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The Use of Research Methods in Psychological Research: A Systematised Review

Salomé elizabeth scholtz.

1 Community Psychosocial Research (COMPRES), School of Psychosocial Health, North-West University, Potchefstroom, South Africa

Werner de Klerk

Leon t. de beer.

2 WorkWell Research Institute, North-West University, Potchefstroom, South Africa

Research methods play an imperative role in research quality as well as educating young researchers, however, the application thereof is unclear which can be detrimental to the field of psychology. Therefore, this systematised review aimed to determine what research methods are being used, how these methods are being used and for what topics in the field. Our review of 999 articles from five journals over a period of 5 years indicated that psychology research is conducted in 10 topics via predominantly quantitative research methods. Of these 10 topics, social psychology was the most popular. The remainder of the conducted methodology is described. It was also found that articles lacked rigour and transparency in the used methodology which has implications for replicability. In conclusion this article, provides an overview of all reported methodologies used in a sample of psychology journals. It highlights the popularity and application of methods and designs throughout the article sample as well as an unexpected lack of rigour with regard to most aspects of methodology. Possible sample bias should be considered when interpreting the results of this study. It is recommended that future research should utilise the results of this study to determine the possible impact on the field of psychology as a science and to further investigation into the use of research methods. Results should prompt the following future research into: a lack or rigour and its implication on replication, the use of certain methods above others, publication bias and choice of sampling method.

Introduction

Psychology is an ever-growing and popular field (Gough and Lyons, 2016 ; Clay, 2017 ). Due to this growth and the need for science-based research to base health decisions on (Perestelo-Pérez, 2013 ), the use of research methods in the broad field of psychology is an essential point of investigation (Stangor, 2011 ; Aanstoos, 2014 ). Research methods are therefore viewed as important tools used by researchers to collect data (Nieuwenhuis, 2016 ) and include the following: quantitative, qualitative, mixed method and multi method (Maree, 2016 ). Additionally, researchers also employ various types of literature reviews to address research questions (Grant and Booth, 2009 ). According to literature, what research method is used and why a certain research method is used is complex as it depends on various factors that may include paradigm (O'Neil and Koekemoer, 2016 ), research question (Grix, 2002 ), or the skill and exposure of the researcher (Nind et al., 2015 ). How these research methods are employed is also difficult to discern as research methods are often depicted as having fixed boundaries that are continuously crossed in research (Johnson et al., 2001 ; Sandelowski, 2011 ). Examples of this crossing include adding quantitative aspects to qualitative studies (Sandelowski et al., 2009 ), or stating that a study used a mixed-method design without the study having any characteristics of this design (Truscott et al., 2010 ).

The inappropriate use of research methods affects how students and researchers improve and utilise their research skills (Scott Jones and Goldring, 2015 ), how theories are developed (Ngulube, 2013 ), and the credibility of research results (Levitt et al., 2017 ). This, in turn, can be detrimental to the field (Nind et al., 2015 ), journal publication (Ketchen et al., 2008 ; Ezeh et al., 2010 ), and attempts to address public social issues through psychological research (Dweck, 2017 ). This is especially important given the now well-known replication crisis the field is facing (Earp and Trafimow, 2015 ; Hengartner, 2018 ).

Due to this lack of clarity on method use and the potential impact of inept use of research methods, the aim of this study was to explore the use of research methods in the field of psychology through a review of journal publications. Chaichanasakul et al. ( 2011 ) identify reviewing articles as the opportunity to examine the development, growth and progress of a research area and overall quality of a journal. Studies such as Lee et al. ( 1999 ) as well as Bluhm et al. ( 2011 ) review of qualitative methods has attempted to synthesis the use of research methods and indicated the growth of qualitative research in American and European journals. Research has also focused on the use of research methods in specific sub-disciplines of psychology, for example, in the field of Industrial and Organisational psychology Coetzee and Van Zyl ( 2014 ) found that South African publications tend to consist of cross-sectional quantitative research methods with underrepresented longitudinal studies. Qualitative studies were found to make up 21% of the articles published from 1995 to 2015 in a similar study by O'Neil and Koekemoer ( 2016 ). Other methods in health psychology, such as Mixed methods research have also been reportedly growing in popularity (O'Cathain, 2009 ).

A broad overview of the use of research methods in the field of psychology as a whole is however, not available in the literature. Therefore, our research focused on answering what research methods are being used, how these methods are being used and for what topics in practice (i.e., journal publications) in order to provide a general perspective of method used in psychology publication. We synthesised the collected data into the following format: research topic [areas of scientific discourse in a field or the current needs of a population (Bittermann and Fischer, 2018 )], method [data-gathering tools (Nieuwenhuis, 2016 )], sampling [elements chosen from a population to partake in research (Ritchie et al., 2009 )], data collection [techniques and research strategy (Maree, 2016 )], and data analysis [discovering information by examining bodies of data (Ktepi, 2016 )]. A systematised review of recent articles (2013 to 2017) collected from five different journals in the field of psychological research was conducted.

Grant and Booth ( 2009 ) describe systematised reviews as the review of choice for post-graduate studies, which is employed using some elements of a systematic review and seldom more than one or two databases to catalogue studies after a comprehensive literature search. The aspects used in this systematised review that are similar to that of a systematic review were a full search within the chosen database and data produced in tabular form (Grant and Booth, 2009 ).

Sample sizes and timelines vary in systematised reviews (see Lowe and Moore, 2014 ; Pericall and Taylor, 2014 ; Barr-Walker, 2017 ). With no clear parameters identified in the literature (see Grant and Booth, 2009 ), the sample size of this study was determined by the purpose of the sample (Strydom, 2011 ), and time and cost constraints (Maree and Pietersen, 2016 ). Thus, a non-probability purposive sample (Ritchie et al., 2009 ) of the top five psychology journals from 2013 to 2017 was included in this research study. Per Lee ( 2015 ) American Psychological Association (APA) recommends the use of the most up-to-date sources for data collection with consideration of the context of the research study. As this research study focused on the most recent trends in research methods used in the broad field of psychology, the identified time frame was deemed appropriate.

Psychology journals were only included if they formed part of the top five English journals in the miscellaneous psychology domain of the Scimago Journal and Country Rank (Scimago Journal & Country Rank, 2017 ). The Scimago Journal and Country Rank provides a yearly updated list of publicly accessible journal and country-specific indicators derived from the Scopus® database (Scopus, 2017b ) by means of the Scimago Journal Rank (SJR) indicator developed by Scimago from the algorithm Google PageRank™ (Scimago Journal & Country Rank, 2017 ). Scopus is the largest global database of abstracts and citations from peer-reviewed journals (Scopus, 2017a ). Reasons for the development of the Scimago Journal and Country Rank list was to allow researchers to assess scientific domains, compare country rankings, and compare and analyse journals (Scimago Journal & Country Rank, 2017 ), which supported the aim of this research study. Additionally, the goals of the journals had to focus on topics in psychology in general with no preference to specific research methods and have full-text access to articles.

The following list of top five journals in 2018 fell within the abovementioned inclusion criteria (1) Australian Journal of Psychology, (2) British Journal of Psychology, (3) Europe's Journal of Psychology, (4) International Journal of Psychology and lastly the (5) Journal of Psychology Applied and Interdisciplinary.

Journals were excluded from this systematised review if no full-text versions of their articles were available, if journals explicitly stated a publication preference for certain research methods, or if the journal only published articles in a specific discipline of psychological research (for example, industrial psychology, clinical psychology etc.).

The researchers followed a procedure (see Figure 1 ) adapted from that of Ferreira et al. ( 2016 ) for systematised reviews. Data collection and categorisation commenced on 4 December 2017 and continued until 30 June 2019. All the data was systematically collected and coded manually (Grant and Booth, 2009 ) with an independent person acting as co-coder. Codes of interest included the research topic, method used, the design used, sampling method, and methodology (the method used for data collection and data analysis). These codes were derived from the wording in each article. Themes were created based on the derived codes and checked by the co-coder. Lastly, these themes were catalogued into a table as per the systematised review design.

Systematised review procedure.

According to Johnston et al. ( 2019 ), “literature screening, selection, and data extraction/analyses” (p. 7) are specifically tailored to the aim of a review. Therefore, the steps followed in a systematic review must be reported in a comprehensive and transparent manner. The chosen systematised design adhered to the rigour expected from systematic reviews with regard to full search and data produced in tabular form (Grant and Booth, 2009 ). The rigorous application of the systematic review is, therefore discussed in relation to these two elements.

Firstly, to ensure a comprehensive search, this research study promoted review transparency by following a clear protocol outlined according to each review stage before collecting data (Johnston et al., 2019 ). This protocol was similar to that of Ferreira et al. ( 2016 ) and approved by three research committees/stakeholders and the researchers (Johnston et al., 2019 ). The eligibility criteria for article inclusion was based on the research question and clearly stated, and the process of inclusion was recorded on an electronic spreadsheet to create an evidence trail (Bandara et al., 2015 ; Johnston et al., 2019 ). Microsoft Excel spreadsheets are a popular tool for review studies and can increase the rigour of the review process (Bandara et al., 2015 ). Screening for appropriate articles for inclusion forms an integral part of a systematic review process (Johnston et al., 2019 ). This step was applied to two aspects of this research study: the choice of eligible journals and articles to be included. Suitable journals were selected by the first author and reviewed by the second and third authors. Initially, all articles from the chosen journals were included. Then, by process of elimination, those irrelevant to the research aim, i.e., interview articles or discussions etc., were excluded.

To ensure rigourous data extraction, data was first extracted by one reviewer, and an independent person verified the results for completeness and accuracy (Johnston et al., 2019 ). The research question served as a guide for efficient, organised data extraction (Johnston et al., 2019 ). Data was categorised according to the codes of interest, along with article identifiers for audit trails such as authors, title and aims of articles. The categorised data was based on the aim of the review (Johnston et al., 2019 ) and synthesised in tabular form under methods used, how these methods were used, and for what topics in the field of psychology.

The initial search produced a total of 1,145 articles from the 5 journals identified. Inclusion and exclusion criteria resulted in a final sample of 999 articles ( Figure 2 ). Articles were co-coded into 84 codes, from which 10 themes were derived ( Table 1 ).

Journal article frequency.

Codes used to form themes (research topics).

These 10 themes represent the topic section of our research question ( Figure 3 ). All these topics except, for the final one, psychological practice , were found to concur with the research areas in psychology as identified by Weiten ( 2010 ). These research areas were chosen to represent the derived codes as they provided broad definitions that allowed for clear, concise categorisation of the vast amount of data. Article codes were categorised under particular themes/topics if they adhered to the research area definitions created by Weiten ( 2010 ). It is important to note that these areas of research do not refer to specific disciplines in psychology, such as industrial psychology; but to broader fields that may encompass sub-interests of these disciplines.

Topic frequency (international sample).

In the case of developmental psychology , researchers conduct research into human development from childhood to old age. Social psychology includes research on behaviour governed by social drivers. Researchers in the field of educational psychology study how people learn and the best way to teach them. Health psychology aims to determine the effect of psychological factors on physiological health. Physiological psychology , on the other hand, looks at the influence of physiological aspects on behaviour. Experimental psychology is not the only theme that uses experimental research and focuses on the traditional core topics of psychology (for example, sensation). Cognitive psychology studies the higher mental processes. Psychometrics is concerned with measuring capacity or behaviour. Personality research aims to assess and describe consistency in human behaviour (Weiten, 2010 ). The final theme of psychological practice refers to the experiences, techniques, and interventions employed by practitioners, researchers, and academia in the field of psychology.

Articles under these themes were further subdivided into methodologies: method, sampling, design, data collection, and data analysis. The categorisation was based on information stated in the articles and not inferred by the researchers. Data were compiled into two sets of results presented in this article. The first set addresses the aim of this study from the perspective of the topics identified. The second set of results represents a broad overview of the results from the perspective of the methodology employed. The second set of results are discussed in this article, while the first set is presented in table format. The discussion thus provides a broad overview of methods use in psychology (across all themes), while the table format provides readers with in-depth insight into methods used in the individual themes identified. We believe that presenting the data from both perspectives allow readers a broad understanding of the results. Due a large amount of information that made up our results, we followed Cichocka and Jost ( 2014 ) in simplifying our results. Please note that the numbers indicated in the table in terms of methodology differ from the total number of articles. Some articles employed more than one method/sampling technique/design/data collection method/data analysis in their studies.

What follows is the results for what methods are used, how these methods are used, and which topics in psychology they are applied to . Percentages are reported to the second decimal in order to highlight small differences in the occurrence of methodology.

Firstly, with regard to the research methods used, our results show that researchers are more likely to use quantitative research methods (90.22%) compared to all other research methods. Qualitative research was the second most common research method but only made up about 4.79% of the general method usage. Reviews occurred almost as much as qualitative studies (3.91%), as the third most popular method. Mixed-methods research studies (0.98%) occurred across most themes, whereas multi-method research was indicated in only one study and amounted to 0.10% of the methods identified. The specific use of each method in the topics identified is shown in Table 2 and Figure 4 .

Research methods in psychology.

Research method frequency in topics.

Secondly, in the case of how these research methods are employed , our study indicated the following.

Sampling −78.34% of the studies in the collected articles did not specify a sampling method. From the remainder of the studies, 13 types of sampling methods were identified. These sampling methods included broad categorisation of a sample as, for example, a probability or non-probability sample. General samples of convenience were the methods most likely to be applied (10.34%), followed by random sampling (3.51%), snowball sampling (2.73%), and purposive (1.37%) and cluster sampling (1.27%). The remainder of the sampling methods occurred to a more limited extent (0–1.0%). See Table 3 and Figure 5 for sampling methods employed in each topic.

Sampling use in the field of psychology.

Sampling method frequency in topics.

Designs were categorised based on the articles' statement thereof. Therefore, it is important to note that, in the case of quantitative studies, non-experimental designs (25.55%) were often indicated due to a lack of experiments and any other indication of design, which, according to Laher ( 2016 ), is a reasonable categorisation. Non-experimental designs should thus be compared with experimental designs only in the description of data, as it could include the use of correlational/cross-sectional designs, which were not overtly stated by the authors. For the remainder of the research methods, “not stated” (7.12%) was assigned to articles without design types indicated.

From the 36 identified designs the most popular designs were cross-sectional (23.17%) and experimental (25.64%), which concurred with the high number of quantitative studies. Longitudinal studies (3.80%), the third most popular design, was used in both quantitative and qualitative studies. Qualitative designs consisted of ethnography (0.38%), interpretative phenomenological designs/phenomenology (0.28%), as well as narrative designs (0.28%). Studies that employed the review method were mostly categorised as “not stated,” with the most often stated review designs being systematic reviews (0.57%). The few mixed method studies employed exploratory, explanatory (0.09%), and concurrent designs (0.19%), with some studies referring to separate designs for the qualitative and quantitative methods. The one study that identified itself as a multi-method study used a longitudinal design. Please see how these designs were employed in each specific topic in Table 4 , Figure 6 .

Design use in the field of psychology.

Design frequency in topics.

Data collection and analysis —data collection included 30 methods, with the data collection method most often employed being questionnaires (57.84%). The experimental task (16.56%) was the second most preferred collection method, which included established or unique tasks designed by the researchers. Cognitive ability tests (6.84%) were also regularly used along with various forms of interviewing (7.66%). Table 5 and Figure 7 represent data collection use in the various topics. Data analysis consisted of 3,857 occurrences of data analysis categorised into ±188 various data analysis techniques shown in Table 6 and Figures 1 – 7 . Descriptive statistics were the most commonly used (23.49%) along with correlational analysis (17.19%). When using a qualitative method, researchers generally employed thematic analysis (0.52%) or different forms of analysis that led to coding and the creation of themes. Review studies presented few data analysis methods, with most studies categorising their results. Mixed method and multi-method studies followed the analysis methods identified for the qualitative and quantitative studies included.

Data collection in the field of psychology.

Data collection frequency in topics.

Data analysis in the field of psychology.

Results of the topics researched in psychology can be seen in the tables, as previously stated in this article. It is noteworthy that, of the 10 topics, social psychology accounted for 43.54% of the studies, with cognitive psychology the second most popular research topic at 16.92%. The remainder of the topics only occurred in 4.0–7.0% of the articles considered. A list of the included 999 articles is available under the section “View Articles” on the following website: https://methodgarden.xtrapolate.io/ . This website was created by Scholtz et al. ( 2019 ) to visually present a research framework based on this Article's results.

This systematised review categorised full-length articles from five international journals across the span of 5 years to provide insight into the use of research methods in the field of psychology. Results indicated what methods are used how these methods are being used and for what topics (why) in the included sample of articles. The results should be seen as providing insight into method use and by no means a comprehensive representation of the aforementioned aim due to the limited sample. To our knowledge, this is the first research study to address this topic in this manner. Our discussion attempts to promote a productive way forward in terms of the key results for method use in psychology, especially in the field of academia (Holloway, 2008 ).

With regard to the methods used, our data stayed true to literature, finding only common research methods (Grant and Booth, 2009 ; Maree, 2016 ) that varied in the degree to which they were employed. Quantitative research was found to be the most popular method, as indicated by literature (Breen and Darlaston-Jones, 2010 ; Counsell and Harlow, 2017 ) and previous studies in specific areas of psychology (see Coetzee and Van Zyl, 2014 ). Its long history as the first research method (Leech et al., 2007 ) in the field of psychology as well as researchers' current application of mathematical approaches in their studies (Toomela, 2010 ) might contribute to its popularity today. Whatever the case may be, our results show that, despite the growth in qualitative research (Demuth, 2015 ; Smith and McGannon, 2018 ), quantitative research remains the first choice for article publication in these journals. Despite the included journals indicating openness to articles that apply any research methods. This finding may be due to qualitative research still being seen as a new method (Burman and Whelan, 2011 ) or reviewers' standards being higher for qualitative studies (Bluhm et al., 2011 ). Future research is encouraged into the possible biasness in publication of research methods, additionally further investigation with a different sample into the proclaimed growth of qualitative research may also provide different results.

Review studies were found to surpass that of multi-method and mixed method studies. To this effect Grant and Booth ( 2009 ), state that the increased awareness, journal contribution calls as well as its efficiency in procuring research funds all promote the popularity of reviews. The low frequency of mixed method studies contradicts the view in literature that it's the third most utilised research method (Tashakkori and Teddlie's, 2003 ). Its' low occurrence in this sample could be due to opposing views on mixing methods (Gunasekare, 2015 ) or that authors prefer publishing in mixed method journals, when using this method, or its relative novelty (Ivankova et al., 2016 ). Despite its low occurrence, the application of the mixed methods design in articles was methodologically clear in all cases which were not the case for the remainder of research methods.

Additionally, a substantial number of studies used a combination of methodologies that are not mixed or multi-method studies. Perceived fixed boundaries are according to literature often set aside, as confirmed by this result, in order to investigate the aim of a study, which could create a new and helpful way of understanding the world (Gunasekare, 2015 ). According to Toomela ( 2010 ), this is not unheard of and could be considered a form of “structural systemic science,” as in the case of qualitative methodology (observation) applied in quantitative studies (experimental design) for example. Based on this result, further research into this phenomenon as well as its implications for research methods such as multi and mixed methods is recommended.

Discerning how these research methods were applied, presented some difficulty. In the case of sampling, most studies—regardless of method—did mention some form of inclusion and exclusion criteria, but no definite sampling method. This result, along with the fact that samples often consisted of students from the researchers' own academic institutions, can contribute to literature and debates among academics (Peterson and Merunka, 2014 ; Laher, 2016 ). Samples of convenience and students as participants especially raise questions about the generalisability and applicability of results (Peterson and Merunka, 2014 ). This is because attention to sampling is important as inappropriate sampling can debilitate the legitimacy of interpretations (Onwuegbuzie and Collins, 2017 ). Future investigation into the possible implications of this reported popular use of convenience samples for the field of psychology as well as the reason for this use could provide interesting insight, and is encouraged by this study.

Additionally, and this is indicated in Table 6 , articles seldom report the research designs used, which highlights the pressing aspect of the lack of rigour in the included sample. Rigour with regards to the applied empirical method is imperative in promoting psychology as a science (American Psychological Association, 2020 ). Omitting parts of the research process in publication when it could have been used to inform others' research skills should be questioned, and the influence on the process of replicating results should be considered. Publications are often rejected due to a lack of rigour in the applied method and designs (Fonseca, 2013 ; Laher, 2016 ), calling for increased clarity and knowledge of method application. Replication is a critical part of any field of scientific research and requires the “complete articulation” of the study methods used (Drotar, 2010 , p. 804). The lack of thorough description could be explained by the requirements of certain journals to only report on certain aspects of a research process, especially with regard to the applied design (Laher, 20). However, naming aspects such as sampling and designs, is a requirement according to the APA's Journal Article Reporting Standards (JARS-Quant) (Appelbaum et al., 2018 ). With very little information on how a study was conducted, authors lose a valuable opportunity to enhance research validity, enrich the knowledge of others, and contribute to the growth of psychology and methodology as a whole. In the case of this research study, it also restricted our results to only reported samples and designs, which indicated a preference for certain designs, such as cross-sectional designs for quantitative studies.

Data collection and analysis were for the most part clearly stated. A key result was the versatile use of questionnaires. Researchers would apply a questionnaire in various ways, for example in questionnaire interviews, online surveys, and written questionnaires across most research methods. This may highlight a trend for future research.

With regard to the topics these methods were employed for, our research study found a new field named “psychological practice.” This result may show the growing consciousness of researchers as part of the research process (Denzin and Lincoln, 2003 ), psychological practice, and knowledge generation. The most popular of these topics was social psychology, which is generously covered in journals and by learning societies, as testaments of the institutional support and richness social psychology has in the field of psychology (Chryssochoou, 2015 ). The APA's perspective on 2018 trends in psychology also identifies an increased amount of psychology focus on how social determinants are influencing people's health (Deangelis, 2017 ).

This study was not without limitations and the following should be taken into account. Firstly, this study used a sample of five specific journals to address the aim of the research study, despite general journal aims (as stated on journal websites), this inclusion signified a bias towards the research methods published in these specific journals only and limited generalisability. A broader sample of journals over a different period of time, or a single journal over a longer period of time might provide different results. A second limitation is the use of Excel spreadsheets and an electronic system to log articles, which was a manual process and therefore left room for error (Bandara et al., 2015 ). To address this potential issue, co-coding was performed to reduce error. Lastly, this article categorised data based on the information presented in the article sample; there was no interpretation of what methodology could have been applied or whether the methods stated adhered to the criteria for the methods used. Thus, a large number of articles that did not clearly indicate a research method or design could influence the results of this review. However, this in itself was also a noteworthy result. Future research could review research methods of a broader sample of journals with an interpretive review tool that increases rigour. Additionally, the authors also encourage the future use of systematised review designs as a way to promote a concise procedure in applying this design.

Our research study presented the use of research methods for published articles in the field of psychology as well as recommendations for future research based on these results. Insight into the complex questions identified in literature, regarding what methods are used how these methods are being used and for what topics (why) was gained. This sample preferred quantitative methods, used convenience sampling and presented a lack of rigorous accounts for the remaining methodologies. All methodologies that were clearly indicated in the sample were tabulated to allow researchers insight into the general use of methods and not only the most frequently used methods. The lack of rigorous account of research methods in articles was represented in-depth for each step in the research process and can be of vital importance to address the current replication crisis within the field of psychology. Recommendations for future research aimed to motivate research into the practical implications of the results for psychology, for example, publication bias and the use of convenience samples.

Ethics Statement

This study was cleared by the North-West University Health Research Ethics Committee: NWU-00115-17-S1.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

• Aanstoos C. M. (2014). Psychology . Available online at: http://eds.a.ebscohost.com.nwulib.nwu.ac.za/eds/detail/detail?sid=18de6c5c-2b03-4eac-94890145eb01bc70%40sessionmgr4006&vid$=$1&hid$=$4113&bdata$=$JnNpdGU9ZWRzL~WxpdmU%3d#AN$=$93871882&db$=$ers
• American Psychological Association (2020). Science of Psychology . Available online at: https://www.apa.org/action/science/
• Appelbaum M., Cooper H., Kline R. B., Mayo-Wilson E., Nezu A. M., Rao S. M. (2018). Journal article reporting standards for quantitative research in psychology: the APA Publications and Communications Board task force report . Am. Psychol. 73 :3. 10.1037/amp0000191 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bandara W., Furtmueller E., Gorbacheva E., Miskon S., Beekhuyzen J. (2015). Achieving rigor in literature reviews: insights from qualitative data analysis and tool-support . Commun. Ass. Inform. Syst. 37 , 154–204. 10.17705/1CAIS.03708 [ CrossRef ] [ Google Scholar ]
• Barr-Walker J. (2017). Evidence-based information needs of public health workers: a systematized review . J. Med. Libr. Assoc. 105 , 69–79. 10.5195/JMLA.2017.109 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bittermann A., Fischer A. (2018). How to identify hot topics in psychology using topic modeling . Z. Psychol. 226 , 3–13. 10.1027/2151-2604/a000318 [ CrossRef ] [ Google Scholar ]
• Bluhm D. J., Harman W., Lee T. W., Mitchell T. R. (2011). Qualitative research in management: a decade of progress . J. Manage. Stud. 48 , 1866–1891. 10.1111/j.1467-6486.2010.00972.x [ CrossRef ] [ Google Scholar ]
• Breen L. J., Darlaston-Jones D. (2010). Moving beyond the enduring dominance of positivism in psychological research: implications for psychology in Australia . Aust. Psychol. 45 , 67–76. 10.1080/00050060903127481 [ CrossRef ] [ Google Scholar ]
• Burman E., Whelan P. (2011). Problems in / of Qualitative Research . Maidenhead: Open University Press/McGraw Hill. [ Google Scholar ]
• Chaichanasakul A., He Y., Chen H., Allen G. E. K., Khairallah T. S., Ramos K. (2011). Journal of Career Development: a 36-year content analysis (1972–2007) . J. Career. Dev. 38 , 440–455. 10.1177/0894845310380223 [ CrossRef ] [ Google Scholar ]
• Chryssochoou X. (2015). Social Psychology . Inter. Encycl. Soc. Behav. Sci. 22 , 532–537. 10.1016/B978-0-08-097086-8.24095-6 [ CrossRef ] [ Google Scholar ]
• Cichocka A., Jost J. T. (2014). Stripped of illusions? Exploring system justification processes in capitalist and post-Communist societies . Inter. J. Psychol. 49 , 6–29. 10.1002/ijop.12011 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Clay R. A. (2017). Psychology is More Popular Than Ever. Monitor on Psychology: Trends Report . Available online at: https://www.apa.org/monitor/2017/11/trends-popular
• Coetzee M., Van Zyl L. E. (2014). A review of a decade's scholarly publications (2004–2013) in the South African Journal of Industrial Psychology . SA. J. Psychol . 40 , 1–16. 10.4102/sajip.v40i1.1227 [ CrossRef ] [ Google Scholar ]
• Counsell A., Harlow L. (2017). Reporting practices and use of quantitative methods in Canadian journal articles in psychology . Can. Psychol. 58 , 140–147. 10.1037/cap0000074 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Deangelis T. (2017). Targeting Social Factors That Undermine Health. Monitor on Psychology: Trends Report . Available online at: https://www.apa.org/monitor/2017/11/trend-social-factors
• Demuth C. (2015). New directions in qualitative research in psychology . Integr. Psychol. Behav. Sci. 49 , 125–133. 10.1007/s12124-015-9303-9 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Denzin N. K., Lincoln Y. (2003). The Landscape of Qualitative Research: Theories and Issues , 2nd Edn. London: Sage. [ Google Scholar ]
• Drotar D. (2010). A call for replications of research in pediatric psychology and guidance for authors . J. Pediatr. Psychol. 35 , 801–805. 10.1093/jpepsy/jsq049 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Dweck C. S. (2017). Is psychology headed in the right direction? Yes, no, and maybe . Perspect. Psychol. Sci. 12 , 656–659. 10.1177/1745691616687747 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Earp B. D., Trafimow D. (2015). Replication, falsification, and the crisis of confidence in social psychology . Front. Psychol. 6 :621. 10.3389/fpsyg.2015.00621 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ezeh A. C., Izugbara C. O., Kabiru C. W., Fonn S., Kahn K., Manderson L., et al.. (2010). Building capacity for public and population health research in Africa: the consortium for advanced research training in Africa (CARTA) model . Glob. Health Action 3 :5693. 10.3402/gha.v3i0.5693 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ferreira A. L. L., Bessa M. M. M., Drezett J., De Abreu L. C. (2016). Quality of life of the woman carrier of endometriosis: systematized review . Reprod. Clim. 31 , 48–54. 10.1016/j.recli.2015.12.002 [ CrossRef ] [ Google Scholar ]
• Fonseca M. (2013). Most Common Reasons for Journal Rejections . Available online at: http://www.editage.com/insights/most-common-reasons-for-journal-rejections
• Gough B., Lyons A. (2016). The future of qualitative research in psychology: accentuating the positive . Integr. Psychol. Behav. Sci. 50 , 234–243. 10.1007/s12124-015-9320-8 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Grant M. J., Booth A. (2009). A typology of reviews: an analysis of 14 review types and associated methodologies . Health Info. Libr. J. 26 , 91–108. 10.1111/j.1471-1842.2009.00848.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Grix J. (2002). Introducing students to the generic terminology of social research . Politics 22 , 175–186. 10.1111/1467-9256.00173 [ CrossRef ] [ Google Scholar ]
• Gunasekare U. L. T. P. (2015). Mixed research method as the third research paradigm: a literature review . Int. J. Sci. Res. 4 , 361–368. Available online at: https://ssrn.com/abstract=2735996 [ Google Scholar ]
• Hengartner M. P. (2018). Raising awareness for the replication crisis in clinical psychology by focusing on inconsistencies in psychotherapy Research: how much can we rely on published findings from efficacy trials? Front. Psychol. 9 :256. 10.3389/fpsyg.2018.00256 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Holloway W. (2008). Doing intellectual disagreement differently . Psychoanal. Cult. Soc. 13 , 385–396. 10.1057/pcs.2008.29 [ CrossRef ] [ Google Scholar ]
• Ivankova N. V., Creswell J. W., Plano Clark V. L. (2016). Foundations and Approaches to mixed methods research , in First Steps in Research , 2nd Edn. K. Maree (Pretoria: Van Schaick Publishers; ), 306–335. [ Google Scholar ]
• Johnson M., Long T., White A. (2001). Arguments for British pluralism in qualitative health research . J. Adv. Nurs. 33 , 243–249. 10.1046/j.1365-2648.2001.01659.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Johnston A., Kelly S. E., Hsieh S. C., Skidmore B., Wells G. A. (2019). Systematic reviews of clinical practice guidelines: a methodological guide . J. Clin. Epidemiol. 108 , 64–72. 10.1016/j.jclinepi.2018.11.030 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ketchen D. J., Jr., Boyd B. K., Bergh D. D. (2008). Research methodology in strategic management: past accomplishments and future challenges . Organ. Res. Methods 11 , 643–658. 10.1177/1094428108319843 [ CrossRef ] [ Google Scholar ]
• Ktepi B. (2016). Data Analytics (DA) . Available online at: https://eds-b-ebscohost-com.nwulib.nwu.ac.za/eds/detail/detail?vid=2&sid=24c978f0-6685-4ed8-ad85-fa5bb04669b9%40sessionmgr101&bdata=JnNpdGU9ZWRzLWxpdmU%3d#AN=113931286&db=ers
• Laher S. (2016). Ostinato rigore: establishing methodological rigour in quantitative research . S. Afr. J. Psychol. 46 , 316–327. 10.1177/0081246316649121 [ CrossRef ] [ Google Scholar ]
• Lee C. (2015). The Myth of the Off-Limits Source . Available online at: http://blog.apastyle.org/apastyle/research/
• Lee T. W., Mitchell T. R., Sablynski C. J. (1999). Qualitative research in organizational and vocational psychology, 1979–1999 . J. Vocat. Behav. 55 , 161–187. 10.1006/jvbe.1999.1707 [ CrossRef ] [ Google Scholar ]
• Leech N. L., Anthony J., Onwuegbuzie A. J. (2007). A typology of mixed methods research designs . Sci. Bus. Media B. V Qual. Quant 43 , 265–275. 10.1007/s11135-007-9105-3 [ CrossRef ] [ Google Scholar ]
• Levitt H. M., Motulsky S. L., Wertz F. J., Morrow S. L., Ponterotto J. G. (2017). Recommendations for designing and reviewing qualitative research in psychology: promoting methodological integrity . Qual. Psychol. 4 , 2–22. 10.1037/qup0000082 [ CrossRef ] [ Google Scholar ]
• Lowe S. M., Moore S. (2014). Social networks and female reproductive choices in the developing world: a systematized review . Rep. Health 11 :85. 10.1186/1742-4755-11-85 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Maree K. (2016). Planning a research proposal , in First Steps in Research , 2nd Edn, ed Maree K. (Pretoria: Van Schaik Publishers; ), 49–70. [ Google Scholar ]
• Maree K., Pietersen J. (2016). Sampling , in First Steps in Research, 2nd Edn , ed Maree K. (Pretoria: Van Schaik Publishers; ), 191–202. [ Google Scholar ]
• Ngulube P. (2013). Blending qualitative and quantitative research methods in library and information science in sub-Saharan Africa . ESARBICA J. 32 , 10–23. Available online at: http://hdl.handle.net/10500/22397 . [ Google Scholar ]
• Nieuwenhuis J. (2016). Qualitative research designs and data-gathering techniques , in First Steps in Research , 2nd Edn, ed Maree K. (Pretoria: Van Schaik Publishers; ), 71–102. [ Google Scholar ]
• Nind M., Kilburn D., Wiles R. (2015). Using video and dialogue to generate pedagogic knowledge: teachers, learners and researchers reflecting together on the pedagogy of social research methods . Int. J. Soc. Res. Methodol. 18 , 561–576. 10.1080/13645579.2015.1062628 [ CrossRef ] [ Google Scholar ]
• O'Cathain A. (2009). Editorial: mixed methods research in the health sciences—a quiet revolution . J. Mix. Methods 3 , 1–6. 10.1177/1558689808326272 [ CrossRef ] [ Google Scholar ]
• O'Neil S., Koekemoer E. (2016). Two decades of qualitative research in psychology, industrial and organisational psychology and human resource management within South Africa: a critical review . SA J. Indust. Psychol. 42 , 1–16. 10.4102/sajip.v42i1.1350 [ CrossRef ] [ Google Scholar ]
• Onwuegbuzie A. J., Collins K. M. (2017). The role of sampling in mixed methods research enhancing inference quality . Köln Z Soziol. 2 , 133–156. 10.1007/s11577-017-0455-0 [ CrossRef ] [ Google Scholar ]
• Perestelo-Pérez L. (2013). Standards on how to develop and report systematic reviews in psychology and health . Int. J. Clin. Health Psychol. 13 , 49–57. 10.1016/S1697-2600(13)70007-3 [ CrossRef ] [ Google Scholar ]
• Pericall L. M. T., Taylor E. (2014). Family function and its relationship to injury severity and psychiatric outcome in children with acquired brain injury: a systematized review . Dev. Med. Child Neurol. 56 , 19–30. 10.1111/dmcn.12237 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Peterson R. A., Merunka D. R. (2014). Convenience samples of college students and research reproducibility . J. Bus. Res. 67 , 1035–1041. 10.1016/j.jbusres.2013.08.010 [ CrossRef ] [ Google Scholar ]
• Ritchie J., Lewis J., Elam G. (2009). Designing and selecting samples , in Qualitative Research Practice: A Guide for Social Science Students and Researchers , 2nd Edn, ed Ritchie J., Lewis J. (London: Sage; ), 1–23. [ Google Scholar ]
• Sandelowski M. (2011). When a cigar is not just a cigar: alternative perspectives on data and data analysis . Res. Nurs. Health 34 , 342–352. 10.1002/nur.20437 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sandelowski M., Voils C. I., Knafl G. (2009). On quantitizing . J. Mix. Methods Res. 3 , 208–222. 10.1177/1558689809334210 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Scholtz S. E., De Klerk W., De Beer L. T. (2019). A data generated research framework for conducting research methods in psychological research .
• Scimago Journal & Country Rank (2017). Available online at: http://www.scimagojr.com/journalrank.php?category=3201&year=2015
• Scopus (2017a). About Scopus . Available online at: https://www.scopus.com/home.uri (accessed February 01, 2017).
• Scopus (2017b). Document Search . Available online at: https://www.scopus.com/home.uri (accessed February 01, 2017).
• Scott Jones J., Goldring J. E. (2015). ‘I' m not a quants person'; key strategies in building competence and confidence in staff who teach quantitative research methods . Int. J. Soc. Res. Methodol. 18 , 479–494. 10.1080/13645579.2015.1062623 [ CrossRef ] [ Google Scholar ]
• Smith B., McGannon K. R. (2018). Developing rigor in quantitative research: problems and opportunities within sport and exercise psychology . Int. Rev. Sport Exerc. Psychol. 11 , 101–121. 10.1080/1750984X.2017.1317357 [ CrossRef ] [ Google Scholar ]
• Stangor C. (2011). Introduction to Psychology . Available online at: http://www.saylor.org/books/
• Strydom H. (2011). Sampling in the quantitative paradigm , in Research at Grass Roots; For the Social Sciences and Human Service Professions , 4th Edn, eds de Vos A. S., Strydom H., Fouché C. B., Delport C. S. L. (Pretoria: Van Schaik Publishers; ), 221–234. [ Google Scholar ]
• Tashakkori A., Teddlie C. (2003). Handbook of Mixed Methods in Social & Behavioural Research . Thousand Oaks, CA: SAGE publications. [ Google Scholar ]
• Toomela A. (2010). Quantitative methods in psychology: inevitable and useless . Front. Psychol. 1 :29. 10.3389/fpsyg.2010.00029 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Truscott D. M., Swars S., Smith S., Thornton-Reid F., Zhao Y., Dooley C., et al.. (2010). A cross-disciplinary examination of the prevalence of mixed methods in educational research: 1995–2005 . Int. J. Soc. Res. Methodol. 13 , 317–328. 10.1080/13645570903097950 [ CrossRef ] [ Google Scholar ]
• Weiten W. (2010). Psychology Themes and Variations . Belmont, CA: Wadsworth. [ Google Scholar ]

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Introduction to Research Methods in Psychology

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

There are several different research methods in psychology , each of which can help researchers learn more about the way people think, feel, and behave. If you're a psychology student or just want to know the types of research in psychology, here are the main ones as well as how they work.

Three Main Types of Research in Psychology

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Psychology research can usually be classified as one of three major types.

1. Causal or Experimental Research

When most people think of scientific experimentation, research on cause and effect is most often brought to mind. Experiments on causal relationships investigate the effect of one or more variables on one or more outcome variables. This type of research also determines if one variable causes another variable to occur or change.

An example of this type of research in psychology would be changing the length of a specific mental health treatment and measuring the effect on study participants.

2. Descriptive Research

Descriptive research seeks to depict what already exists in a group or population. Three types of psychology research utilizing this method are:

• Case studies
• Observational studies

An example of this psychology research method would be an opinion poll to determine which presidential candidate people plan to vote for in the next election. Descriptive studies don't try to measure the effect of a variable; they seek only to describe it.

3. Relational or Correlational Research

A study that investigates the connection between two or more variables is considered relational research. The variables compared are generally already present in the group or population.

For example, a study that looks at the proportion of males and females that would purchase either a classical CD or a jazz CD would be studying the relationship between gender and music preference.

Theory vs. Hypothesis in Psychology Research

People often confuse the terms theory and hypothesis or are not quite sure of the distinctions between the two concepts. If you're a psychology student, it's essential to understand what each term means, how they differ, and how they're used in psychology research.

A theory is a well-established principle that has been developed to explain some aspect of the natural world. A theory arises from repeated observation and testing and incorporates facts, laws, predictions, and tested hypotheses that are widely accepted.

A hypothesis is a specific, testable prediction about what you expect to happen in your study. For example, an experiment designed to look at the relationship between study habits and test anxiety might have a hypothesis that states, "We predict that students with better study habits will suffer less test anxiety." Unless your study is exploratory in nature, your hypothesis should always explain what you expect to happen during the course of your experiment or research.

While the terms are sometimes used interchangeably in everyday use, the difference between a theory and a hypothesis is important when studying experimental design.

Some other important distinctions to note include:

• A theory predicts events in general terms, while a hypothesis makes a specific prediction about a specified set of circumstances.
• A theory has been extensively tested and is generally accepted, while a hypothesis is a speculative guess that has yet to be tested.

The Effect of Time on Research Methods in Psychology

There are two types of time dimensions that can be used in designing a research study:

• Cross-sectional research takes place at a single point in time. All tests, measures, or variables are administered to participants on one occasion. This type of research seeks to gather data on present conditions instead of looking at the effects of a variable over a period of time.
• Longitudinal research is a study that takes place over a period of time. Data is first collected at the beginning of the study, and may then be gathered repeatedly throughout the length of the study. Some longitudinal studies may occur over a short period of time, such as a few days, while others may take place over a period of months, years, or even decades.

The effects of aging are often investigated using longitudinal research.

Causal Relationships Between Psychology Research Variables

What do we mean when we talk about a “relationship” between variables? In psychological research, we're referring to a connection between two or more factors that we can measure or systematically vary.

One of the most important distinctions to make when discussing the relationship between variables is the meaning of causation.

A causal relationship is when one variable causes a change in another variable. These types of relationships are investigated by experimental research to determine if changes in one variable actually result in changes in another variable.

Correlational Relationships Between Psychology Research Variables

A correlation is the measurement of the relationship between two variables. These variables already occur in the group or population and are not controlled by the experimenter.

• A positive correlation is a direct relationship where, as the amount of one variable increases, the amount of a second variable also increases.
• In a negative correlation , as the amount of one variable goes up, the levels of another variable go down.

In both types of correlation, there is no evidence or proof that changes in one variable cause changes in the other variable. A correlation simply indicates that there is a relationship between the two variables.

The most important concept is that correlation does not equal causation. Many popular media sources make the mistake of assuming that simply because two variables are related, a causal relationship exists.

Psychologists use descriptive, correlational, and experimental research designs to understand behavior . In:  Introduction to Psychology . Minneapolis, MN: University of Minnesota Libraries Publishing; 2010.

Caruana EJ, Roman M, Herandez-Sanchez J, Solli P. Longitudinal studies . Journal of Thoracic Disease. 2015;7(11):E537-E540. doi:10.3978/j.issn.2072-1439.2015.10.63

University of Berkeley. Science at multiple levels . Understanding Science 101 . Published 2012.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Scientific or scholarly inquiry by which efforts are made to discover and confirm facts, or to allow investigation of a particular topic.

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Chapter 1: The Science of Psychology

Scientific Research in Psychology

Learning Objectives

• Describe a general model of scientific research in psychology and give specific examples that fit the model.
• Explain who conducts scientific research in psychology and why they do it.
• Distinguish between basic research and applied research.

A Model of Scientific Research in Psychology

Figure 1.1 presents a more specific model of scientific research in psychology. The researcher (who more often than not is really a small group of researchers) formulates a research question, conducts a study designed to answer the question, analyzes the resulting data, draws conclusions about the answer to the question, and publishes the results so that they become part of the research literature. Because the research literature is one of the primary sources of new research questions, this process can be thought of as a cycle. New research leads to new questions, which lead to new research, and so on. Figure 1.1 also indicates that research questions can originate outside of this cycle either with informal observations or with practical problems that need to be solved. But even in these cases, the researcher would start by checking the research literature to see if the question had already been answered and to refine it based on what previous research had already found.

The research by Mehl and his colleagues is described nicely by this model. Their question—whether women are more talkative than men—was suggested to them both by people’s stereotypes and by published claims about the relative talkativeness of women and men. When they checked the research literature, however, they found that this question had not been adequately addressed in scientific studies. They then conducted a careful empirical study, analyzed the results (finding very little difference between women and men), and published their work so that it became part of the research literature. The publication of their article is not the end of the story, however, because their work suggests many new questions (about the reliability of the result, about potential cultural differences, etc.) that will likely be taken up by them and by other researchers inspired by their work.

As another example, consider that as cell phones became more widespread during the 1990s, people began to wonder whether, and to what extent, cell phone use had a negative effect on driving. Many psychologists decided to tackle this question scientifically (Collet, Guillot, & Petit, 2010) [1] . It was clear from previously published research that engaging in a simple verbal task impairs performance on a perceptual or motor task carried out at the same time, but no one had studied the effect specifically of cell phone use on driving. Under carefully controlled conditions, these researchers compared people’s driving performance while using a cell phone with their performance while not using a cell phone, both in the lab and on the road. They found that people’s ability to detect road hazards, reaction time, and control of the vehicle were all impaired by cell phone use. Each new study was published and became part of the growing research literature on this topic.

Who Conducts Scientific Research in Psychology?

Scientific research in psychology is generally conducted by people with doctoral degrees (usually the  doctor of philosophy [PhD] ) and master’s degrees in psychology and related fields, often supported by research assistants with bachelor’s degrees or other relevant training. Some of them work for government agencies (e.g., the Mental Health Commission of Canada), national associations (e.g., the Canadian Psychological Association), nonprofit organizations (e.g., the Canadian Mental Health Association), or in the private sector (e.g., in product development). However, the majority of them are college and university faculty, who often collaborate with their graduate and undergraduate students. Although some researchers are trained and licensed as clinicians—especially those who conduct research in clinical psychology—the majority are not. Instead, they have expertise in one or more of the many other subfields of psychology: behavioural neuroscience, cognitive psychology, developmental psychology, personality psychology, social psychology, and so on. Doctoral-level researchers might be employed to conduct research full-time or, like many college and university faculty members, to conduct research in addition to teaching classes and serving their institution and community in other ways.

Of course, people also conduct research in psychology because they enjoy the intellectual and technical challenges involved and the satisfaction of contributing to scientific knowledge of human behaviour. You might find that you enjoy the process too. If so, your college or university might offer opportunities to get involved in ongoing research as either a research assistant or a participant. Of course, you might find that you do not enjoy the process of conducting scientific research in psychology. But at least you will have a better understanding of where scientific knowledge in psychology comes from, an appreciation of its strengths and limitations, and an awareness of how it can be applied to solve practical problems in psychology and everyday life.

Scientific Psychology Blogs

A fun and easy way to follow current scientific research in psychology is to read any of the many excellent blogs devoted to summarizing and commenting on new findings.

Among them are the following:

• Brain Blogger
• Research Digest
• Social Psychology Eye
• We’re Only Human

You can also browse through Research Blogging , select psychology as your topic, and read entries from a wide variety of blogs.

The Broader Purposes of Scientific Research in Psychology

People have always been curious about the natural world, including themselves and their behaviour (in fact, this is probably why you are studying psychology in the first place). Science grew out of this natural curiosity and has become the best way to achieve detailed and accurate knowledge. Keep in mind that most of the phenomena and theories that fill psychology textbooks are the products of scientific research. In a typical introductory psychology textbook, for example, one can learn about specific cortical areas for language and perception, principles of classical and operant conditioning, biases in reasoning and judgment, and people’s surprising tendency to obey those in positions of authority. And scientific research continues because what we know right now only scratches the surface of what we  can  know.

Scientific research is often classified as being either basic or applied. Basic research  in psychology is conducted primarily for the sake of achieving a more detailed and accurate understanding of human behaviour, without necessarily trying to address any particular practical problem. The research of Mehl and his colleagues falls into this category.  Applied research  is conducted primarily to address some practical problem. Research on the effects of cell phone use on driving, for example, was prompted by safety concerns and has led to the enactment of laws to limit this practice. Although the distinction between basic and applied research is convenient, it is not always clear-cut. For example, basic research on sex differences in talkativeness could eventually have an effect on how marriage therapy is practiced, and applied research on the effect of cell phone use on driving could produce new insights into basic processes of perception, attention, and action.

Key Takeaways

• Research in psychology can be described by a simple cyclical model. A research question based on the research literature leads to an empirical study, the results of which are published and become part of the research literature.
• Scientific research in psychology is conducted mainly by people with doctoral degrees in psychology and related fields, most of whom are college and university faculty members. They do so for professional and for personal reasons, as well as to contribute to scientific knowledge about human behaviour.
• Basic research is conducted to learn about human behaviour for its own sake, and applied research is conducted to solve some practical problem. Both are valuable, and the distinction between the two is not always clear-cut.
• Practice: Find a description of an empirical study in a professional journal or in one of the scientific psychology blogs. Then write a brief description of the research in terms of the cyclical model presented here. One or two sentences for each part of the cycle should suffice.
• Practice: Based on your own experience or on things you have already learned about psychology, list three basic research questions and three applied research questions of interest to you.
• Watch the following TED Ed video, in which David H. Schwartz provides an introduction to two types of empirical studies along with some methods that scientists use to increase the reliability of their results:

Video Attributions

• “ Understanding driver distraction ” by American Psychological Association . Standard YouTube Licence.
• “ Not all scientific studies are created equal – David H. Schwartz ” by TED-Ed . Standard YouTube Licence.
• Collet, C., Guillot, A., & Petit, C. (2010). Phoning while driving I: A review of epidemiological, psychological, behavioural and physiological studies. Ergonomics, 53 , 589–601. ↵

A doctoral degree generally held by people who conduct scientific research in psychology.

In psychology, research conducted for the sake of achieving a more detailed and accurate understanding of human behaviour, without necessarily trying to address any particular problem.

Research conducted primarily to address some practical problem.

Research Methods in Psychology - 2nd Canadian Edition Copyright © 2015 by Paul C. Price, Rajiv Jhangiani, & I-Chant A. Chiang is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Science of Psychology

The Go-To Science

Curiosity is part of human nature. One of the first questions children learn to ask is “why?” As adults, we continue to wonder. Using empirical methods, psychologists apply that universal curiosity to collect and interpret research data to better understand and solve some of society’s most challenging problems.

It’s difficult, if not impossible, to think of a facet of life where psychology is not involved. Psychologists employ the scientific method — stating the question, offering a theory and then constructing rigorous laboratory or field experiments to test the hypothesis. Psychologists apply the understanding gleaned through research to create evidence-based strategies that solve problems and improve lives.

The result is that psychological science unveils new and better ways for people to exist and thrive in a complex world.

Psychologists in Action

Helping Businesses

Dr. Jack Stark uses psychological science to help NASCAR drivers achieve optimal performance  and keep their team in the winner’s circle.

Improving Lives

Dr. David Strayer uses psychological science to study distracted driving by putting people through rigorous concentration tests during driving simulations.

Promoting Health

Dr. Deborah Tate uses psychological science to identify strategies for improving weight loss . Her research brings the proven benefits of face-to-face weight loss programs to more people through technology.

Helping Organizations

As an organizational psychologist, Dr. Eduardo Salas studies people where they work — examining what they do and how they make decisions.

Working in Schools

Dr. Kathleen Kremer knows a thing or two about fun. Using psychological science, she studies user attitudes, behaviors and emotions to learn what makes a child love a toy.

Science in Action

Psychology is a varied field. Psychologists conduct basic and applied research, serve as consultants to communities and organizations, diagnose and treat people, and teach future psychologists and those who will pursue other disciplines. They test intelligence and personality.

Many psychologists work as health care providers. They assess behavioral and mental function and well-being. Other psychologists study how human beings relate to each other and to machines, and work to improve these relationships.

The application of psychological research can decrease the economic burden of disease on government and society as people learn how to make choices that improve their health and well-being. The strides made in educational assessments are helping students with learning disabilities. Psychological science helps educators understand how children think, process and remember — helping to design effective teaching methods. Psychological science contributes to justice by helping the courts understand the minds of criminals, evidence and the limits of certain types of evidence or testimony.

The science of psychology is pervasive. Psychologists work in some of the nation’s most prominent companies and organizations. From Google, Boeing and NASA to the federal government, national health care organizations and research groups to Cirque du Soleil, Disney and NASCAR — psychologists are there, playing important roles.

Brain science and cognitive psychology

Climate and environmental psychology

Clinical psychology

Counseling psychology

Developmental psychology

Experimental psychology

Forensic and public service psychology

Health psychology

Human factors and engineering psychology

Industrial and organizational psychology

Psychology of teaching and learning

Quantitative psychology

Rehabilitation psychology

Social psychology

Sport and performance psychology

Psychology Careers: What to Know

Psychological Research Methods: Types and Tips

Categories Research Methods

Psychological research methods are the techniques used by scientists and researchers to study human behavior and mental processes. These methods are used to gather empirical evidence.

The goal of psychological research methods is to obtain objective and verifiable data collected through scientific experimentation and observation. 

The research methods that are used in psychology are crucial for understanding how and why people behave the way they do, as well as for developing and testing theories about human behavior.

Table of Contents

Reasons to Learn More About Psychological Research Methods

One of the key goals of psychological research is to make sure that the data collected is reliable and valid.

• Reliability means that the data is consistent and can be replicated
• Validity refers to the accuracy of the data collected

Researchers must take great care to ensure that their research methods are reliable and valid, as this is essential for drawing accurate conclusions and making valid claims about human behavior.

High school and college students who are interested in psychology can benefit greatly from learning about research methods. Understanding how psychologists study human behavior and mental processes can help students develop critical thinking skills and a deeper appreciation for the complexity of human behavior.

Having an understanding of these research methods can prepare students for future coursework in psychology, as well as for potential careers in the field.

Quantitative vs. Qualitative Psychological Research Methods

Psychological research methods can be broadly divided into two main types: quantitative and qualitative. These two methods differ in their approach to data collection and analysis.

Quantitative Research Methods

Quantitative research methods involve collecting numerical data through controlled experiments, surveys, and other objective measures.

The goal of quantitative research is to identify patterns and relationships in the data that can be analyzed statistically.

Researchers use statistical methods to test hypotheses, identify significant differences between groups, and make predictions about future behavior.

Qualitative Research Methods

Qualitative research methods, on the other hand, involve collecting non-numerical data through open-ended interviews, observations, and other subjective measures.

Qualitative research aims to understand the subjective experiences and perspectives of individuals and groups.

Researchers use methods such as content analysis and thematic analysis to identify themes and patterns in the data and to develop rich descriptions of the phenomenon under study.

How Quantitative and Qualitative Methods Are Used

While quantitative and qualitative research methods differ in their approach to data collection and analysis, they are often used together to gain a more complete understanding of complex phenomena.

For example, a researcher studying the impact of social media on mental health might use a quantitative survey to gather numerical data on social media use and a qualitative interview to gain insight into participants’ subjective experiences with social media.

Types of Psychological Research Methods

There are several types of research methods used in psychology, including experiments, surveys, case studies, and observational studies. Each method has its strengths and weaknesses, and researchers must choose the most appropriate method based on their research question and the data they hope to collect.

Case Studies

A case study is a research method used in psychology to investigate an individual, group, or event in great detail. In a case study, the researcher gathers information from a variety of sources, including:

• Observation
• Document analysis

These methods allow researchers to gain an in-depth understanding of the case being studied.

Case studies are particularly useful when the phenomenon under investigation is rare or complex, and when it is difficult to replicate in a laboratory setting.

Surveys are a commonly used research method in psychology that involve gathering data from a large number of people about their thoughts, feelings, behaviors, and attitudes.

Surveys can be conducted in a variety of ways, including:

• In-person interviews
• Online questionnaires
• Paper-and-pencil surveys

Surveys are particularly useful when researchers want to study attitudes or behaviors that are difficult to observe directly or when they want to generalize their findings to a larger population.

Experimental Psychological Research Methods

Experimental studies are a research method commonly used in psychology to investigate cause-and-effect relationships between variables. In an experimental study, the researcher manipulates one or more variables to see how they affect another variable, while controlling for other factors that may influence the outcome.

Experimental studies are considered the gold standard for establishing cause-and-effect relationships, as they allow researchers to control for potential confounding variables and to manipulate variables in a systematic way.

Correlational Psychological Research Methods

Correlational research is a research method used in psychology to investigate the relationship between two or more variables without manipulating them. The goal of correlational research is to determine the extent to which changes in one variable are associated with changes in another variable.

In other words, correlational research aims to establish the direction and strength of the relationship between two or more variables.

Naturalistic Observation

Naturalistic observation is a research method used in psychology to study behavior in natural settings, without any interference or manipulation from the researcher.

The goal of naturalistic observation is to gain insight into how people or animals behave in their natural environment without the influence of laboratory conditions.

Meta-Analysis

A meta-analysis is a research method commonly used in psychology to combine and analyze the results of multiple studies on a particular topic.

The goal of a meta-analysis is to provide a comprehensive and quantitative summary of the existing research on a topic, in order to identify patterns and relationships that may not be apparent in individual studies.

Tips for Using Psychological Research Methods

Here are some tips for high school and college students who are interested in using psychological research methods:

Understand the different types of research methods: 

Before conducting any research, it is important to understand the different types of research methods that are available, such as surveys, case studies, experiments, and naturalistic observation.

Each method has its strengths and limitations, and selecting the appropriate method depends on the research question and variables being investigated.

Develop a clear research question: 

A good research question is essential for guiding the research process. It should be specific, clear, and relevant to the field of psychology. It is also important to consider ethical considerations when developing a research question.

Use proper sampling techniques: 

Sampling is the process of selecting participants for a study. It is important to use proper sampling techniques to ensure that the sample is representative of the population being studied.

Random sampling is considered the gold standard for sampling, but other techniques, such as convenience sampling, may also be used depending on the research question.

Use reliable and valid measures:

It is important to use reliable and valid measures to ensure the data collected is accurate and meaningful. This may involve using established measures or developing new measures and testing their reliability and validity.

Consider ethical issues:

It is important to consider ethical considerations when conducting psychological research, such as obtaining informed consent from participants, maintaining confidentiality, and minimizing any potential harm to participants.

In many cases, you will need to submit your study proposal to your school’s institutional review board for approval.

Analyze and interpret the data appropriately : 

After collecting the data, it is important to analyze and interpret the data appropriately. This may involve using statistical techniques to identify patterns and relationships between variables, and using appropriate software tools for analysis.

Communicate findings clearly: 

Finally, it is important to communicate the findings clearly in a way that is understandable to others. This may involve writing a research report, giving a presentation, or publishing a paper in a scholarly journal.

Clear communication is essential for advancing the field of psychology and informing future research.

Frequently Asked Questions

What are the 5 methods of psychological research.

The five main methods of psychological research are:

• Experimental research : This method involves manipulating one or more independent variables to observe their effect on one or more dependent variables while controlling for other variables. The goal is to establish cause-and-effect relationships between variables.
• Correlational research : This method involves examining the relationship between two or more variables, without manipulating them. The goal is to determine whether there is a relationship between the variables and the strength and direction of that relationship.
• Survey research : This method involves gathering information from a sample of participants using questionnaires or interviews. The goal is to collect data on attitudes, opinions, behaviors, or other variables of interest.
• Case study research : This method involves an in-depth analysis of a single individual, group, or event. The goal is to gain insight into specific behaviors, attitudes, or phenomena.
• Naturalistic observation research : This method involves observing and recording behavior in natural settings without any manipulation or interference from the researcher. The goal is to gain insight into how people or animals behave in their natural environment.

What is the most commonly used psychological research method?

The most common research method used in psychology varies depending on the research question and the variables being investigated. However, correlational research is one of the most frequently used methods in psychology.

This is likely because correlational research is useful in studying a wide range of psychological phenomena, and it can be used to examine the relationships between variables that cannot be manipulated or controlled, such as age, gender, and personality traits. 

Experimental research is also a widely used method in psychology, particularly in the areas of cognitive psychology , social psychology , and developmental psychology .

Other methods, such as survey research, case study research, and naturalistic observation, are also commonly used in psychology research, depending on the research question and the variables being studied.

How do you know which research method to use?

Deciding which type of research method to use depends on the research question, the variables being studied, and the practical considerations involved. Here are some general guidelines to help students decide which research method to use:

• Identify the research question : The first step is to clearly define the research question. What are you trying to study? What is the hypothesis you want to test? Answering these questions will help you determine which research method is best suited for your study.
• Choose your variables : Identify the independent and dependent variables involved in your research question. This will help you determine whether an experimental or correlational research method is most appropriate.
• Consider your resources : Think about the time, resources, and ethical considerations involved in conducting the research. For example, if you are working on a tight budget, a survey or correlational research method may be more feasible than an experimental study.
• Review existing literature : Conducting a literature review of previous studies on the topic can help you identify the most appropriate research method. This can also help you identify gaps in the literature that your study can fill.
• Consult with a mentor or advisor : If you are still unsure which research method to use, consult with a mentor or advisor who has experience in conducting research in your area of interest. They can provide guidance and help you make an informed decision.

Scholtz SE, de Klerk W, de Beer LT. The use of research methods in psychological research: A systematised review . Front Res Metr Anal . 2020;5:1. doi:10.3389/frma.2020.00001

Palinkas LA. Qualitative and mixed methods in mental health services and implementation research . J Clin Child Adolesc Psychol . 2014;43(6):851-861. doi:10.1080/15374416.2014.910791

Crowe S, Cresswell K, Robertson A, Huby G, Avery A, Sheikh A. The case study approach . BMC Med Res Methodol . 2011;11(1):100. doi:10.1186/1471-2288-11-100

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2 Chapter 2: Principles of Research

Principles of research, 2.1  basic concepts.

Before we address where research questions in psychology come from—and what makes them more or less interesting—it is important to understand the kinds of questions that researchers in psychology typically ask. This requires a quick introduction to several basic concepts, many of which we will return to in more detail later in the book.

Research questions in psychology are about variables. A variable is a quantity or quality that varies across people or situations. For example, the height of the students in a psychology class is a variable because it varies from student to student. The sex of the students is also a variable as long as there are both male and female students in the class. A quantitative variable is a quantity, such as height, that is typically measured by assigning a number to each individual. Other examples of quantitative variables include people’s level of talkativeness, how depressed they are, and the number of siblings they have. A categorical variable is a quality, such as sex, and is typically measured by assigning a category label to each individual. Other examples include people’s nationality, their occupation, and whether they are receiving psychotherapy.

“Lots of Candy Could Lead to Violence”

Although researchers in psychology know that  correlation does not imply causation , many journalists do not. Many headlines suggest that a causal relationship has been demonstrated, when a careful reading of the articles shows that it has not because of the directionality and third-variable problems.

One article is about a study showing that children who ate candy every day were more likely than other children to be arrested for a violent offense later in life. But could candy really “lead to” violence, as the headline suggests? What alternative explanations can you think of for this statistical relationship? How could the headline be rewritten so that it is not misleading?

As we will see later in the book, there are various ways that researchers address the directionality and third-variable problems. The most effective, however, is to conduct an experiment. An experiment is a study in which the researcher manipulates the independent variable. For example, instead of simply measuring how much people exercise, a researcher could bring people into a laboratory and randomly assign half of them to run on a treadmill for 15 minutes and the rest to sit on a couch for 15 minutes. Although this seems like a minor addition to the research design, it is extremely important. Now if the exercisers end up in more positive moods than those who did not exercise, it cannot be because their moods affected how much they exercised (because it was the researcher who determined how much they exercised). Likewise, it cannot be because some third variable (e.g., physical health) affected both how much they exercised and what mood they were in (because, again, it was the researcher who determined how much they exercised). Thus experiments eliminate the directionality and third-variable problems and allow researchers to draw firm conclusions about causal relationships.

2.2  Generating Good Research Questions

Good research must begin with a good research question. Yet coming up with good research questions is something that novice researchers often find difficult and stressful. One reason is that this is a creative process that can appear mysterious—even magical—with experienced researchers seeming to pull interesting research questions out of thin air. However, psychological research on creativity has shown that it is neither as mysterious nor as magical as it appears. It is largely the product of ordinary thinking strategies and persistence (Weisberg, 1993). This section covers some fairly simple strategies for finding general research ideas, turning those ideas into empirically testable research questions, and finally evaluating those questions in terms of how interesting they are and how feasible they would be to answer.

Finding Inspiration

Research questions often begin as more general research ideas—usually focusing on some behaviour or psychological characteristic: talkativeness, memory for touches, depression, bungee jumping, and so on. Before looking at how to turn such ideas into empirically testable research questions, it is worth looking at where such ideas come from in the first place. Three of the most common sources of inspiration are informal observations, practical problems, and previous research.

Informal observations include direct observations of our own and others’ behaviour as well as secondhand observations from nonscientific sources such as newspapers, books, and so on. For example, you might notice that you always seem to be in the slowest moving line at the grocery store. Could it be that most people think the same thing? Or you might read in the local newspaper about people donating money and food to a local family whose house has burned down and begin to wonder about who makes such donations and why. Some of the most famous research in psychology has been inspired by informal observations. Stanley Milgram’s famous research on obedience, for example, was inspired in part by journalistic reports of the trials of accused Nazi war criminals—many of whom claimed that they were only obeying orders. This led him to wonder about the extent to which ordinary people will commit immoral acts simply because they are ordered to do so by an authority figure (Milgram, 1963).

Practical problems can also inspire research ideas, leading directly to applied research in such domains as law, health, education, and sports. Can human figure drawings help children remember details about being physically or sexually abused? How effective is psychotherapy for depression compared to drug therapy? To what extent do cell phones impair people’s driving ability? How can we teach children to read more efficiently? What is the best mental preparation for running a marathon?

Probably the most common inspiration for new research ideas, however, is previous research. Recall that science is a kind of large-scale collaboration in which many different researchers read and evaluate each other’s work and conduct new studies to build on it. Of course, experienced researchers are familiar with previous research in their area of expertise and probably have a long list of ideas. This suggests that novice researchers can find inspiration by consulting with a more experienced researcher (e.g., students can consult a faculty member). But they can also find inspiration by picking up a copy of almost any professional journal and reading the titles and abstracts. In one typical issue of Psychological Science, for example, you can find articles on the perception of shapes, anti-Semitism, police lineups, the meaning of death, second-language learning, people who seek negative emotional experiences, and many other topics. If you can narrow your interests down to a particular topic (e.g., memory) or domain (e.g., health care), you can also look through more specific journals, such as Memory Cognition or Health Psychology.

Generating Empirically Testable Research Questions

Once you have a research idea, you need to use it to generate one or more empirically testable research questions, that is, questions expressed in terms of a single variable or relationship between variables. One way to do this is to look closely at the discussion section in a recent research article on the topic. This is the last major section of the article, in which the researchers summarize their results, interpret them in the context of past research, and suggest directions for future research. These suggestions often take the form of specific research questions, which you can then try to answer with additional research. This can be a good strategy because it is likely that the suggested questions have already been identified as interesting and important by experienced researchers.

But you may also want to generate your own research questions. How can you do this? First, if you have a particular behaviour or psychological characteristic in mind, you can simply conceptualize it as a variable and ask how frequent or intense it is. How many words on average do people speak per day? How accurate are children’s memories of being touched? What percentage of people have sought professional help for depression? If the question has never been studied scientifically—which is something that you will learn in your literature review—then it might be interesting and worth pursuing.

If scientific research has already answered the question of how frequent or intense the behaviour or characteristic is, then you should consider turning it into a question about a statistical relationship between that behaviour or characteristic and some other variable. One way to do this is to ask yourself the following series of more general questions and write down all the answers you can think of.

·         What are some possible causes of the behaviour or characteristic?

·         What are some possible effects of the behaviour or characteristic?

·         What types of people might exhibit more or less of the behaviour or characteristic?

·         What types of situations might elicit more or less of the behaviour or characteristic?

In general, each answer you write down can be conceptualized as a second variable, suggesting a question about a statistical relationship. If you were interested in talkativeness, for example, it might occur to you that a possible cause of this psychological characteristic is family size. Is there a statistical relationship between family size and talkativeness? Or it might occur to you that people seem to be more talkative in same-sex groups than mixed-sex groups. Is there a difference in the average level of talkativeness of people in same-sex groups and people in mixed-sex groups? This approach should allow you to generate many different empirically testable questions about almost any behaviour or psychological characteristic.

If through this process you generate a question that has never been studied scientifically—which again is something that you will learn in your literature review—then it might be interesting and worth pursuing. But what if you find that it has been studied scientifically? Although novice researchers often want to give up and move on to a new question at this point, this is not necessarily a good strategy. For one thing, the fact that the question has been studied scientifically and the research published suggests that it is of interest to the scientific community. For another, the question can almost certainly be refined so that its answer will still contribute something new to the research literature. Again, asking yourself a series of more general questions about the statistical relationship is a good strategy.

·         Are there other ways to operationally define the variables?

·         Are there types of people for whom the statistical relationship might be stronger or weaker?

·         Are there situations in which the statistical relationship might be stronger or weaker—including situations with practical importance?

For example, research has shown that women and men speak about the same number of words per day—but this was when talkativeness was measured in terms of the number of words spoken per day among college students in the United States and Mexico. We can still ask whether other ways of measuring talkativeness—perhaps the number of different people spoken to each day—produce the same result. Or we can ask whether studying elderly people or people from other cultures produces the same result. Again, this approach should help you generate many different research questions about almost any statistical relationship.

2.3  Evaluating Research Questions

Researchers usually generate many more research questions than they ever attempt to answer. This means they must have some way of evaluating the research questions they generate so that they can choose which ones to pursue. In this section, we consider two criteria for evaluating research questions: the interestingness of the question and the feasibility of answering it.

Interestingness

How often do people tie their shoes? Do people feel pain when you punch them in the jaw? Are women more likely to wear makeup than men? Do people prefer vanilla or chocolate ice cream? Although it would be a fairly simple matter to design a study and collect data to answer these questions, you probably would not want to because they are not interesting. We are not talking here about whether a research question is interesting to us personally but whether it is interesting to people more generally and, especially, to the scientific community. But what makes a research question interesting in this sense? Here we look at three factors that affect the interestingness of a research question: the answer is in doubt, the answer fills a gap in the research literature, and the answer has important practical implications.

First, a research question is interesting to the extent that its answer is in doubt. Obviously, questions that have been answered by scientific research are no longer interesting as the subject of new empirical research. But the fact that a question has not been answered by scientific research does not necessarily make it interesting. There has to be some reasonable chance that the answer to the question will be something that we did not already know. But how can you assess this before actually collecting data? One approach is to try to think of reasons to expect different answers to the question—especially ones that seem to conflict with common sense. If you can think of reasons to expect at least two different answers, then the question might be interesting. If you can think of reasons to expect only one answer, then it probably is not. The question of whether women are more talkative than men is interesting because there are reasons to expect both answers. The existence of the stereotype itself suggests the answer could be yes, but the fact that women’s and men’s verbal abilities are fairly similar suggests the answer could be no. The question of whether people feel pain when you punch them in the jaw is not interesting because there is absolutely no reason to think that the answer could be anything other than a resounding yes.

A second important factor to consider when deciding if a research question is interesting is whether answering it will fill a gap in the research literature. Again, this means in part that the question has not already been answered by scientific research. But it also means that the question is in some sense a natural one for people who are familiar with the research literature. For example, the question of whether human figure drawings can help children recall touch information would be likely to occur to anyone who was familiar with research on the unreliability of eyewitness memory (especially in children) and the ineffectiveness of some alternative interviewing techniques.

A final factor to consider when deciding whether a research question is interesting is whether its answer has important practical implications. Again, the question of whether human figure drawings help children recall information about being touched has important implications for how children are interviewed in physical and sexual abuse cases. The question of whether cell phone use impairs driving is interesting because it is relevant to the personal safety of everyone who travels by car and to the debate over whether cell phone use should be restricted by law.

Feasibility

A second important criterion for evaluating research questions is the feasibility of successfully answering them. There are many factors that affect feasibility, including time, money, equipment and materials, technical knowledge and skill, and access to research participants. Clearly, researchers need to take these factors into account so that they do not waste time and effort pursuing research that they cannot complete successfully.

Looking through a sample of professional journals in psychology will reveal many studies that are complicated and difficult to carry out. These include longitudinal designs in which participants are tracked over many years, neuroimaging studies in which participants’ brain activity is measured while they carry out various mental tasks, and complex non-experimental studies involving several variables and complicated statistical analyses. Keep in mind, though, that such research tends to be carried out by teams of highly trained researchers whose work is often supported in part by government and private grants. Keep in mind also that research does not have to be complicated or difficult to produce interesting and important results. Looking through a sample of professional journals will also reveal studies that are relatively simple and easy to carry out—perhaps involving a convenience sample of college students and a paper-and-pencil task.

A final point here is that it is generally good practice to use methods that have already been used successfully by other researchers. For example, if you want to manipulate people’s moods to make some of them happy, it would be a good idea to use one of the many approaches that have been used successfully by other researchers (e.g., paying them a compliment). This is good not only for the sake of feasibility—the approach is “tried and true”—but also because it provides greater continuity with previous research. This makes it easier to compare your results with those of other researchers and to understand the implications of their research for yours, and vice versa.

Key Takeaways

·         Research ideas can come from a variety of sources, including informal observations, practical problems, and previous research.

·         Research questions expressed in terms of variables and relationships between variables can be suggested by other researchers or generated by asking a series of more general questions about the behaviour or psychological characteristic of interest.

·         It is important to evaluate how interesting a research question is before designing a study and collecting data to answer it. Factors that affect interestingness are the extent to which the answer is in doubt, whether it fills a gap in the research literature, and whether it has important practical implications.

·         It is also important to evaluate how feasible a research question will be to answer. Factors that affect feasibility include time, money, technical knowledge and skill, and access to special equipment and research participants.

References from Chapter 2

Milgram, S. (1963). Behavioral study of obedience. Journal of Abnormal and Social Psychology, 67, 371–378.

Stanovich, K. E. (2010). How to think straight about psychology (9th ed.). Boston, MA: Allyn Bacon.

Weisberg, R. W. (1993). Creativity: Beyond the myth of genius. New York, NY: Freeman.

Research Methods in Psychology & Neuroscience Copyright © by Dalhousie University Introduction to Psychology and Neuroscience Team. All Rights Reserved.

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Meta Analysis: definition, meaning and steps to conduct

Meta-analysis: This article explains the concept of meta-analysis in a practical way. The article begins with an introduction to this concept, followed by a definition and a general explanation. You will also find a practical example and tips for conducting a simple analysis yourself. Enjoy reading!

What is a meta-analysis?

Have you ever wondered how doctors and researchers often make the right decisions about complex (medical) treatments? A powerful tool they use is the so-called meta-analysis. With this approach, they combine the results of multiple scientific studies to get a clearer picture of the overall effectiveness of a treatment.

Definition and meaning

But what exactly is meta-analysis? It’s a research process that systematically brings together the findings of individual studies and uses statistical methods to calculate an overall or ‘absolute’ effect.

It’s not just about merging data from smaller studies to increase sample size. Analysts also use systematic methods to account for differences in research approaches, treatment outcomes, and sample sizes.

For example, they also test the sensitivity and validity of their results for their own research protocols and statistical analyses.

Admittedly, that sounds difficult. It can also be described as putting puzzle pieces together to see the bigger picture. According to experts, scientists are often confronted with valuable but sometimes contradictory results in individual studies.

Meta-analyses play an important role in putting these puzzle pieces together and combining the findings of multiple studies to provide a more complete understanding.

Due to the combination of several scientific studies, it is considered the most comprehensive form of scientific research. This creates more confidence in the conclusions drawn, as a larger body of research is considered.

A practical example

Imagine this: there are several studies examining the same medical treatment, and each study reports slightly different results due to some degree of error.

Meta-analysis helps the researcher by combining these results to get closer to the truth.

By using statistical approaches, an estimated mean can be derived that reflects the common effect observed in the studies.

Steps in conducting a meta-analysis

Meta-analyses are usually preceded by a systematic review, as this helps identify and assess all relevant facts. It is an extremely precise and complex process, which is almost exclusively performed in a scientific research setting.

The general steps are as follows:

• Formulating the research question , for example by using the PICO model
• Searching for literature
• Selection of studies based on certain criteria
• Selection of specific studies on a well-defined topic
• Deciding whether to include unpublished studies to avoid publication bias
• Determining which dependent variables or summary measures are allowed
• Selection of the right model, for example a fixed-effect or random-effect meta-analysis
• Investigating sources of heterogeneity between studies, for example by meta-regression or by subgroup analysis
• Following formal guidelines for conducting and reporting the analysis as described in the Cochrane Handbook
• Use of Reporting Guidelines

By following these steps, meta-analyses can be performed to obtain reliable summaries and conclusions from a wide range of research data.

Meta-analyses have very valuable advantages.

First, it provides an estimate of the unknown effect size, which helps us understand how effective a treatment really is.

It also allows us to compare and contrast results from different studies. It helps identify patterns between the findings, uncover sources of disagreement, and uncover interesting connections that may emerge when multiple studies are analyzed together.

However, like any research method, meta-analysis also has its limitations. A concern is possible bias in individual studies due to questionable research practices or publication bias.

If such biases are present, the overall treatment effect calculated via this type of analysis may not reflect the true efficacy of a treatment.

Another challenge lies in dealing with heterogeneous studies.

Each study can have its own unique characteristics and produce different results. When we average these differences in a meta-analysis, the result may not accurately represent a specific group studied.

It’s like averaging the weight of apples and oranges – the result may not accurately represent both the apples and the oranges.

This means that researchers must make careful choices during the analysis process, such as how to search for studies, which studies to select based on specific criteria, how to handle incomplete data, analyze the data, and take publication bias into account.

Despite these challenges, meta-analysis remains a valuable tool in evidence-based research.

It is often an essential part of systematic reviews, where multiple studies are extensively analyzed. By combining evidence from different sources, it provides a more comprehensive insight into the effectiveness of medical treatments, for example.

Meta-analysis in psychology

Meta-analysis plays an important role in various fields, including psychology. It provides value primarily through its ability to bring together results from different studies.

Imagine there are many little puzzle pieces of information scattered across different studies. Meta-analysis helps us put all those pieces together and get a complete picture.

It helps psychologists discover patterns and trends and draw more reliable conclusions about certain topics, such as the effectiveness of a treatment or the relationship between certain factors.

Now it’s your turn

What do you think? Do you recognize the explanation of meta-analysis? Have you ever heard of this research method? Have you ever performed this analysis yourself? What do you think are the benefits its use? How would you explain its importance to someone who has no experience with research methods? What tips or comments can you share with us?

Share your experience and knowledge in the comments box below.

More information

• Guzzo, R. A., Jackson, S. E., & Katzell, R. A. (1987). Meta-analysis. Research in organizational behavior, 9(1), 407-442.
• Becker, B. J. (2000). Multivariate meta-analysis. Handbook of applied multivariate statistics and mathematical modeling, 499-525.
• Haidich, A. B. (2010). Meta-analysis in medical research. Hippokratia, 14(Suppl 1), 29.
• Field, A. P., & Gillett, R. (2010). How to do a meta‐analysis. British Journal of Mathematical and Statistical Psychology, 63(3), 665-694.

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Ben Janse is a young professional working at ToolsHero as Content Manager. He is also an International Business student at Rotterdam Business School where he focusses on analyzing and developing management models. Thanks to his theoretical and practical knowledge, he knows how to distinguish main- and side issues and to make the essence of each article clearly visible.

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Undergraduate Program

Psychology is the scientific study of the mind, and as such, we investigate the minds of humans and other species. Through gaining a fundamental understanding of the human mind, other goals will also be achieved: the skill to critically assess quantitative evidence from experimental and correlational data, to learn to take difficult and previously unstudied problems of mind and society and bring them under experimental scrutiny, to learn to speak and write about questions of theoretical and social importance that involve the mind.

The Bachelor of Liberal Arts degree is designed for industry professionals with years of work experience who wish to complete their degrees part time, both on campus and online, without disruption to their employment. Our typical student is over 30, has previously completed one or two years of college, and works full time.

Graduate study in the Department of Psychology is organized into four areas: clinical science, developmental, social psychology, and cognition, brain, and behavior. These areas consist of faculty members whose combined interests span a coherent program of advanced study and research in some subfield of psychology. Students enrolled in the Ph.D. program may follow one of two tracks. The first is the Common Curriculum, which embraces social psychology, cognitive psychology, cognitive neuroscience, behavioral neuroscience, and perception. The second track is Clinical Science. Students may only be considered for Clinical Science during the graduate school application process.

Students enrolled in the Master of Liberal Arts program in Psychology will examine the science of psychology and gain an understanding of human behavior. Students explore core theories and the latest research, gaining insights into how human beings think, feel, behave, and navigate their social world.

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Positive Psychology

At its core, positive psychology is the scientific study of human flourishing and optimal functioning. It emerged in the late 20th century, and its roots trace back to humanistic thinkers like Abraham Maslow, who emphasized the importance of self-actualization. In the late 1990s, Dr. Martin Seligman, often regarded as the “father” of positive psychology, introduced it as a distinct sub-field. Unlike traditional psychology, which centers on alleviating mental illnesses and disorders, positive psychology shifts the lens toward the brighter side of the human experience. It emphasizes character strengths, virtues, and factors contributing to human happiness and well-being. It offers a holistic approach that identifies and remedies weaknesses while recognizing and fostering individual strengths and potentials.

How it works

Positive psychology operates on core principles that pivot around understanding human strengths, fostering positive emotions, and ensuring optimal functioning. These principles signify a paradigm shift from traditional therapeutic techniques that mainly target dysfunction to those that accentuate the positive and thriving aspects of human nature.

Positive psychology employs a range of methods to actualize its principles. For instance, gratitude exercises encourage individuals to acknowledge and reflect upon the positive facets and occurrences in their lives, enhancing well-being. Similarly, strength identification tools like the VIA (Values in Action) Survey help individuals recognize their innate talents and virtues, fostering a sense of purpose and direction.

The theoretical foundations of positive psychology are deeply embedded in the works of several important figures. Martin Seligman, renowned for his research on learned helplessness, championed the idea of “learned optimism.” He introduced the PERMA model, which breaks down well-being into five measurable elements: Positive Emotion, Engagement, Relationships, Meaning, and Accomplishments. On the other hand, Mihaly Csikszentmihalyi introduced the concept of ‘flow’--a state of deep absorption where individuals lose the sense of time and self, often experienced during peak moments of creativity and skillful performance. Their works and theories have not only carved the path for understanding happiness and well-being but have also set a precedent for further research and interventions in positive psychology.

What to expect

Individuals may begin a positive psychology journey with an initial assessment. This foundational step aims to identify personal strengths and areas ripe for growth. Customized questionnaires and introspective exercises might be employed to glean insight into an individual’s character strengths, values, and predominant emotional patterns.

Post-assessment, positive interventions form the crux of the therapeutic process. These techniques are designed to amplify positive emotions and fortify personal strengths. For instance, someone might be introduced to journaling exercises that magnify gratitude or activities that leverage their intrinsic strengths, propelling them toward more fulfilling life experiences.

Continuous reflection is also integral to the process. Regular feedback loops, through discussions or reflective practices, allow the individual and therapist to gauge progress, celebrate successes, and recalibrate strategies as needed, addressing areas that need further nurturing or redirection.

Many experience a notable transformation: a shift in perspective. The lens through which life is viewed transitions from being predominantly problem-centric to being strength-centric. Such a shift can alter one’s approach to challenges, relationships, and personal aspirations.

However, while these steps provide a general roadmap, each person’s journey in positive psychology is unique. As with any therapeutic endeavor, individual experiences will vary, shaped by personal histories, goals, and the nuances of individual lives.

Who it benefits

Due to its wide range of applicability, positive psychology can benefit individuals, organizations, educational institutions, and communities.

Individuals

Positive psychology has the profound ability to revolutionize personal journeys. Emphasizing strengths and virtues offers individuals tools and perspectives to enhance their personal development. The reflective activities and interventions commonly used aid individuals in recognizing their intrinsic values and strengths, resulting in a heightened sense of purpose, increased happiness, and overall well-being. Through its focus, individuals learn to manage and overcome challenges and thrive, maximizing their potential in various areas of their lives.

Organizations

Positive psychology’s principles apply in many ways in the corporate world. Organizations that recognize the value of employee well-being are integrating positive psychology practices to foster a healthier work environment. By prioritizing employee strengths and encouraging positive interpersonal relationships, companies can boost morale, which, in turn, often translates to enhanced productivity. Workshops centered around gratitude, resilience, and strength-based leadership are increasingly common, reshaping workplace dynamics and fostering a more engaged and satisfied workplace.

Educational institutions

Schools and educational institutions are also using the power of positive psychology. With its emphasis on strengths and positive emotions, educators can craft a more holistic learning experience. Integrating practices focusing on student well-being ensures mental and emotional while paving the way for optimal learning. By recognizing and nurturing each student’s strengths and providing them with tools to cope with academic pressures, institutions create environments where students can thrive academically, socially, and personally.

Communities

Beyond individual or institutional benefits, positive psychology has the potential to help communities. By fostering communal strengths, promoting positive experiences, and nurturing resilience, communities can experience enhanced cohesion and well-being. Whether through community workshops or public awareness campaigns, initiatives centered around the tenets of positive psychology can lead to more engaged, resilient, and harmonious communities.

Goals for therapy

Positive psychology in therapy seeks to foster holistic growth and improvement in individuals. Below are some key goals, techniques, and outcomes that therapy may aim to achieve:

• Techniques: Interventions might include gratitude journaling, visualization exercises, and mindfulness practices.
• Outcome: These techniques aim to amplify positive emotions, foster a sense of contentment, and enhance overall happiness in daily life.
• Tools: Individuals may learn cognitive-behavioral strategies, exposure to controlled challenges, and reframing adversities.
• Outcome: These tools equip individuals to bounce back from setbacks, face challenges head-on, and cultivate a resilient spirit against life’s inevitable adversities.
• Methods: Assessments like the VIA Character Strengths Survey and strength-based feedback sessions.
• Outcome: By identifying innate strengths, individuals can channel their energies more productively and gain satisfaction from tasks and roles that align with their intrinsic abilities and values.
• Strategies: Individuals may be taught active listening exercises, empathy-building activities, and communication skill workshops.
• Outcome: Fostering healthier relationships starts with oneself and radiates outwards. By enhancing self-understanding and interpersonal skills, individuals can cultivate more fulfilling relationships with themselves and others.
• Approaches: Engaging in activities that induce "flow", seeking out new and engaging experiences, and creating environments conducive to spontaneous moments of joy.
• Outcome: By immersing oneself in challenging and enjoyable activities, one can achieve moments of deep engagement and pleasure, leading to peak experiences and a profound sense of fulfillment.

With a skilled therapist, these goals can act as pillars upon which self-discovery, growth, and well-being can be built using the principles of positive psychology. 

During the ongoing COVID-19 crisis, there has been a significant emphasis on addressing the rising mental health challenges. A study published in The Journal of Positive Psychology highlights the potential of positive psychology in buffering against mental illness and fortifying mental health amidst the pandemic. It includes nine positive psychology elements, including gratitude, self-compassion, and positive emotions, emphasizing their roles in offering resilience during dire times. While many research efforts are understandably centered on mitigating negative psychological impacts, exploring how individuals can be empowered and uplifted can be equally important. This study supports the integration of positive psychology practices, showcasing their efficacy in alleviating mental illness and enhancing overall mental well-being and resilience during and beyond the pandemic.

Additionally, a meta-analysis published in the  International Journal of Applied Positive Psychology investigates the effectiveness of Positive Psychology Interventions (PPIs) in the workplace. Drawing on Positive Work and Organization (PWO) theories, the study analyzed 22 empirical investigations encompassing 52 independent samples and 6027 participants from 10 countries. The findings indicate that PPIs have a small positive impact on desirable work outcomes and a more pronounced effect on reducing undesirable work results. These interventions improved workplace well-being, engagement, and other outcomes, although their impact on overall performance was not significant. Interventions based on employee gratitude and strengths showed more substantial effects on positive work outcomes. This analysis reinforces the value of PPIs in fostering positive work environments and combating negative workplace behaviors.

Finding therapy

Navigating mental well-being can be daunting, but one can find effective and transformative therapy with the proper guidance and resources. Here’s a guide on how to locate a therapist specializing in positive psychology:

Licensed practitioners 

When seeking therapy, it’s helpful to consult licensed professionals. These individuals have undergone rigorous training and are equipped with the expertise to provide evidence-based interventions. Credentialed practitioners uphold ethical standards and offer a higher likelihood of beneficial therapeutic outcomes.

Online resources

With the rise of digital platforms, finding therapy has become more convenient. Websites like  BetterHelp  connect individuals with qualified online therapists, including those specializing in positive psychology. This means that individuals who live in areas with few therapists can reap the benefits of positive psychology from the comfort of home.

Positive psychology institutions

Established organizations such as the International Positive Psychology Association (IPPA) are committed to the scientific study and promotion of positive psychology. They often maintain directories of qualified practitioners and provide resources for those interested in this therapeutic approach. 

Additionally, it may be helpful to ask potential therapists questions to ensure they are a good fit for a positive psychology journey. Some questions to ask include:

What is your educational background and training in positive psychology?

• This helps them understand their depth of knowledge in the specific domain.

Can you discuss some success stories or outcomes using positive psychology techniques?

• This provides insight into their practical experience and efficacy. 

How do you tailor positive psychology interventions to individual needs?

• This ensures a personalized approach rather than a one-size-fits-all methodology.

What tools or assessments do you employ to gauge progress?

• This indicates a structured approach to therapy with measurable outcomes.

Starting any kind of therapy requires trust and assurance. Armed with the right information and resources, one can embark on a fulfilling journey toward well-being using the principles of positive psychology.

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Research Hypothesis In Psychology: Types, & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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A research hypothesis, in its plural form “hypotheses,” is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method .

Hypotheses connect theory to data and guide the research process towards expanding scientific understanding

Some key points about hypotheses:

• A hypothesis expresses an expected pattern or relationship. It connects the variables under investigation.
• It is stated in clear, precise terms before any data collection or analysis occurs. This makes the hypothesis testable.
• A hypothesis must be falsifiable. It should be possible, even if unlikely in practice, to collect data that disconfirms rather than supports the hypothesis.
• Hypotheses guide research. Scientists design studies to explicitly evaluate hypotheses about how nature works.
• For a hypothesis to be valid, it must be testable against empirical evidence. The evidence can then confirm or disprove the testable predictions.
• Hypotheses are informed by background knowledge and observation, but go beyond what is already known to propose an explanation of how or why something occurs.

Predictions typically arise from a thorough knowledge of the research literature, curiosity about real-world problems or implications, and integrating this to advance theory. They build on existing literature while providing new insight.

Types of Research Hypotheses

Alternative hypothesis.

The research hypothesis is often called the alternative or experimental hypothesis in experimental research.

It typically suggests a potential relationship between two key variables: the independent variable, which the researcher manipulates, and the dependent variable, which is measured based on those changes.

The alternative hypothesis states a relationship exists between the two variables being studied (one variable affects the other).

A hypothesis is a testable statement or prediction about the relationship between two or more variables. It is a key component of the scientific method. Some key points about hypotheses:

• Important hypotheses lead to predictions that can be tested empirically. The evidence can then confirm or disprove the testable predictions.

In summary, a hypothesis is a precise, testable statement of what researchers expect to happen in a study and why. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.

An experimental hypothesis predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and are significant in supporting the theory being investigated.

The alternative hypothesis can be directional, indicating a specific direction of the effect, or non-directional, suggesting a difference without specifying its nature. It’s what researchers aim to support or demonstrate through their study.

Null Hypothesis

The null hypothesis states no relationship exists between the two variables being studied (one variable does not affect the other). There will be no changes in the dependent variable due to manipulating the independent variable.

It states results are due to chance and are not significant in supporting the idea being investigated.

The null hypothesis, positing no effect or relationship, is a foundational contrast to the research hypothesis in scientific inquiry. It establishes a baseline for statistical testing, promoting objectivity by initiating research from a neutral stance.

Many statistical methods are tailored to test the null hypothesis, determining the likelihood of observed results if no true effect exists.

This dual-hypothesis approach provides clarity, ensuring that research intentions are explicit, and fosters consistency across scientific studies, enhancing the standardization and interpretability of research outcomes.

Nondirectional Hypothesis

A non-directional hypothesis, also known as a two-tailed hypothesis, predicts that there is a difference or relationship between two variables but does not specify the direction of this relationship.

It merely indicates that a change or effect will occur without predicting which group will have higher or lower values.

For example, “There is a difference in performance between Group A and Group B” is a non-directional hypothesis.

Directional Hypothesis

A directional (one-tailed) hypothesis predicts the nature of the effect of the independent variable on the dependent variable. It predicts in which direction the change will take place. (i.e., greater, smaller, less, more)

It specifies whether one variable is greater, lesser, or different from another, rather than just indicating that there’s a difference without specifying its nature.

For example, “Exercise increases weight loss” is a directional hypothesis.

Falsifiability

The Falsification Principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory or hypothesis to be considered scientific, it must be testable and irrefutable.

Falsifiability emphasizes that scientific claims shouldn’t just be confirmable but should also have the potential to be proven wrong.

It means that there should exist some potential evidence or experiment that could prove the proposition false.

However many confirming instances exist for a theory, it only takes one counter observation to falsify it. For example, the hypothesis that “all swans are white,” can be falsified by observing a black swan.

For Popper, science should attempt to disprove a theory rather than attempt to continually provide evidence to support a research hypothesis.

Can a Hypothesis be Proven?

Hypotheses make probabilistic predictions. They state the expected outcome if a particular relationship exists. However, a study result supporting a hypothesis does not definitively prove it is true.

All studies have limitations. There may be unknown confounding factors or issues that limit the certainty of conclusions. Additional studies may yield different results.

In science, hypotheses can realistically only be supported with some degree of confidence, not proven. The process of science is to incrementally accumulate evidence for and against hypothesized relationships in an ongoing pursuit of better models and explanations that best fit the empirical data. But hypotheses remain open to revision and rejection if that is where the evidence leads.

• Disproving a hypothesis is definitive. Solid disconfirmatory evidence will falsify a hypothesis and require altering or discarding it based on the evidence.
• However, confirming evidence is always open to revision. Other explanations may account for the same results, and additional or contradictory evidence may emerge over time.

We can never 100% prove the alternative hypothesis. Instead, we see if we can disprove, or reject the null hypothesis.

If we reject the null hypothesis, this doesn’t mean that our alternative hypothesis is correct but does support the alternative/experimental hypothesis.

Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct. We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist which could refute a theory.

How to Write a Hypothesis

• Identify variables . The researcher manipulates the independent variable and the dependent variable is the measured outcome.
• Operationalized the variables being investigated . Operationalization of a hypothesis refers to the process of making the variables physically measurable or testable, e.g. if you are about to study aggression, you might count the number of punches given by participants.
• Decide on a direction for your prediction . If there is evidence in the literature to support a specific effect of the independent variable on the dependent variable, write a directional (one-tailed) hypothesis. If there are limited or ambiguous findings in the literature regarding the effect of the independent variable on the dependent variable, write a non-directional (two-tailed) hypothesis.
• Make it Testable : Ensure your hypothesis can be tested through experimentation or observation. It should be possible to prove it false (principle of falsifiability).
• Clear & concise language . A strong hypothesis is concise (typically one to two sentences long), and formulated using clear and straightforward language, ensuring it’s easily understood and testable.

Consider a hypothesis many teachers might subscribe to: students work better on Monday morning than on Friday afternoon (IV=Day, DV= Standard of work).

Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and a Friday afternoon and then measuring their immediate recall of the material covered in each session, we would end up with the following:

• The alternative hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.
• The null hypothesis states that there will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.

More Examples

• Memory : Participants exposed to classical music during study sessions will recall more items from a list than those who studied in silence.
• Social Psychology : Individuals who frequently engage in social media use will report higher levels of perceived social isolation compared to those who use it infrequently.
• Developmental Psychology : Children who engage in regular imaginative play have better problem-solving skills than those who don’t.
• Clinical Psychology : Cognitive-behavioral therapy will be more effective in reducing symptoms of anxiety over a 6-month period compared to traditional talk therapy.
• Cognitive Psychology : Individuals who multitask between various electronic devices will have shorter attention spans on focused tasks than those who single-task.
• Health Psychology : Patients who practice mindfulness meditation will experience lower levels of chronic pain compared to those who don’t meditate.
• Organizational Psychology : Employees in open-plan offices will report higher levels of stress than those in private offices.
• Behavioral Psychology : Rats rewarded with food after pressing a lever will press it more frequently than rats who receive no reward.

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Who is really an excellent university student and how to identify them? A development of a comprehensive framework of excellence in higher education

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This paper addresses the need for a more comprehensive framework of excellence in higher education, which goes beyond academic achievement alone, placing emphasis on its integration with personal characteristics, and acknowledging the diversity in the student population. Two research studies were conducted to establish this comprehensive framework. The pilot study aimed to offer a conceptual definition of the excellent university student according to the perceptions of the academic community. The study, which involved 26 teachers and 159 students, was informed by teacher interviews, student essays, and focus group discussions. The established conceptual framework of excellence was based on a subset of essential attributes that could be embodied by a real student. The conceptual framework comprises facets of expertness, proactive learning, and being a good person organised within the dimensions of educational and personal excellence. It is complemented by academic achievement and underpinned by genuine study motivation. Building upon the findings of the pilot study, the main study aimed to develop and implement a systematic procedure for identifying excellent students. The study, which involved 53 teachers and 112 students, was based on a multisource assessment of multiple contextually relevant criteria of excellence. The identification procedure involved three phases: teacher nomination and assessment, academic achievement assessment, and peer assessment. As a result, 10 excellent students were identified who met all the conceptual criteria of excellence. In conclusion, this paper presents a comprehensive conceptual and methodological framework for defining and identifying excellent university students, grounded in both theoretical principles and empirical findings.

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Since ancient times, individuals excelling in any field of human endeavour have been the subject of broad fascination and admiration serving as role models and inspiring others to reach their own highest potential. Naturally, excellent individuals generate considerable interest also for psychologists and researchers who have devoted special attention to studying, understanding, and explaining excellence to find which specific behaviours, traits, and experiences excellent athletes, professionals, or students share, and which developmental paths they follow (Chen et al., 2020 ; Fuster de Hernàndez, 2020 ; Hirsch & Segolsson, 2021 ; Kallas, 2014 ). Although we believe that the notion of excellence is highly relevant across disciplines, in this paper, we highlight the importance of concentrating on individual-level excellence within the context of higher education. In the field of higher education, there is a growing body of research focused on investigating the characteristics, motivations, and trajectories of excellent university students. The ultimate goal of this research is to identify qualities associated with excellence that universities could cultivate in other students as well (e.g. López et al., 2013 ; Mirghani et al., 2015 ). To ensure that such research generates valid findings and meaningful conclusions which can accurately navigate educational policy and practice, it is first necessary to develop a rigorous conceptualisation and operationalisation of excellence in higher education. However, the field lacks clear and practical guidance on how to conduct research on individual-level excellence in a conceptually and methodologically sound way.

In the theoretical part of this paper, we discuss limitations of the current approaches to define and identify excellent university students, which tend to focus solely on high academic achievement. We advocate for a more holistic framework that integrates academic achievement with the personal and motivational characteristics, acknowledging student diversity and the variability of ways in which excellence manifests itself (Gardner, 2015 ; Miller & Kerr, 2002 ). We argue that the exceedingly high value placed on academic achievement may have negative consequences, such as a decrease of student well-being, an increase of academic dishonesty, and a switch towards unfavourable motivational patterns (Kötter et al., 2017 ; Luthar & Kumar, 2018 ; Yaniv et al., 2017 ). Methodologically, we advocate for greater consideration of the socially-construed context-dependent nature of the construct of excellence (Terzi, 2020 ).

The empirical part of this paper consists of two distinct yet interrelated research studies, conducted at a European university. The first pilot study, based on qualitative data from teachers and students, seeks to establish a context-specific conceptual framework of the excellent university student. The second study aims to develop and implement a methodological framework for identifying excellent university students. As a result, this paper presents a comprehensive framework of excellence that could be particularly helpful for research based on sampling excellent university students.

Conceptual underpinnings of excellence

Since the term excellence has recently become a ubiquitous buzzword in social science and beyond, it is often used broadly to refer to any field-specific desired outcome. Thus, despite its growing appeal, excellence has been documented as an ambiguous, or even as an empty concept (Bruno-Jofré & Hills, 2011 ; Brusoni et al., 2014 ). To unravel the real meaning behind the term, it is necessary to first review its conceptual underpinnings.

Excellence is generally defined as an “outstanding merit or quality” (“Excellence”, n. d. ); the corresponding adjective excellent as “extremely good, of very high quality” (Summers, 2003 , p. 350). The specific understanding of excellence at an individual level, however, diverges into distinct viewpoints along a continuum with technical goodness (being good at or doing well) at one end, and non-instrumental moral goodness (doing good) at the other (Franks, 1996 , p. 297). The first perspective acknowledges a person’s excellence through the resulting product they created (Norton, 1980, as cited in Franks, 1996 ). Put differently, excellence equals outstanding performance, and individuals are considered excellent when they reach a certain level of a key performance indicator (Brusoni et al., 2014 ). The opposite view is in accordance with the original understanding of the term and has its roots in the ethical theory of the Ancient Greeks. From this historical perspective, excellence, or arete , had to do with values and ideals rather than performance, since it was related to quality of character, and thus a feature of the noble and good human (Jahanbegloo, 2014 ).

The most comprehensive conceptualisation of excellence integrates the duality of professional or performance excellence (observable, measurable outcomes) and personal excellence (personal qualities or virtues) into a single framework (Miller & Kerr, 2002 ). While the integrative approach to excellence is rare in the existing literature, parallels can be identified, mainly in integrative conceptual frameworks of giftedness. Although the conceptual definitions of excellence may not explicitly include high intellectual abilities, the indirect link between excellence and intellectual abilities can be presumed based on the predictive power of cognitive ability on academic achievement, which is an integral part of excellence (Rohde & Thompson, 2007 ). In this respect, the conceptual frameworks of giftedness emphasise several closely related but distinct elements, including, but also going beyond, ability as measured by conventional test scores (Renzulli & Reis, 2020 ; Sternberg, 2009 ). As each of these elements plays a crucial role in contributing to the manifestation of gifted behaviour, their synthesis becomes imperative. In essence, an individual cannot be considered gifted if they lack any of the qualities that together form the theoretical foundation of giftedness. For example, the Three Ring Conception of Giftedness is based on three interacting clusters of traits: above-average, though not necessarily superior, ability in terms of both general and specific ability; task commitment, referring to focused motivation directed toward a specific task or performance area; and creativity (Renzulli & Reis, 2020 ). Likewise, the conceptual framework of giftedness proposed by Sternberg ( 2009 ) synthesises wisdom, intelligence, and creativity. Here giftedness is considered

a function of creativity in generating ideas, analytical intelligence in evaluating the quality of these ideas, practical intelligence in implementing the ideas and convincing others to value and follow the ideas, and wisdom to ensure that the decisions and their implementation is for the common good of all stakeholders. (p. 255)

Specifically, wisdom, regarded as the most crucial yet rarest quality of a gifted individual in the giftedness conceptual framework (Sternberg, 2009 ), clearly aligns with personal excellence in the excellence framework.

The present study strongly advocates the holistic approach to excellence, since it promotes the realisation of the human potential to its fullest extent. We argue that the emphasis on both facets of excellence is particularly important when conceptualising excellence in higher education to be in line with the core mission of higher education institutions: to help individuals fulfil their potential by fostering intellectual, personal, and moral growth (Astin & Antonio, 2012 ; Hoff, 2009 ). Moreover, excellence in higher education goes beyond the academic world since it also represents that which students take with them after leaving university to become excellent professionals, parents, and citizens (Gardner, 2015 ). Given that excellence in the workplace entails high-quality work and ethical and social responsibility at its centre (Gardner et al., 2001 ), it can be assumed that excellence in higher education cannot only pertain to high-quality academic work, but also to the development of personality and character (Hoff, 2009 ).

Conceptual frameworks of the excellent student

Although excellence is one of the most fashionable concepts in education these days (Astin & Antonio, 2012 ), relevant literature providing solid conceptual underpinnings of the construct of the excellent student is limited and entails predominantly theoretical work that lacks empirical data on how the construct is perceived by students and teachers. Since sources focusing exclusively on university students are extremely scarce, all the literature presented here covers students of all educational levels. In this respect, several integrative conceptualisations of the excellent student that acknowledge both achievement and personal attributes can be found in the existing literature. The technical facet of excellence, equivalent to individual expertness, comprises the knowledge and strategies that are needed to address specific tasks, and it is manifested in high academic achievement (Ferrari, 2002 ; Li, 2004 ; Parkash and Waks, 1985, as cited in Bruno-Jofré & Hills, 2011 ). The non-technical personal facet, on the other hand, prevents the reduction of excellence to merely a matter of technical expertness, and emphasises the importance of the values, skills, and outcomes that people need to function well in a particular community (Ferrari, 2002 ). Hence, an integral part of excellence is outstanding academic achievement coupled with personal qualities that have been conceptualised as (a) being a good person (Ferrari, 2002 ) or possessing a moral and virtuous character (Li, 2004 ), (b) showing personal mastery including the desire for self-improvement, curiosity, and willingness to work hard to fulfil this curiosity (Erez, 2004 ), and (c) having good work habits in terms of neatness, persistence, efficient time use, and self-discipline (Franks, 1996 ). In addition, a certain level of intellectual skills may be considered part of excellence, as intellectual skills, particularly general cognitive ability, strongly influence academic achievement (Rohde & Thompson, 2007 ). Indeed, being intelligent was identified as one of the relevant aspects in conceptualising an ideal university student (as discussed below; Wong et al., 2021 ).

In contrast to limited conceptualisations of excellence, a rich empirically-based conceptual framework of what is valued in a student provides the related notion of the ideal university student. In this respect, the characteristics of the ideal student include good grades and personal qualities, such as reflectiveness and supportiveness towards others, but also the education-related qualities of engagement, interest, and taking responsibility for their own learning (Llamas, 2006 ; Wong et al., 2021 ). Nevertheless, whereas the features of an excellent university student can be embodied by a real person, the notion of the ideal student constitutes the aspirations and imaginations of desirable student characteristics that may not exist in one individual (Wong et al., 2021 ). Thus, although the aforementioned conceptualisations may resemble the conceptual frameworks of the excellent student to a certain extent, they are not intended to guide research in student sampling as they are far too complex and not very realistic.

Research on excellence in higher education

This paper specifically highlights the relevance of a notion of excellence in the context of higher education. University students are a specific population in multiple ways. They find themselves in the final stage of formal education, and, at the same time, at the beginning of an unfolding career path. Since excellence in higher education is considered a direct antecedent of occupational (and citizenship) excellence (Gardner, 2015 ), it is of particular significance. Moreover, the stage of emerging adulthood, which usually overlaps with studying at university, is characterised by malleability of attitudes, traits, and behaviours. Emerging adults may greatly benefit from interventions focused on establishing positive behaviour patterns that may, in turn, help them to fulfil their potential and live a fulfilling and meaningful life in the long term (Arnett & Schwab, 2012 ; Nelson et al., 2008 ). If universities use the unique opportunity of this life stage to cultivate excellence in university students, it may not only have a direct effect on students’ personal growth, but also promote the growth of communities, organisations, and the whole society (Gardner, 2015 ; Hoff, 2009 ).

Educational researchers may already be aware of the above-mentioned significance of investigating and cultivating excellence in higher education, as there is a growing body of empirical research focused on concrete excellent students. These research studies typically aim to explain determinants of excellence, such as contextual or personal factors that impact the development of excellence (e.g. López et al., 2013 ; Monteiro et al., 2014 ). Alternatively, they explore the career paths pursued by excellent students with the aim of better understanding, for instance, their career choice decisions (e.g. Fuster de Hernàndez, 2020 ; Kass & Miller, 2018 ). Reviewing the approaches adopted by the most recent research, several criteria have been used to operationally define excellent university students. Sampled excellent students achieve high grades (e.g. Mirghani et al., 2015 ; Monteiro et al., 2014 ), exceed a set cut-off point of the grade point average (GPA; e.g. Al Shawwa et al., 2015 ), or they are enrolled in degree programmes designed for high-achieving students (e.g. Shonfeld & Ronen, 2015 ). Additionally, the samples of excellent students were constituted of those scoring high (exceeding a set cut-off point) on admission examinations (e.g. Kass & Miller, 2018 ; López et al., 2013 ) or national standardised examinations (e.g. Fuster de Hernàndez, 2020 ).

The above-described approaches suggest that current higher education research favours the unidimensional technical view of excellence and equals excellence with high achievement. From the standpoint of the present study, defining excellent university students solely via academic achievement indicators is problematic in several ways as discussed in the following sections.

Shortcomings of approaches equating excellence with high academic achievement

The negative side of high academic achievement.

Excellence, by its nature, is an inherently positive construct (Gardner, 2015 ). Likewise, high academic achievement has commonly been perceived as a surrogate of desirable and positive outcomes, linked for instance to job performance or earnings (e.g. French et al., 2014 ). Nevertheless, there are several less-considered negative aspects associated with high academic achievement, including problematic motivational patterns, an increased tendency towards academic dishonesty, and psychological vulnerability in high-achieving students. Performance pressure resulting from the high value placed on academic achievement may be deemed a common culprit of these issues (Bardach et al., 2020 ; Luthar & Kumar, 2018 ; Ma et al., 2013 ).

First, a matter of concern may be the motivation of high-achieving students that does not necessarily derive from genuine interest in the study material, but tends to be fuelled by the external pressure to stand out (Luthar & Kumar, 2018 ). As a consequence, high achievers may be more interested in obtaining a high GPA, high class ranks, and awards than in true learning (Geddes, 2011 ). In the classroom, high achievers tend to pursue performance-competitive goals, which means that they are primarily motivated by the desire to outperform their peers. On the contrary, the students who display a genuine interest in the course material and strive to develop knowledge and skills are lower achieving mastery-oriented individuals (Senko & Miles, 2008 ).

Even more problematic is the potential link between academic achievement and academic dishonesty. While evidence based on self-reported survey data suggests that students with higher GPA cheat less (Whitley, 1998 ), research based on observation of actual or experimentally-driven behaviour showed that high achievers behave in a dishonest way just as much as low achievers (e.g. Williamson & Assadi, 2005 ). Further, Yaniv et al. ( 2017 ) showed that under competitive conditions, high-achieving students (in terms of GPA, high-school matriculation average grades, and psychometric exam scores) were more likely to cheat in an examination compared to their lower achieving counterparts. The obvious discrepancy between survey-based and actual data can be explained by the inverse relationship between actual and self-reported cheating since the students who cheat more are also more likely to be dishonest in self-reports about their cheating (West et al., 2004 ).

These results suggest that high-achieving students tend to behave dishonestly at least in that they may pretend to behave in a more favourable way than they actually do. Since the desire to do better than others can significantly increase the likelihood of cheating (Van Yperen et al., 2011 ), the suggested link between academic achievement and academic dishonesty may be mediated by the above-mentioned performance-oriented motivation (Senko & Miles, 2008 ). In fact, both performance-oriented motivation and dishonest behaviour may be directly promoted by the high value placed on academic achievement (Bardach et al., 2020 ; Ma et al., 2013 ). With respect to cheating in the university setting, grade pressure was identified as one of its strongest determinants (Ma et al., 2013 ).

Finally, performance pressure can have detrimental effects on the well-being, healthy personal development, and even cognitive functioning of students. There is a consistent body of evidence showing that the highest-achieving students display the highest levels of both subjectively perceived stress and physiological stress reactions (Kötter et al., 2017 ; Yoo et al., 2021 ). The elevated levels of stress resulting from the high and ongoing pressure to achieve can make high-achieving students a particularly vulnerable group prone to psychological health issues, such as depression and anxiety, or to the misuse of drugs and alcohol (Luthar & Kumar, 2018 ). Moreover, the findings of Modrek and Kuhn ( 2017 ) suggest that high-performing students in demanding, highly competitive academic settings may be at risk not only with respect to their well-being, but also to cognitive regulation and independent learning skills.

Such findings further highlight the need for a more sustainable framework of excellence particularly in higher education settings. From this study’s perspective, linking excellence solely to high academic achievement may induce performance pressure, leading to detrimental effects on students’ motivation, moral behaviour, and healthy development, potentially resulting in high-achieving students displaying behavioural and motivational patterns incongruent with personal excellence attributes. Moreover, among the various occupational and age groups, university students tend to be the most psychologically vulnerable in terms of poor mental health outcomes (Evans et al., 2018 ; Stallman, 2010 ; Wittchen et al., 1998 ). Thus, we argue that university students could particularly benefit from a framework of excellence that attenuates the excessively high value placed on academic achievement.

Lack of attention to diversity in the student population

Currently, higher education is characterised by a substantial increase in diversity of the student body related to student demographics, socio-economic status, language, cultural and educational background, skills, values, and attitudes (Smit, 2012 ). This trend has been followed by the emerging discourse calling on universities to acknowledge and appreciate diversity, and to actively search for ways to understand student competences and find ways to recognise the dignity of difference (Sacks, 2002 ; Smit, 2012 ). The notion of excellence is in accordance with this discourse as it concerns student’s heterogeneity in terms of the diverse abilities, interests, dispositions, and ambitions of students. Since also diverse paths to excellence are acknowledged, excellence becomes a plural rather than a uniform concept (Terzi, 2020 ). In this respect, Gardner ( 2015 ) noted that

in the intellectual field alone there are many kinds of excellence. There is the kind of intellectual activity that leads to a new theory, and the kind that leads to a new machine. There is the mind that finds its most effective expression in teaching and the mind that is most at home in research. There is the mind that works best in quantitative terms and the mind that luxuriates in poetic imagery. (p. 127-128)

From this perspective, Gardner ( 2015 ) encouraged “to honour the many facets and depths and dimensions of human experience and to seek the many kinds of excellence of which the human spirit is capable” (p. 134).

The current research approach towards excellence in higher education, however, fails to consider the diversity of student biographies, experience, and competences promoting instead a very narrow view of excellence that can be achieved only by the students whose talents and interests match the one-sided criteria of excellence. Moreover, equating excellence with high academic achievement contradicts the call for a widening diversity in the student population and for addressing equity issues because it is inattentive to the vulnerable students. Specifically, using GPA as a proxy of excellence seems to put vulnerable students at a further disadvantage. GPA tends to be lowered, for instance, by students with learning difficulties or physical health issues, or by students who work during their studies (Bergey et al., 2017 ; DeBerard et al., 2004 ; Tessema et al., 2014 ). Thus, the narrow approach to sampling excellent students may overlook vulnerable individuals, such as students with conditions that affect their learning, those from disadvantaged backgrounds who work to pay for their university studies, individuals who approach learning tasks differently, and those with highly specialised talents, interests, creativity, or motivation (Renzulli & Reis, 2020 ).

The present paper adopts a view on excellence that refers to the culmination and realisation of an individual’s potential to the fullest extent, and it manifests itself in an individual-specific way by extraordinary doing and thinking (Astin & Antonio, 2012 ; Gardner, 2015 ). Indeed, the perception of excellence in this paper aligns with the current perspective on high ability and talent development. As Van de Vijver and Mathijssen ( 2024 ) suggest

the ultimate goal of talent development is self-actualization in the meaning of realizing one’s potential and having a meaningful way of living driven by self-determined goals that integrate personal interests and societal contributions. This also implies that a wide range of talents should be nurtured and developed, including moral talents, in order to be able to capture the uniqueness of each individual. (p. 34)

Thus, we argue that more attention should be paid to diversity in the student population and that a broader set of criteria needs to be employed to sample excellent university students.

The nature of excellence: the attribute of context specificity

In literature, two significant attributes of the construct of excellence have been identified, and research on individual-level excellence should align with these for conceptual and methodological soundness. These attributes are: (a) the attribute of diversity (Gardner, 2015 ; Terzi, 2020 ), as discussed above, and (b) the attribute of context specificity (Terzi, 2020 ), which is explored in this section.

Excellence is a social construct made real through social processes and interactions. By their definition, social constructs are complex, dynamic social realities that can be (re)interpreted and (re)shaped in different ways and hence, different populations and cultures may promote different meanings of excellence (Ferrari, 2002 ; Terzi, 2020 ; Young & Collin, 2004 ). Thus, the relevance of criteria employed to operationally define excellent individuals should closely match the perception of a prototypical excellent individual in the target population to enhance the ecological validity of a study. In other words, the fundamental task for research on individual-level excellence should be the rigorous conceptualisation and operationalisation of the phenomenon under investigation to ensure valid findings and meaningful conclusions (Mašková & Kučera, 2022 ; Terzi, 2020 ).

In this respect, occupational research focusing on excellent professionals in various occupations gives an example of good practice in dealing with the construct of excellence. In this area, the selection of excellent individuals has been based mainly on the evaluative judgements of a particular reference group in relation to its standards, such as awards received from the professional communities (e.g. Chen et al., 2020 ), nomination or recommendation by supervisors (e.g. Hirsch & Segolsson, 2021 ; Kallas, 2014 ), peers (e.g. Collinson, 1999 ), or students (in the case of teachers; e.g. Fichten et al., 2018 ). Thus, the methodologies of these studies reflect the context-dependent nature of excellence, since they operationalise excellence in accordance with its socially-construed definition arising out of the communities which excellent individuals are members of. The contextual relevance of criteria used to define and identify excellent university students in higher educational research is, however, unclear, since there is a lack of justification for the use of particular criteria in studies on excellent university students.

Research setting

The research was conducted in the setting of the Faculty of Education, University of South Bohemia (FE USB), which is a public higher education institution in the Czech Republic that ensures bachelor’s, master’s, and doctoral degree programmes mainly in teacher education, and provides also several non-pedagogical degree programmes, such as psychology, geography, informatics, and linguistics. In 2019, when the research was conducted, 2160 students were enrolled at the FE USB. Out of this number, 1693 were full-time students (71% females; 1% doctoral students; < 1% international students). The FE USB provides only Czech-language study programmes free of charge. The population of the Czech Republic is ethnically homogenous (Czech Statistical Office, 2014 ); thus, the number of minority students at the FE USB is negligible.

Research ethics

The research was undertaken in accordance with the tenets of the Declaration of Helsinki and was approved by the FE USB Ethics Committee (Ref No EK 003/2018). All participants approved informed consent statements before participating in the study.

Pilot study

There is a paucity of empirical data on how the construct of the excellent university student is perceived by teachers and students in various cultural settings. This study makes an initial step in attempting to fulfil this gap by investigating the perspective of the academic community at the FE USB. The purpose of this study is twofold. First, we aim at providing a comprehensive overview of the characteristics attributed to the excellent student by teachers and students. Second, we attempt to establish a realistic set of essential attributes that may be embodied by an actual student and to convert them into a rating scale. The results of this study should inform the procedure of the excellent student identification that is designed and implemented in the main study. The central research question for this study is:

How is excellence defined in university students?

In addition, we address the specific research sub-questions:

What are the attributes of the excellent student according to the FE USB academic community?

What are the essential attributes of the excellent student?

In this respect, we established three criteria, all of which need to be fulfilled for an attribute to be considered essential Footnote 1 : (a) the attribute is a core attribute of the excellent student, i.e. a student cannot be considered excellent if they fail to show the respective attribute, (b) the attribute is universal in that it applies to students across different disciplines and study levels, and (c) the attribute is broadly agreed upon by students and teaching staff members at the FE USB.

Participants

A total of 185 individuals participated in this study, thereof 26 teaching staff members (66% females, 77% assistant professors, 15% associate professors, 8% full professors, mean age = 45.92, SD  = 6.82) representing the various departments at the FE USB and 159 full-time students (73% females, mean age = 23.06, SD  = 3.82) pursuing bachelor’s or master’s degree courses of varying specialisations including teacher education, psychology, informatics, and geography. The first phase of the study included 107 student participants enrolled on a psychology course designed for students of various degree courses and study levels, and 14 teaching staff members who represented all the departments participating in full-time student education at the FE USB. To recruit teacher participants, the heads of respective departments were informed about the study aims and invited to either participate themselves or recommend a colleague who might be interested. The second phase involved 12 teacher participants and 52 student participants from various departments at the FE USB. The teacher and student participants were recruited through an e-mail invitation and classroom announcements (in the case of students). The participants of the third phase were 40 teacher education students enrolled in a psychology-focused course. The student participants of the first and third phase were invited to participate during their respective lectures.

In the first phase of the study, which aimed at providing a comprehensive description of the attributes of the excellent student, the student participants were asked to write a short essay in answer to the questions: “In your opinion, who is the excellent university (undergraduate and full-time) student? How do they typically behave and what characteristics make them stand out among other university students?” Concurrently, interviews were conducted with teacher participants (for the interview schedule see Supplementary Material 1 ). The recordings of the interviews, typically lasting 20—30 min, were transcribed and further analysed, along with the content of the essays, which varied from one to several paragraphs. To enhance the credibility of the findings, we subsequently shared a draft of the list of the attributes of the excellent student with the participants (Creswell, 2012 ). Specifically, we asked the entire group of student participants and two teacher participants to reflect on its accuracy.

In the second phase, which aimed at extracting a subset of the essential attributes of the excellent student, focus group discussions with students and teaching staff members at the FE USB were conducted. Focus group discussions were selected as the optimal research method because they facilitate gathering a broad range of perspectives while also providing valuable data on consensus and diversity among participants (Hennink, 2014 ). Four student focus group discussions and two teacher focus group discussions were conducted. The student focus group size varied from 12 to 15 participants, whereas the teacher focus groups comprised 5 and 7 teaching staff members. The duration of the focus group discussions ranged from 80 to 120 min. Each focus group discussion was moderated by the first author, accompanied by a research assistant (a trained psychology undergraduate student) responsible for taking detailed notes on the key points raised and any significant nonverbal behaviour. Subsequently, the first author reviewed the notes to prevent observer bias. Each session began with introductions and an overview of the study’s purpose, schedule, and ethical considerations. The participants then engaged in a data-generating activity where they discussed the relevance of the pre-established set of the excellent student’s attributes and suggested modifications (for the discussion guide see Supplementary Material 1 ). All focus group sessions were audio-recorded and the discussions were transcribed verbatim. After each session, the data were analysed to derive a preliminary set of the essential attributes of the excellent student, which was then presented to the participants in a consecutive focus group to discuss the credibility of the findings. In this step, we employed the process of progressive, iterative content validation (Kidd & Parshall, 2000 ). Data saturation was reached after the sixth focus group session when no new data emerged that would lead to further refining the final set of essential attributes of the excellent student (Saunders et al., 2018 ).

In the third and final phase, which aimed at developing an other-rating scale to assess an individual’s match with the essential attributes, the resulting list of essential attributes of the excellent university student was converted into an evaluative instrument by adding a Likert-type scale and instructions. The suitability of the other-rating scale for the purposes of identifying excellent students at the FE USB was tested by administering it to the participants involved in the third phase of the study with the instruction to assess a fellow student they considered excellent. In addition, the participants were asked to reflect on the accuracy of the list of essential attributes of the excellent university student to enhance the credibility of the results.

Qualitative analysis – interviews and essays

To process the qualitative data from the individual interviews with teachers and student essays, thematic analysis was used, which is a well-established method for identifying, analysing, and reporting themes within qualitative data (Boyatzis, 1998 ). A theme is a pattern found in data that describes and organises the dataset or even interprets aspects of the research topic. For the purpose of this study, inductive thematic analysis was conducted, which means that data were coded in an inductive (data-driven) way without being informed by a pre-existing coding frame (Boyatzis, 1998 ; Braun & Clarke, 2006 , 2013 ). To enhance the rigor of the analysis, multiple coders took part in the coding process to bring diverse perspectives on the data, thus resulting in a more robust data analysis and enhanced credibility of the analytical framework (Boyatzis, 1998 ; Olson et al., 2016 ). Specifically, the first author and two research assistants (trained psychology undergraduates) analysed the data collaboratively using the systematic six-stage procedure suggested by Braun and Clarke ( 2006 , 2013 ).

In the first phase of familiarisation with the data, each coder independently read and re-read all textual materials (interview transcripts and student essays) to identify potential patterns in the data.

In the second phase of generating initial codes, all coders produced preliminary codes, i.e. the most basic elements of raw data or information that can be assessed in a meaningful way regarding the research topic (Boyatzis, 1998 ) from the data. Coding was performed manually without the assistance of any commercially available software. During this phase, the coders met regularly to discuss the individually produced codes, which were refined, merged, and deleted to avoid redundant and irrelevant codes. This resulted in the early version of a codebook which was applied to the data set. The process of mutual discussions, revising and refining the codebook, and reapplying it to the data was repeated until full agreement on the coding system was reached.

In the third phase of searching for themes, the codes and the collated data relating to each code were reviewed to identify a thematic overlap of different codes. After discussion, the codes were sorted into potential themes.

In the fourth phase of reviewing themes, the collaborative analysis was followed by a revision of the themes, whereby the coders returned to all the coded data in the first step and then to the entire data set to ensure that the themes fit the data well. To determine whether the coders were consistent in assigning text segments to the themes, we calculated the percentage of agreement as suggested by Creswell ( 2012 ), which showed a 100% agreement among coders. As a result, a set of 24 coherent, distinctive, and conceptually significant themes was established to provide a meaningful overview of the data in terms of breath and diversity.

In the last phase of defining and naming themes, each theme was provided with a fitting label, description, and an illustrative sample of extracts from the data.

Qualitative analysis – focus group discussions

Qualitative content analysis was used to study the focus group discussions systematically (Krippendorff, 2019 ). The concept-driven (i.e. based on what is already known) and data-driven (i.e. based on the actual data) approaches of qualitative content analysis were combined to develop the main categories. These categories were based on the pre-established comprehensive set of the excellent student’s attributes, and they specified the essential observable qualities and behaviours related to such attributes in a data-driven way (Schreier, 2012 ). The transcripts were double-coded by two coders (the first author and the research assistant involved in the focus group sessions) after each successive focus group session. As in the above-described process of interviews and essay analysis, coding was performed manually without the assistance of any commercially available software.

In the first step, an initial coding frame was generated containing data both relevant and irrelevant to the research question to avoid bias when selecting the relevant parts of the material. The criteria for considering the data relevant were: (a) the attribute was a core attribute of the excellent student, i.e. it was necessary for a student to be considered excellent, (b) the attribute was universally applicable to students across disciplines and study levels, (c) the attribute matching criteria (a) and (b) was agreed upon within and between focus groups. The main criterion for considering the data irrelevant was that it described the non-essential attributes of an excellent student. For such attributes, broad agreement within and between focus groups was not reached in that one or more participants considered an attribute unnecessary/redundant and/or specifically related to a particular discipline and/or study level. The consistency of the coding between the two coders was checked with respect to relevant and irrelevant data.

The second step involved the creation of a substantive coding frame that applied only to the relevant data. The coders then jointly divided the material into coding units according to thematic criteria allowing each unit to correspond to one topic, which fit exactly one category in the coding frame (Schreier, 2012 ).

In the third step, they performed the coding independently, checked the consistency of the coding, and modified the coding frame until full agreement on the set of essential attributes of an excellent student was reached. Each essential attribute was then converted into an item referring to readily observable and quantifiable student behaviours and qualities.

In the last step, the final set of attributes was further analysed and structured in higher-order categories describing the nature of the essential attributes of the excellent student. The coders inductively generated three comprehensive and fittingly labelled categories, to which the respective attributes were assigned. Finally, informed by the conceptual underpinnings of the construct of excellence, they subsequently assigned each of these categories to an overarching dimension of either educational or personal excellence , which represent the basic conceptual distinction related to the construct (Ferrari, 2002 ; Miller & Kerr, 2002 ).

Main findings

The first phase of the study resulted in a set of 24 attributes of the excellent university student, validated through the member checking procedure. This set provides a comprehensive overview of characteristics attributed to the excellent student by the academic community at the FE USB. The attributes range from prerequisites or direct manifestations of professional success, such as cognitive abilities, integration of theory and practice, achievement, through inter- and intrapersonal skills, such as healthy self-esteem, respectful behaviour and good manners, to intrinsically motivated and proactive study behaviour, such as genuine study motivation, engagement in classes, and field of study as a hobby. The labels and descriptions of the attributes, along with sample quotes are presented in Supplementary Table  1 (see Supplementary Material 2 ).

The second phase of the study revealed that although all the attributes are perceived as desirable student characteristics, only a subset can be considered essential. During the focus group discussions, the participants acknowledged that reducing the entire set to a subset of core attributes was necessary because these attributes should pertain to a real person: “An excellent student is not a superhero, just a human being of flesh and blood that has the right to not be perfect (student participant, 3rd student focus group)”. Nevertheless, the crucial role of personal excellence in the conceptualisation of the excellent student was strongly emphasised: “A good student has to be a good person in the first place. They can have the best grades in the world and the rest, but it matters little if they are a horrible person (student participant, 2nd student focus group)”. In this respect, a final set of 10 essential attributes of the excellent student was established that matched the dualistic conceptualisation of the construct of excellence. Specifically, the three essential attributes of thoroughness and punctuality, deep and complex knowledge, and integration of theory and practice were aggregated into the category labelled expertness . Another set of four essential attributes, (engagement in classes, openness to interdisciplinarity, openness to extra learning and experience, and field of study as a hobby) were aggregated into the category labelled proactive learning . Finally, the three essential attributes of fairness and honesty, cooperativeness and helpfulness, and self-reflection were aggregated into the category labelled being a good person . Whereas the category of being a good person matches the personal excellence dimension, the expertness and proactive learning categories correspond to the educational excellence dimension. The 10 items describing the essential attributes of an excellent student are displayed in Table  1 . The presentation of the items is structured according to the overarching categories and dimensions, which altogether constitute the conceptual framework of the excellent university student.

Finally, the third phase of the study, which aimed at pre-testing the newly developed rating scale based on the 10 items, identified no problems concerning the clarity of the instructions, item formulation, or the feasibility of assessment. In addition, the list of essential attributes was validated by the participants. For the instructions and the answer options regarding the rating scale see Table  1 .

Other relevant findings

To gain a comprehensive picture of the conceptual framework of the excellent university student, further relevant findings that resulted from the focus group discussions have to be acknowledged. Specifically, two additional attributes – genuine study motivation and academic achievement – were considered a fundamental part of the conceptual framework of the excellent student although items referring to these attributes were not included in the rating scale.

First, the focus group discussions revealed that genuine study motivation was broadly perceived as a core attribute of the excellent student. However, it was not included in the rating scale due to the fact that in current psychological research, it is uncommon for an external observer to assess an individual’s motivation. It is also questionable whether such methodology would generate reliable results unless combined with other approaches (Fulmer & Frijters, 2009 ). Nevertheless, study participants perceived that genuine study motivation is inherently expressed through the behaviours and qualities referring to the excellent student’s essential attributes:

An individual has to be genuinely motivated to display all the qualities we are talking about here [participants were discussing the final set of essential attributes]. I cannot imagine that without being genuinely motivated an individual could be like this. I mean, if they were just extrinsically motivated, maybe they would display one or two of those qualities, but definitely not the entire set. Genuine motivation is a fundamental prerequisite for a student to be excellent. (teacher participant, 2 nd teacher focus group)

Thus, although the rating scale lacks an item explicitly referring to genuine study motivation, this attribute is considered an inherent underlying attribute upon which the conceptual framework of the excellent university student is built. For purposes of further empirical investigation, genuine study motivation was conceptualised as a combination of mastery-goal orientation and the deep learning approach to learning (Biggs, 1987 ; Elliot & Harackiewicz, 1996 ). For further details see the main study and Mašková and Nohavová ( 2019 ).

Second, academic achievement plays an important role in the conceptual framework of the excellent student although the participants had moderate and non-specific expectations for the academic achievement of the excellent student. The participants acknowledged that a student’s excellence should be translated into more tangible outcomes: “An excellent student should excel in something, but not necessarily in everything (student participant, 3rd student focus group)”. Further, grades were perceived as a complementary indicator of student excellence since it is not necessary for an excellent student to achieve the best grades although they need to have an above-average GPA. “Grades aren’t everything; however, a student with under-average grades definitely cannot be considered excellent (teacher participant, 1st teacher focus group)”. Academic achievement was not integrated into the rating scale, since objective methods of academic achievement assessment were available, and they were preferred to external assessment.

The conceptual framework of the excellent university student, displayed in Fig.  1 , consists of 10 items organised within the dimensions of educational and personal excellence. The dimension of educational excellence is complemented by academic achievement and both dimensions are underpinned by genuine study motivation.

A conceptual framework of the excellent university student

This study aimed to fill the gap in the empirically-based conceptualisations of the excellent university student by providing the perspective of the FE USB academic community. To fulfil the objectives of the study, three subsequent steps were undertaken. First, based on the data from interviews with teachers and student essays, we established a comprehensive overview of the desirable characteristics attributed to the excellent student. Second, based on data from focus group discussions, a subset of broadly agreed-upon essential attributes of the excellent student was established. Finally, we developed a rating scale based on these attributes, allowing for assessment by teachers and peers. Importantly, our results support the multidimensionality of the construct of excellence, recognised in theoretical literature but neglected empirically (e.g. Ferrari, 2002 ; Parkash & Waks, 1985, as cited in Bruno-Jofré & Hills, 2011 ). The 24 characteristics constituting the comprehensive depiction of the excellent student are congruent with the theoretical underpinnings of excellence in that they include but also go beyond academic achievement. The identified excellence-related qualities range from cognitive abilities (Rohde & Thompson, 2007 ), through good working habits (e.g. thoroughness and punctuality, time management skills; Franks, 1996 ), to qualities associated with personal mastery (e.g. self-development, genuine study motivation; Erez, 2004 ), as well as morality and virtuousness (e.g. fairness and honesty, cooperativeness and helpfulness; Li, 2004 ). Because of its complexity, the overview largely overlaps with the conceptual framework of the ideal student by Wong et al. ( 2021 ). In contrast, the more parsimonious conceptual framework of the excellent student based on three categories (expertness, proactive learning, and being a good person) and two overarching dimensions (educational and personal excellence), is more realistic and applicable to real students. The category of expertness emphasises mastery of study-related knowledge and skills, aligning with the technical dimension of excellence (e.g. Li, 2004 ; Parkash and Waks, 1985, cited in Bruno-Jofré & Hills, 2011 ). The category of proactive learning involves students’ active engagement beyond requirements, reflecting the conceptual characteristics of taking responsibility for their own learning, curiosity, and self-motivation (Erez, 2004 ; Llamas, 2006 ). The category of being a good person represents the ethical aspect of excellence, such as morality, virtuousness, and supportiveness towards peers (Ferrari, 2002 ; Li, 2004 ; Llamas, 2006 ; Wong et al., 2021 ).

The findings lay the groundwork for reconsidering individual-level excellence as a multifaceted phenomenon that goes beyond academic achievement alone. Moreover, they have practical value for higher education institutions, offering a conceptual framework for understanding desirable student qualities.

The objective of this study is to develop and implement a procedure for identifying excellent students. Specifically, we aim to identify students who meet all the conceptual criteria of excellence as presented in the pilot study. The key research question specific to this study is:

How can students meeting all the conceptual criteria of excellence be identified?

Three groups of participants took part in the study: members of the teaching staff (teachers), students nominated as excellent by their teachers (nominees), and the nominees’ fellow students (peers).

Regarding the participating teachers, only holders of a PhD degree and primary faculty members at the FE USB participated in the study. External teaching staff and lecturers without a PhD degree were excluded since these teachers may have had limited contact with students. 106 teachers fitting the above-mentioned criteria were invited to participate via a paper form delivered to them by the assistants of their respective departments; thereof 53 (50%) were both willing and able to participate since they knew at least one student who they considered excellent.

All participating nominees were full-time students at the FE USB pursuing a bachelor’s or master’s degree. Doctoral students were excluded, since their study duties as well as their roles at the university significantly differ from that of undergraduate students. Part-time students were excluded because they attend in-person lessons less frequently and have limited contact with both teachers and peers. Out of the 80 nominees who were invited to participate personally or by e-mail, 60 (75%) actually participated; thereof 49 were once nominees and 11 were multiple nominees (nominated by more than one teacher). Out of the 60 participating nominees, 16 were classified as the most eligible nominees (based on the criteria mentioned in the Procedure section), and 13 of the most eligible nominees actually participated (3 once nominees and 10 multiple nominees).

A peer was considered a fellow student enrolled in the same study programme and in the same year of study as the most eligible nominee. To select suitable peers, the list of each of the nominee’s peers was displayed in the university information system. Peers who were nominees themselves were excluded from the list to reduce assessment bias potentially resulting from different perspectives on the assessed behaviours. Based on the course record data of the students available in the system, suitable peers were ordered according to the number of classes they had shared with the nominee in the recent academic year. Peers sharing exactly the same course record with the nominee were listed randomly. Four peers at the top of the list were invited to participate via e-mail. If one or more peers refused to participate, a subsequent peer was invited until four peers for each of the 13 most eligible nominees agreed to participate. Totally, 79 peers were invited to participate, thereof 52 (66%) actually participated.

For the purposes of assessment of a nominee by teachers and peers, we employed the rating scale of the excellent student’s essential attributes (further referred to as the rating scale; see Table  1 ).

Further, two types of objective indicators of academic achievement were formulated for the purposes of academic achievement assessment: (a) GPA and (b) other academic achievement indicators falling into four distinct categories. Data obtained in the pilot study suggest that GPA can be considered a legitimate indicator of excellence in higher education. To further confirm that GPA was a suitable indicator in the setting of the FE USB, we examined the link between GPA and the underlying attribute of genuine study motivation (see the pilot study). The results, which were published elsewhere (see Mašková & Nohavová,  2019 ), revealed that GPA does not contradict the underlying motivational attribute. These findings allowed us to conclude that the use of cumulative GPA for excellent student identification was acceptable. Since academic achievement is a multidimensional construct (Steinmayr et al., 2015 ), besides GPA, we considered other significant indicators of academic achievement of contextual relevance for our research setting: (a) significant achievement in a subject-related contest or student competition (i.e. awards for various kinds of achievement, e.g. The Outstanding Thesis Award), (b) membership of academic organisations/societies (e.g. University Senate), (c) a leadership role in extracurricular activities (e.g. Biology Olympiad organising committee member), and (d) significant achievement in research (e.g. authorship of a peer reviewed publication; Benbow, 1992 ; Kuncel et al., 2001 ; Mould & DeLoach, 2017 ).

The procedure of excellent student identification was grounded in a multisource assessment approach, which enhances the validity of the results by requiring convergent outcomes across multiple sources for a student to be considered excellent (Mathison, 1988 ). The procedure comprised three phases: teacher nomination and assessment, academic achievement assessment, and peer assessment. Each phase involved collecting and evaluating the data (objective data on academic achievement and subjective teacher- and peer-level data) against the set criteria – eligibility thresholds. The procedure and eligibility criteria are displayed graphically in Fig.  2 . An overview of all data collected and evaluated is displayed in Table  2 .

A procedure of excellent student identification

Since we considered teachers the most qualified source for student assessment, the initial step was to ask teachers to nominate the students they considered excellent. At the same time, teachers assessed the nominees on the rating scale. All eligible teaching staff members were provided with a form that asked them to nominate up to three students they considered excellent according to their own criteria of excellence, and to assess them on the rating scale. To ensure the anonymity of the responses, no personal identification was required. Participants were asked to place the forms in sealed boxes in the office of their respective department assistants. The attached instructions asked them not to inform students about the ongoing research to avoid (a) familiarising the nominees with the research interest until the investigation was finalised, (b) promoting an undesirable competitive environment among students, and (c) hurting the feelings of non-nominated students. To ensure that the teachers’ own criteria of excellence corresponded with the perception of the prototypical excellent university student at the FE USB, we set an initial eligibility threshold: a nominee should score at least something between on each of the rating scale items. Therefore, a nominee scoring disagree or fully disagree on any of the rating scale items in the teacher assessment phase would not be further considered an eligible candidate for the study. In sum, 80 students were nominated, thereof 15 by more than one teacher. All nominees passed the initial eligibility threshold.

Subsequently, cumulative GPA and data on the other academic achievement indicators were obtained from the participating nominees. Out of the 80 nominees, 60 agreed to participate in an online survey that asked them to provide basic demographic characteristics, academic achievement indicators (cumulative GPA and data on the other four academic achievement indicators), and to complete a set of psychological questionnaires (not relevant for the present study). The obtained academic achievement data were verified to the highest possible degree by consulting external sources, such as university records. Based on the findings of the pilot study and findings by Mašková and Nohavová ( 2019 ), we set the GPA cut-off threshold that a student needs to pass to be considered excellent. This cut-off value should distinguish between above average and below average students in terms of grades. Whereas the first can be conceptually considered excellent, the latter cannot. Since we had found that the mean value of cumulative GPA in a sample of second-year students at the FE USB was 2.13 (Mašková & Nohavová,  2019 ), we set the GPA cut-off value to 2.0 Footnote 2 after taking into consideration the effect of GPA inflation. Footnote 3 Regarding the other indicators of academic achievement, an eligibility threshold was set for a student to comply with at least one of the indicators to be considered excellent.

The GPA cut-off threshold was passed by 34 once nominees and 10 multiple nominees. Thereof, 18 once nominees and all 10 multiple nominees complied with one other academic achievement indicator. Additionally, six once nominees and seven multiple nominees complied with more than one other academic achievement indicator. The high number of eligible nominees necessitated narrowing the sample to the most eligible ones to make the subsequent step (peer assessment) manageable. In this respect, our decisions were guided by the principles of the multisource assessment methodology, requiring convergence of outcomes across multiple sources to enhance the research validity (Mathison, 1988 ). We primarily relied on the convergence of multiple nominators, as teacher nomination and assessment were more comprehensive, covering both dimensions of excellence. However, this approach was exclusive for once nominees. Thus, for once nominees, the subjective data obtained by a single source had to be confirmed by available objective data. Consequently, we narrowed the pool of candidates to (a) multiple nominees who passed the academic achievement thresholds and (b) once nominees who passed the GPA eligibility threshold and complied with more than one other academic achievement indicator. The 16 most eligible participants were contacted by a research assistant and asked whether they agreed with the peer assessment. Out of the 16 most eligible candidates, 13 agreed and signed an additional informed consent. The participants were informed about the nature of the peer assessment procedure, and that their peers would assess their common study-related behaviour.

Finally, peer assessment was considered an integral part of the procedure of excellent student identification. Given that peers see their student colleagues from a different perspective than teachers, they can provide unique information beyond teacher assessment (Lavrijsen & Verschueren, 2020 ). Peers are likely to know the nominees for a longer time (since the beginning of their studies), and to observe them on more occasions and in less formal settings than teachers, who usually meet them on limited occasions (mainly in classes of short-term courses). Thus, peers tend to be highly accurate in their judgements of each other’s qualities (Funder, 2012 ). Research has shown that four peer assessors are able to achieve satisfactory inter-rater reliability (Conway & Huffcutt, 1997 ). Thus, we asked four suitable peers to assess a candidate using the rating scale in an online form. Only such peers were invited to participate in the study who objectively (based on the data of the course records in the university information system) shared most of the classes with the nominee, and thus were expected to know the nominee well. Nevertheless, to ensure that the peers actually knew the nominee, they were asked to proceed with the assessment only if they perceived their level of familiarity with the nominee sufficient to assess their study-related behaviour and qualities displayed in the university setting. The participants (peers) were ensured about the confidentiality of the data, and they submitted their responses anonymously with no personal identification. The administration of the peer assessment phase was ensured by a research assistant who was informed about the participants’ identities but had no access to the data. The researchers who could access the data had no information about the participants’ identities.

For each candidate, the ratings were first assessed separately to determine the extent to which they match the attributes, and to exclude candidates that clearly mismatch any of the attributes. Although several studies suggest that the rater-ratee interpersonal relationship has only a minimal effect on peer assessment accuracy in higher education (e.g. Azarnoosh, 2013 ; Magin, 2001 ), the severity bias deriving from negative interpersonal affects could still influence individual ratings (Taggar & Brown, 2006 ). Thus, when setting the baseline eligibility threshold for peer assessment, we paid attention primarily to inter-rater agreement which is associated with enhanced validity (Conway & Huffcutt, 1997 ). The eligibility threshold was set as follows: an inter-agreement occurs when a nominee scores at least something between on each of the rating scale items according to at least three peer assessors. On the contrary, should a nominee score disagree or fully disagree on a single item according to two or more assessors, this nominee would no longer be considered an eligible candidate for the study. The evaluation of the individual peer assessments revealed that seven multiple nominees and all three once nominees satisfied the eligibility threshold. In contrast, three multiple nominees were excluded as they were assigned ratings of somewhat disagree or fully disagree on the same item by more than one peer assessor. For the three excluded candidates, these items were 2, 4, and 10, respectively (see Table  1 for item wording).

The second eligibility threshold was based on composite scores for each of the three scales (expertness, proactive learning, and being a good person), derived from the combined teacher and peer ratings. To determine an individual’s scale composite scores, we first calculated the item composite scores, which involved summing all teacher and peer scores for each item and dividing by the number of assessors who provided valid ratings (an invalid rating was considered 0 =  I don’t know/I’m unable to assess ). Then, we calculated the scale composite scores by averaging the item composite scores across all items within that scale. To ensure that a candidate matched each of the three facets of excellence, we established that each of their scale composite scores should equal or be higher than 4.0. All 10 remaining candidates passed this threshold.

To prevent participants from biasing the results of the investigation, the basis of participant selection and participants’ role in the present study was deliberately withheld until the investigation was finalised. All participating nominees were debriefed immediately after it ended. The debriefing provided the information that they had been nominated as excellent. Concurrently, they were asked not to share this information with their fellow students to avoid hurting their feelings.

Psychometric properties of the rating scale

We first tested whether the developed instrument had satisfactory psychometric properties before excellent students’ profiles were analysed. In this respect, item analysis and scale properties were evaluated using the full set of ratings of 10 excellent students. Although these ratings are not independent, since multiple assessors rated the same ratee, using the full set of ratings was necessary to improve the accuracy of the results by obtaining higher rater-to-item ratio (Stewart et al., 2009 ). Still, the small sample size of 63 ratings only informed of the general trends in item properties (Penfield, 2013 ). The main weakness detected was the low reliability estimate of the expertness scale (α = 0.59; ω = 0.66), which was considered marginally acceptable given the tentative nature of the results and explorative purpose of the study (Hair et al., 2018 ). In sum, the corrected item-total correlation coefficients and reliability estimates indicate an acceptable homogeneity of items and internal consistency of the three scales, which correspond to three distinct facets of the conceptual framework of the excellent university student. Thus, the instrument was left unmodified for the purposes of this study. Item and scale properties are displayed in Table  3 .

Excellent students’ profiles

The pilot implementation of the procedure of excellent student identification resulted in a final sample of 10 excellent students. The excellent student sample included two males and eight females; their age ranged from 20 to 28 years (mean age = 24.2,  SD  = 1.99). All excellent students were enrolled in teacher education study programmes at the FE USB. One student was pursuing a bachelor’s degree, the remaining students were studying on a master’s programme. An overview of their background- and excellence-related data is presented in Table  4 , and a detailed overview of assessment-related data can be found in Supplementary Table 2 (see Supplementary Material 2 ).

The highest number of nominations in the sample, which exceeded the modus number of two nominations, was reached by student “A”, who was nominated six times. Student “A” displayed also a very high absolute value of GPA = 1.08, which nearly corresponds to straight A’s, and complied with three out of the four other achievement indicators. Likewise, her composite scores were the highest for all three scales compared to other excellent students. The highest absolute value of GPA = 1.0, which corresponds to straight A’s, was displayed by student “B”, who, on the other side, complied with a single other academic achievement indicator. In contrast, student “I”, who was derived from the group of once nominees, was unique in that she complied with all four other academic achievement indicators.

Table 5 presents the individual rankings based on the composite scores for the scales of expertness, proactive learning, and being a good person, along with the respective number of nominations and objective academic achievement indicators a student complied with. The ranking based on expertness scores showed that the most highly ranked students were those with the highest number of teacher nominations and exceeding the modus of two nominations. Likewise, with exception of student “C” who displayed the lowest GPA in the sample, the nominees who ranked highest were also those with the highest absolute value of GPA. Proactive learning scores, on the other hand, tended to be associated with the number of other academic achievement indicators a student complied with. Additionally, compared to students who ranked lower, the most highly ranked students had gained significant achievement in a subject-related contest or student competition, and were members of academic organisations/societies. Regarding scores on the being-a-good-person scale, the highest rank was achieved by students “A” with the highest number of nominations and “I” who complied with all other academic achievement indicators. For the remaining students, there was no clear pattern of association between the scores on the being-a-good-person scale and academic achievement.

Figure  3 displays the inter- and intra-individual variabilities in the individual scale composite scores for expertness, proactive learning, and being a good person. The individual profiles based on the scores of the three scales tend to have non-flat and individually-unique shapes indicating that (a) the scales adequately represent the essential attributes of the prototypical excellent student as a multifaceted rather than unidimensional construct, and (b) individuals differ in terms of achieving the highest/lowest scores on distinct scales in a unique way.

A line graph of individual composite scores for expertness, proactive learning, and being-a-good-person scales

This study presents the results of the implementation of a specific methodological framework to identify excellent university students, which is based on a multisource assessment of multiple contextually relevant criteria of excellence. Specifically, a scale of the excellent student’s essential attributes and objective academic achievement indicators were employed. The identification of excellent students was informed by subjective teacher- and peer-level data on the rating scale (comprising of the subscales of expertness, proactive learning, and being a good person) and objective data on academic achievement. Both types of data were evaluated against the set eligibility criteria in order to select a final excellent student sample that reliably met all the conceptually derived criteria of excellence. In line with the nature of excellence as a plural rather than uniform construct (Gardner, 2015 ), we intentionally set the criteria broad and flexible to maintain diversity in the sample. As a result, the students in the final sample were excellent in their unique ways and, with the exception of student “A” who manifested excellence in every aspect, their major strengths lay in various areas. In addition, the differences in job status and involvement of vulnerable students with conditions affecting their learning (learning difficulties in student “D” and chronic medical conditions in student “E”) indicate that the developed methodological framework respects diversity in the student population.

The data generated by implementing the framework at the FE USB provides findings that support the need to (a) use multiple sources in student assessment and to (b) apply a multifaceted approach to excellence. First, the teacher and peer assessment discrepancies resulting in the exclusion of three of the most eligible candidates highlighted the importance of relying on more than one source in the subjective assessment of a student to ensure the validity of the results. Such a discrepancy implies that the teacher’s view may be biased due to limited exposure to only a narrow portion of a student’s behaviour and/or qualities. For example, high engagement in classes may be limited only to a teacher’s classes/subjects, and the qualities of a good person may apply to teacher-student interaction but not student–student interaction. Thus, to gain a holistic picture of a student’s behaviour and qualities displayed in various situational contexts, both teacher and peer assessment are required as each source can provide important and unique information.

Second, we found that both subjective and objective data were an integral part of the developed framework. In this respect, although expertness was likely to be associated with GPA, GPA tended to be an unreliable indicator of mastery of study-related knowledge and skills. Support for this argument can be found in student “C” who ranked high in expertness despite showing the lowest GPA in the excellent student sample. This argument is further supported by the case of one of the most eligible candidates who was rated low on one of the expertness scale items although they passed the GPA threshold. Further, the fact that several nominees did not pass the GPA threshold shows that teacher assessment alone is not a sufficient indicator of educational excellence unless corroborated by objective measures. This discrepancy may be explained by a large influence of other student characteristics, particularly perceived engagement, on teacher and peer nominations. Such influence was found to bias identification of students with high abilities (Lavrijsen & Verschueren, 2020 ). Thus, by combining the subjective assessment of educational excellence-related attributes with objective academic achievement assessment, it is possible to reliably identify educationally excellent students. From the perspective of personal excellence assessment, we may conclude that the being-a-good-person scale was an irreplaceable part of the identification method, since it was independent of educational excellence-related data. Supported by the ultimate exclusion of another eligible candidate who was rated low on the being-a-good-person item, we argue that GPA or any academic achievement measure alone cannot guarantee that high-achieving students also display a moral and virtuous character. These findings highlight the requirement to assess the two dimensions of excellence simultaneously to sample such students who meet the conceptual criteria of excellence in higher education.

In conclusion, the methodology of the multisource assessment of multiple criteria of excellence seems to be an appropriate method to reduce bias in excellent student sampling.

General discussion

This paper was underpinned by two main research questions: How is excellence defined in university students? How can students meeting all the conceptual criteria of excellence be identified? To answer these questions, two studies were conducted at a higher education institution in the Czech Republic.

With regard to the first research question, our findings corroborated the theoretical assumptions that the excellent student is an individual who embodies both educational and personal excellence (e.g. Ferrari, 2002 ). These dimensions of excellence were found to be independent of each other (as discussed later), yet conceptually, they are co-existing entities that should occur simultaneously in an individual for them to be considered truly excellent. In this complex view, individual-level excellence refers to students who are deeply knowledgeable, capable of turning their knowledge and skills into action to achieve desirable high-quality outcomes, engaged in learning, and seeking the enhancement of knowledge and experience by doing more than what is required. Concurrently, they are prosocial, moral, self-reflective, and genuinely motivated as that they adopt mastery-goal orientation and a deep learning approach to learning (Biggs, 1987 ; Elliot & Harackiewicz, 1996 ).

To provide a clear answer to the second research question, we developed and piloted a methodological framework based on the two-dimensional concept of excellence. Educational excellence was covered by subjective measures: scales of expertness and proactive learning, as well as objective measures: cumulative GPA and four other academic achievement criteria. Personal excellence, which could hardly be covered by objective indicators, was addressed by the subjective measure of the being-a-good-person scale. The multisource assessment procedure of excellent student identification was initiated by teacher nominations and assessment, and followed by academic achievement assessment and peer assessment. Before providing readers with more specific guidelines on how to identify excellent university students in more general settings, it is necessary to review and integrate the outputs generated during the process of excellent student identification.

The present research revealed that educational and personal excellence are mutually independent, since personal excellence cannot be reliably predicted from educational excellence indicators. In contrast, various indicators of educational excellence seem to be interrelated to a large extent. First, expertness, which refers to mastery of study-related knowledge and skills, tends to be closely linked to (a) the highest GPA values fully or nearly corresponding to straight A’s and (b) the highest number of nominations. Second, proactive learning, which refers to students’ engagement in learning and the enhancement of knowledge and experience by doing more than what is required, might be to some extent indicated by other academic achievement indicators (both in terms of quality and quantity).

Considering the procedural aspects of excellent student identification, the method of nomination, which has been usually employed in research on individual-level occupational excellence (e.g. Kallas, 2014 ), might be one of the most crucial steps in sampling excellent individuals. Our findings confirm that teachers nominated only such students that (at least in the nominator’s view) complied with the agreed-upon socially-construed definition of a prototypical excellent student arising from the academic community at our particular institution. However, since about one-fourth of the nominees displayed under-average GPA, the subjective assessment of educational excellence needs to be combined with the objective assessment of academic achievement to prevent nomination bias and ensure a reliable evaluation of educational excellence. Further, a bias resulting from limited exposure to only a narrow portion of a student’s behaviour and/or qualities in specific situational contexts can be reduced by combining the perspective of teachers with that of peers. The integration of various perspectives is especially important in the evaluation of personal excellence, which cannot be corroborated by objective measures.

Based on the synthesis of the above-presented findings, a more straightforward methodology for excellent student identification can be proposed. Considering that only such individuals are nominated, who (at least from the nominator’s perspective) comply with the attributes related to personal excellence, the collection and cross validation of both teacher and peer nominations could ensure that only personally excellent individuals are included in the pool of nominees. The nomination phase should be followed by the assessment of objective achievement indicators. In this respect, we assume that the criteria of excellence would most likely be met by multiple nominees who display high GPA and comply with multiple other academic achievement indicators. Nevertheless, a cautious approach towards the procedure of peer nomination is warranted. It is advisable to invite only a small group of peer nominators, since the invitation of the entire student community at an institution from which an excellent student sample should be drawn could lead to (a) promoting an undesirable competitive environment among students and (b) unintentional prior familiarisation of the selected excellent students with the research interest, which would disallow researchers to make participants blind (to deliberately withhold key information from the participants until the investigation is finalised) if required. For a step-by-step guideline for implementing the framework in general university settings, see Supplementary Material 3 .

Limitations

The main limitation of the new conceptual and methodological framework of excellence is that it was developed within the culturally and contextually specific setting of a single higher education institution. Regarding the conceptual framework of the excellent university student, it may clearly serve as a solid base for further research to build upon; nevertheless, it reflects the views of a specific academic community which can differ cross-institutionally as well as cross-culturally. The limitation of reduced generalisability applies also for the methodological framework, which is based on preliminary findings from a limited number of participants. In particular, the other-rating scale should be considered a tentative instrument that needs to be subjected to further psychometric analyses.

A related limitation is the specific context of a small higher education institution. First, the settings of a small institution enable a more convenient assessment of students due to the smaller number of nominees. In this respect, we expect that implementing the framework in the settings of larger institutions will prove to be more challenging. Second, teacher nomination and assessment, and especially peer assessment, depend on the extent of familiarity with nominees, which is facilitated by the setting of an institution with smaller classes, and groups of fellow students that tend to know each other well. In this study, we relied on the results of peer assessment with reasonable confidence since the addressed peer assessors regularly interacted with and observed the target student in class, a factor which could help them provide fitting ratings. Thus, the level of familiarity between peers and nominees was not pre-assessed. Such pre-assessment is, however, advisable when implementing the framework in the settings of larger institutions. Likewise, in this study, we did not assess the closeness of friendship between the peer and the nominee. This procedure, however, may be useful when a large pool of suitable peers is available, and it is necessary to standardise the peer assessors (e.g. only ratings by peers in a neutral relationship with a ratee may be considered). Further, the requirement of a reasonable extent of mutual familiarity among students and teachers makes the framework less suitable for part-time students. In addition, the proposed framework is better suited for small-scale studies with a qualitative research design that requires only a relatively small sample of subjects.

Finally, the comprehensive framework of excellence was primarily developed for research purposes to provide a conceptually and methodologically sound method of sampling excellent students. Consequently, the identification procedure required narrowing the final sample to students meeting all set criteria of excellence, with convergence of outcomes across multiple assessment sources. However, a weakness of this procedure is that students whose qualities are overlooked by teachers and/or peers may be excluded, as teacher/peer nomination, along with their convergence, are integral to the proposed identification process. Theoretically, this disadvantage could be addressed by initially assessing academic achievement before moving on to teacher and peer assessment (without nomination). However, implementing this approach could pose significant challenges, especially with a large student population, making the identification process exceedingly complex. Nevertheless, when the framework is intended for talent development rather than research, adjustments to the identification procedure are essential to guarantee a wider pool of candidates, providing opportunities for talent development. This may involve eliminating the need for convergence of assessment sources.

This research presents a comprehensive framework of excellence in higher education that (a) recognises both academic achievement and the personal qualities of a student, (b) acknowledges the variability of student potential that leads to different ways in which excellence manifests itself, and (c) reflects the nature of excellence as a contextually dependent social construct. As a result, this research represents an initial step towards searching for, identifying, and examining truly excellent university students, while also opening up a fruitful research area. With the aid of the framework, educational and psychological research could learn more about excellent individuals, recognise their strengths, and the paths that lead them to becoming excellent. Additionally, their post-university careers can be followed and the assumed transfer of higher education excellence to occupational excellence could be investigated more closely.

Within the article, the following terminology is used for clarity: a core attribute is one that a student must demonstrate to be considered excellent, while an essential attribute meets all three criteria set by the authors. Participants determine whether an attribute is core based on their judgment, while authors determine whether an attribute is essential based on all the data collected during focus group discussions.

According to the Czech university grading system, the best grade is 1 (= A), the worst is 4 (= F). Hence, the higher absolute value of GPA indicates poorer performance.

GPA inflation refers to an upward shift in university students’ GPA over an extended period of time without a corresponding increase in their academic ability. Consequently, GPA could exhibit an inconsistent pattern of development over time, typically a sharp decrease in the second semester followed by a steady increase during the later periods of study before a repeated drop in the final term (Grove & Wasserman, 2004 ). Thus, we expect that second-year students who participated in the study by Mašková and Nohavová ( 2019 ) may exhibit a worse GPA compared to students of other years of study. Consequently, the observed mean GPA was rounded to a higher GPA threshold value.

Al, S. L., Abulaban, A. A., Abulaban, A. A., Merdad, A., Baghlaf, S., Algethami, A., Abu-Shanab, J., & Balkhoyor, A. (2015). Factors potentially influencing academic performance among medical students. Advances in Medical Education and Practice, 6 , 65–75. https://doi.org/10.2147/AMEP.S69304

Article   Google Scholar  

Arnett, J. J., & Schwab, J. (2012). The Clark University poll of emerging adults: Thriving, struggling and hopeful . https://www2.clarku.edu/clark-poll-emerging-adults/pdfs/clark-university-poll-emerging-adults-findings.pdf

Astin, A. W., & Antonio, A. L. (2012). Assessment to excellence : The philosophy and practice of assessment and evaluation in higher education (2nd ed.). Rowman & Littlefield Publishers, Inc.

Azarnoosh, M. (2013). Peer assessment in an EFL context: Attitudes and friendship bias. Language Testing in Asia, 3 (1), 1–10. https://doi.org/10.1186/2229-0443-3-11

Bardach, L., Oczlon, S., Pietschnig, J., & Lüftenegger, M. (2020). Has achievement goal theory been right? A meta-analysis of the relation between goal structures and personal achievement goals. Journal of Educational Psychology, 112 (6), 1197–1220. https://doi.org/10.1037/edu0000419

Benbow, C. P. (1992). Academic achievement in mathematics and science of students between ages 13 and 23: Are there differences among students in the top one percent of mathematical ability? Journal of Educational Psychology, 84 (1), 51–61. https://doi.org/10.1037/0022-0663.84.1.51

Bergey, B. W., Deacon, S. H., & Parrila, R. K. (2017). Metacognitive reading and study strategies and academic achievement of university students with and without a history of reading difficulties. Journal of Learning Disabilities, 50 (1), 81–94. https://doi.org/10.1177/0022219415597020

Biggs, J. B. (1987).  Study Process Questionnaire manual. Student approaches to learning and studying . Australian Council for Educational Research Ltd.

Boyatzis, R. E. (1998). Transforming qualitative information: Thematic analysis and code development . SAGE Publications, Inc.

Braun, V., & Clarke, V. (2013). Successful qualitative research: A practical guide for beginners . SAGE Publications Ltd.

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3 (2), 77–101. https://doi.org/10.1191/1478088706qp063oa

Bruno-Jofré, R., & Hills, G. (2011). Changing visions of excellence in Ontario School Policy: The cases of living and learning and for the love of learning. Educational Theory, 61 (3), 335–349. https://doi.org/10.1111/j.1741-5446.2011.00407.x

Brusoni, M., Damian, R., Sauri, J. G., Jackson, S., Komurcugil, H., Malmedy, M., Matveeva, O., Motova, G., Pisarz, S., Pol, P., Rostlund, A., Soboleva, E., Tavares, O., & Zobel, L. (2014). The concept of excellence in higher education. ENQA AISBL.

Chen, J., Lee, J. C. K., & Dong, J. (2020). Emotional trajectory at different career stages: Two excellent teachers’ stories. Frontiers in Psychology, 11 , 1034. https://doi.org/10.3389/fpsyg.2020.01034

Collinson, V. (1999). Redefining teacher excellence. Theory into Practice, 38 (1), 4–11. https://doi.org/10.1080/00405849909543824

Conway, J. M., & Huffcutt, A. I. (1997). Psychometric properties of multisource performance ratings: A meta-analysis of subordinate, supervisor, peer, and self-ratings. Human Performance, 10 (4), 331–360. https://doi.org/10.1207/s15327043hup1004_2

Creswell, J. W. (2012). Qualitative inquiry and research design: Choosing among five approaches (3rd ed.). SAGE Publications, Inc.

Czech Statistical Office. (2014). Národnostní struktura obyvatel [Population by nationality]. https://www.czso.cz/documents/10180/20551765/170223-14.pdf

DeBerard, M. S., Spielmans, G. I., & Julka, D. L. (2004). Predictors of academic achievement and retention among college freshmen: A longitudinal study. College Student Journal, 38 (1), 66–80.

Google Scholar  

Elliot, A. J., & Harackiewicz, J. M. (1996). Approach and avoidance achievement goals and intrinsic motivation: A mediational analysis. Journal of Personality and Social Psychology, 70 (3), 461–475. https://doi.org/10.1037/0022-3514.70.3.461

Erez, R. (2004). Freedom and creativity: An approach to science education for excellent students and its realization in the Israel arts and science academy’s curriculum. Journal of Secondary Gifted Education, 15 (4), 133–140. https://doi.org/10.4219/jsge-2004-461

Evans, T. M., Bira, L., Gastelum, J. B., Weiss, L. T., & Vanderford, N. L. (2018). Evidence for a mental health crisis in graduate education. Nature Biotechnology, 36 (3), 282–284. https://doi.org/10.1038/nbt.4089

Excellence. (n.d.). In Oxford English Dictionary . Retrieved March 21, 2021, from https://www.oed.com/search/dictionary/?scope=Entries&q=excellence

Ferrari, M. (2002). Conclusion: What is excellence and how to study it? In M. Ferrari (Ed.), The pursuit of excellence through education (pp. 221–239). Psychology Press.

Fichten, C., Jorgensen, M., Havel, A., King, L., Lussier, A., Asuncion, J., Budd, J., Nguyen, M. N., & Amsel, R. (2018). Information and communication technologies: Views of Canadian college students and “excellent” professors. Journal of Education and Training Studies, 6 (9), 1–12. https://doi.org/10.11114/jets.v6i9.3390

Franks, D. R. (1996). A situational study of the meanings of personal excellence for secondary mathematics teachers and students. Educational Studies in Mathematics, 31 (3), 295–317. https://doi.org/10.1007/BF00376324

French, M. T., Homer, J. F., Popovici, I., & Robins, P. K. (2014). What you do in high school matters: High school GPA, educational attainment, and labor market earnings as a young adult. Eastern Economic Journal, 41 (3), 370–386. https://doi.org/10.1057/eej.2014.22

Fulmer, S. M., & Frijters, J. C. (2009). A review of self-report and alternative approaches in the measurement of student motivation. Educational Psychology Review, 21 , 219–246. https://doi.org/10.1007/s10648-009-9107-x

Funder, D. C. (2012). Accurate personality judgment. Current Directions in Psychological Science, 21 (3), 177–182. https://doi.org/10.1177/0963721412445309

Fuster de Hernàndez, M. M. (2020). Choosing to be a teacher: Understanding the career choice of academically excellent university students who had graduated from high performing high schools in the Dominican Republic. [Doctoral Dissertation, Western Michigan University]. https://scholarworks.wmich.edu/dissertations/3579

Gardner, H., Csikszentmihalyi, M., & Damon, W. (2001). Good work: When excellence and ethics meet. Basic Books.

Gardner, J. W. (2015). Can we be equal and excellent too? Harper Colophon Books.

Geddes, K. A. (2011). Academic dishonesty among gifted and high-achieving students. Gifted Child Today, 34 (2), 50–56. https://doi.org/10.1177/107621751103400214

Grove, W. A., & Wasserman, T. (2004). The life-cycle pattern of collegiate GPA: Longitudinal cohort analysis and grade inflation. The Journal of Economic Education, 35 (2), 162–174. https://doi.org/10.3200/JECE.35.2.162-174

Hair, J. F., Babin, B. J., Anderson, R. E., & Black, W. C. (2018). Multivariate data analysis (8th ed.). Cengage Learning EMEA.

Hennink, M. M. (2014). Focus group discussions: Understanding qualitative research. Oxford University Press.

Hirsh, Å., & Segolsson, M. (2021). “Had there been a Monica in each subject, I would have liked going to school every day”: A study of students’ perceptions of what characterizes excellent teachers and their teaching actions. Education Inquiry, 12 (1), 35–53. https://doi.org/10.1080/20004508.2020.1740423

Hoff, J. W. (2009). Excellence and the pursuit of competence in contemporary universities. In J. Strain, R. Barnett, & P. Jarvis (Eds.), Universities, ethics, and professions: Debate and scrutiny (pp. 95–108). Routledge.

Jahanbegloo, R. (2014). The pursuit of excellence and the role of philosophy.  Co-herencia ,  11 (20), 179–186. http://www.scielo.org.co/pdf/cohe/v11n20/v11n20a09.pdf

Kallas, K. D. (2014). Profile of an excellent nurse manager. Nursing Administration Quarterly, 38 (3), 261–268. https://doi.org/10.1097/NAQ.0000000000000032

Kass, E., & Miller, E. C. (2018). Career choice among academically excellent students: Choosing teaching career as a corrective experience. Teaching and Teacher Education, 73 , 90–98. https://doi.org/10.1016/j.tate.2018.03.015

Kidd, P. S., & Parshall, M. B. (2000). Getting the focus and the group: Enhancing analytical rigor in focus group research. Qualitative Health Research, 10 (3), 293–308. https://doi.org/10.1177/104973200129118453

Kötter, T., Wagner, J., Brüheim, L., & Voltmer, E. (2017). Perceived medical school stress of undergraduate medical students predicts academic performance: An observational study. BMC Medical Education, 17 , 256. https://doi.org/10.1186/s12909-017-1091-0

Krippendorff, K. (2019). Content analysis: An introduction to its methodology . SAGE Publications, Inc.

Kuncel, N. R., Hezlett, S. A., & Ones, D. S. (2001). A comprehensive meta-analysis of the predictive validity of the graduate record examinations: Implications for graduate student selection and performance. Psychological Bulletin , 127 , 162-181. https://doi.org/10.1037//q033-2909.127.1.162

Lavrijsen, J., & Verschueren, K. (2020). Student characteristics affecting the recognition of high cognitive ability by teachers and peers. Learning and Individual Differences, 78 , 101820. https://doi.org/10.1016/j.lindif.2019.101820

Li, J. (2004). High abilities and excellence: A cultural perspective. In L. V. Shavinina & M. Ferrari (Eds.), Beyond knowledge: Extracognitive aspects of developing high ability (pp. 187–208). Lawrence Erlbaum Associates Inc.

Llamas, J. M. C. (2006). Technologies of disciplinary power in action: The norm of the ‘Good student.’ Higher Education, 52 (4), 665–686. https://doi.org/10.1007/s10734-004-1449-1

López, B. G., Cerveró, G. A., Rodríguez, J. M. S., Félix, E. G., & Esteban, P. R. G. (2013). Learning styles and approaches to learning in excellent and average first-year university students. European Journal of Psychology of Education, 28 (4), 1361–1379. https://doi.org/10.1007/s10212-012-0170-1

Luthar, S., & Kumar, N. L. (2018). Youth in high-achieving schools: Challenges to mental health and directions for evidence-based interventions. In A. W. Leschied, D. H. Saklofske, & G. L. Flett (Eds.), Handbook of school-based mental health promotion: An evidence-informed framework (pp. 441–458). Springer.

Chapter   Google Scholar  

Ma, Y., McCabe, D. L., & Liu, R. (2013). Students’ academic cheating in Chinese universities: Prevalence, influencing factors, and proposed action. Journal of Academic Ethics, 11 (3), 169–184. https://doi.org/10.1007/s10805-013-9186-7

Magin, D. (2001). Reciprocity as a source of bias in multiple peer assessment of group work. Studies in Higher Education, 26 (1), 53–63. https://doi.org/10.1080/03075070020030715

Mašková, I., & Kučera, D. (2022). Performance, achievement, and success in psychological research: Towards a more transparent use of the still ambiguous terminology. Psychological Reports, 125 (2), 1218–1261. https://doi.org/10.1177/0033294121996000

Mašková, I., & Nohavová, A. (2019). Beyond grades: GPA and its relationship to achievement goal orientations and student approaches to learning. GRANT Journal, 8 (2), 49–54.

Mathison, S. (1988). Why triangulate? Educational Researcher, 17 (2), 13–17. https://doi.org/10.3102/0013189X017002013

Miller, P. S., & Kerr, G. A. (2002). Conceptualizing excellence: Past, present, and future. Journal of Applied Sport Psychology, 14 (3), 140–153. https://doi.org/10.1080/10413200290103464

Mirghani, H. O., Mohammed, O. S., Almurtadha, Y. M., & Ahmed, M. S. (2015). Good sleep quality is associated with better academic performance among Sudanese medical students. BMC Research Notes, 8 (1), 1–5. https://doi.org/10.1186/s13104-015-1712-9

Modrek, A., & Kuhn, D. (2017). A cognitive cost of the need to achieve? Cognitive Development, 44 , 12–20. https://doi.org/10.1016/j.cogdev.2017.08.003

Monteiro, S., Almeida, L. S., Vasconcelos, R. M., & Cruz, J. F. A. (2014). Be (com) ing an excellent student: A qualitative study with engineering undergraduates. High Ability Studies, 25 (2), 169–186. https://doi.org/10.1080/13598139.2014.966066

Mould, T., & DeLoach, S. B. (2017). Moving beyond GPA: Alternative measures of success and predictive factors in Honors programs.  Journal of the National Collegiate Honors Council ,  18 (1), 149–168. http://digitalcommons.unl.edu/nchcjournal/552

Nelson, M. C., Story, M., Larson, N. I., Neumark-Sztainer, D., & Lytle, L. A. (2008). Emerging adulthood and college-aged youth: An overlooked age for weight-related behavior change. Obesity, 16 (10), 2205–2211. https://doi.org/10.1038/oby.2008.365

Olson, J. D., McAllister, C., Grinnell, L. D., Gehrke Walters, K., & Appunn, F. (2016). Applying constant comparative method with multiple investigators and inter-coder reliability. The Qualitative Report, 21 (1), 26–42. https://doi.org/10.46743/2160-3715/2016.2447

Penfield, R. D. (2013). Item analysis. In K. F. Geisinger, B. A. Bracken, J. F. Carlson, J.-I. C. Hansen, N. R. Kuncel, S. P. Reise, & M. C. Rodriguez (Eds.),  APA handbook of testing and assessment in psychology: Vol. 1. Test theory and testing and assessment in industrial and organizational psychology  (pp. 121–138). American Psychological Association.  https://doi.org/10.1037/14047-007

Renzulli, J. S., & Reis, M. S. (2020). The Three Ring Conception of Giftedness: A change in direction from being gifted to the development of gifted behaviors. In R. J. Sternberg & D. Ambrose (Eds.), Conceptions of giftedness and talent (pp. 335–355). Springer.

Rohde, T. E., & Thompson, L. A. (2007). Predicting academic achievement with cognitive ability. Intelligence, 35 (1), 83–92. https://doi.org/10.1016/j.intell.2006.05.004

Sacks, J. (2002). The dignity of difference: How to avoid the clash of civilizations . Continuum.

Saunders, B., Sim, J., Kingstone, T., Baker, S., Waterfield, J., Bartlam, B., Burroughs, H., & Jinks, C. (2018). Saturation in qualitative research: Exploring its conceptualization and operationalization. Quality & Quantity, 52 , 1893–1907. https://doi.org/10.1007/s11135-017-0574-8

Schreier, M. (2012). Qualitative content analysis in practice . SAGE Publications Ltd.

Senko, C., & Miles, K. M. (2008). Pursuing their own learning agenda: How mastery-oriented students jeopardize their class performance. Contemporary Educational Psychology, 33 (4), 561–583. https://doi.org/10.1016/j.cedpsych.2007.12.001

Shonfeld, M., & Ronen, I. (2015). Online learning for students from diverse backgrounds: Learning disability students, excellent students and average students.  IAFOR Journal of Education ,  3 (2), 13–29. https://files.eric.ed.gov/fulltext/EJ1100564.pdf

Smit, R. (2012). Towards a clearer understanding of student disadvantage in higher education: Problematising deficit thinking. Higher Education Research & Development, 31 (3), 369–380. https://doi.org/10.1080/07294360.2011.634383

Stallman, H. M. (2010). Psychological distress in university students: A comparison with general population data. Australian Psychologist, 45 (4), 249–257. https://doi.org/10.1080/00050067.2010.482109

Steinmayr, R. Meißner, A., Weidinger, A. F., & Wirthwein, L. (2015). Academic achievement. In. Oxfordbibliographies.com. https://doi.org/10.1093/obo/9780199756810-0108

Sternberg, R. (2009). Wisdom, intelligence, creativity, synthesised: A conceptual framework of giftedness. In T. Balchin, B. Hymer, & D. J. Matthews (Eds.), The Routledge International Companion to Gifted Education (pp. 255–264). Routledge.

Stewart, S. M., Bing, M. N., Davison, H. K., Woehr, D. J., & McIntyre, M. D. (2009). In the eyes of the beholder: A non-self-report measure of workplace deviance. Journal of Applied Psychology, 94 (1), 207–215. https://doi.org/10.1037/a0012605

Summers, D. (Ed.) (2003). Longman dictionary of contemporary English . Longman.

Taggar, S., & Brown, T. C. (2006). Interpersonal affect and peer rating bias in teams. Small Group Research, 37 (1), 86–111. https://doi.org/10.1177/1046496405284382

Terzi, L. (2020). On educational excellence. Philosophical Inquiry in Education, 27 (2), 92–105. https://doi.org/10.7202/1074040ar

Tessema, M. T., Ready, K. J., & Astani, M. (2014). Does part-time job affect college students’ satisfaction and academic performance (GPA)? The case of a mid-sized public university. International Journal of Business Administration, 5 (2), 1–10. https://doi.org/10.5430/ijba.v5n2p

Van de Vijver, A., & Mathijssen, S. (2024). A Philosophical Approach to Talent Development. Roeper Review, 46 (1), 27–38. https://doi.org/10.1080/02783193.2023.2285053

Van Yperen, N. W., Hamstra, M. R., & van der Klauw, M. (2011). To win, or not to lose, at any cost: The impact of achievement goals on cheating. British Journal of Management, 22 , S5–S15. https://doi.org/10.1111/j.1467-8551.2010.00702.x

West, T., Ravenscroft, S., & Shrader, C. (2004). Cheating and moral judgment in the college classroom: A natural experiment. Journal of Business Ethics, 54 (2), 173–183. https://doi.org/10.1007/s10551-004-9463-x

Whitley, B. E. (1998). Factors associated with cheating among college students: A review. Research in Higher Education , 39 (3), 235–274. https://www.jstor.org/stable/40196379

Williamson, W. P., & Assadi, A. (2005). Religious orientation, incentive, self-esteem, and gender as predictors of academic dishonesty: An experimental approach. Archive for the Psychology of Religion, 27 (1), 137–158. https://doi.org/10.1163/008467206774355411

Wittchen, H. U., Nelson, C. B., & Lachner, G. (1998). Prevalence of mental disorders and psychosocial impairments in adolescents and young adults. Psychological Medicine, 28 (1), 109–126. https://doi.org/10.1017/S0033291797005928

Wong, B., DeWitt, J., & Chiu, Y. L. T. (2021). Mapping the eight dimensions of the ideal student in higher education.  Educational Review , 1–19. https://doi.org/10.1080/00131911.2021.1909538

Yaniv, G., Siniver, E., & Tobol, Y. (2017). Do higher achievers cheat less? An experiment of self-revealing individual cheating. Journal of Behavioral and Experimental Economics, 68 , 91–96. https://doi.org/10.1016/j.socec.2017.04.005

Yoo, H. H., Yune, S. J., Im, S. J., Kam, B. S., & Lee, S. Y. (2021). Heart rate variability-measured stress and academic achievement in medical students. Medical Principles and Practice, 30 (2), 193–200. https://doi.org/10.1159/000513781

Young, R. A., & Collin, A. (2004). Constructivism and social constructionism in the career field. Journal of Vocational Behavior, 64 (3), 373–398. https://doi.org/10.1016/j.jvb.2003.12.005

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Mašková, I., Kučera, D. & Nohavová, A. Who is really an excellent university student and how to identify them? A development of a comprehensive framework of excellence in higher education. Eur J Psychol Educ (2024). https://doi.org/10.1007/s10212-024-00865-y

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Article contents

Video games as meaningful or eudaimonic experiences.

• Daniel Possler Daniel Possler Institute for Human-Computer-Media, Julius-Maximilians-Universität Würzburg
• https://doi.org/10.1093/acrefore/9780190228613.013.1485
• Published online: 18 June 2024

Research on meaningful or eudaimonic gaming experiences explores players’ profound responses to video games. It rests on the observation that video games have ‘grown up’ in the 2000s and 2010s. While the medium traditionally aimed at providing fun, modern games increasingly afford meaningful experiences, for example by addressing serious topics (e.g., loss). Drawing on philosophical and psychological well-being research, these meaningful experiences are often termed “eudaimonic.” Beyond this shared categorization, however, no consensual definition of eudaimonic/meaningful gaming experiences has yet been developed. Instead, various competing and partially overlapping conceptualizations exist in the literature, including (a) appreciation, (b) the covariation of meaningfulness, being emotionally moved or challenged, and self-reflection, (c) deep social connectedness, and (d) specific emotional responses (e.g., nostalgia, awe). The formation of eudaimonic/meaningful gaming experiences has mostly been attributed to game characteristics, including (1) game mechanics that allow rare performances or promote reflection by disrupting players’ gameplay expectations; (2) narratives that address emotionally challenging topics, feature moral dilemmas, or facilitate deep social bonds with game characters; (3) multiplayer features that enable cooperative interactions with close co-players; and (4) game aesthetics that facilitate awe or aesthetic contemplation. In contrast, little is known about how player characteristics affect the formation of eudaimonic/meaningful gaming experiences. Similarly, research on the effects of these experiences is sparse. However, initial studies suggest that eudaimonic/meaningful experiences may benefit players beyond gaming by increasing their well-being or promoting pro-social behavior. Additionally, eudaimonic/meaningful gaming experiences appear to have a motivational appeal , as preliminary studies suggest that seeking such experiences can motivate playing games in general and specific titles in particular. Overall, this burgeoning line of research is still in its infancy but has already provided valuable insights into the quality and formation of eudaimonic/meaningful experiences in interactive media and the attraction and positive effects of video games.

• video games
• entertainment
• meaningful media
• media effects
• appreciation

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