example of research procedure in thesis

Research Methodology Example

Detailed Walkthrough + Free Methodology Chapter Template

If you’re working on a dissertation or thesis and are looking for an example of a research methodology chapter , you’ve come to the right place.

In this video, we walk you through a research methodology from a dissertation that earned full distinction , step by step. We start off by discussing the core components of a research methodology by unpacking our free methodology chapter template . We then progress to the sample research methodology to show how these concepts are applied in an actual dissertation, thesis or research project.

If you’re currently working on your research methodology chapter, you may also find the following resources useful:

• Research methodology 101 : an introductory video discussing what a methodology is and the role it plays within a dissertation
• Research design 101 : an overview of the most common research designs for both qualitative and quantitative studies
• Variables 101 : an introductory video covering the different types of variables that exist within research.
• Sampling 101 : an overview of the main sampling methods
• Methodology tips : a video discussion covering various tips to help you write a high-quality methodology chapter
• Private coaching : Get hands-on help with your research methodology

PS – If you’re working on a dissertation, be sure to also check out our collection of dissertation and thesis examples here .

FAQ: Research Methodology Example

Research methodology example: frequently asked questions, is the sample research methodology real.

Yes. The chapter example is an extract from a Master’s-level dissertation for an MBA program. A few minor edits have been made to protect the privacy of the sponsoring organisation, but these have no material impact on the research methodology.

Can I replicate this methodology for my dissertation?

As we discuss in the video, every research methodology will be different, depending on the research aims, objectives and research questions. Therefore, you’ll need to tailor your literature review to suit your specific context.

You can learn more about the basics of writing a research methodology chapter here .

Where can I find more examples of research methodologies?

The best place to find more examples of methodology chapters would be within dissertation/thesis databases. These databases include dissertations, theses and research projects that have successfully passed the assessment criteria for the respective university, meaning that you have at least some sort of quality assurance.

The Open Access Thesis Database (OATD) is a good starting point.

How do I get the research methodology chapter template?

You can access our free methodology chapter template here .

Is the methodology template really free?

Yes. There is no cost for the template and you are free to use it as you wish.

Great insights you are sharing here…

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

• Print Friendly

• Staff Directory
• Library Policies
• Hege Research Award
• Quaker Archives
• Art Gallery
• Student Support
• Teaching & Learning
• Reserving spaces
• Technology Lending
• Interlibrary Loan
• Course Reserves
• Copyright & Fair Use
• Poster Printing
• Virtual Reference
• Research Guides
• Off-campus access
• Digital Scholarship
• Guilford Sources
• Open Educational Resources
• Quaker Collections
• Digital Collections
• College Archives
• Underground Railroad
• Universities Studying Slavery
• Images & Exhibitions

Service Alert

Hege Library & Learning Technologies

Guide for Thesis Research

• Introduction to the Thesis Process
• Project Planning
• Literature Review
• Theoretical Frameworks
• Research Methodology
• GC Honors Program Theses
• Thesis Submission Instructions This link opens in a new window
• Accessing Guilford Theses from 1898 to 2020 This link opens in a new window

Basics of Methodology

Research is a process of inquiry that is carried out in a pondered, organized, and strategic manner. In order to obtain high quality results, it is important to understand methodology.

Research methodology refers to how your project will be designed, what you will observe or measure, and how you will collect and analyze data. The methods you choose must be appropriate for your field and for the specific research questions you are setting out to answer.

A strong understanding of methodology will help you:

• apply appropriate research techniques
• design effective data collection instruments
• analyze and interpret your data
• develop well-founded conclusions

Below, you will find resources that mostly cover general aspects of research methodology. In the left column, you will find resources that specifically cover qualitative, quantitative, and mixed methods research.

General Works on Methodology

Qualitative Research

Quantitative Research

Mixed Methods Research

• << Previous: Theoretical Frameworks
• Next: Citation >>
• Last Updated: Jul 22, 2024 10:48 AM
• URL: https://library.guilford.edu/thesis-guide

Research Procedures

• Open Access
• First Online: 28 March 2023

Cite this chapter

You have full access to this open access chapter

• Ivan Buljan   ORCID: orcid.org/0000-0002-8719-7277 3  

Part of the book series: Collaborative Bioethics ((CB,volume 1))

8736 Accesses

This chapter offers a guide on how to implement good research practices in research procedures, following the logical steps in research planning from idea development to the planning of analysis of collected data and data sharing. This chapter argues that sound research methodology is a foundation for responsible science. At the beginning of each part of the chapter, the subtitles are formulated as questions that may arise during your research process, in the attempt to bring the content closer to the everyday questions you may encounter in research. We hope to stimulate insight into how much we can predict about a research study before it even begins. Research integrity and research ethics are not presented as separate aspects of research planning, but as integral parts that are important from the beginning, and which often set the directions of research activities in the study.

You have full access to this open access chapter,  Download chapter PDF

Similar content being viewed by others

Ethical Issues in Research Methods

Research Integrity: Responsible Conduct of Research

• Research plan
• Research question
• Study design
• Measurement
• Protocol registration
• Reproducibility

What This Chapter Is About

Case scenario: planning research.

This hypothetical scenario was adapted from a narrative about the process of poor research planning and its consequences. The original case scenario is developed by the Members of The Embassy of Good Science and is available at the Embassy of Good Science . The case is published under the Creative Commons Attribution-ShareAlike License, version 4.0 (CC BY-SA 4.0).

Professor Gallagher is a leader of a research project on moral intuitions in the field of psychology. She is working on the project with Dr. Jones, a philosopher, and Mr. Singh, a doctoral student. Although she has little experience in the matter, Dr. Jones is put as the principal investigator in the study design and analysis of the two experiments, while Mr. Singh prepares materials and conducts the experiments.

After the first experimental study, Mr. Singh sends the results to Dr. Jones for analysis. After some time, eager to enter the results in his thesis, Singh asks Dr. Jones about the results of the study. She admits that she forgot to formulate the hypothesis before data analysis, and now the results can be interpreted as confirmatory, regardless of the direction. They decide to formulate a hypothesis that will result in a positive finding.

Mr. Singh and Dr. Jones present the results to Dr. Gallagher, who is satisfied and proceeds with paper writing. In the second study, Dr. Jones formulates multiple hypotheses before the study begins. Mr. Singh conducts the study and sends the results to Dr. Jones. She performs the analysis by trying to find only significant differences between groups. Finally, to achieve significance, she excludes participants over 60 years from the analysis and while presenting the results, admits that to Prof Gallagher. Prof Galagher is happy about the results and proceeds with the paper writing, while Mr. Singh enters the results in his dissertation.

Before Mr. Singh has the public defense of his dissertation, one of the internal reviewers notices that some data has been excluded from the second study and only significant results were reported. She invites Mr. Singh for an examination board meeting during which MR Singh admits that the data has been excluded and that in the first study hypothesis was formulated after the results were known.

Questions for You

Why is hypothesizing after the results are known, as described in the first study, considered problematic?

What was wrong about reporting only significant results in Study 2?

How would you improve the entire research process described in the scenario?

Good research practice from the European Code of Conduct for Research Integrity:

Researchers take into account the state-of-the-art in developing research ideas.

Researchers make proper and conscientious use of research funds.

What to Do First When You Have an Idea?

It is difficult to come up with a good research idea, and if you struggle to come up with a new research direction, that is perfectly fine. Creative processes are the highest form of learning and developing an idea requires significant cognitive effort. In some cases, you may have an epiphany, where you would suddenly come up with a great idea for your research project. This is something popularized by stereotypes about scientists as eccentric figures who come up with brilliant ways of tackling things using only their intelligence and intuition. However, scientific work resembles ore mining. It takes a tremendous effort to read relevant scientific literature, communicate with your peers, plan, and, in some cases, attempt and fail before you even start digging for gold. As in a mine, you will need to dig a lot of rocks before you come across diamonds and gold.

Usually, the most important decisions are made before digging even begins. To decide where you will start mining, you start with the exploration of the terrain. In research, this means knowing your field of study. You may read an interesting piece in the scientific literature or listen to a presentation at a conference and then think of a hypothesis whose testing will answer an interesting and important question in your research field. On the other hand, sometimes you have to adjust your research interest so that they fit the specific aims of grant funding calls. It does not matter what the source of the idea is, there are always two things to consider when developing research ideas: the current state of the field and the resources available to you. Good research practice is to consider the state of the art in developing your research ideas and make the proper use of research funds. This does not mean that you are not allowed to develop research ideas if they address a research topic that has been neglected. It is the responsibility of a researcher to combine the best of the “old” evidence with new research developments. It is important to keep in mind that research is not performed in a vacuum and that the funds and resources provided by public or private funders are given with an expectation of an honest answer to a specific research question. The main responsibility for the proper use of research funds is on the researcher, and this is overseen by funders during and at the end of the proposal. Another recommendation refers to the use of state-of-the-art information as a basis for your research. The control system in this case is other scientists who read or evaluate your research, and who will recognize outdated research results.

Let’s get back to the analogy of the mine for a moment. If you are paid to dig in the mine, you are expected to find important ore. In our case, a research funder is an employer, and the researchers are workers who need to go down the mine and get their hands dirty in the search for new true information. If you are set to dig a deep hole in the ground with the possibility of finding gold and diamonds, but you do not get any guarantee that you will find them unless you chose an appropriate place in a specific period, you would probably spend a lot of time planning and trying to decide where to start digging, what to do when specific problems arise and to avoid ending with a huge number of worthless rocks instead of gold and diamonds. The process is similar to research planning since a significant amount of the research process can be defined before data collection begins. As valuable as it can be, a research idea is just a thought which needs to be translated into research practice to gain its full impact.

How to Formulate a Good Research Question?

Research is performed to answer a specific question. The research process can be observed as a complex tool that, if used properly, can give a clear answer to a posed question. The research question is the compass of the research process (or the mine if we continue with our mine analogy) since it determines the steps of the research process. It translates into specific research aims and, consequently, into testable research hypotheses. Formulation of a research question is a skill that develops over time, a skill that can be learned. Your research question should have a FINER structure, which stands for: F easible, I nteresting, N ovel, E thical and R elevant. Although initially developed as a set of recommendations for quantitative research, FINER recommendations can be applied to formulating a research question in any given field of science.

The feasibility of a research aim is often defined by time restrictions and funding because research is often burdened by deadlines and output requirements set by the funders. F easibility is also affected by the availability of technology, geographical restrictions, availability of participants, or availability of collaborators. If one considers all those factors, it is obvious that research interests play only a small part in the formulation of a research question. Ask yourself: What research can be published in an excellent journal if you have limited funds and only 1 year for research, with limited access to a specific technology? (Today, highly specialized experts may be a greater problem than the technology in question). You might experience that the formulation of the research question is mostly defined by non-research factors, because, in the end, it is better to have a completed than never-finished research.

There are other elements of the research question that are as important as feasibility. The first one to consider is E thics, which affects all parts of the research process due to its broad nature. If research is not ethical, then it should not be conducted. In a mining analogy, ethics is training and safety, which helps you to protect others and yourself during the entire process. To get back to the best research practices, researchers should make proper use of research funds and fulfill the basic research aim – the benefit to society. This also implies treating members of that society with respect, respecting their privacy and dignity, and being honest and transparent about the research process and results. Therefore, when determining the feasibility of a research study, ethics aspects are the first to consider, along with the objective factors of time, cost, and manpower.

I nterest, N ovelty, and R elevance from the FINER guidance are the elements of the research question that increase the chances of getting funding or the chances for a journal publication, and they are closely aligned. Regardless of the audience (researchers, publishers, non-experts), research should be new to be interesting and relevant. However, doing research just for the novelty’s sake is analogous to the digger who starts digging a new mine every couple of days. It gives you the thrill of a new beginning, but you have not dug deep enough to get to the real results. Relevance, defined in this context as a significant add-on to the current knowledge, can be assessed with a high probability of success by a thorough search for available evidence. The main aim of that process is to identify research or practice gaps that can be filled to improve general knowledge.

Interest is related to the principal internal motivation of an individual to pursue research goals. The interest to pursue research aims is difficult to assess. When planning research, do you consider that research is interesting to you, your peers, potential users, or all three? Probably the last, but here is the catch. Interest is the most subjective part of research planning. Research planning could be understood as a balance between your interest and all other factors that affect the research outcome. A good research idea is often the compromise between objective possibilities and a desire to make a research discovery. If the research idea is interesting but extremely difficult (or even impossible) to conduct in given circumstances, you will end up frustrated. On the other hand, if you decide to perform research based solely on convenience (because it is something for which is easy to get funded or someone is offering you a research topic you are not interested in), it will be very difficult to stay motivated to complete the study.

The more structured your research question is, the easier it is to determine which research design is best to test the hypothesis and statistical analysis is more straightforward. Let’s look at several examples of research questions in biomedical research: Are psychedelics more effective in the treatment of psychosis than the standard treatment? What are the opinions of young fathers on exclusive breastfeeding of their spouses? Which percentage of the population has suffered from post-COVID-19 syndrome? Intuitively, for each of posed research questions, we would try to find answers differently. In cases of comparison of treatment methods and assessment of population percentage, we could express the results quantitatively, e.g., we could state explicitly how much the psychedelics treatment is better compared to standard methods in terms of days of remission or everyday functionality or an explicit number of people in the sample who had COVID-19-related symptoms. On the other hand, the answers to the question about the opinions of young fathers about exclusive breastfeeding are not straightforward or numerical, but more textual and descriptive. It is an example of the research question that would be more suitable for qualitative research. Qualitative and quantitative study designs answer different types of research questions and are therefore suitable for different situations. It is important to carefully consider and choose the most appropriate study design for your research question because only then can you get valid answers.

To conclude, research question development is the crucial factor in setting research direction. Although framed as a single sentence, it defines numerous parts of the research process, from research design to data analysis. On the other hand, non-research factors also have an equal role in research questions and need to be considered.

Literature Search

In a literature search, researchers go through the relevant information sources to systematically collect information, i.e. foreground knowledge, about a specific research phenomenon and/or procedure. While research information is readily available online not only to researchers but to the whole public, the skill of systematic literature search and critical appraisal of evidence is a specific research skill. A literature search is closely tied with the development of the research aim, because you may want to change it after you read about previous research.

When doing a literature search, you must be careful not to omit previous studies about the topic. Here we have two directions that must be balanced. The first one is to do a very precise search to find specific answers, and the other one is to perform a wide, sensitive search that will include many synonyms and combinations of words to discover articles that related to a specific term. Both of those approaches have their advantages and disadvantages: a precise search is less time-consuming and retrieves a small number of studies. However, it may omit important results, so you may end up performing studies for which we already have established conclusions. This creates waste in research because you will spend time and resources, and involve participants in unnecessary work, which would be unethical. You may also miss citing important studies. On the other hand, if you perform a search that is too wide, you will spend a lot of time filtering for useful articles, which leaves less time for doing research.

Researchers design, carry out, analyze and document research in a careful and well-considered manner.

Researchers report their results in a way that is compatible with the standards of the discipline and, where applicable, can be verified and reproduced.

What Is the Optimal Study Design for My Research?

Study designs are one of the main heuristics related to the reader’s perception of the credibility of research information. Also, different study designs give answers to different research questions. It is intuitively easy to understand that different approaches should be taken if the question is about the percentage of infected people in the population vs about which drug is the most effective in the treatment of the disease. The roughest categorization of the study designs is observational and experimental (Box 3.1 ). However, in different scientific areas, even that type of categorization is not enough, since study designs can be theoretical, as in physics or mathematics, or critical, as in humanities, and those types of research will not be covered in this chapter.

Box 3.1 Types of Study Designs

Observational study designs :.

Case study / case series / qualitative study : All three types of study designs take into account a small number of participants and examine the phenomenon of interest in-depth but cannot make generalizations about the entire population.

Case-control study : Individuals with a certain outcome or disease are selected and then information is obtained on whether the subjects have been exposed to the factor under investigation more frequently than the carefully selected controls. This approach is quick and cost-effective in the determination of factors related to specific states (e.g., risk factors), but it relies too much on records and/or self-report, which may be biased.

Cross-sectional study : Best study design for determining the prevalence and examination of relationships between variables that exist in the population at a specific time. Although it is simple to perform, and relatively cheap, it is susceptible to various types of bias related to participant selection, recall bias, and potential differences in group sizes.

Cohort study : Participants are followed over a certain period (retrospectively or prospectively) and data are compared between exposed and unexposed groups to determine predictive factors for the phenomenon of interest.

Experimental study designs :

Randomized controlled trial (RCT) : Participants are allocated to treatment or control groups using randomization procedures to test the strength of the interventions.

Quasi-experimental trial : Participants are allocated to treatment or control groups to test the strengths of the interventions, but there is no randomization procedure.

For some research areas (e.g. health sciences, social sciences), there is another type of research often referred to as evidence synthesis, or literature review. The literature review is a review of evidence-based on a formulated research question and elements. They differ in their scope and methodology (Box 3.2 ).

Box 3.2 Most Common Types of Review

Systematic review : A type of review that searches systematically for, appraises, and synthesizes research evidence, often adhering to guidelines on the conduct of a review.

Scoping review : Type of review which serves as a preliminary assessment of the potential size and scope of available research literature to identify the nature and extent of research evidence (usually including ongoing research).

Meta-analysis : Statistical synthesis of the results from quantitative studies to provide a more precise effect of the results.

Rapid review : A type of review that assesses what is already known about a policy or practice issue, by using systematic review methods to quickly search and critically appraise existing research to inform practical steps.

Umbrella review : Specific type of review that searches and assesses compiling evidence from multiple reviews into one accessible and usable document. Focuses on broad conditions or problems for which there are competing interventions and highlights reviews that address these interventions and their results.

How to Assess which Study Design Is Most Suitable for Your Research Question?

Based on the research aim, one may already get a hint about which study design will be applied, since different study designs give answers to different research questions. However, very often a research question is not so straightforward. Sometimes the research aim could be to determine whether category X is superior to category Y, related to the specific outcome. In those cases, one must determine what the core outcome of the study is (e.g., testing of the effectiveness of two interventions, the scores on current differences between two groups, or the changes over time between different groups), and then it is not difficult to determine the study type in question. In principle, a single research question can be answered with a single study design. However, what we can also use are substitute study designs that can give approximate answers to the question we are asking but will never give as clear an answer as the appropriate design. For example, if we want to explore the reasons early-career researchers seek training in research integrity using a survey approach, we could list all possible answers and say to participants to choose everything that applies to them. The more appropriate study design would be to use a qualitative approach instead because in the survey approach the assumption is that we already know most of the reasons. The survey approach gives us the answer which answer is the most frequent of all. It is a subtle, but important difference. Similarly, although we can test causation using a cohort approach, the evidence for causation is never strong enough in a cohort study as it would be in an experimental study, simply because in a cohort study the researcher does not have control over the independent variable. For example, if we would test the effects of alcohol uptake on the occurrence of cancer, we would compare participants who drink versus those who do not drink to determine the incidence of cancer and make the conclusion about the association between alcohol and cancer. However, the true study design for testing the causation is the randomized controlled trial, where participants are randomized into the interventional and control group, the researcher can give an exact amount of alcohol based on persons’ weight, over a specific period, and in the end, compare the incidence between two groups. However, that type of study would not be an ethical study, so it is not possible to do it. So, there are subtle, but important differences which answer whether can specific and good formulated research questions can be tested and answered fully with only one study design, but due to the various reasons (time restrictions, ethics, cost-benefit analysis) we often use substitute study designs.

When describing people involved in the research process, researchers often refer to them as “participants” or “respondents” (in the case of surveys). A more precise term would be to name the group based on the population they are drawn from (children, people with specific diseases, or people from a specific geographical area). The appropriate term to use would be “participants”, since people are willingly involved in the research process, and the generation of new findings depends on them. Being a participant in a research process means that a person has willingly entered into a research, without any real or imagined coercion, possesses respect and interest for the research topic, with the understanding that positive aspects of research findings encompass the research situation and contribute to general knowledge. This would be a definition of an ideal participant and the researcher should avoid a situation where the participants are coerced to enter research, whether by situational factors or personal reasons because that will probably result in a decrease in motivation for participation and lower quality of research findings. To act ethically and to improve the quality of the research you have to inform participants about the reasons for the study, its purpose, research procedure, their rights, and expected outcomes. A potential pitfall in the research process can happen if all information were not given to participants at the beginning of a research. On the other hand, if a participant enters willingly into the trial, but possesses no real interest in the research topic, it may also affect the motivation for participation in research, because those participants may consider the topic irrelevant and not take the research process seriously (it is easy to imagine a situation where teenagers in a classroom willingly decide to take the survey and participate in research about personality traits, but quickly lose interest after the second page of the questionnaire). All those things are not reflected in the research report but may have an enormous influence on the research findings. Therefore, it is important to define the population of interest and try to motivate participants by providing them with all information before the research begins. Some additional ways to increase participant retention are financial rewards or similar incentives. There are several sampling strategies used when approaching participants for a study (Box 3.3 ).

Box 3.3 Most Common Sampling Methods

Simple random sampling : Each member of the defined population has an equal chance of being included in the study. The sampling is often performed by a coin toss, throwing dice, or (most commonly) using a computer program.

Stratified random sampling : The population of interest is first divided into strata (subgroups) and then we perform random sampling from each subgroup. In this way, the sample with better reflects the target population in specific (relevant) characteristics.

Cluster random sampling : In cluster sampling, the parts of the population (subgroups) are used as sampling units instead of individuals.

Systematic sampling : Participants are selected by equal intervals set before the data collection begins (e.g., every third of every fifth participant who enters the hospital).

Convenience sampling : Participants are approached based on availability. This is perhaps the most common sampling method, especially for survey research.

Purposive sampling : This is the most common approach in qualitative study designs. Researchers choose participants (or they define their characteristics in detail), based on their needs since participants with those special characteristics are the research topic.

It is difficult to give clear criteria on when to stop collecting data. In the case of pre-registered studies, the stopping rule is defined in the protocol. Examples include time restrictions (e.g. 1 month), or the number of participants (e.g. after collecting data on 100 participants). If the research protocol has not been pre-registered, then the stopping rule should be explained in detail in the publication, with reasons. In the latter case, it is never completely clear if the stopping happened after researchers encountered the desired result or if it has been planned. The practice of stopping after you collect sufficient data to support your desired hypothesis is highly unethical since it can lead to biased findings. Therefore, the best way of deciding to terminate the data collection is to pre-register your study, or at least define the desired number of participants by performing sample size calculation before the study begins and pre-registering your study. More about pre-registration and biases which it eliminates will be said later in the chapter.

Ethics of the Sample Size: Too Small and Too Big Samples

A common problem in sampling is that researchers often determine the desired number of participants in a study. The problem is that the response rate is always lower than 100% (in survey research it is often around 15–20%), and a certain percentage of participants drops out of research, resulting in a sample size significantly lower than initially planned. The sample used in research can be too small, and there is a possibility that you will not find a true effect between groups, and in that case, you would make a type II error. The reason is that in small-scale studies the error margin is big, and you would need an extremely large effect size to reach statistical significance. On the other hand, in cases of a big sample, the problem is different. If you have big samples, even small effects will be statistically significant, but the effect size may be negligible. The reason is that within big samples, the margin of error is small, and consequently, every difference is statistically significant. Once again, the proper solution (practically and ethically) for this issue is to calculate the minimum sample size needed to determine the desired difference between groups to avoid the issues with small samples and report effect sizes also, to avoid issues related to (too) big samples.

What We Can and What Cannot Measure?

When it comes to measuring in research, that part is mostly associated with statistical analysis of research data. The principal thing in statistical analysis is to determine the nature of the main outcome variable. In qualitative research (e.g. interview, focus group) or a systematic review without meta-analysis, statistical analysis is not necessary. On the other hand, for quantitative studies (a term often used for mostly case-control, cross-sectional, cohort, and interventional studies) the most important part of the research plan is to define the outcome which can be measured.

In general, there are two types of variables: qualitative and quantitative. When it comes to statistical analysis of qualitative variables (in a statistical context you will encounter the terms nominal and ordinal variables), we can do only basic functions, like counting and comparing the proportions between different groups, but we are not able to calculate mean or standard deviation, because those variables do not possess numerical characteristics. Examples of qualitative variables in research can be the number of surviving patients in a group at the end of the trial, self-reported socioeconomic status as a demographic characteristic, or any binary (yes/no) question in a questionnaire. In some cases, qualitative variables may be coded with numbers, but that does not make them quantitative. A good example is jersey numbers where numbers serve only as a label and not as a measure of quantity (e.g. if you have team player numbers 2, 4, 6, you probably will not state that the average jersey number is 4 because the very concept of the “average” jersey number is absurd). On the other hand, for quantitative variables, differences between numbers indicate the differences in value (e.g. if you say that person X is 1.80 m high, you know that that person is taller than person Y who is 1.70 m tall). You can also calculate different statistical parameters, like mean and median, and dispersion measures, which gives you a more flexible approach in the choice of statistical tests, especially those tests for differences between groups. On the other hand, applying statistical tests would mean that you are more familiar with statistics, which sometimes may present a problem for less (and more) experienced researchers.

When Is the Time to Consult with a Statistician (and Do You Have to)?

Some (lucky) researchers possess sufficient knowledge to perform data analysis themselves. They usually do not need to rely on somebody else to do the statistical analysis for their study. For everybody else, statistical analysis is a crossroad where one needs to decide on including a person with statistical knowledge in a research team or to learn statistical analyses by themselves. The usual process is that the research team defines the research aim, spends time collecting data, collects data, and then tries to find a statistician who will analyse the data. If we keep in mind that research often has high stakes (e.g. doctoral diploma) and researchers are under a great time and financial pressure, the decision to include a statistician is sound and logical, but is it really necessary? The inclusion of a statistician in research when the data are already collected is similar to the situation when you give a cook an already finished stew and ask him/her how it can be improved. The cook may help with the decorations and give some spice which would make the food look and taste better but cannot change the essence of the food since it is already cooked. It is the same with data. The golden rule of statistics is “garbage in, garbage out”, referring to a situation where poorly collected data or data of poor quality will give rise to wrong conclusions. Researchers should know statistics, not only because of the statistical analysis but because statistical reasoning is important in the formulation of measurable research aims. Therefore, statistical analysis is an important part of responsible research and begins with the formulation of the research aim. Statistical experts should be included in the study at that point.

Statisticians usually analyse data based on the initially set research aim. They send back the results of the data analysis to the research team, and they all together (in an ideal scenario) write the manuscript. The dataset remains in the possession of the principal researcher and the paper is published in a journal. Many journals and funders require that the data are publicly available so that anyone can use it, respecting the FAIR principles. Keeping that in mind, the process when somebody else is doing statistical analysis for you requires an enormous level of trust for statisticians, because they can do analysis wrong but you may never know it. Unless, of course, someone else analyses publicly available data and sees the error. In that case, you are also responsible for the analysis because it does not matter that you did not perform it. In some cases, this may lead to the retraction of the paper, which consequently may lead to certain consequences for you (especially if the articles are the basis for a doctoral thesis). If you are willing to put trust in someone to do data analysis, that is perfectly fine, just be aware of this risk, and remember that people make mistakes, very often unintentionally, and therefore a double check by a third party would be recommended.

On the other hand, if you are willing to learn how to do statistical analysis, the good news is that today there are lots of resources to help you. The first thing about statistics you need to know is that you do not need to know all statistics to do statistics. The only knowledge about statistics and statistical programs you need is the one that would help you do the analysis of your research aim and test the research hypothesis. To do that, you will have to see the data you have and search online for ways to analyze a specific problem. You can use tutorials of the statistical program that simultaneously give instructions about the statistical principles and procedures for analysis. Today, most of those programs have online videos and detailed tutorials. Some of those programs are user-friendly and free (e.g., JAMOVI or JASP ), some are commercial (e.g., SPSS, Statistica), and some are less user-friendly but free and available (e.g., R programming language ). If you are a beginner, use a more user-friendly program that has detailed instructions and try to do the statistical analysis by yourself. It is expected that you will make errors, so it would be good if someone more experienced looked at the results and provides feedback on your first attempts.

There are many tutorials on how to do statistical analysis, but far less on how to do proper data entry, which is the preparation of data for statistical analysis. Usually, the data entry table is made in a computer program that provides a tabular view of the data (e.g., Microsoft Excel). The golden rule is that each column represents a variable collected in research, by the order it was collected in the research and that each row represents the unit of the analysis (usually participant, text, article, or any other unit). In a separate sheet or a document, there should be a codebook that contains information about each level of each variable in the dataset, in a way that a person who is not familiar with research can understand the nature of the variable. The codebook should always accompany the dataset, so if the dataset is shared publicly, the codebook should also be shared. The rule of thumb for the data entry is that textual variables are entered as texts and quantitative variables as numbers, and textual variables can later be coded with numbers if necessary. The table for data entry should be made before the research begins, and it is good to seek help from a statistician when defining that, too.

Researchers publish results and interpretations of research in an open, honest, transparent, and accurate manner, and respect the confidentiality of data or findings when legitimately required to do so.

Preregistration of Research Findings

Pre-registration refers to the presentation of the research plan before the research begins. This process serves as the quality control mechanism because it prevents a change in the research hypothesis and methodology to fit the data collected. Pre-registration of research findings should be done after the research has been approved by the ethics committee. There are various registries, some of which are more discipline-specific (e.g., ClinicalTrials.gov for clinical studies) while others are open to different disciplines and study designs (e.g., Open Science Framework ). For the pre-registration of a study, one should clearly define all steps related to the research aim, methods, planned analysis, and planned use of data. Pre-registration of data is nothing more than the public sharing of a research plan. However, even that relatively simple procedure helps eliminate specific biases and decreases the probability of unethical behavior. Pre-registration eliminates the problem of h ypothesizing a fter the r esults are k nown (so-called HARKing) because you need to state your hypothesis publicly before the research begins. Pre-registration should be done before the actual research begins, since you may have already collected the data and modified your hypothesis so that it fits your data (this is called PARKing – p re-registering a fter the r esults are k nown), which should be avoided since it is not a true pre-registration.

Why is pre-registration good for research? When a study is pre-registered, researchers will follow the research plan and planned analysis and will not alter the study protocol and statistical analysis unless there is a valid and strong reason for protocol modification. Many journals today require that studies are pre-registered and that research data are shared. It is recommended to pre-register not only the study aim and methods, planned analysis, but also planned impact, data use, and authorship. When pre-registering authorship, you make clear from the beginning of the study the roles and expectations of each member of the research team. If during the research process some changes happen with the study protocol, those should be clearly explained and pointed out in the final publication, because deviations from the protocol can sometimes bring suspicion in the interpretation of the results if they are not reported. Pre-registration can be peer-reviewed and some problems, which would affect the final interpretation of the results, can be addressed even before the study begins. Finally, when pre-registered, you have the evidence that it was you who came up first with a specific research idea.

One problem that pre-registration cannot prevent is research spin or exaggeration in the scope of study results. Even if data have been carefully collected and properly analyzed, the interpretation of the results is up to the researcher. You should be honest (and modest) when interpreting the results of your study, by stating the true magnitude of your results and putting them in the context of the previous studies.

After the research has been published, the data used in research should be made available to everyone who wants to use them, since data sharing helps research replication and evidence synthesis. You can read more about data sharing in the chapter on Data Management and the chapter on Publication and Dissemination.

With this knowledge in mind, how would you improve the research procedure from the case scenario at the beginning of this chapter?

Author information

Authors and affiliations.

Department of Research in Biomedicine and Health and Center for Evidence-based Medicine, University of Split School of Medicine, Split, Croatia

Ivan Buljan

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Ivan Buljan .

Editor information

Editors and affiliations.

School of Medicine, University of Split, Split, Croatia

Ana Marusic

If You Want to Learn More

The embassy of good science.

Replicability

AllTrials campaign: https://embassy.science/wiki/Theme:0bb5e4f7-9336-4ca8-92e3-c506413d1450

Forensic statistics to detect data fabrication: https://embassy.science/wiki/Theme:467f5cf6-d41f-42a0-9b19-76556579845d

Pre-registration of animal study protocols

Prospective registration of clinical trials

Statistical pre-registration

Data driven hypothesis without disclosure (“HARKing”)

Insufficiently reported study flaws and limitations

Spin of research results .

Published Articles

Cooke A, Smith D, Booth A (2012) Beyond PICO: the SPIDER tool for qualitative evidence synthesis. Qual Health Res 22(10):1435–1443. https://doi.org/10.1177/1049732312452938

Grant MJ, Booth A (2009) A typology of reviews: an analysis of 14 review types and associated methodologies. Health Inf Libr J 26(2):91–108. https://doi.org/10.1111/j.1471-1842.2009.00848.x

Kerr NL (1998) HARKing: hypothesizing after the results are known. Personal Soc Psychol Rev 2(3):196–217. https://doi.org/10.1207/s15327957pspr0203_4

Lipowski EE (2008) Developing great research questions. Am J Health Syst Pharm 65(17):1667–1670. https://doi.org/10.2146/ajhp070276

Onwuegbuzie AJ, Leech NL (2006) Linking research questions to mixed methods data analysis procedures. Qual Rep 11(3):474–498

Rau JL (2004) Searching the literature and selecting the right references. Respir Care 49(10):1242–1245

Richardson WS, Wilson MC, Nishikawa J, Hayward RS (1995) The well-built clinical question: a key to evidence-based decisions. ACP J Club 123(3):A12–A13

Yamada Y (2018) How to crack pre-registration: toward transparent and open science. Front Psychol 9:1831. https://doi.org/10.3389/fpsyg.2018.01831

Hulley SB. Designing clinical research: Lippincott Williams & Wilkins; 2007.

University of Oxford. The Centre for Evidence-Based Medicine .

HealthKnowledge .

Cummings SR, Browner WS, Hulley SB. Conceiving the research question and developing the study plan. In: Designing Clinical Research. 4th ed. Philadelphia: Lippincott Williams and Wilkins; 2013. p. 14–22.

Rights and permissions

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Reprints and permissions

Copyright information

© 2023 The Author(s)

About this chapter

Buljan, I. (2023). Research Procedures. In: Marusic, A. (eds) A Guide to Responsible Research. Collaborative Bioethics, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-031-22412-6_3

Download citation

DOI : https://doi.org/10.1007/978-3-031-22412-6_3

Published : 28 March 2023

Publisher Name : Springer, Cham

Print ISBN : 978-3-031-22411-9

Online ISBN : 978-3-031-22412-6

eBook Packages : Biomedical and Life Sciences Biomedical and Life Sciences (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

• Resources Home 🏠
• Try SciSpace Copilot
• Search research papers
• Add Copilot Extension
• Try AI Detector
• Try Paraphraser
• Try Citation Generator
• April Papers
• June Papers
• July Papers

Here's What You Need to Understand About Research Methodology

Table of Contents

Research methodology involves a systematic and well-structured approach to conducting scholarly or scientific inquiries. Knowing the significance of research methodology and its different components is crucial as it serves as the basis for any study.

Typically, your research topic will start as a broad idea you want to investigate more thoroughly. Once you’ve identified a research problem and created research questions , you must choose the appropriate methodology and frameworks to address those questions effectively.

What is the definition of a research methodology?

Research methodology is the process or the way you intend to execute your study. The methodology section of a research paper outlines how you plan to conduct your study. It covers various steps such as collecting data, statistical analysis, observing participants, and other procedures involved in the research process

The methods section should give a description of the process that will convert your idea into a study. Additionally, the outcomes of your process must provide valid and reliable results resonant with the aims and objectives of your research. This thumb rule holds complete validity, no matter whether your paper has inclinations for qualitative or quantitative usage.

Studying research methods used in related studies can provide helpful insights and direction for your own research. Now easily discover papers related to your topic on SciSpace and utilize our AI research assistant, Copilot , to quickly review the methodologies applied in different papers.

The need for a good research methodology

While deciding on your approach towards your research, the reason or factors you weighed in choosing a particular problem and formulating a research topic need to be validated and explained. A research methodology helps you do exactly that. Moreover, a good research methodology lets you build your argument to validate your research work performed through various data collection methods, analytical methods, and other essential points.

Just imagine it as a strategy documented to provide an overview of what you intend to do.

While undertaking any research writing or performing the research itself, you may get drifted in not something of much importance. In such a case, a research methodology helps you to get back to your outlined work methodology.

A research methodology helps in keeping you accountable for your work. Additionally, it can help you evaluate whether your work is in sync with your original aims and objectives or not. Besides, a good research methodology enables you to navigate your research process smoothly and swiftly while providing effective planning to achieve your desired results.

What is the basic structure of a research methodology?

Usually, you must ensure to include the following stated aspects while deciding over the basic structure of your research methodology:

1. Your research procedure

Explain what research methods you’re going to use. Whether you intend to proceed with quantitative or qualitative, or a composite of both approaches, you need to state that explicitly. The option among the three depends on your research’s aim, objectives, and scope.

2. Provide the rationality behind your chosen approach

Based on logic and reason, let your readers know why you have chosen said research methodologies. Additionally, you have to build strong arguments supporting why your chosen research method is the best way to achieve the desired outcome.

3. Explain your mechanism

The mechanism encompasses the research methods or instruments you will use to develop your research methodology. It usually refers to your data collection methods. You can use interviews, surveys, physical questionnaires, etc., of the many available mechanisms as research methodology instruments. The data collection method is determined by the type of research and whether the data is quantitative data(includes numerical data) or qualitative data (perception, morale, etc.) Moreover, you need to put logical reasoning behind choosing a particular instrument.

4. Significance of outcomes

The results will be available once you have finished experimenting. However, you should also explain how you plan to use the data to interpret the findings. This section also aids in understanding the problem from within, breaking it down into pieces, and viewing the research problem from various perspectives.

5. Reader’s advice

Anything that you feel must be explained to spread more awareness among readers and focus groups must be included and described in detail. You should not just specify your research methodology on the assumption that a reader is aware of the topic.  

All the relevant information that explains and simplifies your research paper must be included in the methodology section. If you are conducting your research in a non-traditional manner, give a logical justification and list its benefits.

6. Explain your sample space

Include information about the sample and sample space in the methodology section. The term "sample" refers to a smaller set of data that a researcher selects or chooses from a larger group of people or focus groups using a predetermined selection method. Let your readers know how you are going to distinguish between relevant and non-relevant samples. How you figured out those exact numbers to back your research methodology, i.e. the sample spacing of instruments, must be discussed thoroughly.

For example, if you are going to conduct a survey or interview, then by what procedure will you select the interviewees (or sample size in case of surveys), and how exactly will the interview or survey be conducted.

7. Challenges and limitations

This part, which is frequently assumed to be unnecessary, is actually very important. The challenges and limitations that your chosen strategy inherently possesses must be specified while you are conducting different types of research.

The importance of a good research methodology

You must have observed that all research papers, dissertations, or theses carry a chapter entirely dedicated to research methodology. This section helps maintain your credibility as a better interpreter of results rather than a manipulator.

A good research methodology always explains the procedure, data collection methods and techniques, aim, and scope of the research. In a research study, it leads to a well-organized, rationality-based approach, while the paper lacking it is often observed as messy or disorganized.

You should pay special attention to validating your chosen way towards the research methodology. This becomes extremely important in case you select an unconventional or a distinct method of execution.

Curating and developing a strong, effective research methodology can assist you in addressing a variety of situations, such as:

• When someone tries to duplicate or expand upon your research after few years.
• If a contradiction or conflict of facts occurs at a later time. This gives you the security you need to deal with these contradictions while still being able to defend your approach.
• Gaining a tactical approach in getting your research completed in time. Just ensure you are using the right approach while drafting your research methodology, and it can help you achieve your desired outcomes. Additionally, it provides a better explanation and understanding of the research question itself.
• Documenting the results so that the final outcome of the research stays as you intended it to be while starting.

Instruments you could use while writing a good research methodology

As a researcher, you must choose which tools or data collection methods that fit best in terms of the relevance of your research. This decision has to be wise.

There exists many research equipments or tools that you can use to carry out your research process. These are classified as:

a. Interviews (One-on-One or a Group)

An interview aimed to get your desired research outcomes can be undertaken in many different ways. For example, you can design your interview as structured, semi-structured, or unstructured. What sets them apart is the degree of formality in the questions. On the other hand, in a group interview, your aim should be to collect more opinions and group perceptions from the focus groups on a certain topic rather than looking out for some formal answers.

In surveys, you are in better control if you specifically draft the questions you seek the response for. For example, you may choose to include free-style questions that can be answered descriptively, or you may provide a multiple-choice type response for questions. Besides, you can also opt to choose both ways, deciding what suits your research process and purpose better.

c. Sample Groups

Similar to the group interviews, here, you can select a group of individuals and assign them a topic to discuss or freely express their opinions over that. You can simultaneously note down the answers and later draft them appropriately, deciding on the relevance of every response.

d. Observations

If your research domain is humanities or sociology, observations are the best-proven method to draw your research methodology. Of course, you can always include studying the spontaneous response of the participants towards a situation or conducting the same but in a more structured manner. A structured observation means putting the participants in a situation at a previously decided time and then studying their responses.

Of all the tools described above, it is you who should wisely choose the instruments and decide what’s the best fit for your research. You must not restrict yourself from multiple methods or a combination of a few instruments if appropriate in drafting a good research methodology.

Types of research methodology

A research methodology exists in various forms. Depending upon their approach, whether centered around words, numbers, or both, methodologies are distinguished as qualitative, quantitative, or an amalgamation of both.

1. Qualitative research methodology

When a research methodology primarily focuses on words and textual data, then it is generally referred to as qualitative research methodology. This type is usually preferred among researchers when the aim and scope of the research are mainly theoretical and explanatory.

The instruments used are observations, interviews, and sample groups. You can use this methodology if you are trying to study human behavior or response in some situations. Generally, qualitative research methodology is widely used in sociology, psychology, and other related domains.

2. Quantitative research methodology

If your research is majorly centered on data, figures, and stats, then analyzing these numerical data is often referred to as quantitative research methodology. You can use quantitative research methodology if your research requires you to validate or justify the obtained results.

In quantitative methods, surveys, tests, experiments, and evaluations of current databases can be advantageously used as instruments If your research involves testing some hypothesis, then use this methodology.

3. Amalgam methodology

As the name suggests, the amalgam methodology uses both quantitative and qualitative approaches. This methodology is used when a part of the research requires you to verify the facts and figures, whereas the other part demands you to discover the theoretical and explanatory nature of the research question.

The instruments for the amalgam methodology require you to conduct interviews and surveys, including tests and experiments. The outcome of this methodology can be insightful and valuable as it provides precise test results in line with theoretical explanations and reasoning.

The amalgam method, makes your work both factual and rational at the same time.

Final words: How to decide which is the best research methodology?

If you have kept your sincerity and awareness intact with the aims and scope of research well enough, you must have got an idea of which research methodology suits your work best.

Before deciding which research methodology answers your research question, you must invest significant time in reading and doing your homework for that. Taking references that yield relevant results should be your first approach to establishing a research methodology.

Moreover, you should never refrain from exploring other options. Before setting your work in stone, you must try all the available options as it explains why the choice of research methodology that you finally make is more appropriate than the other available options.

You should always go for a quantitative research methodology if your research requires gathering large amounts of data, figures, and statistics. This research methodology will provide you with results if your research paper involves the validation of some hypothesis.

Whereas, if  you are looking for more explanations, reasons, opinions, and public perceptions around a theory, you must use qualitative research methodology.The choice of an appropriate research methodology ultimately depends on what you want to achieve through your research.

Frequently Asked Questions (FAQs) about Research Methodology

1. how to write a research methodology.

You can always provide a separate section for research methodology where you should specify details about the methods and instruments used during the research, discussions on result analysis, including insights into the background information, and conveying the research limitations.

2. What are the types of research methodology?

There generally exists four types of research methodology i.e.

• Observation
• Experimental
• Derivational

3. What is the true meaning of research methodology?

The set of techniques or procedures followed to discover and analyze the information gathered to validate or justify a research outcome is generally called Research Methodology.

4. Where lies the importance of research methodology?

Your research methodology directly reflects the validity of your research outcomes and how well-informed your research work is. Moreover, it can help future researchers cite or refer to your research if they plan to use a similar research methodology.

You might also like

Consensus GPT vs. SciSpace GPT: Choose the Best GPT for Research

Literature Review and Theoretical Framework: Understanding the Differences

Using AI for research: A beginner’s guide

• Privacy Policy

Home » Research Process – Steps, Examples and Tips

Research Process – Steps, Examples and Tips

Table of Contents

Research Process

Definition:

Research Process is a systematic and structured approach that involves the collection, analysis, and interpretation of data or information to answer a specific research question or solve a particular problem.

Research Process Steps

Research Process Steps are as follows:

Identify the Research Question or Problem

This is the first step in the research process. It involves identifying a problem or question that needs to be addressed. The research question should be specific, relevant, and focused on a particular area of interest.

Conduct a Literature Review

Once the research question has been identified, the next step is to conduct a literature review. This involves reviewing existing research and literature on the topic to identify any gaps in knowledge or areas where further research is needed. A literature review helps to provide a theoretical framework for the research and also ensures that the research is not duplicating previous work.

Formulate a Hypothesis or Research Objectives

Based on the research question and literature review, the researcher can formulate a hypothesis or research objectives. A hypothesis is a statement that can be tested to determine its validity, while research objectives are specific goals that the researcher aims to achieve through the research.

Design a Research Plan and Methodology

This step involves designing a research plan and methodology that will enable the researcher to collect and analyze data to test the hypothesis or achieve the research objectives. The research plan should include details on the sample size, data collection methods, and data analysis techniques that will be used.

Collect and Analyze Data

This step involves collecting and analyzing data according to the research plan and methodology. Data can be collected through various methods, including surveys, interviews, observations, or experiments. The data analysis process involves cleaning and organizing the data, applying statistical and analytical techniques to the data, and interpreting the results.

Interpret the Findings and Draw Conclusions

After analyzing the data, the researcher must interpret the findings and draw conclusions. This involves assessing the validity and reliability of the results and determining whether the hypothesis was supported or not. The researcher must also consider any limitations of the research and discuss the implications of the findings.

Communicate the Results

Finally, the researcher must communicate the results of the research through a research report, presentation, or publication. The research report should provide a detailed account of the research process, including the research question, literature review, research methodology, data analysis, findings, and conclusions. The report should also include recommendations for further research in the area.

Review and Revise

The research process is an iterative one, and it is important to review and revise the research plan and methodology as necessary. Researchers should assess the quality of their data and methods, reflect on their findings, and consider areas for improvement.

Ethical Considerations

Throughout the research process, ethical considerations must be taken into account. This includes ensuring that the research design protects the welfare of research participants, obtaining informed consent, maintaining confidentiality and privacy, and avoiding any potential harm to participants or their communities.

Dissemination and Application

The final step in the research process is to disseminate the findings and apply the research to real-world settings. Researchers can share their findings through academic publications, presentations at conferences, or media coverage. The research can be used to inform policy decisions, develop interventions, or improve practice in the relevant field.

Research Process Example

Following is a Research Process Example:

Research Question : What are the effects of a plant-based diet on athletic performance in high school athletes?

Step 1: Background Research Conduct a literature review to gain a better understanding of the existing research on the topic. Read academic articles and research studies related to plant-based diets, athletic performance, and high school athletes.

Step 2: Develop a Hypothesis Based on the literature review, develop a hypothesis that a plant-based diet positively affects athletic performance in high school athletes.

Step 3: Design the Study Design a study to test the hypothesis. Decide on the study population, sample size, and research methods. For this study, you could use a survey to collect data on dietary habits and athletic performance from a sample of high school athletes who follow a plant-based diet and a sample of high school athletes who do not follow a plant-based diet.

Step 4: Collect Data Distribute the survey to the selected sample and collect data on dietary habits and athletic performance.

Step 5: Analyze Data Use statistical analysis to compare the data from the two samples and determine if there is a significant difference in athletic performance between those who follow a plant-based diet and those who do not.

Step 6 : Interpret Results Interpret the results of the analysis in the context of the research question and hypothesis. Discuss any limitations or potential biases in the study design.

Step 7: Draw Conclusions Based on the results, draw conclusions about whether a plant-based diet has a significant effect on athletic performance in high school athletes. If the hypothesis is supported by the data, discuss potential implications and future research directions.

Step 8: Communicate Findings Communicate the findings of the study in a clear and concise manner. Use appropriate language, visuals, and formats to ensure that the findings are understood and valued.

Applications of Research Process

The research process has numerous applications across a wide range of fields and industries. Some examples of applications of the research process include:

• Scientific research: The research process is widely used in scientific research to investigate phenomena in the natural world and develop new theories or technologies. This includes fields such as biology, chemistry, physics, and environmental science.
• Social sciences : The research process is commonly used in social sciences to study human behavior, social structures, and institutions. This includes fields such as sociology, psychology, anthropology, and economics.
• Education: The research process is used in education to study learning processes, curriculum design, and teaching methodologies. This includes research on student achievement, teacher effectiveness, and educational policy.
• Healthcare: The research process is used in healthcare to investigate medical conditions, develop new treatments, and evaluate healthcare interventions. This includes fields such as medicine, nursing, and public health.
• Business and industry : The research process is used in business and industry to study consumer behavior, market trends, and develop new products or services. This includes market research, product development, and customer satisfaction research.
• Government and policy : The research process is used in government and policy to evaluate the effectiveness of policies and programs, and to inform policy decisions. This includes research on social welfare, crime prevention, and environmental policy.

Purpose of Research Process

The purpose of the research process is to systematically and scientifically investigate a problem or question in order to generate new knowledge or solve a problem. The research process enables researchers to:

• Identify gaps in existing knowledge: By conducting a thorough literature review, researchers can identify gaps in existing knowledge and develop research questions that address these gaps.
• Collect and analyze data : The research process provides a structured approach to collecting and analyzing data. Researchers can use a variety of research methods, including surveys, experiments, and interviews, to collect data that is valid and reliable.
• Test hypotheses : The research process allows researchers to test hypotheses and make evidence-based conclusions. Through the systematic analysis of data, researchers can draw conclusions about the relationships between variables and develop new theories or models.
• Solve problems: The research process can be used to solve practical problems and improve real-world outcomes. For example, researchers can develop interventions to address health or social problems, evaluate the effectiveness of policies or programs, and improve organizational processes.
• Generate new knowledge : The research process is a key way to generate new knowledge and advance understanding in a given field. By conducting rigorous and well-designed research, researchers can make significant contributions to their field and help to shape future research.

Tips for Research Process

Here are some tips for the research process:

• Start with a clear research question : A well-defined research question is the foundation of a successful research project. It should be specific, relevant, and achievable within the given time frame and resources.
• Conduct a thorough literature review: A comprehensive literature review will help you to identify gaps in existing knowledge, build on previous research, and avoid duplication. It will also provide a theoretical framework for your research.
• Choose appropriate research methods: Select research methods that are appropriate for your research question, objectives, and sample size. Ensure that your methods are valid, reliable, and ethical.
• Be organized and systematic: Keep detailed notes throughout the research process, including your research plan, methodology, data collection, and analysis. This will help you to stay organized and ensure that you don’t miss any important details.
• Analyze data rigorously: Use appropriate statistical and analytical techniques to analyze your data. Ensure that your analysis is valid, reliable, and transparent.
• I nterpret results carefully : Interpret your results in the context of your research question and objectives. Consider any limitations or potential biases in your research design, and be cautious in drawing conclusions.
• Communicate effectively: Communicate your research findings clearly and effectively to your target audience. Use appropriate language, visuals, and formats to ensure that your findings are understood and valued.
• Collaborate and seek feedback : Collaborate with other researchers, experts, or stakeholders in your field. Seek feedback on your research design, methods, and findings to ensure that they are relevant, meaningful, and impactful.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Data Collection – Methods Types and Examples

Research Paper – Structure, Examples and Writing...

Research Project – Definition, Writing Guide and...

Critical Analysis – Types, Examples and Writing...

Research Recommendations – Examples and Writing...

Problem Statement – Writing Guide, Examples and...

Economics 145                                                                                                                 Prof. Yang

In Chapter 1, we covered the basic concepts of research in economics first by reviewing key terms in research and the roles of theory and data in the study of economics. We noted that the study of economics proceeds within the framework of scientific methods and we engaged in a general discussion of scientific method before moving on to a discussion of various terms and concepts within the scientific method. Clearly, learning about the scientific method and the basic concepts of the scientific method is essential to carry out research in economics. However, our discussion was general in nature and, basically, conceptual. While interesting, this kind of learning about the scientific method, and its basic concepts of research in general terms, offers little help in actually starting and successfully completing an economic research project. Thus, given the understanding of basic concepts of research, what we need to learn now are the specifics of to start our research, and to do it.

When students take a research methods course, they usually learn about research procedures in general terms. When they attempt to apply what they have learned in the research methods course to a real research project, they arguably find that they are not adequately prepared to start and complete a meaningful project.

This outcome is at least in part due to our way of teaching students mainly by feeding information without requiring a deeper understanding the subject. This is also partly due to the fact that undergraduate economic education is organized largely by subject-matter packages. Economics undergraduate students typically take intermediate level micro and macro economic theories, several upper division field-specific courses, depending on their interests, as well as one or two quantitative economics courses. More often than not, economics students do not have an opportunity to take a research methods course. Students in upper division field-specific economics courses seldom have the opportunity to conduct any serious and meaningful research, except for the rare occasions to do an honor’s thesis with individualized assistance from faculty members. Consequently, with or without a research methods course, undergraduate students typically do not learn the subject well enough to apply their conceptual knowledge to finding answers in the context of applied and quantitative research. There is a crying need for a practical guide to conducting applied quantitative economic research. Meeting this need is the main motivation to write this book.

The procedures and steps to follow in research are well-known, and I present below a set of standard procedures for conducting applied quantitative economic research. At each step, I will explain you do, you do it, and finally you do it. But merely listing and describing each step at a time is not enough. What I am striving to accomplish is to integrate research problems, theory, and the analysis of data, and to show how and why decisions are being made throughout the research process. To assure comprehension, I will present discussions on research procedures by working with specific examples.

Research is an orderly and systematic procedure, and this procedure may be presented sequentially from the first step of problem definition through the final step of the writing-up of the final report. However, it is also essential to understand that the research process is essentially in the sense that each preceding and succeeding step feeds on each other.

The five major steps in any typical applied and quantitative economic research process are as follows:

1. Statement of Research Problem
2. Survey of Related Literature
3. Theoretical Model: Formulation of Hypothesis
4. Analysis of Data: Testing of Hypothesis
5. Write-up of Research Report

Research Problem

John Dewey

In starting research one obviously must first decide what problem to investigate. Clearly, therefore, a clear definition and statement of the research problem is the most important part of any research activity. When a research topic is given by your professor or supervisor there is no problem to choosing it. But the responsibility to choose research problem is yours, it becomes more daunting and difficult task. In this case, it will be first something your are interested. It is appropriate to choose a topic within a field which the student is familiar. But even if the student identifies a potential topic he or she is interested in and reasonably familiar with, it is still a difficult task to define and state the problem clearly and adequately. It takes a fair amount of mental struggle to understand the research issue thoroughly; and it takes at least an equal amount of effort to be able to write a succinct problem statement. The importance of a succinct problem statement cannot be too emphasized. Inadequate and incomplete delineation of the research problem results in wasting precious time in gathering materials, and may also result in activities not directly related to the research problem.

A research problem may be disciplinary, subject-matter, or problem-solving oriented and, indeed, combination of the three.

Finally, the requirements of a good research problem include , , and .

B. The research problem has to be specific so that it can be addressed given the constraints of time and budget. Broad and general research problems are neither meaningful nor manageable. If one wishes to study the Pacific Ocean, it is a too broad and wide topic. Research has to focus on a specific aspect of the Pacific Ocean.

C. Finally, the research problem has be "manageable" because it has to be finished within the assigned time and budget constraint.

To define the research problem adequately, however, requires substantial knowledge of the problem itself. One way of obtaining this knowledge is to read background materials. Text-books or, better, a recent review article on the topic are often useful as starting points, since they give a balanced summary of present knowledge, and also provide useful references. But as you study these books and articles by other investigators, you need to evaluate these previous works in light of your own research problem. As you read, you must always ask yourself what you are trying to find out. If you cannot state clearly what it is you want to find out, it is obvious that you are going to waste a lot of time before you settle on the specific research problem.

The problem or problem area to research depends on your interest, experience, and career goals. But once you have a research problem or problem area, a decision must be made to focus on one or two specific aspects of the problem. To accomplish this, one has to be familiar with the area; to actually know quite bit about it, fact. How does one gain enough knowledge to embark on serious research? If you are familiar with the subject matter, it is largely a matter of intuition and insight for you to come up with the specific problem. If not, you have to read background materials to gain this knowledge. Suppose, for instance, that you are interested in studying the reasons for the continual increase over time in health care expenditure in the U.S. Searching through the literature, you will find that Chapter 4, "The Economics of Health Care" in the and two symposia articles on health economics and health care reform in (Summer 1992 and Summer 1994) provide adequate background.

As you gain knowledge on the chosen topic, it is also helpful to decide what specific research problems and issues you are interested in. Specifically, are you interested in establishing a comparison? To find a cause or an effect? With the cause-effect relationship in the health care expenditure in mind, one can choose to study the roles of third-party payment mechanism through insurance, of technology, or of government. How about measuring the magnitude of some interesting and important issue, such as measuring the welfare cost of national health care insurance? There are many more interesting questions and issues to be investigated within the general rubric of health care expenditure in the U.S.

The formulation of the statement of the problem usually requires the following two steps.

1. Overview of the problem
2. Narrowing down to specific aspect(s) of the problem

One good way to start the problem statement is to present the problem in an overview. The reason why researchers usually start with presenting the problem in the context of an overview is to present the problem in perspective. This way, the reader is introduced to terminology, definitions, and the relationship under consideration, as well as the relationship of the topic to related questions and fields.

An example of good overview is quoted below. [See Craig S. Hakkio’s article entitled "Is Purchasing Power Parity a Useful Guide to the Dollar?" in pp. 37-51]

(PPP) is a measure of the dollar’s equilibrium value - the exchange rate toward which the dollar moves over time. Because the value of the dollar is currently below its PPP value, academic and business economists

use the concept of purchasing power parity to advocates argue that the dollar is undervalued and therefore likely to rise.

Other economists acknowledge that PPP may help forecast the value of the dollar over the long run but doubt its usefulness as a short-term guide. They often cite the 1970s, when the dollar frequently strayed from its PPP value and sometimes took years to return. They also note that economic and political forces regularly buffet the dollar, keeping its value away from equilibrium. Thus, even though the dollar is currently below its PPP value, these economists maintain there is no guarantee it will rise in value in the near term.

The research problem in Hakkio’s paper is to evaluate the purchasing power parity as a guide to future direction of the US dollar. In his overview of the problem, Hakkio first defines the term "purchasing power parity" and presents two alternative views to the use of PPP as a guide to the future direction of the US dollar. According to one view, namely the purchasing power parity view, the dollar is likely to rise because current value of the dollar is below its PPP value. According to other view, however, there is no guarantee that the dollar value will rise soon, because the PPP view is known to hold only in the long run. What he skillfully accomplished in the two short paragraphs are: 1) to clearly define the problem, 2) to define the key term, and 3) to present two alternative views on the future direction of the dollar.

After having presented an overview of the problem, the researcher usually focuses in one or two specific issues or aspects of the problem. In the case of Hakkio’s paper, however, he moves directly on to the objective statement because there is no further need to narrow it down. His objective statement and a general outline of his paper is presented in the third paragraph of his paper and is quoted below:

"This article argues that PPP is a useful guide to the dollar in the long run

and - to a lesser extent - in the short run. The first section of the article defines

the concept and discusses why most economists believe it is a useful long-run guide. The second section shows the dollar generally moves toward its PPP

the measure says little about whether the dollar will rise in the near term."

Suppose a student proposes to write a research paper with the title "Trade Deficit", "Budget Deficit", "Exchange Rate", or "U.S. Banking". One can tell immediately see that each of these subjects is too broad and general to be a meaningful research topic. How does one go about narrowing down these broad and general topics to a manageable research problem? To learn how to do that, we need to learn to reduce the topic to manageable size by limiting it by time, space, or geographically a certain aspect of the problem.

With the trade deficit as an example, one may limit it to "U.S. Trade Deficit" or, more specifically, to "U.S.-Japan Trade deficit". Similarly, it may limited to a specific time period as "Persistent U.S. Trade Deficit During 1980s". Indeed, it might be a combination of two aspects, like "U.S.-Japan Trade Deficit during the 1980s". Or one might decide to focus on one particular aspect, such as the major determinants of the U.S. trade deficit or the effect of U.S. trade deficits on the exchange value of the U.S. dollar..

With the budget deficit in mind, one might similarly limit it to "U.S. budget Deficits" or to the well known issue of the twin deficits between the "U.S. Budget Deficits and Trade Deficits". Or perhaps one might decide to limit the topic to the impact of a reduction in U.S. budget deficit on the value of the U.S. dollar..

With the U.S. banking in mind, one may ask whether or not U.S. Banking is declining". If the answer is positive, one can further examine why U.S. banking is not declining

To illustrate further, consider health care expenditure in the United States over the past several decades. We see from the data that U.S. health case expenditure has steadily increased and that it has increased faster than other categories of expenditure. One can raise a number of important and meaningful questions with respect to heath care spending vis a vis other categories of expenditures. One can ask: Why are health care expenditures in the U.S. increasing over time? Or why has health care expenditure in the U.S. increased substantially faster than other expenditure categories? With this general question in mind, one may pose a more specific question like: Why has the share of health care expenditure of total U.S. consumer expenditure increased during the 1960-95 period? To answer this question adequately, one has to examine the major determinants of health care expenditure in the U.S. Relating to this question, one might pursue the financing side of health care expenditure. To restrain this rapid growth, many health care reform proposals deal with the question of how to finance health care expenditure.

For the last illustration of narrowing down to specific aspect of the research problem, let us take up the recent surge in gasoline prices in California. One can raise a number of important and useful questions about this topic. First, why did gasoline price increase so dramatically in the first part of 1996 compared with those in the previous five years? Is it due to higher gasoline taxes or is it due to the "rigging" of gasoline price by big oil companies? Would the proposed sale of the navy petroleum reserve announced by President Clinton help lower gasoline prices or is it merely a political ploy? A second question might be: Why, in recent years, are gasoline prices in California substantially higher than in the rest of U.S.? What, if any, is the role of environmental costs in California’s recent gasoline price hike?

When you have finally defined the research problem adequately, the next task is to state the problem clearly. We often say that it is necessary to state the research problem Success depends largely on one’s ability in organization and in technical writing. Since the writing aspect of research activity is not the primary focus of this chapter, one may refer to publications devoted to this aspect. Two good resources which many students find useful are: by John S. Harris and Reed H. Blake (Nelson-Hall) and by William Strunk Jr. and E.B. White. (Macmillan)

 

Review of Related Literature

Almost all research topics have been studied by other researchers. Nothing under the sun is new, as one verse in the Old Testament says. As you consider some problem or research issue, you can be almost assured that somebody else has studied the problem previously. Therefore, the obvious fact is: The more one knows about what was studied earlier, the better the researcher can approach and solve the problem. What then is the purpose of this review? Obviously, it is to assist you in attacking the problem you choose to study. As you review previous research done in related areas, directly and indirectly, you will be better prepared to handle the problem.

The benefits of a review of related literature are many and a few of them are listed here:

How should one go about preparing "Review of Related Literature". While there is no one way of doing this, following the several steps listed below will be helpful.

Before you begin a review of related literature, you first need to find out what has been done on the topic you are interested in. How does one find out what has been done on a particular topic? There are several ways to do this. An increasingly popular method to do a literature search is to tap into two popular electronic data bases. The first one is the compact disc search This handy and economical search software consists of one compact disc and user manual. It has a complete list of all the articles and working papers listed in the , an official publication of the American Economic Association. To use it, however, requires access to an EconLit compact disc and a personal computer with a compact disc drive. The second electronic source is one of several search procedures available on Internet.

When using electronic search procedures, it is important to type in two or three key words to facilitate your search. If the word chosen for your search is broad and general, these search procedures will give you literately few hundreds citations and sorting through so many is not efficient. Thus, when you search previous studies directly or indirectly related to your chosen topic, type only in two or three few key words directly related to your topic.

After your electronic search procedure has produced the necessary citations, you often need to select only those citations which have a direct bearing on your topic. It is a survey of literature, and only the researcher can make the determination of the degree of relatedness.

If you do not have access to an electronic search, you have to rely on manual search procedures through the library. You can go to library and find current and old issues of the This publication lists articles published by journal and by field. It would be advisable to start from a current issue and see if one can find articles and other publications on the topic. Then go to older issues and do the same by writing down citations of all related works.

Having identified all important previous studies which have a direct or indirect bearing on your topic, the next step of writing the review of related literature is to make a plan of how you want to organize your literature review. Without a plan, the literature survey easily becomes "Smith did this; Jones did that" by merely listing what they have done. In making a literature review plan, good advice is always to be mindful of the research problem itself. Without a clear understanding of the research issue and problem, one cannot make a plan for a good literature survey. Any serious attempt to understand the different aspects of each previous work requires substantial effort before one can see clearly how seemingly unrelated work fit together. Only then one can develop a good plan. In organizing your related literature, it is useful first to identify one or two major, or classic, studies. Then you can see the contributions of other works in relation to what was already done or not done in the major works. There may be situations where, in some cases, it is difficult to classify previous works by "major or minor" categories, because the contributions of each work are similar. In this case, it would be reasonable to review previous works by commenting only on the different aspects or focus of these works. One way or another, understanding the main research problem and the contributions of previous works is essential before one can make a plan for literature survey. Finally, it is always a good idea to see how each prior study is related to the problem you are focusing on. The importance of the literature cannot be emphasized too much.

Remember what you are doing is a review of literature. This means that you are presenting your own discussion of existing literature. Because of this, it is to avoid direct quotation. Paraphrasing or restating in your own words is the way to do it. What you are doing is evaluating prior work to shed light on your study.

 

"Science is built with facts as a house is built with stones, but a collection of facts is no more science than a heap of stones is a house." -Jules Henri Poincare

"A person "can stare stupidly at phenomena; but in the absence of imagination they will not connect themselves together in any rational way". -C.S. Peirce

 

After the researcher has chosen a problem and has ascertained what investigations have already been done on it, the next step is to conceptualize the problem.

1. is Conceptual Framework and do we need one?

The conceptual or theoretical framework is the process of conceptualizing the problem by reasoning, recognizing, and synthesizing the problem. It is an abstract process in which the researcher identifies the central versus the peripheral, or the primary versus the secondary components of the problem, and understands how these components fit together.

The economic world is incredibly complex. The economist’s task of explaining the behavior of people, institutions and their interactions is, therefore, a very difficult task. To understand why and how it works, we need, as in all other fields of science, to abstract from reality. Abstraction requires ignoring many details in order to focus on the most important elements in order to understand the functioning of a complex phenomenon. Theorizing is a combined effort of abstracting (from details) and connecting (the essential components), and . As Pierce said, one can stupidly stare at the facts and data. Only with theory, one can begin to attempt to understand it. The process of arriving at a logical structure for organizing and analyzing the problem is, in fact, a deliberate simplification (abstraction) of the factual relationship to explain how those relationships work. So the theory is an of the mechanism behind observed phenomena.

A couple of examples: 1) road map; 2) ocean waves on the surface as facts. Undercurrent and forces below. Focusing on waves make you dizzy with no understanding of why and how’s of pattern of ocean waves. 3) gasoline price and shift in the demand and supply.

Therefore, the first role of conceptualization is to provide a logical structure for organizing and analyzing the problem. The second role of conceptualization is to lead to a hypothesis, which in turn leads to the testing of the hypothesis. Hypotheses are the results of the conceptualization of the problem. One definition: An hypothesis is a tentative assertion that is subject to testing. "As a tentative assertion, it can take the form of a simple proposition of an expected outcome or an assertion of a relationship, or relationships, between or among forces, variables, or events". [Eldridge, p. 136]

:

2) to Star and Develop the Conceptual Framework

How does one get started on the conceptual framework? Source materials for developing the conceptual framework for your research come from existing theories. You will recall that the research process is in the sense that the knowledge obtained in each stage feed on each other from the problem statement, the survey of related literature, and the conceptual framework.

From the review of related literature, one must first existing theory or theories on the problem. Second, when there are competing theories, one has to a particular theory suitable to the problem. Third, and finally, one has to the chosen theory to solve the problem.

To illustrate how to start and develop a conceptual framework, it is best to work with a specific topic. Suppose we are interested in the relationship between transit fares in Sacramento, California and the revenue that the transit system takes in. Specifically, let our question be whether or not a hike in fares, say from a current $1.25 to $1.50, would increase revenue.

In organizing and analyzing our problem to answer that question, we will develop a conceptual framework or, as the economist calls it, build a model. From our knowledge from the micro-economics principles course, we learned that total fare collection (total revenue) is equal to the average fare times the number of rides. In an equational form, this relationship can be stated as:

Equation (1) helps us organize our thinking about the two key variables, namely the fare and number of rides, in the determination of the total revenue. The fare is under the control of and is set by the Sacramento Regional Transit Authority. However, the number rides depends on the fare. The problem is to know how the number of rides will be affected by proposed fare hike by the Transit Authority. Or, more broadly, the question is what determines the number of rides. The economist’s way of answering this question is to view the number of rides as depending on the consumer’s decision to choose between taking transit and alternative transportation modes. The choice of transportation mode is basically an economic decision based on the relative cost and convenience of alternative means of transportation. Once viewed this way, one can see that this is the demand for regional transit.

From the theory of demand, we know that the number of rides, or using the economist’s term, the quantity of transit rides demanded, depends first on fare and the cost of alternative mode of transportation, as well as consumer income. Formally, we can write:

The next step is then to combine the equations (1) and (2) to have the model of total revenue from fare collection. Combining equations (1) and (2) yields:

Total Subway Revenue = fare x quantity of transit ride demanded

We now have a complete theoretical model of transit revenue with the key determining factors. However, one may note that the model specified above is a simplified description of the process involved when compared with real world complexities. It is obvious that important explanatory variables such as parking in downtown, the frequency and quality of service of regional transit and so forth, have been omitted. So the natural question is what is the "right" degree of abstraction. But there is no such thing as one right level of abstraction for all analytical purposes. The proper degree of abstraction obviously depends on the objective of the analysis at hand.

Once the theoretical model is specified, we need to evaluate the model qualitatively. An increase in the fare is expected to do two things. First, an increase in fare, holding constant all factors other than fare, tends to increase revenue. But second, as the theory of demand tells us, an increase in transit fare is expected to reduce the number of transit rides demanded, holding constant other factors, namely taxi cab fare, cost of owning automobile driving, and consumer income. The prediction of a negative relationship between the subway ride demanded and subway fare would make sense intuitively. But to really understand and an increase in the transit fare usually leads to a reduction in the transit rides demanded, one has to go beyond principles of economics and dig into microeconomics at intermediate level. There we will learn that there are income and substitution effects associated with an increase in subway fare, and that the combined income and substitution effects cause transit rides demanded to fall as a result of the increase in its fare. [See for instance, Nicholson’s Chapter 4, 1994]. Since these two effects work in opposite directions, it is not a priori clear whether the increase in fare will lead to an increase in total revenue or not. There are three possibilities: Total revenue may increase, remain the same, or decrease, all depending on the price elasticity of demand of ridership.

What does the theory of demand tells us about expected impact of an increase in cost of two alternative means of transportation? Theory tells us that an increase in taxi fare and cost of automobile driving, given the transit fare, will raise transit rides demanded because transit ride will become relatively cheaper (through the substitution effect). But how about the effect of an increase in consumer income on transit rides demanded? The theory of demand tells us that whether or not an increase in consumer income will raise or reduce transit rides demanded depends on whether consumers perceive transit rides as an inferior good or as a luxury good.

Now we realize that the theory of demand will provide answers only . That is, the number of transit rides demanded will rise or fall, if such and such conditions are satisfied. But theory of demand will not and cannot provide answers, which is necessary to answer our initial question of whether or not a hike in transit fare would increase transit revenue. What the theory does is to conceptualize the problem to provide a logical structure for organizing and analyzing the problem, and it can predict the direction of change of a change in determining factors only . What we need is a quantitative and empirical answer. The next section is devoted to the discussion of the formulation of an empirical model and its estimation with real-world data, as well as testing of hypotheses.

Empirical analysis covers a wide range of activity of measurement, estimation, and verification of phenomena under consideration. Since it covers a broad range of activity, it is difficult to present discussion of general rules to follow in conducting empirical analysis. But it seems reasonable to classify various empirical analysis into two types of empirical analysis: descriptive empirical analysis and cause-effect analysis which involves estimation and the testing of hypothesis. First, we will present discussions of how to conduct a descriptive empirical analysis with an example.

A descriptive empirical analysis is based on data analysis usually consisting of descriptive statistics and other quantitative measures in analyzing a particular issue(s) or question(s). It does not involve the statistical estimation of relationship and the testing of the hypothesis, as is done in the case of analysis of an assumed cause-effect relationship.

A descriptive empirical analysis may also involve and of the extent and degree of a certain phenomenon

To illustrate how to conduct a descriptive empirical analysis, consider a well-known macroeconomic issue of the trade-off between inflation and unemployment. Some background on this issue may be useful. During the 1950s and 1960s, many empirical studies examined inflation and unemployment data for numerous countries and time periods; and in many cases finding a negative relationship between unemployment and inflation. This negative empirical relationship between unemployment and inflation is known as the In the following decades, however, this relationship between unemployment and inflation failed to hold. In the latter part of the 1960s and early 1970s some economists, notably Milton Friedman and Edmund Phelps, question the logic of the Phillips curve. They argued on theoretical ground that we should not expect a stable relationship between inflation and unemployment. Rather, a stable negative relationship should exist between inflation and the unemployment rate.

Incorporating the negative relationship between unanticipated inflation and cyclical unemployment, we may write

- = )

where h refers to a positive number that measures the strength of the relationship between unanticipated inflation and cyclical unemployment. The latter is defined as the difference between the actual unemployment rate (u) and the natural rate of unemployment (u ). The above equation states that given the expected inflation rate, unanticipated inflation will be positive when the cyclical unemployment rate is negative, negative when cyclical unemployment is positive, and zero when cyclical unemployment is zero.

With the concept of a trade-off between the unemployment rate and the inflation rate, policy makers try to gauge the amount of slack in the economy in formulating monetary policy. When the economy’s resources are not pushed beyond capacity levels, inflation tends to remain under control. But when the economy’s resources are pushed to or beyond the capacity level, then inflation is expected to surge. In assessing the capacity in the labor markets, the natural rate of unemployment (NRU) is a key concept. The natural rate of unemployment is defined as that rate of unemployment at which there is no tendency for inflation to change.

With the above background in mind, let us now consider how Stuart Weiner at the Federal Reserve Bank of Kansas examined the relationship between unemployment and inflation in recent papers. First, he defined the concept of the natural rate of unemployment and provided general background information about the relationship between inflation and unemployment, as expected. He then presented two line graphs with data on the U.S. actual unemployment rate and natural unemployment rate for the 1959-1994 period. Since both unemployment rates are measured in percentages on the vertical axis, the vertical difference between them may be considered as the cyclical unemployment rate. In the second graph, Weiner first identified the four episodes of sustained increases in inflation during the period by shaded areas, and then superimposed a line graph of the cyclical unemployment rate.

Using the second graph, he then analyzed whether or not the increases in inflation were accompanied by the actual unemployment rate going below the natural unemployment rate. Examining the graph, he found that "at no times has the actual unemployment rate gone below the natural rate without the economy ultimately experiencing a rise in inflation".

In the discussion of policy implications of his findings, he made several observations. First, he noted that the lead time between a move below the natural rate and the eventual increase in inflation varies. Second, evaluating changes in the demographics and labor market conditions, he made several comments about why he believes that the natural unemployment rate would not be declining from the then currently estimated rate of 6.25%.

 

Regression analysis of Sacramento Regional transit demand

"Even the best scientific research is useless unless it is communicated to others" Ghebremdhin and Tweeten, 1988, p. 44

1. Introduction
2. Review of Literature
3. Theoretical Model
4. Empirical Analysis
5. Summary and Conclusions
6. Footnotes
7. Tables
8. Appendix
9. References

 

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Methodology

Research Design | Step-by-Step Guide with Examples

Published on 5 May 2022 by Shona McCombes . Revised on 20 March 2023.

A research design is a strategy for answering your research question  using empirical data. Creating a research design means making decisions about:

• Your overall aims and approach
• The type of research design you’ll use
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research aims and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, frequently asked questions.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities – start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative approach	Quantitative approach

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

Prevent plagiarism, run a free check.

Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types. Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

Type of design	Purpose and characteristics
Experimental
Quasi-experimental
Correlational
Descriptive

With descriptive and correlational designs, you can get a clear picture of characteristics, trends, and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analysing the data.

Type of design	Purpose and characteristics
Grounded theory
Phenomenology

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study – plants, animals, organisations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region, or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalise your results to the population as a whole.

Probability sampling	Non-probability sampling

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalise to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviours, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.

Questionnaires	Interviews

Observation methods

Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Quantitative observation

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

Field	Examples of data collection methods
Media & communication	Collecting a sample of texts (e.g., speeches, articles, or social media posts) for data on cultural norms and narratives
Psychology	Using technologies like neuroimaging, eye-tracking, or computer-based tasks to collect data on things like attention, emotional response, or reaction time
Education	Using tests or assignments to collect data on knowledge and skills
Physical sciences	Using scientific instruments to collect data on things like weight, blood pressure, or chemical composition

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected – for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are reliable and valid.

Operationalisation

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalisation means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in – for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced , while validity means that you’re actually measuring the concept you’re interested in.

Reliability	Validity

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample – by mail, online, by phone, or in person?

If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method, how will you avoid bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organising and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymise and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.

On their own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyse the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarise your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarise your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

Approach	Characteristics
Thematic analysis
Discourse analysis

There are many other ways of analysing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

McCombes, S. (2023, March 20). Research Design | Step-by-Step Guide with Examples. Scribbr. Retrieved 21 August 2024, from https://www.scribbr.co.uk/research-methods/research-design/

Is this article helpful?

Shona McCombes

• About English Wizard Online
• Parts of Speech, Phrases, Sentences
• Spelling/ Vocabulary
• Research/ Thesis Writing
• Lesson Plans
• Worksheets, eBooks, Workbooks, Printables
• Poems (Text and Video)
• Speech/ Pronunciation Lessons
• Classroom Ideas/ Materials
• Teacher's Comments
• Creative and Technical Writing
• Teaching Articles/ Teaching Strategies
• Essays/ Book and Movie Reviews
• Yearbook/ Student Publication/ Magazine/ Newsletter

Sample Methods of Research and Procedure in Chapter 3 (Thesis Writing)

No comments:

Post a comment.

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base
• How to Write a Thesis Statement | 4 Steps & Examples

How to Write a Thesis Statement | 4 Steps & Examples

Published on January 11, 2019 by Shona McCombes . Revised on August 15, 2023 by Eoghan Ryan.

A thesis statement is a sentence that sums up the central point of your paper or essay . It usually comes near the end of your introduction .

Your thesis will look a bit different depending on the type of essay you’re writing. But the thesis statement should always clearly state the main idea you want to get across. Everything else in your essay should relate back to this idea.

You can write your thesis statement by following four simple steps:

• Start with a question
• Write your initial answer
• Develop your answer
• Refine your thesis statement

Instantly correct all language mistakes in your text

Upload your document to correct all your mistakes in minutes

Table of contents

What is a thesis statement, placement of the thesis statement, step 1: start with a question, step 2: write your initial answer, step 3: develop your answer, step 4: refine your thesis statement, types of thesis statements, other interesting articles, frequently asked questions about thesis statements.

A thesis statement summarizes the central points of your essay. It is a signpost telling the reader what the essay will argue and why.

The best thesis statements are:

• Concise: A good thesis statement is short and sweet—don’t use more words than necessary. State your point clearly and directly in one or two sentences.
• Contentious: Your thesis shouldn’t be a simple statement of fact that everyone already knows. A good thesis statement is a claim that requires further evidence or analysis to back it up.
• Coherent: Everything mentioned in your thesis statement must be supported and explained in the rest of your paper.

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

The thesis statement generally appears at the end of your essay introduction or research paper introduction .

The spread of the internet has had a world-changing effect, not least on the world of education. The use of the internet in academic contexts and among young people more generally is hotly debated. For many who did not grow up with this technology, its effects seem alarming and potentially harmful. This concern, while understandable, is misguided. The negatives of internet use are outweighed by its many benefits for education: the internet facilitates easier access to information, exposure to different perspectives, and a flexible learning environment for both students and teachers.

You should come up with an initial thesis, sometimes called a working thesis , early in the writing process . As soon as you’ve decided on your essay topic , you need to work out what you want to say about it—a clear thesis will give your essay direction and structure.

You might already have a question in your assignment, but if not, try to come up with your own. What would you like to find out or decide about your topic?

For example, you might ask:

After some initial research, you can formulate a tentative answer to this question. At this stage it can be simple, and it should guide the research process and writing process .

Prevent plagiarism. Run a free check.

Now you need to consider why this is your answer and how you will convince your reader to agree with you. As you read more about your topic and begin writing, your answer should get more detailed.

In your essay about the internet and education, the thesis states your position and sketches out the key arguments you’ll use to support it.

The negatives of internet use are outweighed by its many benefits for education because it facilitates easier access to information.

In your essay about braille, the thesis statement summarizes the key historical development that you’ll explain.

The invention of braille in the 19th century transformed the lives of blind people, allowing them to participate more actively in public life.

A strong thesis statement should tell the reader:

• Why you hold this position
• What they’ll learn from your essay
• The key points of your argument or narrative

The final thesis statement doesn’t just state your position, but summarizes your overall argument or the entire topic you’re going to explain. To strengthen a weak thesis statement, it can help to consider the broader context of your topic.

These examples are more specific and show that you’ll explore your topic in depth.

Your thesis statement should match the goals of your essay, which vary depending on the type of essay you’re writing:

• In an argumentative essay , your thesis statement should take a strong position. Your aim in the essay is to convince your reader of this thesis based on evidence and logical reasoning.
• In an expository essay , you’ll aim to explain the facts of a topic or process. Your thesis statement doesn’t have to include a strong opinion in this case, but it should clearly state the central point you want to make, and mention the key elements you’ll explain.

If you want to know more about AI tools , college essays , or fallacies make sure to check out some of our other articles with explanations and examples or go directly to our tools!

• Ad hominem fallacy
• Post hoc fallacy
• Appeal to authority fallacy
• False cause fallacy
• Sunk cost fallacy

College essays

• Choosing Essay Topic
• Write a College Essay
• Write a Diversity Essay
• College Essay Format & Structure
• Comparing and Contrasting in an Essay

 (AI) Tools

• Grammar Checker
• Paraphrasing Tool
• Text Summarizer
• AI Detector
• Plagiarism Checker
• Citation Generator

A thesis statement is a sentence that sums up the central point of your paper or essay . Everything else you write should relate to this key idea.

The thesis statement is essential in any academic essay or research paper for two main reasons:

• It gives your writing direction and focus.
• It gives the reader a concise summary of your main point.

Without a clear thesis statement, an essay can end up rambling and unfocused, leaving your reader unsure of exactly what you want to say.

Follow these four steps to come up with a thesis statement :

• Ask a question about your topic .
• Write your initial answer.
• Develop your answer by including reasons.
• Refine your answer, adding more detail and nuance.

The thesis statement should be placed at the end of your essay introduction .

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

McCombes, S. (2023, August 15). How to Write a Thesis Statement | 4 Steps & Examples. Scribbr. Retrieved August 21, 2024, from https://www.scribbr.com/academic-essay/thesis-statement/

Is this article helpful?

Shona McCombes

Other students also liked, how to write an essay introduction | 4 steps & examples, how to write topic sentences | 4 steps, examples & purpose, academic paragraph structure | step-by-step guide & examples, what is your plagiarism score.