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Sports (Basel)
Current Research and Statistical Practices in Sport Science and a Need for Change
Jake r. bernards.
1 Center of Excellence for Sport Science and Coach Education, Department of Sport, Exercise, Recreation, and Kinesiology, East Tennessee State University, Johnson City, TN 37614, USA; ude.uste@1kotas (K.S.); ude.uste@relyzab (C.D.B.)
Kimitake Sato
G. gregory haff.
2 Center for Exercise and Sport Science Research, Edith Cowan University, Joondalup, WA 6027, Australia; [email protected]
Caleb D. Bazyler
Associated data.
Current research ideologies in sport science allow for the possibility of investigators producing statistically significant results to help fit the outcome into a predetermined theory. Additionally, under the current Neyman-Pearson statistical structure, some argue that null hypothesis significant testing (NHST) under the frequentist approach is flawed, regardless. For example, a p-value is unable to measure the probability that the studied hypothesis is true, unable to measure the size of an effect or the importance of a result, and unable to provide a good measure of evidence regarding a model or hypothesis. Many of these downfalls are key questions researchers strive to answer following an investigation. Therefore, a shift towards a magnitude-based inference model, and eventually a fully Bayesian framework, is thought to be a better fit from a statistical standpoint and may be an improved way to address biases within the literature. The goal of this article is to shed light on the current research and statistical shortcomings the field of sport science faces today, and offer potential solutions to help guide future research practices.
1. The Problem
Although the common goal of many researchers remains the same, the validity of the sport science body of literature may be in question because of common research practices and the current statistical framework applied [ 1 , 2 , 3 ]. Recently, journals are beginning to address existing research and statistical practices through journal wide initiatives to increase the reproducibility of results found in the literature. While such initiatives act as a first step, a shift in the current statistical framework may be a better solution to ensure the field of sport science continually progresses. While pressure to increase the volume of publications in the academic setting progresses, it has become increasingly tempting to deviate from the scientific framework that has been proven to be crucial for discovery; rigor, reproducibility, and transparency [ 4 ]. Although the ambition to develop novel and innovative findings remains the primary goal of the current academic infrastructure, the outcome can become biased and unchallenged [ 5 ]. The occurrence of biased and unchallenged outcomes in sport science can be attributed to at least two factors.
First, the current academic/publication structure to only reward new, unique and ground-breaking findings instils a need for producing research that has statistically significant results. Second, existing statistical practices lend themselves to nearly any result, to be interpreted at the author’s discretion. With biased interpretations of nearly any statistical result, a more transparent statistical structure could help lead the reader to make claims based off the results, rather than the authors’ interpretations of such results [ 1 , 6 ]. This is very different from the current dogma of p < 0.05, therefore the intervention works. Therefore, the goal of this article is to shed light on the potential problems the field of sport science faces, and to offer solutions to help guide future research practices.
A persistent problem in current research practice involves the multitude of ways an individual can manipulate their data to produce statistically significant results in the absence of a true effect. Examples include manipulation of statistics to produce statistically significant results ( p -hacking) and hypothesis after results are known (HARKing) [ 7 , 8 ]. Such practices make it is nearly impossible for the reader to know which findings are a discovery and which are produced. While the production of statistically significant results may be advantageous for the researcher in the short-term, it can be detrimental to the literature in the long-term. When non-statistical data is manipulated to produce a significant p -value, it appears to the reader of the investigation that there is likely an effect of the treatment. It is plausible that a future reader will be interested in expanding on the topic, even though the effect of the treatment may have been unsuccessful, but only appeared to be effective following manipulation of the data. Repeat this process over and over, and the body of literature can venture down research avenues that are based off an original study that had no true effect to begin with. A similar concept was the underlining focus of Thomas Kuhn’s essay, The Structure of Scientific Revolutions [ 9 ]. If researchers meld their data to fit individual theories through p -hacking and HARKing, individual theories will persist and the field of sport science is likely to progress at a much slower rate.
A secondary struggle that has stemmed from current research practice includes the “ file-drawer effect ”. By striving to primarily publish the latest findings within the field, journals have likely created a biased body of literature for coaches and investigators to pull from as a result of the “ file-drawer effect ” [ 10 ]. Due to the “ file-drawer effect ”, common strength and conditioning practices that are “ evidence based ” may appear to be effective simply because a handful of studies showed statistical significance; however, similar studies may have revealed no significance and never made it to publication. This “ file-drawer effect ” causes two key problems in the body of literature. First, this system can often cause researchers to undertake unwarranted research by basing their hypotheses, theories, and future experiments on a study that may have no effect but was shown as statistically significant by p -hacking or HARKing. Second, by primarily including studies that show statistical significance and not including unpublished, non-significant studies, the body of literature becomes biased and, therefore, the common practice of performing a meta-analysis is likely to also be biased.
Furthermore, under null hypothesis significance testing (NHST), p < 0.05 is sufficient to state an intervention was effective without any regard to the magnitude of the effect. This shortcoming allows researchers to dredge their data looking for any relationship that will lead to a statistically significant finding. This can lead to an inflated rate of Type I errors that may go unnoticed with the lack of a replication process. Beyond the strategies to meld the data into the p < 0.05 box, some argue that the frequentist approach of NHST is flawed, regardless [ 11 , 12 , 13 , 14 ].
For example, when a hypothesis is not specified prior to data collection and analysis, the widely used multiway ANOVA exhibits a multiple comparisons issue [ 11 ]. As Cramer et al. [ 11 ] point out, when a 2 × 3 ANOVA is computed with a “ let’s see what we can find ” approach, the probability of making a minimum of one Type I error (familywise error rate) inflates to 0.14 or 14% as opposed to the thought error rate set to five-percent when a multiple correction adjustment is omitted. Currently, it is only taken on faith that the author had a hypothesis prior to the analysis process.
Cumming [ 12 ] argues that NHST prompts researchers to see the world as black or white, and to formulate research aims to make conclusions in absolute terms—an effect is statistically significant or it is not; it exists or it does not. However, rarely is our field of sports science black and white. Furthermore, the sole use of p -values shifts investigators’ focus away from the practicality of a finding to simply claim statistical significance without providing a detailed description of what may have occurred.
From a statistical standpoint, a p -value cannot; (1) measure the probability that the studied hypothesis is true, (2) measure the size of an effect or the importance of a result, or (3) provide a good measure of evidence regarding a model or hypothesis [ 1 , 15 ]. Moreover, decisions about an effect based on some “magic threshold” may be biased, regardless of how the threshold is defined [ 13 , 16 ]. Lastly, p -values are sample-size dependent, a major limitation in the field of sport science if the study is done with an elite athletic population. For example, a strength program may show significance with a sample of 12, yet with two dropouts may miss the effect [ 14 ]. Therefore, an alternative research and statistical model may better suit our field.
2. The Solution
In sports science, there are two primary study designs: hypothesis generating (exploratory) and hypothesis testing (experimental). While both types of studies are central components of the applied research model typically used in sport science [ 17 ], it is important that there is a clear understanding of their roles in research and when the appropriate design is necessitated. As Tukey (1997) stated, if we do not explore, we might miss valuable insights that could suggest new research directions. This statement resonates within our field, where the smallest variances can be the difference in medalling or going home empty handed. However, in an exploratory analysis of the data, results must be clearly identified as speculative, and warrant further investigation with a developed hypothesis revealed during the analysis, a key concept that is often forgotten [ 18 ].
When planning a study, one proposed model sports scientists can follow is the Applied Research Model for Sport Science (ARMSS) [ 17 ]. The model incorporates both exploratory and experimental study designs linked together in a sequential manner to maximize the transferability of the research to a sport setting. ARMSS is an eight-stage model that includes;
(1) Defining the problem
(2) Descriptive research (hypothesis generating)
(3) Predictors of performance
(4) Experimental testing of predictors
(5) Determinants of key performance predictors
(6) Efficacy studies (controlled laboratory or field)
Once a problem has been defined, an investigator can then begin exploratory research to determine relationships that specific variables may have to the problem. This process helps to provide investigators domains of where to look for potential solutions. Following an exploratory finding, results need to be verified via replication to ensure an effect is in fact present. However, because scientific journals tend to favour novel findings, this crucial step of replication rarely occurs [ 17 ]. Therefore, during subsequent novel studies on the same topic, researchers should also attempt to replicate previous findings within their investigations by including previously identified correlations alongside the novel aspects [ 17 ].
The process of replication can then repeat itself to continually progress a topic forward while also presenting novel findings to advance the investigators academic career. Ideally, a replication study that is built with this framework will keep key features of the original investigation while modifying others to give a converging perspective. This method will not only increase the confidence in the original finding but will also begin to explore additional variables that may influence it [ 12 ]. Once a relationship between key variables and a specific problem have been determined, the researchers can then proceed to more traditional research designs that help determine causal relationship between the variables and a problem [ 17 ]. Once a causal relationship has been determined, the efficacy of specific investigations addressing the problem can be investigated. Finally, for the transfer and adoption of research outcomes to be effective, evidence must show that the use of the innovation is both feasible and effective in practice. This can be accomplished by evaluating the findings in a sport setting to ensure it is an improvement to current practice [ 17 ]. However, considering the flaws of NHST, novel statistical approaches are needed to support the ARMSS model.
3. Current Alternative Statistical Methods
Detailed in this section are common alternative statistics, and a brief explanation of how to conduct/interpret them.
3.1. Smallest Worthwhile Change
A key struggle of studying elite athletics, whether it is for research purposes or to determine if a training program is moving an individual in a meaningful direction, is sample size. Under the current statistical model of NHST, it is often impossible to achieve a statistically significant p -value due to extremely small samples and considerably small effects. However, the smallest of effects can be the sole difference when you are dealing with athletes of the highest calibre. Two metrics can be utilized in determining the smallest difference that can lead to a meaningful change in performance; smallest worthwhile change and smallest real difference.
The smallest worthwhile change, also termed smallest meaningful change and smallest clinically important difference is calculated one of two ways, dependent on the nature of the sport. For team sports, the smallest worthwhile change can be calculated as; 0.2 × between-subject SD [ 19 , 20 ]. For a variable to be considered capable of detecting the smallest worthwhile change under this formula, the typical error of the measurement must be less than the smallest worthwhile change.
When calculating the smallest worthwhile change in individual sports, you must first determine the sport-specific coefficient of variation, whether in the literature or through your own research. After obtaining this value, 0.3 of the coefficient of variation equates to a top-tiered athlete medaling once for every ten races when racing equally matched elite athletes [ 21 , 22 ]. A value of 0.3 was determined via simulation, and equates to a top-tiered athlete gaining one extra medal every 10 races performed. This same technique was used to determine Hopkins’ guidelines for interpreting effect sizes. Values of 0.9, 1.6, 2.5, and 4.0 of a CV give an extra 3, 5, 7, and 9 medals per 10 races, respectively. When assessing a fitness test in a team setting, rather than using the coefficient of variation, one should use the standardized change (Cohen’s d z ), as there is no clear relationship between fitness-test performance and team performance [ 23 ]. The smallest worthwhile change is then equal to 0.2 of d [ 23 ].
The smallest real differences, also termed the smallest detectable difference, is the smallest measurement change that can be interpreted as a real difference beyond zero [ 24 ]. Calculation of this metric can be computed as:
where SEM equals the standard error of measurement [ 25 ]. Because measurement error causes the observed measurement to differ from the individual’s true value, an error band can be calculated to express the uncertainty of the difference between the two observed scores. When the smallest real difference error band contains zero, the difference between the two measurements may have been induced by error alone, and may not be a result of the intervention [ 25 ].
3.2. Comparing Correlations
A common question in sports science is to compare two correlations obtained from a single sample of subjects, often a team, between multiple predictor variables and a single common dependent variable. Much like d can compare results measured in various units, the r to z-transformation allows an investigator to compare the correlation coefficients between a dependent variable and a set of independent variables. The first step to comparing multiple correlation coefficients to a dependent variable is to perform Fisher’s z-transformation, defined as:
where ln is the natural log and r is the correlation of the two variables. Fisher’s z-transformation of sample correlation coefficients improves the normality substantially, especially for small sample sizes [ 26 ]. Following the transformation from r to z , the two correlated correlations can then be compared to determine what predictors can do a better job in predicting the variable in question [ 26 ]. This method can also be used when testing whether correlations with a common variable follow the pattern of magnitudes that a theory would predict [ 26 ].
Correlations that have been calculated from different samples can also be tested against one another. Comparing correlations from two samples can help determine if there is a significant difference in the correlations of two groups. For example, imagine you are collecting data on training age and jump height from men and women. The two resulting correlations can be tested against each other to determine if there is a significant difference in the correlation of both groups. To compare correlations from independent samples, we must first convert the coefficients to z -scores than can determine the z -score difference as:
This score can then be used to determine the one- or two-tailed probabilities to determine significance [ 27 ].
3.3. Effect Size
Cohen’s d effect size is a z -score that will take the difference of two group means and divide the result by a standardizer if the assumption of homogeneity of variance is met. While there are multiple calculations used to determine d , the standardizer chosen is dependent on the study design [ 28 ]. For example, d s is calculated by using the pooled standard deviation of the groups and is used when investigating independent groups. When determining the effect in a one sample group, the standard deviation difference in scores can be used as the standardizer to calculate d z . When dealing with a small sample size and meta analyses, a Hedges’ g correction can be computed. Calculating Cohen’s d s based off sample averages may give a biased estimate of the population effect size, especially for samples under twenty participants [ 29 ]. Cohen’s d s can be converted to the adjusted Hedge’s g s by [ 28 ]:
As outlined by Cohen (1988), d is also a metric of the magnitude of the effect with guidelines originally set forth by both Cohen and later updated specifically for sport science by Hopkins [ 30 ], and for resistance training studies by Rhea [ 31 ], to guide investigators toward interpreting the magnitude of the effect. As it currently stands, the guidelines set forth by Cohen Hopkins, and Rhea are detailed in Table 1 .
Effect size interpretation guidelines.
Trivial
Small
Moderate
Large
Very Large
Nearly Perfect
Cohen [ ]
N/A
0.1
0.3
0.5
N/A
N/A
Hopkins [ ]
0–0.2
0.2–0.6
0.6–1.2
1.2–2.0
2.0–4.0
>4.0–∞
Rhea [ ]: Untrained
<0.5
0.5–1.25
1.25–1.9
>2.0
N/A
N/A
Rhea [ ]: Recreationally Trained
<0.35
0.35–0.8
0.8–1.5
>1.5
N/A
N/A
Rhea [ ]: Highly Trained
<0.25
0.25–0.5
0.5–1.0
>1.0
N/A
N/A
3.4. Confidence Intervals
The confidence interval (CI) can be defined as, “the likely range of the true, real, or population value of the statistic” within a given probability [ 22 ]. This range of values is unique in that, rather than there being a set probability of the CI containing the true population value of a given statistic, the CI will include the population’s true value a given number of times when replicated indefinitely. For example, if a study was replicated an indefinite number of times, 95% of the calculated CIs would include the population’s true value of its accompanying statistic, while 5% of the calculated CIs will not. Although ambiguous alone, the use of confidence intervals in combination with an effect size can help to show the precision of the effect size estimate.
Confidence intervals can be computed for a wide range of commonly calculated statistics. Examples of metrics that often include a confidence interval in the literature are group means, mean difference, and effect size. When used in conjunction with an effect size, confidence intervals can be especially useful in making magnitude-based inferences.
3.5. Magnitude-Based Inferences
Pioneered by Dr. Hopkins and colleges in the early 2000s, making inferences from magnitude-based metrics can be accomplished by using a multi-level scale [ 22 ]. To do so, one can inspect the magnitudes covered by its effect size confidence interval and infer to what degree of the true value it could be [ 22 ]. While metrics such as a confidence interval may be vague when used alone, the confidence interval in conjunction with another statistic like effect size and smallest worthwhile change can take the typical polar reject-nonreject decision and transform it into a 3-level scale of magnitude (beneficial, trivial, and harmful) that inferences can be based off [ 22 ]. Inferences stemming from the 3-level scale of magnitudes result in “beneficial”, “trivial”, “harmful”, or “unclear”, dependent on the statistic/resulting confidence interval, a much more useful approach than the current “the effect is not statistically significant” response [ 22 ]. Incorporating such a method also has the added benefit of opening transparency and allowing the reader to determine their own inferences based on the results. Magnitude-based inferences can be made more accurate and informative by qualifying them with probabilities that help to reflect the uncertainty in the true value [ 33 ]. The qualitative probabilistic terms can be assigned using the scale put forth by Hopkins (2007); <0.5%, most unlikely or almost certainly not; 0.5–5%, very unlikely; 5–25%, unlikely or probably not; 25–75%, possibly; 75–95%, likely or probably; 95–99.5%, very likely; >99.5%, most likely or almost certainly [ 34 ].
3.6. Counter-Argument against Magnitude-Based Inferences
There are three common limitations of the magnitude-based inference model that advocates acknowledge. These include:
(1) A defined a priori with both a magnitude of the smallest important effect and the thresholds used to qualify likelihoods is needed [ 14 ].
(2) The investigator is invited to include his/her bias into the final interpretation [ 22 ].
(3) The potential of inflating the inferential error rate in increased [ 35 , 36 ].
The need for a strongly defined a priori should not be looked at as a limitation, but rather an advantage over the current NHST system. Rather than simply testing against the likelihood that all groups came from the same population, a defined a priori necessitates that the investigator adopts a conscious process when analysing and interpreting their data [ 14 ]. By simply testing a set of data against the NULL, you cannot strengthen a theory, but simply say that it effects the population in some way. There is no direction or strength to the claim. By defining a priori , the magnitude-based inference model can strengthen a theory by testing directly against itself. You are no longer testing against the void of the NULL, but against tangible expectations. The result is either a stronger or weaker than theory, dependent on the results. Moreover, the magnitude of the change can also help the degree to which the theory is strengthened or weakened.
The claim that magnitude-based inference increases the bias of a decision from the researcher does not hold substance as bias has already seeped into science under the current NHST framework. With publications allowing interpretations such as, “ weakly significant ” ( p = 0.11), “ approaching formal significance ” ( p = 0.1052), and “ not significantly … but clinically meaningful ” ( p = 0.072) the NHST already allows for bias [ 37 ]. All the previous examples were pulled from published peer-reviewed journal articles with α set to 0.05. Under the current statistical framework, some investigators mold their findings to tell their own story. From the p -value alone, the reader cannot detect the practical significance or the magnitude of the differences.
Finally, the thought that magnitude-based inference increases the potential of inflating the inferential error rate has been shown not to be true. In a study conducted by Hopkins & Batterham (2016), results from 500,000 simulated randomized controlled trials, magnitude-based inference methods outperformed NHST in respect of inferential error rates. In addition, magnitude based inference also outperformed in terms of rates of publishable outcomes with suboptimal sample sizes and publication bias with such samples [ 35 ].
4. Bayesian Estimation
While the shift towards a magnitude-based inference model may act as a better fit for inference in sport science, a commitment towards a fully Bayesian model may act as a better solution for small effects and small sample sizes [ 38 ]. Criticism towards the magnitude inference model claim that a fully Bayesian approach may be a better solution [ 38 , 39 ].
In a study performed by Mengersen et al. [ 38 ], a fully Bayesian approach was shown to provide more direct probabilistic comparisons of treatments and able to identify small effects of interest, even with small sample sizes. Conclusions based off the Bayesian model were consistent with a magnitude-based inference approach and was determined to be a simple and effective way of analysing small effects while providing a rich set of results that are straightforward to interpret in terms of probabilistic statements [ 38 ].
In Mengersen’s comparison of statistical models, the authors applied a Bayesian model with a traditional statistical model to Humberstone-Gough et al. [ 40 ] study determining the effects of three training regimens; “Live High Train Low”, “Intermittent Hypoxic Exposure”, and “Placebo” on running performance and blood characteristics [ 40 ]. Results from the Bayesian model were consistent with those reported from the original investigation. However, the Bayesian approach allowed for a much more direct probabilistic interpretation of credible intervals and posterior probabilities [ 38 ].
Small effects on competitive performance are vital in elite athletics, and highly relevant for coaches and sport scientists when understanding the likely benefit or harm of a training program, recovery intervention, or any other facet surrounding the preparation process [ 38 ]. Adopting a Bayesian approach may by one approach to providing an answer. For example, in Mengersen’s Bayesian model of the altitude training data collected by Humberstone-Gough et al. [ 40 ], running economy improved by ~0.17 L (4.2%) more in the live high train low group when compared to intermittent hypoxic exposure. Data from the Bayesian model resulted in a 95% credible interval of −0.9 to −7.5% with a probability of ~0.99 that the true decrease in submaximal oxygen consumption is substantial (worthwhile) [ 38 ]. This is just one example of how a Bayesian model can allow for a much more detailed result, enabling the reader to gain better insight to the analysis.
Bayesian methods are different from frequentist approaches in that the parameters are treated as random variables that have a true, but unknown value. These values are described by a posterior probability distribution that reflects the uncertainty associated with how well they are known based on the data [ 38 ]. The posterior distribution is calculated by:
The likelihood describes the probability of observing the data given specified values of the parameters. The prior encapsulates beliefs about the probability of obtaining those independently of the data. Priors may either be developed using a range of information including previous experiments, historical data, and/or expert opinions, or priors can be uninformative to allow inferences to be driven by the observed data alone [ 38 ]. Priors allow the model to explore the consequences of beginning with varying information. Still, there can be many reasonable choices when defining the prior, all of which produce the same inference [ 41 ]. Even in the absence of previous knowledge, there is usually enough information to determine a plausible range of values that can be encoded directly into the prior and discount the plausibility of some parameter values (i.e., the negative associations between height and weight) [ 41 ].
It is important to note that there has been push back against adopting a Bayesian statistical model for various reasons. Historically, one of the biggest criticisms of Bayesian inference revolves around the prior [ 42 ]. While priors can be used to help produce results, much like p -hacking, it becomes increasingly obvious to the reader if done. Both Bayesian and non-Bayesian models are equally harried and dependent on likelihood functions and conventionalized model forms [ 41 ]. Therefore, if nothing more, adopting a Bayesian model is advantageous in both transparency and providing information for the reader to determine their own decision.
A major drawback to adopting a fully Bayesian approach is that most current sport scientists are not trained in Bayesian methods. This is largely due to the progression of Bayesian approaches only recently becoming commonplace following advancements with the computer. However, the unique hurdles that continue to persist in sport science may lend itself to adopting a fully Bayesian approach [ 38 ]. Furthermore, decision making can also be enhanced by the richer probabilistic and inferential capability afforded by the Bayesian analysis [ 38 ]. For example, “the outcome of an intervention to improve athletic performance may be classified as ‘possible’ in some cases (acceptable probability of improving performance within minimal adverse effects) and hence lead to a decision of using, whereas in another context it may be deemed too risky (unacceptable risk of impairing performance due to specified risks) and lead to no action” [ 38 ]. In practice, these decisions may not coincide with the traditional statement of statistically significant effect at the alpha 0.05 level [ 43 ]. The intricacies of Bayesian inference go far beyond the extremely brief introduction provided here. Additional papers have been added to the supplemental section to act as a starting point to begin to make an individual decision in regards to adopting a Bayesian Model. Additionally, as with any cultural shift, many fields of science are currently debating if a Bayesian model is superior with strong arguments from both sides [ 38 , 42 ]. It is important to note that no one model can address every issue that occurs in science. It is the responsibility of the investigator to be as transparent and informative as possible to inform the reader of the results, something a Bayesian framework may be able to offer.
5. Conclusions
The practice of focusing exclusively on a dichotomous reject-nonreject decision strategy of NHST to determine the effect a treatment has on an outcome can impede scientific progress. [ 44 ] The data should lead us, instead of fitting the data to our hypotheses. One way to help ensure the practice of data manipulation does not have a place in our field is to adopt a more transparent and informative statistical model. In doing so, the author can provide a more detailed statistical analysis of what occurred in the investigation and the reader is able to make an individualized interpretation of the effectiveness of an intervention. The inclusion of confidence intervals, effect statistics, and other descriptive metrics to accompany the p -value under the NHST model is an easy and effective first step an investigator can make to produce more transparent research that is also more informative. However, a shift towards a magnitude-based inference model, and eventually a fully Bayesian approach, may be a better fit from a statistical standpoint, a reproducibility standpoint, and may be an improved way to address biases within the literature. All while being a superior model to deal with smaller samples sizes and small effects, two fundamental struggles in the field.
Supplementary Materials
The following are available online at www.mdpi.com/2075-4663/5/4/87/s1 , Bayesian Versus Orthodox Statistics: Which Side Are You On?
Author Contributions
The manuscript was prepared by J.R.B. All authors participated in the correction and revision of the manuscript.
Conflicts of Interest
The authors declare no conflict of interest.
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Sport Psychology Research Methods: Qualitative vs Quantitative
Qualitative and Quantitative
Qualitative and quantitative research methods are two commonly used psychological research approaches with very different procedures and objectives. It is important for researchers to understand the differences between these two modes of research in order to determine which approach is best suited to adequately address the research question. The greatest distinctions between these two fundamentally different research techniques are the genesis of theory and the role that theory plays in the mechanics of research. In the quantitative technique, the research effort begins with a theory: a statement that tries to explain observed phenomena. The theory is then operationalized (that is, stated in terms that can be statistically tested) through hypothesis. Data is gathered, statistical tests are completed, and the results are interpreted. The results either support the hypothesis or they do not. (Downey & Ireland, 1979)
Quantitative research is experimental and objective whereas qualitative research is explorative and is not in numerical form. Quantitative research is used to identify evidence of cause and effect relationships and is used to collect data from a larger population than qualitative research (Downey & Ireland, 1979). Aliaga and Gunderson (2000), explain that qualitative research is ‘Explaining phenomena by collecting numerical data that are analyzed using mathematically based methods’. It is used to quantify attitudes, opinions, behaviors, and other defined variables – and generalize results from a larger sample population.
Quantitative data collection methods are much more structured than qualitative data collection methods. Data collection methods used in qualitative research includes focus groups, triads, dyads, interviews and observation (Creswell, 2013). Qualitative data is descriptive, which is more difficult to analyze then quantitative data which is categorized, ranked, or in units of measurement. One benefit of qualitative research is the ability to observe, collect, and reach data that other methods cannot obtain. It also provides researchers with flexibility in conveying a story without the constraints of formal academic structure (Creswell, 2013). However, Berkwits and Inui (1998) explain that qualitative research is suspect in its usefulness to provide a generalize foundations for clinical decisions and policies.
Qualitative methods derive from a variety of psychological research disciplines and traditions (Crabtree & Miller, 2012). Different in many ways from quantitative research; yet qualitative research does have a quantitative connection. Qualitative research, also recognized as preliminary exploratory research, is used to capture communicative information not conveyed in quantitative data about beliefs, feelings, values, and motivations that trigger behaviors. They are used to learn directly from the participant what is important to them, to provide the context necessary to understand quantitative findings, and to identify variables important for future clinical studies (Crabtree & Miller, 2012). Qualitative research provides insights into the problem and helps to develop ideas or hypotheses for potential quantitative research.
Examining Qualitative Research
Qualitative research is primarily used in investigative research to explore a phenomenon. Creswell (2013) explains that qualitative methods should be used to study complex subjects and topics. Some subjects in which qualitative analysis is the methodology of choice include but are not limited to education, biology, behavior, health care, psychology, human resources, as well as societal issues such as cultural and racial issues, social norms and stigmas. The use of qualitative research is appropriate when the researcher wants to answer questions or solve a problem by collecting data to generate a theory or hypothesis. Qualitative research uses context and a non-judgmental approach to attempt to understand the phenomena in question from the subject’s point of view and is used to capture expressive information not conveyed in quantitative data about beliefs, values, feelings, and motivations that underlie behaviors (Berkwits & Inui, 1998). Qualitative research is a form of inquiry that analyzes information observed in natural settings.
Qualitative Research is also used to uncover trends in thought and opinions, and dive deeper into the problem. Qualitative data collection methods vary using unstructured or semi-structured techniques. Some common methods include focus groups (group discussions), individual interviews, and participation/observations. The sample size is typically small, and respondents are selected to fulfill a given quota. There are four philosophical assumptions of qualitative methodology recognized in psychological research: ontology, epistemology, axiology, and methodology.
Qualitative research comes from a variety of psychological research disciplines and traditions (Crabtree & Miller, 2012). It is a unique research approach because it allows research access to information that goes beyond quantitative measure. However, the main weakness of the qualitative approach is that it is difficult to provide generalizable foundation for scientific decisions and procedures behaviors (Berkwits & Inui, 1998). It is important to mention that some qualitative approaches use technical methods (such as statistical content analysis) to determine the significance of findings, while others rely on researchers thoughtful reflection (Crabtree & Miller, 2012).
Examining Quantitative Research
Quantitative research is experimental and objective. The objective of quantitative research is essentially to collect numerical data to explain a particular phenomenon (Hoe and Hoare, 2012). By using measurable data researchers are able to formulate facts and uncover patterns in research. The quantitative approach involves a systematic empirical investigation of a phenomenon using numerical data. It is used to identify evidence of cause and effect relationships, as well as collect data from a larger population than qualitative research (Downey & Ireland, 1979).
When conducting a quantitative study researchers use statistical tests to analyze research data. Quantitative data collection methods include various forms of surveys, face-to-face interviews, telephone interviews, longitudinal studies, website interceptors, online polls, and systematic observations. For researchers using the quantitative technique, data is primary and context is secondary. This means that researchers gather data that can be counted, but the context in which the data is observed is not very important to the process. The data is analyzed and rational conclusions are drawn from the interpretation of the resulting numbers (Downey & Ireland, 1979).
Researches elect to use quantitative research when their research problem and questions are best suited to being answered using quantitative methods. Quantitative research is designed to quantify a research problem by way of generating numerical data or data that can be transformed into useable statistics. There are four main types of research questions best suited for quantitative research. The first type of question is a question demanding a qualitative answer (Hoe and Hoare, 2012). For example, how many I/O psychology students are currently enrolled at Capella. The second type of questions is when numerical can only be studies using quantitative methods (Hoe and Hoare, 2012). For example, is the number of I/O psychology students enrolled at Capella rising or falling? The third type of question concerns understanding the state of a phenomenon, such as the contributing factors (Hoe and Hoare, 2012). For example, what factors predict the recruitment of I/O psychology students to attend online universities? The final type of question best suited for quantitative methods is the testing of hypotheses?
There are three quantitative research approaches: (1) experimental, (2) quasi-experimental, and (3) non-experimental. Variables are the foundation of quantitative research. Variables are something that takes on different values or categories. The experimental approach is used to study the cause and effect relationship of variables, specifically the independent and dependent variables. This approach involves the use of true random assignments of variables for analysis. The defining characteristic of the experimental approach involves the manipulation of the independent variable. The quasi-experimental approach is similar to the experimental approach however the main difference is that it does not include the use of randomly assigned variables. The final quantitative research approach, non-experimental, is a comparative approach that differs from experimental because there is no manipulation of the independent variable or random assignment of variables (Leedy & Ormrod, 2013). Sources of references: Creswell, J. W. (2013). Qualitative inquiry and research design: Choosing among five approaches (3rd ed.). Newbury Park, CA: Sage Publications. Leedy, P. D., & Ormrod, J. E. (2013). The nature and tools of research. Practical research: Planning and design , 1-26.
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Sports research topics cover a vast array of areas in the world of athletics, from the physical and psychological impacts of sport on athletes to the social and cultural implications of sports on society. Sports research can include studies on training techniques, nutrition, injury prevention, performance enhancement, and much more. It can also explore the societal impact of sports, such as the role of sports in shaping national identities, gender roles, and cultural values. As a result, the field of sports research provides a unique lens through which to understand the complex relationship between sports and society, and offers insights that can benefit athletes, coaches, and sports enthusiasts alike. In this post, we will explore some of the most fascinating and important sports research topics that are currently being investigated.
Sports Research Topics
Sports Research Topics are as follows:
The psychological benefits of participating in team sports
The impact of sports on academic achievement
The role of sports in promoting physical health and fitness
The impact of sports on mental health and well-being
The benefits and drawbacks of early specialization in youth sports
The relationship between sports and character development
The role of sports in building social capital and community cohesion
The impact of technology on sports training and performance
The influence of gender on sports participation and achievement
The impact of culture on sports participation and achievement
The economics of professional sports: salaries, revenue, and team valuations
The role of sports in promoting diversity and inclusion
The impact of sports on political and social change
The impact of sports sponsorship on consumer behavior
The impact of doping in sports on athlete health and performance
The role of nutrition in sports performance
The impact of weather conditions on sports performance
The influence of crowd behavior on sports performance and player behavior
The impact of sports injuries on athlete health and career longevity
The impact of sports on tourism and local economies
The role of sports in promoting peace and conflict resolution
The impact of globalization on sports and their respective cultures
The impact of sports on national identity and patriotism
The impact of sports media on fan behavior and athlete performance
The impact of sports on the environment
The influence of sports fans on team strategy and decision-making
The impact of sports gambling on sports integrity and athlete health
The impact of sports specialization on long-term athlete development
The influence of sports referees and officials on game outcomes
The role of technology in sports officiating and decision-making
The impact of sports on youth development and socialization
The role of sports in promoting gender equality and women’s empowerment
The impact of sports on personal identity and self-esteem
The role of sports in promoting physical literacy and lifelong physical activity
The impact of fan behavior on athlete mental health and well-being
The influence of sports broadcasters on fan behavior and attitudes
The role of sports in promoting healthy competition and fair play
The impact of sports participation on academic performance in children
The influence of social media on athlete behavior and fan engagement
The impact of sports on international diplomacy and political relations
The influence of coach behavior on athlete mental health and performance
The role of sports in promoting cultural understanding and awareness
The impact of sports science on athlete training and performance
The impact of youth sports on parent-child relationships
The influence of sports team culture on athlete behavior and performance
The role of sports in promoting environmental sustainability
The impact of sports on social mobility and economic inequality
The influence of sports on global health issues
The impact of sports on regional and national identity
The role of sports in promoting positive youth development and resilience.
The impact of technology on sports performance
The effects of altitude on ball flight in sports like golf and tennis
The effects of sports on stress management
The impact of COVID-19 on the sports industry
The impact of technology on sports officiating and rule enforcement
The role of sports in promoting cultural heritage and preservation
The impact of sports on mental toughness and resilience among athletes
The effects of different types of recovery interventions on sports injury rehabilitation
The role of sports in promoting intergenerational connections and social capital
The effects of different types of sports psychology interventions on team dynamics and performance in professional sports
The role of sports in promoting peacebuilding and conflict resolution in divided societies
The impact of sports on career development and job satisfaction among sports journalists
The effects of different types of recovery interventions on injury prevention and performance in powerlifting
The role of sports in promoting social innovation and entrepreneurship among youth
The impact of sports on social identity and community building among refugees and immigrants
The effects of different types of sports nutrition interventions on brain health and cognitive function in older adults
The role of sports in promoting sustainable urban development and active transportation
The impact of sports on social capital and political engagement among LGBTQ+ athletes
The effects of different types of training interventions on injury prevention and recovery in equestrian sports.
The impact of sports on body image and self-esteem among female athletes
The effects of different types of sports equipment on performance and injury risk in extreme sports
The role of sports in promoting cultural diplomacy and international relations
The impact of sports on emotional regulation and mental health among adolescent athletes
The effects of different types of nutrition interventions on injury prevention and recovery in team sports
The role of sports in promoting civic engagement and political participation among athletes
The impact of sports on cognitive development and academic achievement in early childhood
The effects of different types of sports psychology interventions on sports performance and mental health
The role of sports in promoting environmental education and sustainability in schools
The impact of sports on career development and employability among retired athletes
The effects of different types of mindfulness interventions on sports performance and well-being
The role of sports in promoting intercultural dialogue and understanding
The impact of sports on emotional intelligence and leadership development among coaches
The effects of different types of sports supplements on performance and health outcomes
The role of sports in promoting disaster risk reduction and resilience in coastal communities
The impact of sports on social identity and group dynamics in fan communities
The effects of different types of sports training on injury prevention and recovery in power sports
The role of sports in promoting digital literacy and technological innovation in youth
The impact of sports on social-emotional learning and character development in schools
The effects of different types of nutrition interventions on sports performance and cognitive function in older adults
The role of sports in promoting gender equity and empowerment in sports organizations
The impact of sports on cultural identity and community building among Indigenous peoples
The effects of different types of training interventions on injury prevention and recovery in para-athletes
The role of sports in promoting global health and disease prevention
The impact of sports on social support and mental health among parents of youth athletes
The effects of different types of recovery interventions on sports performance and injury prevention in master athletes
The role of sports in promoting community-based health education and behavior change
The impact of sports on identity development and socialization among adolescent girls
The effects of different types of sports nutrition interventions on gut microbiota and health outcomes
The role of sports in promoting intercultural communication and language learning
The impact of sports on psychological well-being and job satisfaction among sports officials
The effects of different types of mindfulness interventions on injury prevention and recovery in endurance sports
The role of sports in promoting sustainable tourism and economic development in rural areas
The impact of sports on social integration and inclusion among individuals with disabilities
The effects of different types of sports equipment on biomechanics and performance in precision sports
The role of sports in promoting community resilience and disaster risk reduction in urban areas
The impact of sports on social-emotional development and academic achievement among at-risk youth
The effects of different types of sports nutrition interventions on immune function and health outcomes
The role of sports in promoting social justice and human rights in sport governance
The impact of sports on community development and social capital in post-conflict areas
The effects of different types of resistance training on injury prevention and recovery in endurance athletes
The role of sports in promoting intergenerational relationships and aging well-being
The impact of sports on social support and mental health among retired athletes
The role of sports in promoting civic activism and social change
The impact of sports on sleep quality and quantity in professional athletes
The effects of different types of stretching on recovery and injury prevention
The role of sports in promoting environmental justice and sustainability
The impact of sports on emotional intelligence and social skills among youth athletes
The effects of different types of resistance training on sports performance
The role of sports in promoting peace and conflict resolution in divided societies
The impact of sports on academic achievement and career success among athletes
The effects of different types of endurance training on injury prevention and recovery
The role of sports in promoting cultural diversity and inclusion
The impact of sports on team cohesion and communication
The effects of different types of dietary interventions on sports performance and recovery
The role of sports in promoting mental health and well-being in marginalized communities
The impact of sports on cognitive function and academic achievement in children
The effects of different types of cooling interventions on sports performance and recovery
The role of sports in promoting community resilience and disaster preparedness
The impact of sports on social capital and social mobility in low-income communities
The effects of different types of sports nutrition interventions on bone health and injury prevention
The role of sports in promoting global citizenship and intercultural competence
The impact of sports on personal and professional development among athletes
The effects of different types of training programs on sports performance and injury prevention in older adults
The role of sports in promoting human rights and social justice
The impact of sports on decision-making and risk-taking behavior in adolescents
The effects of different types of aerobic exercise on cognitive function and brain health
The role of sports in promoting sustainable development and social innovation
The impact of sports on social integration and belonging among refugees and immigrants
The effects of different types of sports equipment on injury risk and performance
The role of sports in promoting gender equality and empowerment in developing countries
The impact of sports on academic engagement and achievement among middle school students
The effects of different types of hydration interventions on sports performance and recovery
The role of sports in promoting community-based tourism and economic development
The impact of sports on identity formation and self-concept among athletes
The effects of different types of sports training on bone health and injury prevention in female athletes
The role of sports in promoting environmental conservation and climate action
The impact of sports on personal values and character development among athletes
The effects of different types of sports nutrition interventions on cardiovascular health and performance
The role of sports in promoting community-based disaster response and recovery
The impact of sports on social support and well-being among LGBTQ+ athletes
The effects of different types of recovery interventions on injury rehabilitation and return to play in professional athletes
The role of sports in promoting social entrepreneurship and innovation
The impact of sports on moral reasoning and ethical decision-making among athletes
The effects of different types of training programs on cognitive function and academic achievement in children
The role of sports in promoting social inclusion and equality in urban settings
The impact of sports on social identity and collective action among fans
The effects of different types of recovery interventions on sports performance and injury prevention in adolescent athletes
The effects of different types of recovery modalities on injury prevention in sports
The role of sports in promoting cultural diplomacy
The impact of sports participation on academic achievement among college students
The effects of different types of hydration strategies on sports performance
The role of sports in promoting social cohesion and community building
The impact of sports on physical and cognitive aging
The effects of different types of warm-down on sports performance and injury prevention
The role of sports in promoting positive youth development
The impact of sports on crime and delinquency among youth
The effects of different types of endurance training on sports performance
The role of sports in promoting gender equity and empowerment
The impact of sports on mental health among athletes
The effects of different types of carbohydrate intake on sports performance
The role of sports in promoting international relations and diplomacy
The impact of sports on body image and self-esteem among adolescents
The effects of different types of sports drinks on sports performance
The role of sports in promoting environmental sustainability and conservation
The impact of sports on cognitive function and brain health
The effects of different types of sports psychology interventions on sports performance
The role of sports in promoting social justice and human rights
The impact of sports on physical activity levels and sedentary behavior
The effects of different types of pre-game nutrition on sports performance
The role of sports in promoting economic development and tourism
The impact of sports on cultural and national identity
The effects of different types of footwear on injury risk in sports
The role of sports in promoting civic engagement and democracy
The impact of sports on sleep quality and quantity
The effects of different types of anaerobic training on sports performance
The role of sports in promoting intergenerational relationships and socialization
The impact of sports on body composition and weight management
The effects of different types of sports psychology interventions on injury prevention and recovery
The role of sports in promoting peacebuilding and conflict resolution
The impact of sports on self-efficacy and self-esteem among athletes
The effects of different types of protein intake on sports performance
The role of sports in promoting health equity and reducing health disparities
The impact of sports on social capital and community resilience
The effects of different types of high-intensity interval training on sports performance
The impact of sports on stress and stress-related disorders
The effects of different types of dietary supplements on sports performance
The role of sports in promoting human development and well-being
The impact of sports on emotional regulation and mental health
The effects of different types of strength training on sports performance
The role of sports in promoting social innovation and entrepreneurship
The impact of sports on social identity and belonging
The effects of different types of cognitive training on sports performance
The role of sports in promoting disaster resilience and preparedness
The impact of sports on academic engagement and achievement among high school students
The effects of different types of stretching on injury prevention and sports performance.
The effects of different types of training on athletic performance
The effectiveness of different coaching styles in sports
The role of nutrition in athletic performance
The psychology of injury rehabilitation in sports
The use of performance-enhancing drugs in sports
The role of sports in promoting physical and mental health
The impact of social media on sports marketing
The effectiveness of sports marketing campaigns
The effects of gender and ethnicity on sports participation and performance
The impact of sports sponsorship on athlete performance
The role of sports in promoting teamwork and leadership
The effects of environmental conditions on sports performance
The impact of sports on community development
The psychology of winning and losing in sports
The effects of sleep on sports performance
The use of virtual reality in sports training
The impact of sports injuries on athletes’ careers
The effects of altitude on athletic performance
The use of data analysis in sports performance assessment
The role of sports in reducing stress and anxiety
The impact of sports on academic performance
The effects of different sports on cardiovascular health
The use of cryotherapy in sports recovery
The impact of social media on sports fans and fandom
The effects of different types of footwear on sports performance
The role of sports in promoting physical activity among children and adolescents
The effects of different types of stretching on sports performance
The impact of sports on social and cultural values
The effects of hydration on sports performance
The role of sports in promoting global understanding and diplomacy
The effects of different types of surfaces on sports performance
The impact of sports on economic development
The impact of sports on mental toughness and resilience
The effects of different types of recovery methods on sports performance
The use of mindfulness in sports performance and injury recovery
The impact of sports on environmental sustainability
The effects of different types of warm-up on sports performance
The role of sports in promoting tourism and travel
The impact of sports on crime reduction and community safety
The effects of different types of sports equipment on performance
The impact of sports on job creation and employment opportunities
The effects of different types of physical activity on mental health
The role of sports in promoting social mobility and equality
The impact of sports on identity formation and socialization
The effects of different types of pre-game rituals on sports performance.
The role of sports in promoting healthy aging
The impact of sports on conflict resolution among youth
The effects of sports on job satisfaction and productivity
The role of sports in promoting environmental conservation
The impact of sports on language proficiency and communication skills
The effects of sports on the development of social skills
The role of sports in promoting peaceful coexistence and tolerance
The impact of sports on community building and cohesion
The effects of different types of sports on hand-eye coordination
The impact of sports on personal growth and self-discovery
The effects of sports on cultural competency
The role of sports in promoting social and emotional learning
The impact of sports on community health
The effects of different types of sports on reaction time
The role of sports in promoting social justice and equity
The impact of sports on academic motivation and achievement
The effects of sports on the development of grit and resilience
The role of sports in promoting civic engagement and social responsibility.
The impact of sports on tourism
The role of sports in promoting physical activity
The effects of playing sports on cognitive development
The impact of sports on political identity
The effects of sports on self-esteem and body image
The role of sports in promoting teamwork and collaboration
The effects of different coaching styles on athlete performance
The impact of sports on national security
The role of sports in promoting cultural exchange and diplomacy
The effects of sports on language acquisition
The impact of sports on family dynamics
The role of sports in promoting conflict resolution
The impact of sports on social mobility
The effects of different types of training on injury prevention
The role of sports in promoting global health
The effects of sports on decision-making and risk-taking behavior
The role of sports in promoting physical and mental well-being
The impact of sports on social justice
The effects of sports on academic achievement among at-risk youth
The role of sports in promoting cultural heritage
The impact of sports on personal identity
The effects of sports on emotional intelligence and empathy
The role of sports in promoting gender equality
The impact of sports on identity formation
The effects of different types of sports on balance and coordination
The role of sports in promoting social capital
The impact of sports on social integration and inclusion
The effects of training at high altitudes on athletic performance
The psychological factors that contribute to athlete burnout
The relationship between sleep and athletic performance
The effects of music on sports performance
The effects of caffeine on sports performance
The impact of climate on sports performance
The use of supplements in sports performance
The role of genetics in sports performance
The effects of aging on sports performance
The impact of sports injuries on athlete’s careers
The relationship between sports and mental health
The effects of gender on sports performance
The impact of social media on sports
The effects of sports fandom on mental health
The use of technology in sports coaching
The impact of team culture on sports performance
The effects of sports specialization on athlete development
The role of sports psychology in athlete performance
The effects of plyometric training on athletic performance
The impact of climate change on outdoor sports
The effects of team dynamics on sports performance
The impact of sports participation on academic achievement
The effects of sports sponsorship on athlete performance
The role of biomechanics in sports performance
The effects of stretching on sports performance
The impact of sports equipment on performance
The effects of altitude training on endurance sports performance
The effects of different types of training on sports performance
The role of nutrition in injury prevention
The effects of mental preparation on sports performance
The effects of climate on indoor sports performance
The role of sports in cultural identity
The impact of sports participation on youth development
The effects of strength training on sports performance
The role of coaches in athlete development
The impact of sports on national identity
The effects of different playing surfaces on sports performance
The role of recovery in sports performance
The impact of sports on local economies
The impact of sports on gender and racial equality
The effects of team size on sports performance
The role of sports in promoting social inclusion
The effects of sports on personal development
The impact of sports on conflict resolution
The effects of sports on leadership development
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Methods for Quantitative Research in Psychology
Conducting Research
Psychological Research
August 2023
This seven-hour course provides a comprehensive exploration of research methodologies, beginning with the foundational steps of the scientific method. Students will learn about hypotheses, experimental design, data collection, and the analysis of results. Emphasis is placed on defining variables accurately, distinguishing between independent, dependent, and controlled variables, and understanding their roles in research.
The course delves into major research designs, including experimental, correlational, and observational studies. Students will compare and contrast these designs, evaluating their strengths and weaknesses in various contexts. This comparison extends to the types of research questions scientists pose, highlighting how different designs are suited to different inquiries.
A critical component of the course is developing the ability to judge the quality of sources for literature reviews. Students will learn criteria for evaluating the credibility, relevance, and reliability of sources, ensuring that their understanding of the research literature is built on a solid foundation.
Reliability and validity are key concepts addressed in the course. Students will explore what it means for an observation to be reliable, focusing on consistency and repeatability. They will also compare and contrast different forms of validity, such as internal, external, construct, and criterion validity, and how these apply to various research designs.
The course concepts are thoroughly couched in examples drawn from the psychological research literature. By the end of the course, students will be equipped with the skills to design robust research studies, critically evaluate sources, and understand the nuances of reliability and validity in scientific research. This knowledge will be essential for conducting high-quality research and contributing to the scientific community.
Learning objectives
Describe the steps of the scientific method.
Specify how variables are defined.
Compare and contrast the major research designs.
Explain how to judge the quality of a source for a literature review.
Compare and contrast the kinds of research questions scientists ask.
Explain what it means for an observation to be reliable.
Compare and contrast forms of validity as they apply to the major research designs.
This program does not offer CE credit.
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p-ISSN: 2162-9463 e-ISSN: 2162-8467
2020; 10(2): 41-48
doi:10.5923/j.edu.20201002.03
Received: June 23, 2020; Accepted: July 20, 2020; Published: August 15, 2020
Effects of Sports Participation on the Academic Performance of Grade 12 Students after the K-12 Implementation
Joswa Billonid , Ma. Teresa Cabailo , Winde Rose Mie Dagle , Donna May Godilano , Kyle Robert Kibanoff , Lenny Rose Tasic
Department of Senior High School, Western Institute of Technology, Iloilo, Philippines
Implementing the K-12 program is part of an active effort by stakeholders in Philippine education to solve the shortcomings in the educational system. This new educational framework seeks to deliver a more thorough and well-rounded educational experience for the students. However, this system's implementation and the addition of extracurricular activities like athletics provide several difficulties for students. This study compared the academic performance of athletes and non-athletes in Grade 12 Senior High School students ( N =60) using their midterm scores and a self-report survey questionnaire after the K-12 implementation. Data from the students' midterm scores and a self-report survey questionnaire administered after implementing the K-12 program were analyzed using a t-test. The results showed no significant difference in the midterm scores between non-athletes (78.13 ± 13.71) and athletes (73.7 ± 9.13), with a p-value of 0.146. The results of the survey questionnaire showed that only two (2) questions out of fifteen (15) (Do you skip classes? and Do you have enough allowance?) had a significant difference (p>0.05). The results of the current study demonstrated that student-athletes perform academically on par with non-athletes and can function both inside and outside the classroom. They could maintain attendance despite needing more time for training and getting used to the K–12 system. This study generally demonstrated that with the establishment of K–12, Grade 12 Senior High School pupils' engagement in athletics had little bearing on their academic achievement. The study's findings are crucial for promoting and supporting student participation in sports activities across all education stakeholders.
Keywords: General Academics, K to 12 Programs, Student Participation in Sports Activities, Athletics as Extracurricular, Activities, Self-report Survey Questionnaire
Cite this paper: Joswa Billonid , Ma. Teresa Cabailo , Winde Rose Mie Dagle , Donna May Godilano , Kyle Robert Kibanoff , Lenny Rose Tasic , Effects of Sports Participation on the Academic Performance of Grade 12 Students after the K-12 Implementation, Education , Vol. 10 No. 2, 2020, pp. 41-48. doi: 10.5923/j.edu.20201002.03.
Article Outline
1. introduction, 1.1. objectives of the study, 2. materials and methods, 2.1. research design.
Schematic diagram of the Descriptive-Quantitative Analysis
2.2. Research Instrument
2.3. participants, 2.4. data analysis, 3.1. midterm scores.
Results of Midterm examination
3.2. Survey Questionnaire
Survey questions
. Heat map showing the percentage of the responses categorized according to the respondents. Intense red means the highest percentage, and intense blue means the lowest percentage. The following initials mean: (Q1) Do you attend classes regularly; (Q2) Do you go to school on time; (Q3) Do you skip classes; (Q4) Do you participate in class discussions; (Q5) Do you spend time studying for every examination; (Q6) Do you attend tutorial classes; (Q7) Do you borrow notes from your classmates when missed a lesson; (Q8) Do you cheat during the major exam; (Q9) Do you understand the lesson very well; (Q10) Do you use a cellphone or other gadgets during the class discussion; (Q11) Do you spend more time with your boyfriend/girlfriend rather than studying; (Q12) Does your family pressure you to excel in class; (Q13) Do your peers a bad influence in your studies; (Q14) Do you have enough allowance; (Q15)Do you watch YouTube tutorials related to your subject
Summary of p- values in all questions
4. Discussion
5. conclusion and recommendations, acknowledgements.
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The Importance of Qualitative Data Analysis in Research: A Comprehensive Guide
August 29th, 2024
Qualitative data analysis, in essence, is the systematic examination of non-numerical information to uncover patterns, themes, and insights.
This process is crucial in various fields, from product development to business process improvement.
Key Highlights
Defining qualitative data analysis and its importance
Comparing qualitative and quantitative research methods
Understanding the qualitative data analysis process
Reviewing CAQDAS tools for efficient analysis
Ensuring rigor through triangulation and member checking
Addressing challenges and ethical considerations
Examining future trends in qualitative research
Introduction to Qualitative Data Analysis
Qualitative data analysis is a sophisticated process of examining non-numerical information to extract meaningful insights.
It’s not just about reading through text; it’s about diving deep into the nuances of human experiences, opinions, and behaviors.
This analytical approach is crucial in various fields, from product development to process improvement , and even in understanding complex social phenomena.
Importance of Qualitative Research Methods
The importance of qualitative research methods cannot be overstated. In my work with companies like 3M , Dell , and Intel , I’ve seen how qualitative analysis can uncover insights that numbers alone simply can’t reveal.
These methods allow us to understand the ‘why’ behind the ‘what’, providing context and depth to our understanding of complex issues.
Whether it’s improving a manufacturing process or developing a new product, qualitative research methods offer a rich, nuanced perspective that’s invaluable for informed decision-making.
Comparing Qualitative vs Quantitative Analysis
While both qualitative and quantitative analyses are essential tools in a researcher’s arsenal, they serve different purposes.
Quantitative analysis, which I’ve extensively used in Six Sigma projects, deals with numerical data and statistical methods.
It’s excellent for measuring, ranking, and categorizing phenomena. On the other hand, qualitative analysis focuses on the rich, contextual data that can’t be easily quantified.
It’s about understanding meanings, experiences, and perspectives.
Key Approaches in Qualitative Data Analysis
Explore essential techniques like thematic analysis, grounded theory, content analysis, and discourse analysis.
Understand how each approach offers unique insights into qualitative data interpretation and theory building.
Thematic Analysis Techniques
Thematic analysis is a cornerstone of qualitative data analysis. It involves identifying patterns or themes within qualitative data.
In my workshops on Statistical Thinking and Business Process Charting , I often emphasize the power of thematic analysis in uncovering underlying patterns in complex datasets.
This approach is particularly useful when dealing with interview transcripts or open-ended survey responses.
The key is to immerse yourself in the data, coding it systematically, and then stepping back to see the broader themes emerge.
Grounded Theory Methodology
Grounded theory is another powerful approach in qualitative data analysis. Unlike methods that start with a hypothesis, grounded theory allows theories to emerge from the data itself.
I’ve found this particularly useful in projects where we’re exploring new territory without preconceived notions.
It’s a systematic yet flexible approach that can lead to fresh insights and innovative solutions.
The iterative nature of grounded theory, with its constant comparison of data, aligns well with the continuous improvement philosophy of Six Sigma .
Content Analysis Strategies
Content analysis is a versatile method that can be both qualitative and quantitative.
In my experience working with diverse industries, content analysis has been invaluable in making sense of large volumes of textual data.
Whether it’s analyzing customer feedback or reviewing technical documentation, content analysis provides a structured way to categorize and quantify qualitative information.
The key is to develop a robust coding framework that captures the essence of your research questions.
Discourse Analysis Approaches
Discourse analysis takes a deeper look at language use and communication practices.
It’s not just about what is said, but how it’s said and in what context. In my work on improving communication processes within organizations , discourse analysis has been a powerful tool.
It helps uncover underlying assumptions, power dynamics, and cultural nuances that might otherwise go unnoticed.
This approach is particularly useful when dealing with complex organizational issues or when trying to understand stakeholder perspectives in depth.
The Qualitative Data Analysis Process
Navigate through data collection, coding techniques, theme development, and interpretation. Learn how to transform raw qualitative data into meaningful insights through systematic analysis.
Data collection methods (interviews, focus groups, observation)
The foundation of any good qualitative analysis lies in robust data collection. In my experience, a mix of methods often yields the best results.
In-depth interviews provide individual perspectives, focus groups offer insights into group dynamics, and observation allows us to see behaviors in their natural context.
When working on process improvement projects , I often combine these methods to get a comprehensive view of the situation.
The key is to align your data collection methods with your research questions and the nature of the information you’re seeking.
Qualitative Data Coding Techniques
Coding is the heart of qualitative data analysis. It’s the process of labeling and organizing your qualitative data to identify different themes and the relationships between them.
In my workshops, I emphasize the importance of developing a clear, consistent coding system.
This might involve open coding to identify initial concepts, axial coding to make connections between categories, and selective coding to integrate and refine the theory.
The goal is to transform raw data into meaningful, analyzable units.
Developing Themes and Patterns
Once your data is coded, the next step is to look for overarching themes and patterns. This is where the analytical magic happens.
It’s about stepping back from the details and seeing the bigger picture. In my work with companies like Motorola and HP, I’ve found that visual tools like mind maps or thematic networks can be incredibly helpful in this process.
They allow you to see connections and hierarchies within your data that might not be immediately apparent in text form.
Data Interpretation and Theory Building
The final step in the qualitative data analysis process is interpretation and theory building.
This is where you bring together your themes and patterns to construct a coherent narrative or theory that answers your research questions.
It’s crucial to remain grounded in your data while also being open to new insights. In my experience, the best interpretations often challenge our initial assumptions and lead to innovative solutions.
Tools and Software for Qualitative Analysis
Discover the power of CAQDAS in streamlining qualitative data analysis workflows. Explore popular tools like NVivo, ATLAS.ti, and MAXQDA for efficient data management and analysis .
Overview of CAQDAS (Computer Assisted Qualitative Data Analysis Software)
Computer Assisted Qualitative Data Analysis Software (CAQDAS) has revolutionized the way we approach qualitative analysis.
These tools streamline the coding process, help manage large datasets, and offer sophisticated visualization options.
As someone who’s seen the evolution of these tools over the past two decades, I can attest to their transformative power.
They allow researchers to handle much larger datasets and perform more complex analyses than ever before.
Popular Tools: NVivo, ATLAS.ti, MAXQDA
Among the most popular CAQDAS tools are NVivo, ATLAS.ti, and MAXQDA.
Each has its strengths, and the choice often depends on your specific needs and preferences. NVivo , for instance, offers robust coding capabilities and is excellent for managing multimedia data.
ATLAS.ti is known for its intuitive interface and powerful network view feature. MAXQDA stands out for its mixed methods capabilities, blending qualitative and quantitative approaches seamlessly.
Ensuring Rigor in Qualitative Data Analysis
Implement strategies like data triangulation, member checking, and audit trails to enhance credibility. Understand the importance of reflexivity in maintaining objectivity throughout the research process.
Data triangulation methods
Ensuring rigor in qualitative analysis is crucial for producing trustworthy results.
Data triangulation is a powerful method for enhancing the credibility of your findings. It involves using multiple data sources, methods, or investigators to corroborate your results.
In my Six Sigma projects, I often employ methodological triangulation, combining interviews, observations, and document analysis to get a comprehensive view of a process or problem.
Member Checking for Validity
Member checking is another important technique for ensuring the validity of your qualitative analysis.
This involves taking your findings back to your participants to confirm that they accurately represent their experiences and perspectives.
In my work with various organizations, I’ve found that this not only enhances the credibility of the research but also often leads to new insights as participants reflect on the findings.
Creating an Audit Trail
An audit trail is essential for demonstrating the rigor of your qualitative analysis.
It’s a detailed record of your research process, including your raw data, analysis notes, and the evolution of your coding scheme.
Practicing Reflexivity
Reflexivity is about acknowledging and critically examining your own role in the research process. As researchers, we bring our own biases and assumptions to our work.
Practicing reflexivity involves constantly questioning these assumptions and considering how they might be influencing our analysis.
Challenges and Best Practices in Qualitative Data Analysis
Address common hurdles such as data saturation , researcher bias, and ethical considerations. Learn best practices for conducting rigorous and ethical qualitative research in various contexts.
Dealing with data saturation
One of the challenges in qualitative research is knowing when you’ve reached data saturation – the point at which new data no longer brings new insights.
In my experience, this requires a balance of systematic analysis and intuition. It’s important to continuously review and compare your data as you collect it.
In projects I’ve led, we often use data matrices or summary tables to track emerging themes and identify when we’re no longer seeing new patterns emerge.
Overcoming Researcher Bias
Researcher bias is an ever-present challenge in qualitative analysis. Our own experiences and preconceptions can inadvertently influence how we interpret data.
To overcome this, I advocate for a combination of strategies. Regular peer debriefing sessions , where you discuss your analysis with colleagues, can help uncover blind spots.
Additionally, actively seeking out negative cases or contradictory evidence can help challenge your assumptions and lead to more robust findings.
Ethical Considerations in Qualitative Research
Ethical considerations are paramount in qualitative research, given the often personal and sensitive nature of the data.
Protecting participant confidentiality, ensuring informed consent, and being transparent about the research process are all crucial.
In my work across various industries and cultures, I’ve learned the importance of being sensitive to cultural differences and power dynamics.
It’s also vital to consider the potential impact of your research on participants and communities.
Ethical qualitative research is not just about following guidelines, but about constantly reflecting on the implications of your work.
The Future of Qualitative Data Analysis
As we look to the future of qualitative data analysis, several exciting trends are emerging.
The increasing use of artificial intelligence and machine learning in qualitative analysis tools promises to revolutionize how we handle large datasets.
We’re also seeing a growing interest in visual and sensory methods of data collection and analysis, expanding our understanding of qualitative data beyond text.
In conclusion, mastering qualitative data analysis is an ongoing journey. It requires a combination of rigorous methods, creative thinking, and ethical awareness.
As we move forward, the field will undoubtedly continue to evolve, but its fundamental importance in research and decision-making will remain constant.
For those willing to dive deep into the complexities of qualitative data, the rewards in terms of insights and understanding are immense.
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Religion, Spirituality and Health Research: Warning of Contaminated Scales
IMPRESSIONISTIC REPORTING
Published: 28 August 2024
Cite this article
Harold G. Koenig ORCID: orcid.org/0000-0003-2573-6121 1 , 2 , 3 , 5 &
Lindsay B. Carey 4 , 6 , 7
The relationship between religiosity, spirituality and health has received increasing attention in the academic literature. Studies involving quantitative measurement of religiosity and/or spirituality (R/S) and health have reported many positive associations between these constructs. The quality of various measures, however, is very important in this field, given concerns that some measures of R/S have been contaminated with indicators of mental health. When this occurs, that is when R/S is defined and measured a priori, this subsequently guarantees a positive association between R/S and health (especially mental health). Such associations are called tautological, which involves correlating a construct with itself, thus producing associations that are uninterpretable and misleading. In this article, concerns about the measurement of R/S are discussed, examples of contaminated and potentially probelmatic measures of R/S are noted, and recommendations are made regarding uncontaminated measures of R/S that should be used in future studies of R/S and health.
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Defining “spirituality": Please note that it is not the intent nor within the scope of this review to consider the differences in meaning between "spirituality" and the actual practice of spiritual care or pastoral care. Please refer to Carey et al. ( 2024 , p. 2, and associated Footnote ) for a brief comparative discussion.
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Department of Psychiatry & Behavioral Sciences, Duke University Medical Center, Durham, NC, USA
Harold G. Koenig
Department of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
Department of Psychiatry, Shiraz University of Medical Sciences, Shiraz, Iran
School of Psychology & Public Health, College of Science, Health & Engineering La Trobe University, Victoria, 3086, Australia
Lindsay B. Carey
Duke University Medical Center, Box 3400, Durham, NC, 27710, USA
Institute for Ethics and Society, University of Notre Dame, Sydney, New South Wales, 2007, Australia
College of Sport, Health and Engineering, Victoria University, Melbourne, New South Wales, 3011, Australia
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Koenig, H.G., Carey, L.B. Religion, Spirituality and Health Research: Warning of Contaminated Scales. J Relig Health (2024). https://doi.org/10.1007/s10943-024-02112-6
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Published: 02 September 2024
Weaving equity into infrastructure resilience research: a decadal review and future directions
Infrastructure resilience plays an important role in mitigating the negative impacts of natural hazards by ensuring the continued accessibility and availability of resources. Increasingly, equity is recognized as essential for infrastructure resilience. Yet, after about a decade of research on equity in infrastructure resilience, what is missing is a systematic overview of the state of the art and a research agenda across different infrastructures and hazards. To address this gap, this paper presents a systematic review of equity literature on infrastructure resilience in relation to natural hazard events. In our systematic review of 99 studies, we followed an 8-dimensional assessment framework that recognizes 4 equity definitions including distributional-demographic, distributional-spatial, procedural, and capacity equity. Significant findings show that (1) the majority of studies found were located in the US, (2) interest in equity in infrastructure resilience has been exponentially rising, (3) most data collection methods used descriptive and open-data, particularly with none of the non-US studies using human mobility data, (4) limited quantitative studies used non-linear analysis such as agent-based modeling and gravity networks, (5) distributional equity is mostly studied through disruptions in power, water, and transportation caused by flooding and tropical cyclones, and (6) other equity aspects, such as procedural equity, remain understudied. We propose that future research directions could quantify the social costs of infrastructure resilience and advocate a better integration of equity into resilience decision-making. This study fills a critical gap in how equity considerations can be integrated into infrastructure resilience against natural hazards, providing a comprehensive overview of the field and developing future research directions to enhance societal outcomes during and after disasters. As such, this paper is meant to inform and inspire researchers, engineers, and community leaders to understand the equity implications of their work and to embed equity at the heart of infrastructure resilience plans.
Introduction
Infrastructures are the backbones of our societies, connecting people to essential resources and services. At the same time, infrastructure systems such as power, water, and transportation play a pivotal role in determining whether a natural hazard event escalates into a disaster 1 . Driven by the combination of accelerating climate hazards and increasing vulnerability, a 2022 Reuters report indicated that natural hazards caused infrastructure and building losses between $732 and $845 billion dollars internationally 2 . In another report by the World Bank (2019), the direct damage to power and transportation systems had an estimated cost of $18 billion annually 3 . Not only do infrastructure disruptions result in economic losses but they also lead to health issues and a decline in quality of life 4 . Since infrastructure systems secure the accessibility and availability of water, health, and electricity, among other critical services, disruptions of infrastructure exacerbate disasters. For example, the Nepal earthquake (2015) caused the collapse of 262 micro-hydropower plants and 104 hospitals, which further weakened the community’s ability to recover from the hazardous event 5 . Hurricane Maria (2017) in Puerto Rico led to year-long power disruptions which contributed to the 2975 estimated human fatalities 6 . Therefore, infrastructure resilience is becoming increasingly prominent in research, policy, and practice.
The National Infrastructure Advisory Council defined infrastructure resilience as the ability of infrastructure systems, to absorb, adapt, or recover from disruptive events such as natural hazards 7 , 8 . From an engineering viewpoint, infrastructure resilience ensures no significant degradation or loss of system performance in case of a shock (robustness), establishes multiple access channels to infrastructure services (redundancy), effectively mobilizes resources and adapts to new conditions (resourcefulness), and accomplishes these goals in a timely manner (rapidity) 9 . From these origins, infrastructure resilience has evolved to include the complex interactions of technology, policy, social, and governance structures 10 . The United Nations Office for Disaster Risk Reduction discusses the need to use transdisciplinary and systemic methods to guide infrastructure resilience 11 . In their Principles of Resilient Infrastructure report, the principles of infrastructure resilience are to develop understanding and insights (continual learning), prepare for current and future hazards (proactively protected), positively work with the natural environment (environmentally integrated), develop participation across all levels of society (socially engaged), share information and expertise for coordinated benefits (shared responsibility), and address changing needs in infrastructure operations (adaptively transforming) 12 .
Based on the argument of Schlor et al. 13 that “social equity is essential for an urban resilience concept,” we also argue that equity in infrastructure resilience will not only benefit vulnerable populations but also lead to more resilient communities. Equity, in a broad sense, refers to the impartial distribution and just accessibility of resources, opportunities, and outcomes, which strive for fairness regardless of location and social group 14 , 15 . Equity in infrastructure resilience ensures that everyone in the community, regardless of their demographic background, geographic location, level of community status, and internal capabilities, have access to and benefits from infrastructure services. It would also address the limitations of infrastructure resilience, which brings short-term benefits to a specific group of people but ultimately results in long-term disaster impacts 16 . A failure to recognize equity in infrastructure resilience could exacerbate the disaster impact and lock in recovery processes, which in turn, reduces future resilience and leads to a vicious cycle 17 .
Even though infrastructure resilience has important equity impacts, the traditional definition of infrastructure resilience is antithetical to equity. Socially vulnerable populations (such as lower income, minority, indigenous, or rural populations) have traditionally been excluded from the development, maintenance, and planning of infrastructure resilience 18 . For instance, resilience strategies do not conventionally consider the unique needs and vulnerabilities of different communities, leading to inadequate one-size-fits-all solutions 19 . Conventional approaches to restoring infrastructure after hazard events are based on the number of outages, the number of affected customers, and extent of damage within an area, depending on the company preferences, and rarely prioritize the inherent vulnerability of affected individuals and areas 20 . Thereby, those who are most dependent on infrastructure systems may also be most affected by their outages. Several reports, such as National Institute of Standards and Technology 21 , United Nations Office for Project Services 11 , United Nations Office for Disaster Risk Reduction and Coalition for Disaster Resilient Infrastructure 22 , and the Natural Hazards Engineering Research Infrastructure 23 have recognized the importance of considering vulnerable populations in infrastructure resilience.
Furthermore, infrastructure resilience efforts often require significant investment at individual, community, and societal levels 24 . For instance, lower income households may not be able to afford power generators or water tanks to replace system losses 25 , 26 , which means they are more dependent on public infrastructure systems. Wealthier communities may receive more funding and resources for resilience projects due to better political representation and economic importance 27 . Improvements in infrastructure can also lead to gentrification and displacement, as an area perceived with increased safety may raise property values and push out underrepresented residents 28 . Infrastructure resilience may not be properly communicated or usable for all members of the community 29 . Research has also shown an association between vulnerable groups facing more intense losses and longer restoration periods of infrastructure disruptions due to planning biases, inadequate maintenance, and governance structures 18 . Due to the limited tools that translate equity considerations, infrastructure managers, owners, and operators are unlikely to recognize inequities in service provision 20 . Finally, resilience planning can prioritize rapid recovery which may not allow for sufficient time to address the underlying social inequities. This form of resilience planning overlooks the range of systematic disparities evident in infrastructure planning, management, operations, and maintenance in normal times and hazardous conditions 18 .
The field of equity in infrastructure resilience has sparked increasing interest over the last decade. First, researchers have distinguished equal and equitable treatment for infrastructure resilience. As stated by Kim and Sutley 30 , equality creates equivalence at the beginning of a process whereas equity seeks equivalence at the end. Second, the term has been interpreted through other social-economic concepts such as social justice 16 , sustainability 31 , vulnerability 32 , welfare 33 , 34 , and environmental justice 35 . Third, equitable infrastructure is frequently associated with pre-existing inequities such as demographic features 36 , 37 , spatial clusters 38 , 39 , 40 , and political processes 41 . Fourth, studies have proposed frameworks to analyze the relationship of equity in infrastructure resilience 42 , 43 , adapted quantitative and qualitative approaches 44 , 45 , and created decision-making tools for equity in infrastructure resilience 31 , 46 .
Despite a decade of increasing interest in integrating equity into infrastructure resilience, the research gap is to systematically evaluate collective research progress and fundamental knowledge. To address this gap, this paper presents a comprehensive systematic literature review of equity-related literature in the field of infrastructure resilience during natural hazards. The aim is to provide a thorough overview of the current state of art by synthesizing the growing body of literature of equitable thinking and academic research in infrastructure resilience. From there, we aim to identify gaps and establish a research agenda. This review focuses on the intersection of natural hazard events, infrastructure resilience, and equity to answer three overarching research questions. As such, this research is important because it explores the critical but often neglected integration of equity into infrastructure resilience against natural hazards. It provides a comprehensive overview and identifies future research opportunities to improve societal outcomes during and after disasters.
What are the prevailing concepts, foci, methods, and theories in assessing the inequities of infrastructure services in association with natural hazard events?
What are the similarities and differences in studying pathways of equity in infrastructure resilience?
What are the current gaps of knowledge and future challenges of studying equity in infrastructure resilience?
To answer the research questions, the authors reviewed 99 studies and developed an 8-dimensional assessment framework to understand in which contexts and via which methods equity is studied. To differentiate between different equity conceptualizations, the review distinguishes four definitions of equity: distributional-demographic (D), distributional-spatial (S), procedural (P), and capacity (C). In our study, “pathways” explores the formation, examination, and application of equity within an 8-dimensional framework. Following Meerow’s framework of resilience to what and of what? 47 , we then analyze for which infrastructures and hazards equity is studied. Infrastructures include power, water, transportation, communication, health, food, sanitation, stormwater, emergency, and general if a specific infrastructure is not mentioned. Green infrastructure, social infrastructure, building structures, and industrial structures were excluded. The hazards studied include flood, tropical cyclone, drought, earthquake, extreme temperature, pandemic, and general if there is no specific hazard.
The in-depth decadal review aims to bring insights into what aspects are fully known, partially understood, or completely missing in the conversation involving equity, infrastructure resilience, and disasters. The review will advance the academic understanding of equity in infrastructure resilience by highlighting understudied areas, recognizing the newest methodologies, and advising future research directions. Building on fundamental knowledge can influence practical applications. Engineers and utility managers can use these findings to better understand potential gaps in the current approaches and practices that may lead to inequitable outcomes. Community leaders and advocates could also leverage such evidence-based insights for advocacy and bring attention to equity concerns in infrastructure resilience policies and guidelines.
Infrastructure resilience in the broader resilience debate
To establish links across the resilience fields, this section embeds infrastructure resilience into the broader resilience debate including general systems resilience, ecological resilience, social resilience, physical infrastructure resilience, and equity in infrastructure resilience. From the variety of literature in different disciplines, we focus on the definitions of resilience and draw out the applicability to infrastructure systems.
Resilience has initially been explored in ecological systems. Holling 48 defines resilience as the ability of ecosystems to absorb changes and maintain their core functionality. This perspective recognizes that ecosystems do not necessarily return to a single equilibrium state, but can exist in multiple steady states, each with distinct thresholds and tipping points. Building on these concepts, Carpenter et al. 49 assesses the capacity of socioecological systems to withstand disturbances without transitioning to alternative states. The research compares resilience properties in lake districts and rangelands such as the dependence on slow-changing variables, self-organization capabilities, and adaptive capacity. These concepts enrich our understanding of infrastructure resilience by acknowledging the complex interdependencies between natural and built systems. It also points out the different temporal rhythms across fast-paced behavioral and slow-paced ecological and infrastructural change 50 .
Social resilience brings the human and behavioral dimension to the foreground. Aldrich and Meyer focuses on the concept of social capital in defining community resilience by emphasizing the role of social networks and relationships to enhance a community’s ability to withstand and recover from disasters 51 . Aldrich and Meyer argues that social infrastructure is as important as physical infrastructure in disaster resilience. Particularly, the depth and quality of social networks can provide crucial support in times of crisis, facilitate information sharing, expedite resource allocation, and coordinate recovery efforts. Resilience, in this context, is defined as the enhancement and utilization of its social infrastructure through social capital. It revolves around the collective capacity of communities to manage stressors and return to normalcy post-disaster through cooperative efforts.
Since community resilience relies on collaborative networks, which in turn are driven by accessibility, community and social resilience are intricately linked to functioning infrastructures 52 . To understand the relationships, we first examine the systems of systems approach thinking. Vitae Systems of Systems aims to holistically resolve complex environmental and societal challenges 53 . It emphasizes strategic, adaptive, and interconnected solutions crucial for long-term system resilience. Individual systems, each with their capabilities and purposes, are connected in ways such that they can achieve together what they cannot achieve alone. Additionally, Okada 54 also shows how the Vitae Systems of Systems can detect fundamental areas of concern and hotspots of vulnerability. It highlights principles of survivability (live through), vitality (live lively), and conviviality (live together) to build system capacity in the overall community. In the context of infrastructure resilience, these approaches bring context to the development of systems and their interdependencies, rather than focusing on the resilience of individual components in isolation.
Expanding on the notion of social and community resilience, Hay’s applies key concepts of being adaptable and capable of maintaining critical functionalities during disruptions to infrastructure 55 . This perspective introduces the concept of “safe-to-fail” systems, which suggests that planning for resilience should anticipate and accommodate the potential for system failures in a way that minimizes overall disruption and aids quick recovery.
As such, the literature agrees that social, infrastructural, and environmental systems handle unexpected disturbances and continue to provide essential services. While Aldrich’s contribution lies in underscoring the importance of social ties and community networks, Hay expands this into the realm of physical systems by considering access to facilities. Infrastructure systems traditionally adapt and change slowly, driven by rigid physical structures, high construction costs, and planning regulations. In contrast, behavioral patterns are relatively fast-changing, even though close social connections and trust also take time to build. Yet, infrastructures form the backbone that enables—or disrupts—social ties. By adopting resilience principles that enable adaptation across infrastructure and social systems, better preparedness, response, and recovery can be achieved.
Given the dynamic, complex nature of resilience, infrastructure resilience, by extension, should not just be considered through the effective engineering of the built environment. Rather, infrastructure resilience must be considered as an integral part of the multi-layered resilience landscape. Crucial questions that link infrastructure to the broader resilience debate include: How will it be used and by whom? How are infrastructure resilience decisions taken, and whose voices are prioritized? These critical questions necessitate the integration of equity perspectives into the infrastructure resilience discourse.
Equity in infrastructure resilience ensures all community members have equitable access to essential services and infrastructure. In her commentary paper, Cutter 56 examines disaster resilience and vulnerability, challenging the prevalent ambiguity in the definitions of resilience. The paper poses two fundamental questions of “resilience to what?” and “resilience to whom?” . Later, Meerow and Newell 47 expanded on these questions in the context of urban resilience, “for whom, what, where, and why?” . They also stress the need for “resilience politics,” which include understanding of how power dynamics shape resilience policies, creating winners and losers 47 .
In a nutshell, resilience strategies must proactively address systemic inequities. This can also be framed around the concept of Rawls’ Theory of Justice principles, such as equal basic rights and fair equality of opportunity 57 , 58 . Rawls advocates for structuring social and economic inequalities to benefit the least advantaged members of society. In the context of infrastructure resilience, the theory would ensure vulnerable communities, such as lower-income households, have priority in infrastructure restoration. Incorporating Walker’s Theory of Abundant Access, this could also mean prioritizing those most dependent on public transit. Access to public transit, especially in lower-income brackets, allows for greater freedom of movement and connection to other essential facilities in the community like water, food, and health 59 , 60 . At the same time, Casali et al. 61 show that access to infrastructures alone is not sufficient for urban resilience to emerge. Such perspectives integrate physical and social elements of a community to equitably distribute infrastructure resilience benefits. Table 1 summarizes the selected definitions of resilience.
Definitions of equity
Equity in infrastructure resilience ensures that individuals have the same opportunity and access to infrastructure services regardless of differing demographics, spatial regions, involvement in the community, and internal capacity. Equity is a multifaceted concept that requires precise definitions to thoroughly assess and address it within the scope of infrastructure resilience. Based on the literature, our systematic literature review proposes four definitions of equity for infrastructure resilience: distributional-demographic (D), distributional-spatial (S), procedural (P), and capacity (C). Distributional-demographic (D) equity represents accessibility to and functionality of infrastructure services considering the vulnerability of demographic groups 62 . Distributional-spatial (S) equity focuses on the equitable distribution of infrastructure services to all spatial regions 63 . Procedural (P) equity refers to inclusive participation and transparent planning with stakeholders and community members 31 . Capacity equity (C) connect the supporting infrastructure to the hierarchy of needs which recognizes the specific capacities of households 64 .
Distributional-demographic (D) addresses the systemic inequities in communities to ensure those of differing demographic status have equitable access to infrastructure services 37 . The purpose is to equitably distribute the burdens and benefits of services by reducing disparity for the most disadvantaged populations 42 . These groups may need greater support due to greater hardship to infrastructure losses, greater dependency on essential services, and disproportionate losses to infrastructure 43 , 65 , 66 . In addition, they may have differing abilities and need to mitigate service losses 33 . Our research bases distributional-demographic on age for young children and elderly, employment, education, ethnicity, people with disabilities, gender, income, tenure of residence, marginalized populations based on additional demographic characteristics, intergenerational, and general-social inequities 67 .
Distributional-spatial (S) recognizes that the operation and optimizations of the systems may leave certain areas in isolation 68 , 69 , 70 . For example, an equitable access to essential services (EAE) approach to spatial planning can identify these service deserts 46 . Urban and rural dynamics may also influence infrastructure inequities. Rural areas have deficient funding sources compared to urban areas 17 while urban areas may have greater vulnerability due to the interconnectedness of systems 71 . Our research labels distributional-spatial as spatial and urban-rural. Spatial involves spatial areas of extreme vulnerability through spatial regression models, spatial inequity hotspots, and specific mentions of vulnerable areas. Urban-rural references the struggles of urban-rural areas.
Procedural (P) equity ensures the inclusion of everyone in the decision-making process from the collection of data to the influence of policies. According to Rivera 72 , inequities in the disaster recovery and reconstruction process originate from procedural vulnerabilities associated with historical and ongoing power relations. The validity of local cultural identities is often overlooked in the participation process of designing infrastructure 73 . Governments and institutions may have excluded certain groups from the conversation to understand, plan, manage, and diminish risk in infrastructure 74 . As argued by Liévanos and Horne 20 , such utilitarian bureaucratic decision rules can limit the recognition of unequal services and the development of corrective actions. These biases can be present in governmental policies, maintenance orders, building codes, and distribution of funding 30 . Our research labels procedural equity as stakeholder input and stakeholder engagement. Stakeholder input goes beyond collecting responses from interviews and surveys. Rather, researchers will ask for specific feedback and validation on final research deliverables like models, results, and spatial maps, but they are not included in the research planning process. Stakeholder engagement are instances where participants took an active role in the research deliverables to change elements of their community.
Capacity (C) equity is the ability of individuals, groups, and communities to counteract or mitigate the effect of infrastructure loss. As mentioned by Parsons, et al. 75 , equity can be enhanced through a network of adaptive capacities at the household or community level. These adaptive capacities are viewed as an integral part of community resilience 76 . Regarding infrastructure, households can prepare for infrastructure losses and have service substitutes such as power generators or water storage tanks 77 , 78 . It may also include the household’s ability to tolerate disruptions and the ability to perceive risk to infrastructure losses 66 . However, capacity can be limited by people’s social connections, social standing, and access to financial resources and personal capital 79 . Our research categorizes capacity equity as adaptations, access, and susceptibility. Adaptations include preparedness strategies before a disaster as well as coping strategies during and after the disaster. Access includes a quantifiable metric in reaching critical resources which may include but is not limited to vehicles, public transportation, or walking. Susceptibility involves a household internal household capability such as tolerance, suffering, unhappiness, and willingness-to-pay models. Although an important aspect of capability, the research did not include social capital since it is outside the scope of research.
Methods of systematic literature review
Our systematic literature review used the Covidence software 80 , which is a production tool to make the process of conducting systematic reviews more efficient and streamlined 80 . As a web-based platform, it supports the collaborative management of uploaded journal references and processes journals through 4-step screening and analysis including title and abstract screening, full-text screening, data abstraction, and quality assessment. The software also follows the guidelines of PRIMSA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis), which provides a clear, transparent way for researchers to document their findings 81 . PRIMSA includes a 27-item checklist and 4-phase flow diagram of identification, screening, eligibility, and inclusion. Figure 1 summarizes the PRIMSA method we followed during our review process by showing the search criteria and final selected articles at each stage, including identification, screening, eligibility, and inclusion.
The figure shows the 4-step screening process of identification, screening, eligibility, and inclusion as well as the specific search criteria for each step. From the initial 2991 articles, 99 articles were selected.
Identification
The search covered Web of Science and Science Direct due to their comprehensive coverage and interdisciplinary sources. To cover a broad set of possible disasters and infrastructures, our search focused on the key areas of equity (“equit- OR fair- OR justice- OR and access-“), infrastructure (“AND infrastructure system- OR service-”), and disasters (“ AND hazard- OR, cris- OR, disaster- OR”). We limited our search to journal articles published in engineering, social sciences, and interdisciplinary journals during January 2010 to March 2023. Excluding duplicates, the combined results of the search engines resulted in 2991 articles.
The articles were screened on their title and abstract. These had to explicitly mention both an infrastructure system (water, transportation, communication, etc.) and natural hazards (tropical cyclone, earthquake, etc.) The specific criteria for infrastructure and natural hazard is found in the 8-dimension framework. This initial screening process yielded 398 articles for full-text review.
Eligibility
The articles were examined based on the extent of discussion in infrastructure, natural hazard, and equity dimension. Insufficient equity discussion means that the paper did not fall within the distributional-demographic, distributional-spatial, procedural, or capacity forms of equity (98). Studies were also excluded for not directly including equity analysis in the infrastructure system (19). Limited infrastructure focus means that the article may have focused on infrastructure outside the scope of the manuscript such as industrial, green, building, or social infrastructure (74). Limited disaster focus means that the article did not connect to the direct or indirect impacts of disasters on infrastructure systems (45). Wrong study design included literature reviews, opinion pieces, policy papers, and unable to access (56). This stage yielded 99 final articles.
Inclusion and assessment framework
To analyze the 99 articles, we designed an 8-dimensional assessment framework (see Fig. 2 ) to analyze the literature. In Fig. 2 , the visualization focuses on equity, infrastructure, and natural hazards since these are the 3 main dimensions of the systematic literature review. The icons on the bottom are the remaining 5 dimensions which add more analysis and context to the first 3 dimensions. Here, we refer to research question 1: what are the prevailing concepts, foci, methods, and theories, in assessing the inequities of disrupted infrastructure services? The framework distinguished the concepts (equity dimensions, infrastructure system, and natural hazard event), foci (geographical scale, geographic location, temporal scale), methods (nature of study and data collection), and theories (theoretical perspective) (Fig. 2 ). The following details each subquestion:
Equity dimensions, infrastructure type, and hazard event type are the main 3 dimensions while geographical location, geographic scale, temporal, nature of the study, and theoretical perspectives are the remaining 5 dimensions which add more information and context.
How is equity conceptualized and measured? First, we label equity into 4 definitions (DPSC). Second, it summarizes the equity conclusions.
Infrastructure type
Which infrastructure services were most and least commonly studied? This category is divided into power, water, transportation, communication, health, food, sanitation, stormwater, emergency, and general if a specific infrastructure is not mentioned. Studies can include more than one infrastructure service. Green infrastructure, social infrastructure, building structures, and industrial structures were excluded.
Hazard event type
Which hazard events are most or least frequently studied? This category includes flood, tropical cyclone, drought, earthquake, extreme temperature, pandemic, and general if there is no specific hazard. To clarify, tropical cyclones include hurricanes and typhoons while extreme temperatures are coldwaves and heatwaves. It determines which studies are specific to hazards and which can be applied to universal events.
Geographic location
Which countries have studied equity the most and least? This category is at the country scale such as the United States, Netherlands, China, and Australia, among others.
Geographic scale
What geographic unit of scale has been studied to represent equity? Smaller scales of study can reveal greater insights at the household level while larger scales of study can reveal comparative differences between regional communities. It ranges from individual, local, regional, and country as well as project. To clarify, ‘individual’ can include survey respondents, households, and stakeholder experts; ‘local’ is census block groups, census tracts, and ZIP codes equivalent scales; ‘regional’ is counties, municipalities, and cities equivalent; ‘project’ refers to studies that focused on specific infrastructure/ construction projects.
Temporal scale
When did themes and priority of equity first emerge? This category determines when equity in infrastructure research is published and whether these trends are increasing, decreasing, or constant.
Nature of the study
How is data for equity being collected and processed? This category analyzed data types used including conceptual, descriptive, open-data, location-intelligence, and simulation data. To clarify, conceptual refers to purely conceptual frameworks or hypothetical datasets; descriptive refers to surveys, questionnaires, interviews, or field observations performed by the researcher; open-data refers to any open-data source that is easily and freely attainable such as census and flood data; location-intelligence refers to social media, human mobility, satellite and aerial images, visit data, and GIS layers; and finally, simulation data can be developed through simulation models like numerical software, Monte-Carlo, or percolation methods. Second, the data can be processed through quantitative or qualitative methods. Quantitative methods may include correlation, principal component analysis, and spatial regression while qualitative methods may include validation, thematic coding, participatory rural appraisal, and citizen science. We focused on analysis explicitly mentioned in the manuscript. For example, it can be assumed that studies of linear regression discussed correlation analysis and other descriptive statistics in their data processing.
Theoretical perspective
Which theoretical frameworks have been created and used to evaluate equity? This category summarizes the reasoning behind the theoretical frameworks which may have informal or formal names such as a service-gap model, well-being approach, and capability approach.
Based on the 8-dimensional assessment framework, the research first examines the spatiotemporal patterns as well as data and methods to evaluate equity. Then, it investigates the definitions of equity to the intersections with infrastructure and hazards. It concludes with a discussion of theoretical frameworks. We use the term “pathways” to identify how equity is constructed, analyzed, and used in relation to the 8-dimensional framework. For instance, the connection between equity and infrastructure is considered a pathway. By defining specific “pathways,” we are essentially mapping out the routes through which equity interacts with various dimensions of a framework, such as infrastructure. The following analysis directly addresses research question 1 (prevailing concepts, focuses, methods, and theories, in assessing the inequities of disrupted infrastructure services) and research question 2 (similar and different pathways of equity). Supplementary Figures 1A – 12A provide additional context to the research findings and can be found in the Supplementary Information .
Spatiotemporal patterns of equity
Overall, there is an increasing number of publications about equity in infrastructure management (Fig. 3 ). A slight decrease observed in 2021 could be because of the focus on COVID-19 research. Spatially, by far the most studies focus on the US (69), followed by India (3), Ghana (3), and Bangladesh (3) (Fig. 5 ). This surprising distribution seems to contradict the intuition that equity and fairness in infrastructure resilience are certainly global phenonmena. Besides the exact phrasing of the search term, this result can be explained by the focus of this review on the intersection of infrastructure resilience and inequity. For infrastructure resilience, prominent reports, such as the CDRI’s 2023 Global Infrastructure Resilience Report 82 still fail to address it. Even though research has called for increasing consideration of equity and distributive justice in infrastructure and risk assessment, inequity is still all too often viewed as a social and economic risk 83 . At the same time, persistent imbalances in terms of data availability have been shown to shift research interest to the US, especially for data intense studies on urban infrastructures 84 . Finally, efforts to mainstream of equity and fairness across all infrastructures as a part of major transitions may explain why equity discussion is less pronounced in the context of crises. For instance, in Europe, according to the EU climate act (Article 9(1)) 85 , all sectors need to be enabled and empowered to make the transition to a climate-resilient society fair and equitable .
The bar graph shows an overall increasing from 2011 to 2023 in publications about equity in infrastructure resilience during natural hazard events. The pie chart shows that countries in the global north with United States (US), England, Australia, Germany, Taiwan, Norway, South Korea, and Japan and global south with Bangladesh, India, Ghana, Mexico, Mozambique, Brazil, Tanzania, Sri Lanka, Pakistan, Nigeria, Kenya, Nepal, Zimbabwe, Central Asia, and South Africa.
Data and methods to interpret equity
Our Sankey diagram (Fig. 4 ) sketches the distribution of data collection pathways which connects quantitative-qualitative data to data type to scale. Most studies start from quantitative data (120) with fewer using mixed (34) or qualitative (18) data. Quantitative studies use descriptive (58), open-data (50) location-intelligence (36), simulation (19), and conceptual (9). The most prominent spatial scale was local (66) which consisted of census tract, census block group, zip code, and equivalent spatial scale of analysis. This was followed by individual or household scale (64) which largely stems from descriptive data of interviews, surveys, and field observations. Within the context of infrastructure, equity, and hazards, non-US studies did not use human mobility data, a specific type of location-intelligence data. This could be due to limitations in data availability and different security restrictions to these researchers such as the European Union’s General Data Protection Regulation 86 . Increasingly, the application of location-intelligence data was used to supplement the understanding of service disruptions. For example, satellite information 87 , telemetry-based data 37 , and human mobility data 88 were used to evaluate the equitable restoration of power systems and access to critical facilities. Social media quantified public emotions to disruptions 89 , 90 .
The Sankey diagram shows the flow from studies containing quantitative, qualitative, or quantitative–qualitative data to the specific type of data of descriptive, open-data, location-intelligence, simulation, and conceptual to spatial scale of data of local, individual, regional, country, and project.
As shown in Fig. 5 , there are distinct quantitative and qualitative methods to interpret equity. Most quantitative methods were focused on descriptive analysis and linear models which can assume simple relationships within equity dimensions. Simple relationships would assume that dependent variables have a straightforward relationship with independent variables. Regarding quantitative analysis, descriptive statistics were correlation (12), chi-square (6), and analysis of variance (ANOVA) (5) means. Spatial analysis included geographic information system (GIS) (15), Moran’s-I spatial autocorrelation (6), and spatial-regression (5). Variables were also grouped together through principal component analysis (PCA) (9) and Index-Weighting (9). Logit models (13) and Monte-Carlo simulations (7) were used to analyze data. Thus, more complex models are needed to uncover the underlying mechanisms associated with equity in infrastructure. In analyzing quantitative data, most research has focused on using descriptive statistics, linear models, and Moran’s I statistic which have been effective in pinpointing areas with heightened physical and social vulnerability 25 , 91 , 92 .
The quantitative pie chart has geographic information system (GIS), logit model, correlation, index-weighting, principal component analysis (PCA), monte-carlo simulation, chi-square, Moran’s- I spatial autocorrelation, analysis of variance (ANOVA), and spatial regression. The qualitative pie chart has validation, thematic coding, citizen science, sentiment analysis, conceptual analysis, participatory rural appraisal, document analysis, participatory assessment, photovoice, and ethnographic.
However, there has been a less frequent yet insightful use of advanced techniques like machine learning, agent-based modeling, and simulation. For example, Esmalian, et al. 66 employed agent-based modeling to explore how social demographic characteristics impact responses to power outages during Hurricane Harvey. In a similar vein, Baeza, et al. 93 utilized agent-based modeling to evaluate the trade-offs among three distinct infrastructure investment policies: prioritizing high-social-pressure neighborhoods, creating new access in under-served areas, and refurbishing aged infrastructure. Simulation models have been instrumental in understanding access to critical services like water 43 , health care 92 , and transportation 33 . Beyond these practical models, conceptual studies have also contributed innovative methods. Notably, Clark, et al. 94 proposed gravity-weighted models, and Kim and Sutley 30 explored the use of genetic algorithms to measure the accessibility to critical resources. These diverse methodologies indicate a growing sophistication in the field, embracing a range of analytical tools to address the complexities of infrastructure resilience.
Regarding qualitative analysis, the methods included thematic coding (7), validation of stakeholders (9), sentiment (4), citizen science (5), conceptual analysis (3) participatory rural appraisal (2), document analysis (2), participatory assessment (1), photovoice (1), and ethnographic (1). Qualitative methods were used to capture diverse angles of equity, offering a depth and context not provided by quantitative data alone. These methods are effective in understanding capacity equity, such as unexpected strategies and coping mechanisms that would go otherwise unnoticed 95 . Qualitative research can also capture the perspectives and voices of stakeholders through procedural equity. Interviews and focus groups can validate and enhance research frameworks 96 . Working collaboratively with stakeholders, as shown with Masterson et al. 97 can lead to positive community changes in updated planning policies. Qualitative methods can narratively convey the personal hardships of infrastructure losses 98 . This approach recognizes that infrastructure issues are not just technical problems but also deeply intertwined with social, economic, and cultural dimensions.
Interlinkages of equity definitions
As shown in Fig. 6 , the frequency of type of equity was distributional-demographic (90), distributional-spatial (55), capacity (54), and procedural (16). It is notable to reflect on the intersections between the four definitions of equity. Between two linkages, the top three linkages between DC (20), DS (16), and DP (9), which all revealed a connection to distributional-demographic equity. There were comparatively fewer studies linking 3 dimensions except for DSC which had 25 connections. Only 3 studies had 4 connections.
Distributional-demographic had the highest number of studies and the greatest overlap with the remaining equity definitions of capacity, procedural, and distributional-spatial. Only 3 studies overlapped with the four equity definitions.
Distributional-demographic equity was the most studied equity definition. Table 2 shows how pathways of demographic equity relate to the different infrastructure systems and variables within distributional-demographic, including 728 unique pathways. As a reminder, pathways explore equity across an 8-dimensional framework. In this case, the distributional-demographic equity is connected to infrastructure, treating these connections as pathways Pathways with power (165), water (147), and transportation (112) were the most frequent while those with stormwater (23) and emergency (9) services were the least frequent. Referencing demographics, the most pathways were income (148), ethnicity (115), and age (122) while least studied were gender (63), employment (35), marginalized populations (5) and intergenerational (1). Note the abbreviations for Tables 2 and 3 are power (P), water (W), transportation (T), food (F), health (H), sanitation (ST), communication (C), stormwater (SW), emergency (E), and general (G). Regarding distributional-demographic, several research papers showed that lower income and minority households were most studied in comparison to the other demographic variables. Lower-income and minority households faced greater exposure, more hardship, and less tolerance to withstand power, water, transportation, and communication outages during Hurricane Harvey 99 . These findings were replicated in disasters such as Hurricane Florence, Hurricane Michael, COVID-19 pandemic, Winter Storm Uri, and Hurricane Hermine, respectively 65 , 91 , 100 , 101 . Several studies found that demographic vulnerabilities are interconnected and compounding, and often, distributional-demographic equity is a pre-existing inequality condition that is exacerbated by disaster impact 102 . For instance, Stough, et al. 98 identified that respondents with disabilities faced increased struggles due to a lack of resources to access proper healthcare and transportation after Hurricane Katrina. Women were often overburdened by infrastructure loss as they were expected to “pick up the pieces,” and substitute the missing service 103 , 104 . Fewer studies involved indigenous populations, young children, or considered future generations. Using citizen-science methods, Ahmed, et al. 105 studied the struggles and coping strategies of the Santal indigenous group to respond to water losses in drought conditions. Studies normally did not account for the direct infrastructure losses on children and instead concentrated on the impacts on their caretakers 106 ; however, this is likely due to restrictions surrounding research with children. Lee and Ellingwood 107 discussed how, “intergenerational discounting makes it possible to allocate costs and benefits more equitably between the current and future generations” (pg.51) A slight difference in discounting rate can lead to vastly different consequences and benefits for future generations. For example, the study found that insufficient investments in design and planning will only increase the cost and burden of infrastructure maintenance and replacement.
Distributional-spatial equity was the second most studied aspect, which includes spatial grouping and urban-rural designation, particularly given the rise of open-data and location-intelligence data with spatial information. Table 3 shows the pathways of spatial equity connected to different infrastructures and variables. In total, 109 unique pathways were found with spatial (83) and urban-rural (26) characteristics. Power (27), transportation (22), water (16), and health (15) systems were the most frequent pathways with stormwater (4), emergency (2), and communication (3) the least frequent. Urban-rural studies on communication and emergency services are entirely missing. Distributional-spatial equity studies, including spatial inequities and urban-rural dynamics, were often linked with distributional-demographic equity. For example, Logan and Guikema 46 defined “access rich” and “access poor” to measure different sociodemographic populations’ access to essential facilities. White populations had less distance to travel to open supermarkets and service stations in North Carolina 46 . Esmalian et al. 108 found that higher income areas had a lower number of stores in their areas, but they still had better access to grocery stores in Harris County, Texas. This could be because higher income areas live in residential areas, but they have the capability to travel further distances and visit more stores. Vulnerable communities could even be indirectly impacted by spatial spillover effects from neighboring areas 26 . Regarding urban-rural struggles, Pandey et al. 17 argued that inequities emerge when urban infrastructure growth lags with respect to the urban population while rural areas face infrastructure deficits. Rural municipalities had fewer resources, longer restoration times, and less institutional support to mitigate infrastructure losses 95 , 109 , 110 .
Capacity was the third most studied dimension and had 150 unique pathways to adaptations (54), access (43), and susceptibility (53). In connecting to infrastructure systems, power (29), water (27), transportation (25), and food (22) had the greatest number of pathways. There were interesting connections between different infrastructures and variables of capacity. Access was most connected to food (11), transportation (10), and health systems (10). Adaptations were most connected to water (15) and power (12) systems. This highlights how capacity equity is reflected differently to infrastructure losses. Capacity equity was often connected with distributional-equity since different sociodemographic groups have varying adaptations to infrastructure losses 78 . For example, Chakalian, et al. 106 found that white respondents were 2.5 more likely to own a power generator while Kohlitz et al. 95 found that poorer households could not afford rainwater harvesting systems. These behaviors may also include tolerating infrastructure disruptions 111 , cutting back on current resources 112 , or having an increased suffering 113 . The capabilities approach offers a valuable perspective on access to infrastructure services 94 . It recognizes the additional time and financial resources that certain groups may need to access the same level of services, especially if travel networks are disrupted 114 , 115 and travel time is extended 33 . In rural regions, women, children, and lower income households often reported traveling further distances for resources 105 , 116 . These disparities are often influenced by socioeconomic factors, emphasizing the need for a nuanced understanding on how different communities are affected by and respond to infrastructure losses. As such, building capacity is not just increasing the preparedness of households but also accommodating infrastructure systems to ensure equitable access, such as the optimization of facility locations 69 .
Procedural was the least studied equity definition with only 26 unique pathways, involving stakeholder input and stakeholder engagement. Pathways to communication and emergency systems were not available. The greatest number of pathways were water services to stakeholder input (7) and stormwater services to stakeholder engagement (4). Stakeholder input can assist researchers in validating and improving their research deliverables. This approach democratizes the decision-making process and enhances the quality and relevance of research and planning outcomes. For instance, the involvement of local experts and residents in Tanzania through a Delphi process led to the development of a more accurate and locally relevant social resilience measurement tool 117 . Stakeholder engagement, such as citizen science methods, can incorporate environmental justice communities into the planning process, educate engineers and scientists, and collect reliable data which can be actively incorporated back to the community 118 , 119 , 120 . Such participatory approaches, including citizen science, allow for a deeper understanding of community needs and challenges. In Houston, TX, the success of engaging high school students in assessing drainage infrastructure exemplified how community involvement can yield significant, practical data 119 . The data was approximately 74% accurate to trained inspectors, which were promising results for communities assessing their infrastructure resilience 119 . In a blend of research and practice, Masterson, et al. 97 illustrated the practical application of procedural equity. By interweaving equity in their policy planning, Rockport, TX planners added accessible services and upgrades to infrastructure for lower-income and racial-ethnic minority neighborhoods, directly benefiting underserved communities.
Pathways between equity, hazard, and infrastructure
For the hazards, tropical cyclones (34.6%) and floods (30.8%) make up over half of the studied hazards (Supplementary Figure 2A ) while power (21.2%), water (19.2%), transportation (15.4%), and health (12.0%) were the most frequently studied infrastructure services (Supplementary Figure 3A ). A pathway is used to connect equity to different dimensions of the framework, in this case, equity to infrastructure to hazard (Fig. 7 ). When considering these pathways, distributional-demographic (270) had the most pathways followed by capacity (175), distributional-spatial (140), and procedural (28). The most common pathway across all infrastructure services was a tropical cyclone and flooding with distributional-demographic equity (Supplementary Figures 6A – 8A ). As shown in Fig. 7 , tropical cyclone (229) and flood (192) had the most pathways while extreme temperatures (20) and pandemic (14) had the least. Although pandemic is seemingly the least studied, it is important to note that most of these studies were post COVID-19. Power (120), transportation (107), and water (104) had the most pathways whereas sanitation (33), communication (27), stormwater (21), and emergency (14) had the least pathways. The figure shows specific gaps in the literature. Whereas the other three equity definitions had connections to each hazard event, procedural equity only had connections to tropical cyclone, flood, general, and drought. There were only pathways from health infrastructure to tropical cyclone, flood, general, earthquake, and pandemic. There were 106 pathways connecting equity to general hazards, which may suggest the need to look at the impacts of specific hazards to equity in infrastructure resilience.
The Sankey diagram shows the flow from the different types of equity, or equity definitions, of distributional-demographic (D), capacity (C), distributional-spatial (S), and procedural (P) to hazard of tropical cyclone, flood, general, drought, earthquake, extreme temperature, and pandemic to infrastructure of power, transportation, water, health, food, communication, general, stormwater, emergency, and sanitation.
Research frameworks
Regarding research question 2, this research aims to understand frameworks of equity in infrastructure resilience. As an exploration of the frameworks. we found common focus areas of adaptations, access, vulnerability, validation, and welfare economics (Table 4 ). The full list of frameworks can be found in the online database that was uploaded in DesignSafe Data Depot. Supplementary Information .
Adaptations
Household adaptations included the ability to prepare before a disaster as well as coping strategies during and after the disaster. Esmalian et al. 111 developed a service gap model based on survey data of residents affected by Hurricane Harvey. Lower-income households were less likely to own power generators, which could lead to an inability to withstand power outages 111 . To understand household adaptations, Abbou et al. 78 asked residents of Los Angeles, California about their experiences in electrical and water losses. The study showed that when compared to men, women used more candles and flashlights. People with higher education, regardless of gender, were more likely to use power generators. In a Pressure and Release model, Daramola et al. 112 examined the level of preparedness to natural hazards in Nigeria. The study found that rural residents tended to use rechargeable lamps while urban areas used generators, likely due to the limited availability of electricity systems. Approximately 73% of participants relied on chemist shops to cope with constrained access to health facilities.
Other frameworks focused on the accessibility to resources. Clark et al. 94 developed the social burden concept which uses resources, conversion factors, capabilities, and functioning into a travel cost method to access critical resources. In an integrated physical-social vulnerability model, Dong et al. 92 calculated disrupted access to hospitals in Harris County, Texas. Logan and Guikema 46 integrated spatial planning, diverse vulnerabilities, and community needs into EAE services. In the case study of Willimgton, North Carolina, they showed how lower-income households had fewer access to grocery stores. In a predictive recovery monitoring spatial model, Patrascu and Mostafavi 26 found that the percentage of Black and Asian subpopulations were significant features to predict recovery of population activity, or the visits to essential services in a community.
Vulnerability
Several of the infrastructure resilience frameworks were grounded in social vulnerability assessments. For instance, Toland et al. 43 created a community vulnerability assessment based on an earthquake scenario that resulted in the need for emergency food and water resources. Using GIS, Oswald and Mohammed developed a transportation justice threshold index that integrated social vulnerability into transportation understanding 121 . In a Disruption Tolerance Index, Esmalian et al. 25 showed how demographic variables are connected with disproportionate losses in power and transportation losses.
Additional studies were based on stakeholder input and expert opinion. Atallah et al. 36 established an ABCD roadmap for health services which included acute life-saving services, basic institutional aspects for low-resource settings, community-driven health initiatives, and disease specific interventions. Health experts were instrumental in providing feedback for the ABCD roadmap. Another example is the development of the social resilience tool for water systems validated by experts and community residents by Sweya et al. 117 . To assess highway resilience, Hsieh and Feng had transportation experts score 9 factors including resident population, income, employment, connectivity, dependency ratio, distance to hospital, number of substitutive links, delay time in substitutions, and average degenerated level of services 122 .
Welfare economics
Willingness-to-pay (WTP) models reveal varied household investments in infrastructure resilience. Wang et al. 123 showed a wide WTP range, from $15 to $50 for those unaffected by disruptions to $120–$775 for affected, politically liberal individuals. Islam et al. 124 found households with limited access to safe drinking water were more inclined to pay for resilient water infrastructure. Stock et al. 125 observed that higher-income households showed greater WTP for power and transportation resilience, likely due to more disposable income and expectations for service quality. These findings highlight the need to consider economic constraints in WTP studies to avoid misinterpreting lower income as lower willingness to invest. Indeed, if a study does not adequately account for a person’s economic constraints, the findings may incorrectly interpret a lower ability to pay as a lower willingness to pay.
In terms of policy evaluation for infrastructure resilience, studies like Ulak et al. 126 prioritized equitable power system recovery for different ethnic groups, favoring network renewal over increasing response crews. Baeza et al. 93 noted that infrastructure decisions are often swayed by political factors rather than technical criteria. Furthermore, Lee and Ellingwood 107 introduced a method for intergenerational discounting in civil infrastructure, suggesting more conservative designs for longer service lives to benefit future generations. These studies underscore the complex factors influencing infrastructure resilience policy, including equity, political influence, and long-term planning.
This systematic review is the first to explore how equity is incorporated into infrastructure resilience against natural hazards. By systematically analyzing the existing literature and identifying key gaps, the paper enhances our understanding of equity in this field and outlines clear directions for future research. This study is crucial for understanding the fundamental knowledge that brings social equity to the forefront of infrastructure resilience. Table 5 summarizes the primary findings of this systematic review of equity in infrastructure resilience literature, including what the studies are currently focusing on and the research gaps and limitations.
Our findings show a great diversity of frameworks and methods depending on the context, in which equity is applied (Table 5 ). Moreover, we identify a lack of integrative formal and analytical tools. Therefore, a clear and standard framework is needed to operationalize inequity across infrastructures and hazards; what is missing are analytical tools and approaches to integrate equity assessment into decision-making.
Referring to question 3, we will further explore the current gaps of knowledge and future challenges of studying equity in infrastructure resilience. In elaborating on the gaps identified in our review, we propose that the next era of research questions and objectives should be (1) monitoring equity performance with improved data, (2) weaving equity in computational models, and (3) integrating equity into decision-making tools. Through principles of innovation, accountability, and knowledge, such objectives would be guided by moving beyond distributional equity, recognizing understudied gaps of equity, and inclusion of all geographic regions, and by extension stakeholders (Fig. 8 ).
The figure demonstrates that previous research has focused on detecting and finding evidence of disparity in infrastructure resilience in hazard events. It supports that the next phase of research will monitor equity performance with improved data, weave equity in computational models, and integrate equity in decision making tools in order to move beyond social and spatial distributions, recognize understudied gaps of equity, and include all geographic regions.
The first research direction is the monitoring equity performance with improved data at more granular scales and greater representation of impacted communities. Increased data availability provides researchers, stakeholders, and community residents with more detailed and accurate assessment of infrastructure losses. Many studies have used reliable, yet inherently approximate data sources, for infrastructure service outages. These sources include human mobility, satellite, points-of-interest visitation, and telemetry-based data (such as refs. 69 , 100 ). Private companies are often reluctant to share utility and outage data with researchers 127 . Thus, we encourage the shift towards transparent and open datasets from utility companies in normal times and outage events. This aligns with open-data initiatives such as Open Infrastructure Outage Data Initiative Nationwide (ODIN) 128 , Invest in Open Infrastructure 129 , and Implementing Act on a list of High-Value Datasets 130 . Transparency in data fosters an environment of accountability and innovation to uphold equity standards in infrastructure resilience 131 . An essential aspect of this transparency involves acknowledging and addressing biases that may render certain groups ‘invisible’ within datasets. These digitally invisible populations may well be among the most vulnerable, such as unhoused people that may not have a digital footprint yet are very vulnerable to extreme weather 132 . Gender serves as a poignant example of such invisibility. Historical biases and societal norms often result in gender disparities being perpetuated in various facets of infrastructure design and resilience planning 133 . Women are frequently placed in roles of caregiving responsibilities, such as traveling to reach water (as shown in refs. 105 , 116 , 134 ) or concern over the well-being of family members (as shown in refs. 103 , 135 ), which have been overlooked or marginalized in infrastructure planning processes.
If instances of social disparities are uncovered, researchers and practitioners could collaboratively cultivate evidence-based recommendations to manage infrastructure resilience. At the same time, approaches for responsible data management need to be developed that protect privacy of individuals, especially marginalized and vulnerable groups 136 . There is a trade-off between proper representation of demographic groups and ensuring the privacy of individuals 45 , 67 . Despite this, very few studies call into question the fairness of the data collection in capturing the multifaceted aspects of equity 137 , or the potential risks to communities as described in the EU’s forthcoming Artificial Intelligence Act 138 .
By extension, addressing the problem of digitally invisible populations and possible bias, Gharaibeh et al. 120 also emphasizes that equitable data should represent all communities in the study area. Choices about data collection and storage can directly impact the management of public services, by extension the management of critical information 139 . For example, a significant problem with location-intelligence data collection is properly representing digitally invisible populations as these groups are often marginalized in the digital space leading to gaps in data 132 , 140 . Human mobility data, a specific type of location-intelligence data derived from cell phone pinpoint data, illustrates this issue. Vulnerable groups may not afford or have frequent access to cell phones, resulting in a skewed understanding of population movements 141 . However, other studies have shown that digital platforms can be empowering for marginalized populations to express sentiments of cultural identity and tragedies through active sharing and communication 142 . Ultimately, Hendricks et al. 118 recommend a “triangulation of data sources,” to integrate quantitative and qualitative data, which would mitigate potential data misrepresentation and take advantage of the online information. Moving ahead, approaches need to be developed for fair, privacy-preserving, and unbiased data collection that empowers especially vulnerable communities. At the same time, realizing that data gaps especially in infrastructure-poor regions may not be easy to address, we also follow Casali et al. 84 in calling for synthetic approaches and models that work on sparse data.
Few studies, such as refs. 45 , 66 , have created computational models to capture equity-infrastructure-hazards interactions, which are initial attempts to quantify both the social impacts and the physical performance of infrastructure. This is echoed in the work of Soden et al. 143 which found only ~28% of studies undertake a quantitative evaluation of differential impacts experienced in disasters. To enhance analytical and computational methods in supporting equitable decision-making, it is imperative for future studies to comprehensively integrate social dimensions of infrastructure resilience. Therefore, the next research direction is the intentional weaving of equity in computational models. Where the majority of studies used descriptive statistics and non-linear modeling, complex computational models—such as agent-based simulations—offer the advantage of capturing the nonlinear interactions of equity in infrastructure systems. These tools also allow decision-makers to gain insights into the emergence of complex patterns over time. These simulation models can then be combined with specific metrics that measure infrastructural or social implications. Metrics might include susceptibility curves 144 , social burden costs estimates 94 , or social resilience assessment 76 . Novel metrics for assessing adaptive strategies, human behaviors, and disproportionate impacts (such as 113 ) could also be further quantified through empirical deprivation costs for infrastructure losses 145 . These metrics also are a stepping-stone for formalizing and integrating equity into decision-making tools.
Another research direction is the integration of equity into decision-making tools. Key performance indicators and monitoring systems are essential for clarifying equity processes and outcomes and creating tangible tools for infrastructure planners, managers, engineers, and policy-makers. In particular, the literature discussed the potential for using equity in infrastructure resilience to direct infrastructure investments (such as refs. 93 , 126 , 146 ). Infrastructure resilience requires significant upfront investment and resource allocations, which generally favors wealthier communities. Communities may hold social, cultural, and environmental values that are not properly quantified in infrastructure resilience 147 . Since traditional standards of cost-benefit analyses used by infrastructure managers and operators primarily focus on monetary gains or losses, they would not favorably support significant investments to mitigate the human impacts of infrastructure losses on those most vulnerable 148 . This limitation also delays investments and leads to inaction in infrastructure resilience, resulting in unnecessary loss of services and social harm, potentially amplifying inequities, and furthering societal fragmentation. To bridge this gap, we propose to measure the social costs of infrastructure service disruptions as a way to determine the broad benefits of resilience investments 147 .
As the literature review found, several studies are following a welfare economics approach to quantify social costs associated with infrastructure losses such as the evaluation of policies (such as ref. 93 ) and willingness-to-pay models (such as ref. 125 ). Such economic functions are preliminary steps in quantifying equity as a cost measure; however, these models must avoid misinterpreting lower income as a lower willingness to invest. Lee and Ellingwood 107 proposed using intergenerational discounting rate; however, it is important to recognize the flexibility of options for future generations 149 . Teodoro et al. 149 points to the challenges of using (fixed) discount rates and advocate for a procedural justice-based approach that maximizes flexibility and adaptability. Further research is needed to quantify the social costs of infrastructure disruptions and integrate them into infrastructure resilience assessments, such as calculating the deprivation costs of service losses for vulnerable populations.
Our review shows that certain demographic groups such as indigenous populations, persons with disabilities, and intergenerational equity issues have not been sufficiently studied 150 . This aligns with the conclusions of Seyedrezaei et al. 151 , who found that the majority of studies about equity in the built-environment focused on lower-income and minority households. Indigenous populations face significant geographical, cultural, and linguistic barriers that make their experiences with disrupted infrastructure services distinct from those of the broader population 152 .
Even though intergenerational justice issues have increasingly sparked attention on the climate change discussion, intergenerational equity issues in infrastructure resilience assessments have received limited attention. We argue that intergenerational equity warrants special attention as infrastructure systems have long life cycles that span across multiple generations, and ultimately the decisions on the finance, restoration, and new construction will have a significant impact on the ability of future generations to withstand the impact of stronger climate hazard events. Non-action may lead to tremendous costs in the long run 149 . It is the responsibility of current research to understand the long-term effects of equity in infrastructure management to mitigate future losses and maintain the flexibility of future generations. As a means of procedural justice, these generations should have the space to make choices, instead of being locked in by today’s decisions. Future studies should develop methods to measure and integrate intergenerational inequity in infrastructure resilience assessments.
Given the specific search criteria and focus on equity, infrastructure, and natural hazard, we found a major geographic focus on the United States. Large portions of the global north and global south were not included in the analysis. This could be due to the search criteria of the literature review; however, it is important to recognize potential geographic areas that are isolated from the academic studies on infrastructure resilience. Different infrastructure challenges (e.g., intermittent services) are present through data availability in the region. A dearth of studies on equitable infrastructure resilience could contribute to greater inequity in those regions due to the absence of empirical evidence and proper methodological solutions. This aligns with other findings on sustainable development goals and climate adaptation broadly 153 . Global research efforts, along with common data platforms, standards and methods (see above), that include international collaborations among researchers across the global north and global south regions can bridge this gap and expand the breadth of knowledge and solutions for equitable infrastructure resilience.
Finally, while significant attention has been paid to distributional demographic and spatial inequity issues 151 , there remain several underutilized definitions of equity. Procedural and capacity equity hold the greatest potential for people to feel more included in the infrastructure resilience process. Instead of depending directly on the infrastructure systems, individual households can adapt to disrupted periods through substituted services and alternative actions (such as ref. 78 ). To advance procedural equity in infrastructure resilience, citizen-science research or participatory studies can begin by empowering locals to understand and monitor their resilience (such as ref. 76 ) or failures in their infrastructure systems (such as ref. 120 ). As referenced by Masterson and Cooper 154 , the ladder of citizen power can serve as a framework for how to ethically engage with community partners for procedural equity. The ladder, originally developed by Arnstein 155 , includes non-participation, tokenism, and citizen power. Table 3 shows that most research falls into non-participation: survey data and information are extracted without any community guidance. Limited studies that have branched into community involvement still stay restricted in the tokenism step, such as models that are validated by stakeholders or receive expert opinions on their conceptual models. Future studies should expand inquiries regarding the procedural and capacity dimension of equity in infrastructure resilience assessments and management. For instance, research could map out where inequities occur in the decision-making process and targeted spatial regions as well as allocate of resources for infrastructure resilience. It could also continue pursuing inclusive methodologies such as participatory action research and co-design processes. It should investigate effective methods to genuinely integrate different stakeholders and community members from conception through evaluation of research.
Although the primary audience of the literature review is academic scholars and fellow researchers, the identified gaps are of importance for practitioners, governmental agencies, community organizations, and advocates. By harnessing the transformative power of equity, studies in infrastructure resilience can transcend its traditional role and develop equity-focused data, modeling, and decision-making tools which considers everyone in the community. The integration of equity aspects within the framework of infrastructure resilience not only enhances the resilience of infrastructure systems but also contributes to the creation of inclusive and resilient communities. Infrastructure resilience would not just be a shield against adversity but also a catalyst for positive social and environmental change.
Data availability
The created excel database which includes information on the key parts of the 8-dimensional equity framework will be uploaded to DesignSafe-CI.
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This material is based in part upon work supported by the National Science Foundation under Grant CMMI-1846069 (CAREER) and the support of the National Science Foundation Graduate Research Fellowship. We would like to thank the contributions of our undergraduate students: Nhat Bui, Shweta Kumaran, Colton Singh, and Samuel Baez.
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Coleman, N., Li, X., Comes, T. et al. Weaving equity into infrastructure resilience research: a decadal review and future directions. npj Nat. Hazards 1 , 25 (2024). https://doi.org/10.1038/s44304-024-00022-x
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Importance of Quantitative Research Across Fields
First of all, research is necessary and valuable in society because, among other things, 1) it is an important tool for building knowledge and facilitating learning; 2) it serves as a means in understanding social and political issues and in increasing public awareness; 3) it helps people succeed in business; 4) it enables us to disprove lies and support truths; and 5) it serves as a means to find, gauge, and seize opportunities, as well as helps in finding solutions to social and health problems (in fact, the discovery of COVID-19 vaccines is a product of research).
Now, quantitative research, as a type of research that explains phenomena according to numerical data which are analyzed by means of mathematically based methods, especially statistics, is very important because it relies on hard facts and numerical data to gain as objective a picture of people’s opinion as possible or an objective understanding of reality. Hence, quantitative research enables us to map out and understand the world in which we live.
In addition, quantitative research is important because it enables us to conduct research on a large scale; it can reveal insights about broader groups of people or the population as a whole; it enables researchers to compare different groups to understand similarities and differences; and it helps businesses understand the size of a new opportunity. As we can see, quantitative research is important across fields and disciplines.
Let me now briefly discuss the importance of quantitative research across fields and disciplines. But for brevity’ sake, the discussion that follows will only focus on the importance of quantitative research in psychology, economics, education, environmental science and sustainability, and business.
First, on the importance of quantitative research in psychology .
We know for a fact that one of the major goals of psychology is to understand all the elements that propel human (as well as animal) behavior. Here, one of the most frequent tasks of psychologists is to represent a series of observations or measurements by a concise and suitable formula. Such a formula may either express a physical hypothesis, or on the other hand be merely empirical, that is, it may enable researchers in the field of psychology to represent by a few well selected constants a wide range of experimental or observational data. In the latter case it serves not only for purposes of interpolation, but frequently suggests new physical concepts or statistical constants. Indeed, quantitative research is very important for this purpose.
It is also important to note that in psychology research, researchers would normally discern cause-effect relationships, such as the study that determines the effect of drugs on teenagers. But cause-effect relationships cannot be elucidated without hard statistical data gathered through observations and empirical research. Hence, again, quantitative research is very important in the field of psychology because it allows researchers to accumulate facts and eventually create theories that allow researchers in psychology to understand human condition and perhaps diminish suffering and allow human race to flourish.
Second, on the importance of quantitative research in economics .
In general perspective, the economists have long used quantitative methods to provide us with theories and explanations on why certain things happen in the market. Through quantitative research too, economists were able to explain why a given economic system behaves the way it does. It is also important to note that the application of quantitative methods, models and the corresponding algorithms helps to make more accurate and efficient research of complex economic phenomena and issues, as well as their interdependence with the aim of making decisions and forecasting future trends of economic aspects and processes.
Third, on the importance of quantitative research in education .
Again, quantitative research deals with the collection of numerical data for some type of analysis. Whether a teacher is trying to assess the average scores on a classroom test, determine a teaching standard that was most commonly missed on the classroom assessment, or if a principal wants to assess the ways the attendance rates correlate with students’ performance on government assessments, quantitative research is more useful and appropriate.
In many cases too, school districts use quantitative data to evaluate teacher effectiveness from a number of measures, including stakeholder perception surveys, students’ performance and growth on standardized government assessments, and percentages on their levels of professionalism. Quantitative research is also good for informing instructional decisions, measuring the effectiveness of the school climate based on survey data issued to teachers and school personnel, and discovering students’ learning preferences.
Fourth, on the importance of quantitative research in Environmental Science and Sustainability.
Addressing environmental problems requires solid evidence to persuade decision makers of the necessity of change. This makes quantitative literacy essential for sustainability professionals to interpret scientific data and implement management procedures. Indeed, with our world facing increasingly complex environmental issues, quantitative techniques reduce the numerous uncertainties by providing a reliable representation of reality, enabling policy makers to proceed toward potential solutions with greater confidence. For this purpose, a wide range of statistical tools and approaches are now available for sustainability scientists to measure environmental indicators and inform responsible policymaking. As we can see, quantitative research is very important in environmental science and sustainability.
But how does quantitative research provide the context for environmental science and sustainability?
Environmental science brings a transdisciplinary systems approach to analyzing sustainability concerns. As the intrinsic concept of sustainability can be interpreted according to diverse values and definitions, quantitative methods based on rigorous scientific research are crucial for establishing an evidence-based consensus on pertinent issues that provide a foundation for meaningful policy implementation.
And fifth, on the importance of quantitative research in business .
As is well known, market research plays a key role in determining the factors that lead to business success. Whether one wants to estimate the size of a potential market or understand the competition for a particular product, it is very important to apply methods that will yield measurable results in conducting a market research assignment. Quantitative research can make this happen by employing data capture methods and statistical analysis. Quantitative market research is used for estimating consumer attitudes and behaviors, market sizing, segmentation and identifying drivers for brand recall and product purchase decisions.
Indeed, quantitative data open a lot of doors for businesses. Regression analysis, simulations, and hypothesis testing are examples of tools that might reveal trends that business leaders might not have noticed otherwise. Business leaders can use this data to identify areas where their company could improve its performance.
Journal Article Reporting Standards (JARS)
APA Style Journal Article Reporting Standards offer guidance on what information should be included in all manuscript sections for quantitative, qualitative, and mixed methods research and include how to best discuss race, ethnicity, and culture.
Introducing Journal Article Reporting Standards for Race, Ethnicity, and Culture (JARS–REC)
JARS–REC were created to develop best practices related to the manner in which race, ethnicity, and culture are discussed within scientific manuscripts in psychological science.
Quantitative research
Use JARS–Quant when you collect your study data in numerical form or report them through statistical analyses.
Qualitative research
Use JARS–Qual when you collect your study data in the form of natural language and expression.
Mixed methods research
Use JARS–Mixed when your study combines both quantitative and qualitative methods.
Race, ethnicity, culture
Use JARS–REC for all studies for guidance on how to discuss race, ethnicity, and culture.
What are APA Style JARS?
APA Style Journal Article Reporting Standards (APA Style Jars ) are a set of standards designed for journal authors, reviewers, and editors to enhance scientific rigor in peer-reviewed journal articles. Educators and students can use APA Style JARS as teaching and learning tools for conducting high quality research and determining what information to report in scholarly papers.
The standards include information on what should be included in all manuscript sections for:
Quantitative research ( Jars –Quant)
Qualitative research ( Jars –Qual)
Mixed methods research ( Jars –Mixed)
Additionally, the APA Style Journal Article Reporting Standards for Race, Ethnicity, and Culture ( Jars – Rec ) provide guidance on how to discuss race, ethnicity, and culture in scientific manuscripts. Jars – Rec should be applied to all research, whether it is quantitative, qualitative, or mixed methods.
Race, Ethnicity, and Culture ( Jars – Rec )
Using these standards will make your research clearer and more accurate as well as more transparent for readers. For quantitative research, using the standards will increase the reproducibility of science. For qualitative research, using the standards will increase the methodological integrity of research.
Jars –Quant should be used in research where findings are reported numerically (quantitative research). Jars –Qual should be used in research where findings are reported using nonnumerical descriptive data (qualitative research). Jars –Mixed should be applied to research that includes both quantitative and qualitative research (mixed methods research). JARS–REC should be applied to all research, whether it is quantitative, qualitative, or mixed methods.
View an infographic (PDF, 453KB) to learn about the benefits of JARS and how they are relevant to you
Listen to a podcast with Drs. Harris Cooper and David Frost discussing JARS and implications for research in psychology
Many aspects of research methodology warrant a close look, and journal editors can promote better methods if we encourage authors to take responsibility to report their work in clear, understandable ways. —Nelson Cowan, Editor, Journal of Experimental Psychology: General
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This video describes and discusses the updated APA Style Journal Article Reporting Standards.
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From the APA Style blog
Introducing APA Style Journal Article Reporting Standards for Race, Ethnicity, and Culture
These standards are for all authors, reviewers, and editors seeking to improve manuscript quality by encouraging more racially and ethnically conscious and culturally responsive journal reporting standards for empirical studies in psychological science.
APA Style JARS for high school students
In this post, we provide an overview of APA Style JARS and resources that can be shared with high school students who want to learn more about effective communication in scholarly research.
Happy 2022, APA Stylers!
This blog post is dedicated to our awesome APA Style users. You can use the many resources on our website to help you master APA Style and improve your scholarly writing.
APA Style JARS on the EQUATOR Network
The APA Style Journal Article Reporting Standards (APA Style JARS) have been added to the EQUATOR Network. The network aims to promote accuracy and quality in reporting of research.
APA Style JARS: Resources for instructors and students
APA Style Journal Article Reporting Standards (APA Style JARS) are a set of guidelines for papers reporting quantitative, qualitative, and mixed methods research that can be used by instructors, students, and all others reading and writing research papers.
MyU : For Students, Faculty, and Staff
Professor William Pomerantz named Institute on the Environment Fellow
MINNEAPOLIS / ST. PAUL (8/29/2024) – Professor William C. K. Pomerantz has been recently selected as a University of Minnesota Institute on the Environment (IonE) Fellow. IonE Fellows are established researchers and innovative thought leaders who have demonstrated excellence in disciplines related to environmental protection or sustainability, maintain a significant publication record regarding subjects of topical relevance to IonE, and have been recognized by national and international colleagues.
Pomerantz’s fellowship recognizes a new interest area in his research program that has been emerging over the last six years, for addressing a key societal challenge, namely the persistence of poly- and perfluorinated alkyl substances (PFAS). IonE provides access to a collaborative educational environment with researchers interested in problems surrounding sustainability and supports interactions with external partners to help solve important societal problems, including but not limited to PFAS. Using his laboratory’s expertise in synthetic organofluorine chemistry, and expertise in quantitative 19F nuclear magnetic resonance spectroscopy (NMR), Pomerantz has begun to work with researchers across campus and with the United States Geological Survey (USGS) to help identify persistent fluorinated functional groups in widely-used pharmaceuticals and pesticides, to help quantify total PFAS in the environment, and to start designing PFAS alternatives for biomedical applications.
Through a collaboration with Bill Arnold in the UMN Department of Civil and Environmental Engineering, Pomerantz has served as a co-PI on several collaborative state and federally funded grants to support this research since 2018 from the Legislative-Citizen Commission on Minnesota Resources Environment and Natural Resources Trust Fund, the USGS, and the National Science Foundation. To continue to expand the impact of this important research, Pomerantz has also worked with the College of Science and Engineering leadership over the last year to engage a community of IonE researchers to help identify collaborative opportunities for pooling expertise across multiple disciplines. He says he looks forward to continuing this work as a member of the IonE.
Since joining the UMN faculty in 2012, Pomerantz’s chemistry research has focused on the development of chemical biology and medicinal chemistry approaches for modulating transcription factor function through disruption of protein-protein interactions, with a significant focus in the area of epigenetics. In addition to work in his lab, he is currently the co-director of the NIH T32 Chemistry Biology Interface Training Grant, which works to provide rigorous and interdisciplinary training to a diverse and inclusive community of biomedical scientists and Topic Editor for ACS Medicinal Chemistry Letters, an ACS Transformative Journal. Pomerantz’s impact on the chemistry community has been recognized with many honors, including the Horace T. Morse Award (2024), the George W. Taylor Award for Distinguished Teaching (2022), the NIH Maximizing Investigators Research Award (2021), the McKnight Presidential Fellowship (2018), the Guillermo E. Borja Career Development Award (2018), being named a Cottrell Scholar by the Research Corporation for Science Advancement (2016), and the international Chemical Biology Society’s Rising Star Award (2016).
Pomerantz Group Website
Institute on the Environment Website
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What Is Quantitative Research In Sport?
In sport and exercise research, qualitative analysis is fundamental to understanding factors such as exercise adherence , the nature of effective training, non-response to interventions and stakeholder priorities.
What is the purpose of qualitative and quantitative analysis in sports?
Qualitative and Quantitative
It is used to quantify attitudes, opinions, behaviors, and other defined variables – and generalize results from a larger sample population . Quantitative data collection methods are much more structured than qualitative data collection methods.
What is the importance of Quantitative Research in education Culture and Sports?
Education research often relies on the quantitative methodology. Quantitative research in education provides numerical data that can prove or disprove a theory , and administrators can easily share the number-based results with other schools and districts.
What is the role of quantitative research in education?
Quantitative research in education provides numerical data that can prove or disprove a theory , and administrators can easily share the number-based results with other schools and districts.
Why do we need quantitative research?
The purpose of quantitative research is to attain greater knowledge and understanding of the social world . Researchers use quantitative methods to observe situations or events that affect people. Quantitative research produces objective data that can be clearly communicated through statistics and numbers.
What is the importance or contribution of quantitative research to arts and design?
While quantitative research aims to measure the impact of the arts on student learning by testing the claims of its advocates through controlled, experimental methods , qualitative research methods may be applied in an effort to describe the impact of the arts in education within the heuristic world of arts education.
Where can quantitative research be applied?
Quantitative research is widely used in the natural and social sciences : biology, chemistry, psychology, economics, sociology, marketing, etc.
How important is qualitative research in your life?
It provides an in-depth understanding of the ways people come to understand, act and manage their day-to-day situations in particular settings . … Qualitative research uses words and images to help us understand more about “why” and “how” something is happening (and, sometimes “what” is happening).
What is the importance of qualitative research in arts?
The main purposes of arts-informed research are: to enhance understanding of the complexities of the human condition through alternative processes and representational forms of inquiry ; and to reach multiple audiences by making scholarship more accessible.
What is qualitative research example?
A good example of a qualitative research method would be unstructured interviews which generate qualitative data through the use of open questions . This allows the respondent to talk in some depth, choosing their own words. … Photographs, videos, sound recordings and so on, can be considered qualitative data.
Why is it important to design your research plan?
A research plan is pivotal to a research project because it identifies and helps define your focus, method, and goals while also outlining the research project from start to finish . This type of plan is often necessary to: Apply for grants or internal company funding.
What is quantitative research examples?
After careful understanding of these numbers to predict the future of a product or service and make changes accordingly. An example of quantitative research is the survey conducted to understand the amount of time a doctor takes to tend to a patient when the patient walks into the hospital .
What is the importance of quantitative research in tourism?
Quantitative tourism research is usually employed to examine and understand tourism-related phenomena (e.g., behavior of tourists, traveler characteristics, destination image assessment and perceptions, decision making and destination selection, demand analysis, performance measures, and general market assessment and …
Which is a type of research method used in sport psychology?
Experimental Approach . The first category of research method in sport and exercise psychology is the experimental approach. … The interest of studying causal relationships in sport and exercise psychology is not different than that of other scientific domains.
What is research design in quantitative?
Quantitative research design is aimed at discovering how many people think, act or feel in a specific way . Quantitative projects involve large sample sizes, concentrating on the quantity of responses, as opposed to gaining the more focused or emotional insight that is the aim of qualitative research.
How do you do quantitative research?
The main methods used in quantitative research are:
Survey. Survey methods collects data gathered from responses given by the participants through questionnaires. …
Tracking. …
Experiments. …
Structured interviews. …
Validity. …
Internal validity. …
External validity. …
Lack of detail.
What is strength of quantitative research?
Quantitative studies provide data that can be expressed in numbers —thus, their name. … Quantitative studies’ great strength is providing data that is descriptive—for example, allowing us to capture a snapshot of a user population—but we encounter difficulties when it comes to their interpretation.
What is the importance and contribution of arts and design?
Art, craft and design introduces participants to a range of intellectual and practical skills . … It provides children, young people and lifelong learners with regular opportunities to think imaginatively and creatively and develop confidence in other subjects and life skills.
How does quantitative research help the society?
The strength of quantitative methods is that they can provide vital information about a society or community , through surveys, examination or records or censuses that no individual could obtain by observation. …
What is quantitative research method?
Quantitative research methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires , and surveys, or by manipulating pre-existing statistical data using computational techniques.
What are the 4 types of quantitative research?
There are four main types of Quantitative research: Descriptive, Correlational, Causal-Comparative/Quasi-Experimental, and Experimental Research.
What is quantitative example?
Quantitative is an adjective that simply means something that can be measured . For example, we can count the number of sheep on a farm or measure the gallons of milk produced by a cow.
What is the importance of quantitative research in our daily life?
The quantitative approach is so vital, even in our daily lives, because in most, if not all things we do in life, we measure to see how much there is of something . Quantitative method is part of our daily life, even from birth, data are constantly being collected, assessed, and re-assessed as we grow.
Current Research and Statistical Practices in Sport Science and a Need
The occurrence of biased and unchallenged outcomes in sport science can be attributed to at least two factors. First, the current academic/publication structure to only reward new, unique and ground-breaking findings instils a need for producing research that has statistically significant results. Second, existing statistical practices lend ...
Sport Psychology Research Methods: Qualitative vs Quantitative
Examining Quantitative Research. Quantitative research is experimental and objective. The objective of quantitative research is essentially to collect numerical data to explain a particular phenomenon (Hoe and Hoare, 2012). By using measurable data researchers are able to formulate facts and uncover patterns in research.
Journal of Quantitative Analysis in Sports
The Journal of Quantitative Analysis in Sports (JQAS), an official journal of the American Statistical Association, publishes timely, high-quality peer-reviewed research on the quantitative aspects of professional and amateur sports, including collegiate and Olympic competition. The scope of application reflects the increasing demand for novel methods to analyze and understand data in the ...
Students' Motivation for Sport Activity and Participation in University
3.1. University Sports. In the quantitative study, 47.6% of students stated that they participated in university sports. ... Our study underlines the importance of combining quantitative and qualitative research approaches, since our qualitative analysis identified additional reasons for sport participation that were not addressed by the ...
Why research in sports science is so important
Finally, sports science research allows us to better understand which types of exercise can be effective. And this, in turn, often filters down to non-athletes. We can therefore use the findings ...
Searching for Meaning and Purpose in Elite Sport: A ...
Purpose of sport: Quantitative (questionnaire data) Task orientation linked to beliefs about intrinsic (skills mastery, personal improvement) and cooperative purposes of sport (teach being honest, decent citizens). Ego oriented student-athletes saw sport as means to an end (outperform others, have competitive success, become famous, bend the ...
PDF Students' Motivation for Sport Activity and Participation in University
quantitative and qualitative methods to analyze university students' motivation for sports engagement. Our study underlines the importance of combining quantitative and qualitativeresearchapproaches,sinceourqualitativeanalysis
The impact of urban form on physical change: A quantitative and
The importance of urban form as a key factor affecting future development and transformation patterns is well-recognised in urban morphology. However, despite the need for a diachronic approach to rigorously understand the form-change relationship, studies utilising longitudinal datasets remain scarce, and only a small fraction employs quantitative methodologies and morphometric approaches.
(PDF) Quantitative Research: A Successful Investigation in Natural and
Quantitative research explains phenomena by collecting numerical unchanging d etailed data t hat. are analyzed using mathematically based methods, in particular statistics that pose questions of ...
The importance of research in sports medicine
The importance of research in sports medicine. Per Renström, Per Renström. Department of Surgical Sciences, Section of Sports Medicine, Karolinska Institute, Stockholm, Sweden, E-mail: [email protected] Search for more papers by this author. Per Renström, Per Renström.
Why Is Quantitative Research Important?
Advantages of Quantitative Research. Quantitative researchers aim to create a general understanding of behavior and other phenomena across different settings and populations. Quantitative studies are often fast, focused, scientific and relatable. 4. The speed and efficiency of the quantitative method are attractive to many researchers.
500+ Sports Research Topics
500+ Sports Research Topics. March 26, 2024. by Muhammad Hassan. Sports research topics cover a vast array of areas in the world of athletics, from the physical and psychological impacts of sport on athletes to the social and cultural implications of sports on society. Sports research can include studies on training techniques, nutrition ...
Methods for quantitative research in psychology
Compare and contrast the major research designs. Explain how to judge the quality of a source for a literature review. Compare and contrast the kinds of research questions scientists ask. Explain what it means for an observation to be reliable. Compare and contrast forms of validity as they apply to the major research designs.
Parasports News, Research and Analysis
Top contributors. Amy Latimer-Cheung Professor, School of Kinesiology, Queen's University, Ontario Janine Coates Senior Lecturer in Qualitative Research Methods, Loughborough University
Effects of Sports Participation on the Academic Performance of Grade 12
Research shows that participation in sports has excellent cognitive and physical benefits [3]. ... The descriptive-quantitative design explores the daily activities inside and outside the school of student-athletes and non-athletes to determine their coping strategies and compare them to the non-athletes. ... This result can be an important ...
(PDF) Impacts of sports on students' life
Using descriptive correlational research design, quantitative data were gathered using a questionnaire as the main instrument. ... The paper shows that sport is an important arena for the creation ...
(PDF) The role of qualitative research in exercise science and sports
Qualitative research methods are. the strategies used in the process of collecting, organising and in-. terpreting text obtained through observation, or from communication. with individuals or ...
Contraception Choice for Female Endurance Athletes: What's Sport Got to
This was an online survey study with female endurance athletes recruited through social media and emails to university/club coaches and sport organizations. Quantitative and qualitative data were analyzed with descriptive statistics and conventional content analysis, respectively. A total of 323 female endurance athletes participated.
Qualitative Data Analysis: A Complete Guide [2024]
Quantitative analysis, which I've extensively used in Six Sigma projects, deals with numerical data and statistical methods. It's excellent for measuring, ranking, and categorizing phenomena. ... As we move forward, the field will undoubtedly continue to evolve, but its fundamental importance in research and decision-making will remain ...
Religion, Spirituality and Health Research: Warning of ...
The relationship between religiosity, spirituality and health has received increasing attention in the academic literature. Studies involving quantitative measurement of religiosity and/or spirituality (R/S) and health have reported many positive associations between these constructs. The quality of various measures, however, is very important in this field, given concerns that some measures ...
The Status of Canadian Radiology Mentorship Programs, Where We Stand
Mentoring programs within medical schools are beneficial for students and have been implemented on a global scale. 2 In addition to promoting student growth, these programs also nurture interest in clinical specialties, instill enthusiasm for research, and provide career guidance. 2 Mentors play a pivotal role in supporting students academically by assisting with studies and materials, thereby ...
Weaving equity into infrastructure resilience research: a decadal
In analyzing quantitative data, most research has focused on using descriptive statistics, linear models, and Moran's I statistic which have been effective in pinpointing areas with heightened ...
Chapter 3
The Importance of Quantitative Research across Fields. null. What communicative behaviors are used to respond to co-workers displaying emotional stress? (Allen, Titsworth, Hunt, 2009) 3. QUANTITATIVE RESEARCH and SPORTS MEDICINE Quantitative research is used to analyze how sports may be used as an alternative way of medicating an illness. An ...
Importance of Quantitative Research Across Fields
Importance of Quantitative Research Across Fields. First of all, research is necessary and valuable in society because, among other things, 1) it is an important tool for building knowledge and facilitating learning; 2) it serves as a means in understanding social and political issues and in increasing public awareness; 3) it helps people ...
Journal Article Reporting Standards (JARS)
Jars -Qual should be used in research where findings are reported using nonnumerical descriptive data (qualitative research). Jars -Mixed should be applied to research that includes both quantitative and qualitative research (mixed methods research). JARS-REC should be applied to all research, whether it is quantitative, qualitative, or ...
Professor William Pomerantz named Institute on the Environment Fellow
MINNEAPOLIS / ST. PAUL (8/29/2024) - Professor William C. K. Pomerantz has been recently selected as a University of Minnesota Institute on the Environment (IonE) Fellow. IonE Fellows are established researchers and innovative thought leaders who have demonstrated excellence in disciplines related to environmental protection or sustainability, maintain a significant publication record ...
Learning Activity Sheet Importance of Quantitative Research Across
This document provides a learning activity sheet on the importance of quantitative research across different fields. It contains background information on quantitative research and how it has been widely used in most disciplines like medicine, dentistry, education, sports, and social sciences. The activity asks students to classify research titles under their appropriate disciplines like ...
What is quantitative research in sport?
Qualitative and Quantitative. It is used to quantify attitudes, opinions, behaviors, and other defined variables - and generalize results from a larger sample population.Quantitative data collection methods are much more structured than qualitative data collection methods. What is the importance of Quantitative Research in education Culture and Sports?
Answers to: Example of importance of quantitative research in sports
Answers. Quantitative research plays a crucial role in sports medication as it enables researchers to quantitatively measure and analyze various aspects of sports performance and injury prevention. One example of the importance of quantitative research in this field is the study of concussion management in athletes. Concussions are a prevalent ...
In quantitative research what is the importance or contribution in sports?
report flag outlined. The purpose of quantitative research is to generate knowledge and create understanding about the social world. Quantitative research is used by social scientists, including communication researchers, to observe phenomena or occurrences affecting individuals. Social scientists are concerned with the study of people.
COMMENTS
The occurrence of biased and unchallenged outcomes in sport science can be attributed to at least two factors. First, the current academic/publication structure to only reward new, unique and ground-breaking findings instils a need for producing research that has statistically significant results. Second, existing statistical practices lend ...
Examining Quantitative Research. Quantitative research is experimental and objective. The objective of quantitative research is essentially to collect numerical data to explain a particular phenomenon (Hoe and Hoare, 2012). By using measurable data researchers are able to formulate facts and uncover patterns in research.
The Journal of Quantitative Analysis in Sports (JQAS), an official journal of the American Statistical Association, publishes timely, high-quality peer-reviewed research on the quantitative aspects of professional and amateur sports, including collegiate and Olympic competition. The scope of application reflects the increasing demand for novel methods to analyze and understand data in the ...
3.1. University Sports. In the quantitative study, 47.6% of students stated that they participated in university sports. ... Our study underlines the importance of combining quantitative and qualitative research approaches, since our qualitative analysis identified additional reasons for sport participation that were not addressed by the ...
Finally, sports science research allows us to better understand which types of exercise can be effective. And this, in turn, often filters down to non-athletes. We can therefore use the findings ...
Purpose of sport: Quantitative (questionnaire data) Task orientation linked to beliefs about intrinsic (skills mastery, personal improvement) and cooperative purposes of sport (teach being honest, decent citizens). Ego oriented student-athletes saw sport as means to an end (outperform others, have competitive success, become famous, bend the ...
quantitative and qualitative methods to analyze university students' motivation for sports engagement. Our study underlines the importance of combining quantitative and qualitativeresearchapproaches,sinceourqualitativeanalysis
The importance of urban form as a key factor affecting future development and transformation patterns is well-recognised in urban morphology. However, despite the need for a diachronic approach to rigorously understand the form-change relationship, studies utilising longitudinal datasets remain scarce, and only a small fraction employs quantitative methodologies and morphometric approaches.
Quantitative research explains phenomena by collecting numerical unchanging d etailed data t hat. are analyzed using mathematically based methods, in particular statistics that pose questions of ...
The importance of research in sports medicine. Per Renström, Per Renström. Department of Surgical Sciences, Section of Sports Medicine, Karolinska Institute, Stockholm, Sweden, E-mail: [email protected] Search for more papers by this author. Per Renström, Per Renström.
Advantages of Quantitative Research. Quantitative researchers aim to create a general understanding of behavior and other phenomena across different settings and populations. Quantitative studies are often fast, focused, scientific and relatable. 4. The speed and efficiency of the quantitative method are attractive to many researchers.
500+ Sports Research Topics. March 26, 2024. by Muhammad Hassan. Sports research topics cover a vast array of areas in the world of athletics, from the physical and psychological impacts of sport on athletes to the social and cultural implications of sports on society. Sports research can include studies on training techniques, nutrition ...
Compare and contrast the major research designs. Explain how to judge the quality of a source for a literature review. Compare and contrast the kinds of research questions scientists ask. Explain what it means for an observation to be reliable. Compare and contrast forms of validity as they apply to the major research designs.
Top contributors. Amy Latimer-Cheung Professor, School of Kinesiology, Queen's University, Ontario Janine Coates Senior Lecturer in Qualitative Research Methods, Loughborough University
Research shows that participation in sports has excellent cognitive and physical benefits [3]. ... The descriptive-quantitative design explores the daily activities inside and outside the school of student-athletes and non-athletes to determine their coping strategies and compare them to the non-athletes. ... This result can be an important ...
Using descriptive correlational research design, quantitative data were gathered using a questionnaire as the main instrument. ... The paper shows that sport is an important arena for the creation ...
Qualitative research methods are. the strategies used in the process of collecting, organising and in-. terpreting text obtained through observation, or from communication. with individuals or ...
This was an online survey study with female endurance athletes recruited through social media and emails to university/club coaches and sport organizations. Quantitative and qualitative data were analyzed with descriptive statistics and conventional content analysis, respectively. A total of 323 female endurance athletes participated.
Quantitative analysis, which I've extensively used in Six Sigma projects, deals with numerical data and statistical methods. It's excellent for measuring, ranking, and categorizing phenomena. ... As we move forward, the field will undoubtedly continue to evolve, but its fundamental importance in research and decision-making will remain ...
The relationship between religiosity, spirituality and health has received increasing attention in the academic literature. Studies involving quantitative measurement of religiosity and/or spirituality (R/S) and health have reported many positive associations between these constructs. The quality of various measures, however, is very important in this field, given concerns that some measures ...
Mentoring programs within medical schools are beneficial for students and have been implemented on a global scale. 2 In addition to promoting student growth, these programs also nurture interest in clinical specialties, instill enthusiasm for research, and provide career guidance. 2 Mentors play a pivotal role in supporting students academically by assisting with studies and materials, thereby ...
In analyzing quantitative data, most research has focused on using descriptive statistics, linear models, and Moran's I statistic which have been effective in pinpointing areas with heightened ...
The Importance of Quantitative Research across Fields. null. What communicative behaviors are used to respond to co-workers displaying emotional stress? (Allen, Titsworth, Hunt, 2009) 3. QUANTITATIVE RESEARCH and SPORTS MEDICINE Quantitative research is used to analyze how sports may be used as an alternative way of medicating an illness. An ...
Importance of Quantitative Research Across Fields. First of all, research is necessary and valuable in society because, among other things, 1) it is an important tool for building knowledge and facilitating learning; 2) it serves as a means in understanding social and political issues and in increasing public awareness; 3) it helps people ...
Jars -Qual should be used in research where findings are reported using nonnumerical descriptive data (qualitative research). Jars -Mixed should be applied to research that includes both quantitative and qualitative research (mixed methods research). JARS-REC should be applied to all research, whether it is quantitative, qualitative, or ...
MINNEAPOLIS / ST. PAUL (8/29/2024) - Professor William C. K. Pomerantz has been recently selected as a University of Minnesota Institute on the Environment (IonE) Fellow. IonE Fellows are established researchers and innovative thought leaders who have demonstrated excellence in disciplines related to environmental protection or sustainability, maintain a significant publication record ...
This document provides a learning activity sheet on the importance of quantitative research across different fields. It contains background information on quantitative research and how it has been widely used in most disciplines like medicine, dentistry, education, sports, and social sciences. The activity asks students to classify research titles under their appropriate disciplines like ...
Qualitative and Quantitative. It is used to quantify attitudes, opinions, behaviors, and other defined variables - and generalize results from a larger sample population.Quantitative data collection methods are much more structured than qualitative data collection methods. What is the importance of Quantitative Research in education Culture and Sports?
Answers. Quantitative research plays a crucial role in sports medication as it enables researchers to quantitatively measure and analyze various aspects of sports performance and injury prevention. One example of the importance of quantitative research in this field is the study of concussion management in athletes. Concussions are a prevalent ...
report flag outlined. The purpose of quantitative research is to generate knowledge and create understanding about the social world. Quantitative research is used by social scientists, including communication researchers, to observe phenomena or occurrences affecting individuals. Social scientists are concerned with the study of people.