research procedure

Research Process Steps: Research Procedure and Examples

Table of Contents

Conducting academic and scientific research can be difficult and lengthy. It requires careful planning, critical thinking, attention to detail, and strong organizational skills to achieve impactful, real-world outcomes. Given the complexities involved, following a structured research process is essential for success.  

The research process is an organized method of gathering information and answering specific academic and scientific questions. It acts like an essential guide for researchers, right from the crucial first step of choosing a topic to the final stage of writing a comprehensive report.

This structured approach guarantees that every aspect of research is meticulously planned and executed. But what exactly are these research process steps, and how do they contribute to effective research?   

Understanding research process steps with examples  

The research process typically involves several steps, each building on the previous one. Below is a detailed breakdown of the research process steps –  

Step 1: Choosing a topic   

This first step is the most crucial part of the research process. It involves selecting a topic that is not only of interest to you but also contributes meaningfully to the academic field. Try to be original in your choice of topic, as it will make your research more engaging, and if you find yourself struggling to narrow down what to work on, consult your supervisor or mentor for guidance.  

Step 2: Conducting a literature review  

The next step in the research process would be to look for information on the subject. A preliminary search will give you an idea of the amount of literature available to support your study.

Once this is done, you can undertake a more detailed review of available literature to get a comprehensive overview of existing information on your topic. This will also help you to find gaps in knowledge and identify trends and findings that will shape and guide your research.  

For your literature review, you can use academic databases like JSTOR, Scopus, and Web of Science to gather information, build upon existing work, avoid repetition, and create a solid foundation for your research.

Additionally, you can also use local libraries and search engines like Google Scholar to find resources online. Remember to write down the citation details and the location of your source for future reference. 

Step 3: Defining the research question  

Once you have chosen a topic and done your basic reading on the subject, it is time to frame a research question. Developing a research question involves finding gaps or inconsistencies in existing research and creating specific, testable statements that will address these unresolved questions.

For example, researchers studying the aurora borealis may find that while there is significant research on what causes the phenomena, there are few studies on why it is being seen further south. Identifying this gap could lead to a meaningful research question about how geomagnetic storms impact the Earth and what can be done to mitigate its effects. A well-articulated research question will provide a clear direction for your study. 

Step 4: Creating a research design   

Based on your research question, you can draw up a framework outlining research methods and techniques that you will be using to conduct your study.  In other words, create a research design. Make sure to choose an appropriate research design based on available resources – it could be qualitative, quantitative, or mixed methods. Whichever you choose, it must align with your research objectives.

For example, researchers investigating the southward movement of the aurora borealis would likely need to use a mixed-methods approach that could include quantitative data collection through satellite imagery combined with qualitative analysis of historical auroral patterns and climate conditions. 

Step 5: Collecting data for study  

This is a crucial step in the research process, as the accuracy, credibility, and quality of data collected from various sources will directly impact the reliability of your research findings.

That is why you should choose data collection techniques best suited to your research objectives. Importantly, irrespective of how it is done – whether collecting primary data through interviews, surveys, and conducting experiments or secondary data gathered from existing studies and reports – it must be done carefully to avoid bias and errors.

For example, researchers studying the northern lights may use geomagnetic observations and atmospheric measurements to collect quantitative data on why the northern lights are being seen in wider areas. In qualitative research, the analysis might involve trying to understand how geomagnetic forces impact the Earth. 

Step 6: Analyzing data  

The next step in the research process is to analyze the raw data collected during the research to see if it supports or contradicts your hypothesis. Start by organizing data into relevant categories. This will help you discover patterns and trends and enable you to draw meaningful inferences.

Keep in mind that the method used to analyze data will depend on the type of data collected. For example, researchers could use the data collected from geomagnetic observations and atmospheric measurements to evaluate how geomagnetic storms are causing the aurora borealis to become more vividly and widely viewed and how this has a much broader impact on Earth.  

Step 7: Drawing inferences   

Once you have analyzed the data, it is time to interpret the findings and draw conclusions. This is one of the most critical steps in the research process because it directly answers the research question.

It is also important to consider what these findings mean for existing theories, practical applications, and directions for future research. At this stage, you should also acknowledge any limitations in your study and propose areas that warrant further investigation.  

Taking our example further, researchers analyzing data from satellite sensors would probably find that geomagnetic storms are enhancing the intensity and geographical reach of the aurora borealis and disrupting power grids, communication systems, and satellite operations. These findings could lead to policy recommendations for mitigating these impacts and preparing for possible disruptions in digital communication systems. 

Step 8: Writing the research report  

Finally, all of the information collected needs to be compiled into a research report or paper that accurately and coherently communicates the research process and outcomes. Ensuring that the research report is well written is crucial, as it not only improves readability but also goes a long way in ensuring that it is accepted by peer reviewers and journal editors.  

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Research Procedures

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• First Online: 28 March 2023

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

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

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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.

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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?

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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 .

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Home » Research Methods – Types, Examples and Guide

Research Methods – Types, Examples and Guide

Table of Contents

Research methods are the techniques, tools, and procedures used to collect, analyze, and interpret data for a study. They provide a systematic approach to solving research questions, ensuring that findings are accurate, reliable, and relevant. Choosing the right research method is crucial for the success of any project, as it determines how data is gathered and interpreted.

This article provides an overview of research methods, their types, practical examples, and a step-by-step guide to selecting and using them effectively.

Research Methods

Research methods are the systematic procedures researchers use to investigate phenomena, test hypotheses, and answer questions. They vary depending on the study’s objectives, data type, and discipline.

For example:

• A researcher exploring consumer preferences might use surveys as a method.
• A scientist studying cell biology might rely on experiments in a lab.

Types of Research Methods

1. qualitative research methods.

• Description: Focus on understanding human behavior, experiences, and social phenomena through non-numerical data.
• Examples of Techniques: Interviews, focus groups, ethnography, and content analysis.
• Applications: Used in social sciences, education, and humanities.
• A study examining students’ perceptions of online learning through in-depth interviews.

2. Quantitative Research Methods

• Description: Involve the collection and analysis of numerical data to identify patterns, test hypotheses, or predict outcomes.
• Examples of Techniques: Surveys, experiments, and statistical analysis.
• Applications: Common in natural sciences, economics, and psychology.
• Measuring the correlation between hours studied and exam scores using a survey.

3. Mixed-Methods Research

• Description: Combines qualitative and quantitative methods to provide a comprehensive understanding of the research problem.
• Examples of Techniques: Sequential explanatory design (quantitative followed by qualitative) or concurrent triangulation (both methods used simultaneously).
• Applications: Useful in multidisciplinary studies.
• Analyzing employee productivity through surveys (quantitative) and focus groups (qualitative).

4. Experimental Research

• Description: Involves manipulating one variable to determine its effect on another, often using control groups.
• Examples of Techniques: Randomized controlled trials (RCTs), field experiments.
• Applications: Widely used in sciences, medicine, and psychology.
• Testing the effectiveness of a new drug compared to a placebo in reducing symptoms.

5. Observational Research

• Description: Involves observing subjects in their natural environment without interference.
• Examples of Techniques: Participant observation, structured observation, and case studies.
• Applications: Common in anthropology, sociology, and environmental studies.
• Observing classroom behavior to assess the impact of teaching styles.

6. Historical Research

• Description: Examines past events to understand trends, causes, and consequences.
• Examples of Techniques: Document analysis, archival research.
• Applications: Used in history, political science, and cultural studies.
• Analyzing historical documents to study the causes of a revolution.

7. Correlational Research

• Description: Investigates the relationship between two or more variables without establishing causation.
• Examples of Techniques: Statistical analysis, scatter plots.
• Applications: Common in psychology, business, and education.
• Studying the relationship between income levels and spending habits.

Examples of Research Methods in Action

1. education research.

• Method: Surveys and focus groups.
• Objective: Understanding students’ preferences for online vs. in-person learning.
• Outcome: Insights into how learning modes impact engagement.

2. Business Research

• Method: Experiments and statistical analysis.
• Objective: Testing the impact of pricing strategies on customer purchasing behavior.
• Outcome: Identifying the optimal price point to maximize sales.

3. Environmental Studies

• Method: Observational research.
• Objective: Monitoring wildlife behavior in response to urbanization.
• Outcome: Data to guide conservation efforts.

4. Healthcare Research

• Method: Randomized controlled trials.
• Objective: Evaluating the effectiveness of a new vaccine.
• Outcome: Evidence supporting the vaccine’s safety and efficacy.

Steps to Choose and Use Research Methods

Step 1: define the research problem.

• Identify the issue or question you want to investigate.
• Example: “What factors influence employee retention in remote work settings?”

Step 2: Determine the Objective

• Decide whether you need to describe, explain, or predict phenomena.
• Example: If predicting retention trends, a quantitative approach may be more appropriate.

Step 3: Select a Research Design

• Choose between qualitative, quantitative, or mixed methods based on your objectives.
• Use qualitative methods to explore employee experiences.
• Use quantitative methods to measure retention rates.

Step 4: Choose Data Collection Methods

• Select techniques like surveys, interviews, or experiments depending on the data required.
• Surveys for numerical data on retention rates.
• Interviews for in-depth insights into employee motivations.

Step 5: Analyze Data

• Use appropriate tools and techniques to interpret the data.
• Example: Statistical software like SPSS or R for quantitative data, and thematic analysis for qualitative data.

Step 6: Evaluate Limitations

• Acknowledge potential biases, constraints, or challenges in your chosen method.
• Example: Limited sample size in qualitative research may affect generalizability.

Step 7: Report Findings

• Present results in a clear and structured manner, linking them to your research objectives.

Advantages of Research Methods

• Systematic Approach: Provides a structured way to investigate complex issues.
• Flexibility: Offers various methods to suit different research questions.
• Credibility: Ensures findings are based on reliable and valid data.
• Reproducibility: Enables other researchers to replicate studies for verification.

Limitations of Research Methods

• Time and Cost: Some methods, such as experiments, can be resource-intensive.
• Bias: Researcher bias or sample bias may affect the validity of results.
• Complexity: Advanced methods like mixed-methods require significant expertise.
• Ethical Concerns: Certain methods may raise ethical issues, especially in sensitive studies.

Common Tools for Research Methods

• Surveys and Questionnaires: Google Forms, Qualtrics, SurveyMonkey.
• Statistical Software: SPSS, R, SAS, Microsoft Excel.
• Qualitative Analysis Tools: NVivo, ATLAS.ti, MAXQDA.
• Data Visualization Tools: Tableau, Power BI, Matplotlib.
• Experiment Platforms: MATLAB, LabVIEW.

Tips for Selecting Research Methods

• Align with Objectives: Ensure the method matches your research goals.
• Consider Resources: Choose methods that fit within your time, budget, and expertise.
• Ensure Ethical Compliance: Obtain permissions and protect participant privacy.
• Be Open to Adjustments: Modify methods if initial plans prove impractical.

Research methods are essential tools for exploring, analyzing, and understanding complex issues. By choosing the right method—whether qualitative, quantitative, or mixed—you can ensure the accuracy and relevance of your findings. Following the steps and examples in this guide will help you select and apply research methods effectively, enhancing the overall quality and impact of your study.

• Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . Sage Publications.
• Bryman, A. (2015). Social Research Methods . Oxford University Press.
• Kumar, R. (2019). Research Methodology: A Step-by-Step Guide for Beginners . Sage Publications.
• Saunders, M., Lewis, P., & Thornhill, A. (2019). Research Methods for Business Students . Pearson.
• Flick, U. (2018). An Introduction to Qualitative Research . Sage Publications.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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The Research Process | Steps, How to Start & Tips

Introduction

Basic steps in the research process, conducting a literature review, designing the research project, collecting and analyzing data.

• Interpretation, conclusion and presentation of findings

Key principles for conducting research

The research process is a systematic method used to gather information and answer specific questions. The process ensures the findings are credible, high-quality, and applicable to a broader context. It can vary slightly between disciplines but typically follows a structured pathway from initial inquiry to final presentation of results.

What is the research process?

At its core, the research process involves several fundamental activities: identifying a topic that needs further investigation, reviewing existing knowledge on the subject, forming a precise research question , and designing a method to investigate it. This is followed by collecting and analyzing data , interpreting the results, and reporting the findings. Each step builds upon the previous one, requiring meticulous attention to detail and rigorous methodology.

The research process is important because it provides a scientific basis for decision-making. Whether in academic, scientific, or commercial fields, research helps us understand complex issues, develop new tools or products, and improve existing practices. By adhering to a structured research process , researchers can produce results that are insightful and transparent so that others can understand how the findings were developed and build on them in future studies. The integrity of the research process is essential for advancing knowledge and making informed decisions that can have significant social, economic, and scientific impacts.

The research process fosters critical thinking and problem-solving skills. It demands a clear articulation of a problem, thorough investigation, and thoughtful interpretation of data, all of which are valuable skills in any professional field. By following this process, researchers are better equipped to tackle complex questions and contribute meaningful solutions to real-world problems.

From finding the key theoretical concepts to presenting the research findings in a report, every step in the research process forms a cohesive pathway that supports researchers in systematically uncovering deep insights and generating meaningful knowledge, which is instrumental to the success of any qualitative study.

Identifying key theoretical concepts

The first step in the research process involves finding the key theoretical concepts or words that specify the research topic and are always included in the title of the investigation. Without a definition, these words have no sense or meaning (Daft, 1995). To identify these concepts, a researcher must ask which theoretical keywords are implicit in the study. To answer this question, a researcher should identify the logical relationships among the two words that catch the focus of the investigation. Researchers should also provide clear definitions for their theoretical keywords. The title of the research can then include these theoretical keywords and signal how they are being studied.

A piece of useful advice is to draw a conceptual map to visualize the direct or indirect relationships between the key theoretical words and choose a relationship between them as the focus of the investigation.

Developing a research question

One of the most important steps in the research endeavor is identifying a research question. Research questions answer aspects of the topic that need more knowledge or shed light on information that has to be prioritized before others. It is the first step in identifying which participants or type of data collection methods. Research questions put into practice the conceptual framework and make the initial theoretical concepts more explicit.

A research question carries a different implicit meaning depending on how it is framed. Questions starting with what, who, and where usually identify a phenomenon or elements of one, while how, why, when and how much describe, explain, predict or control a phenomenon.

Overall, research questions must be clear, focused and complex. They must also generate knowledge relevant to society and the answers must provide a thorough understanding that contributes to the scientific community.

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A literature review is the synthesis of the existing body of research relevant to a research topic . It allows researchers to identify the current state of the art of knowledge of a particular topic. When conducting research, it is the foundation that guides the researcher toward the knowledge gaps that need to be covered to best contribute to the scientific community.

Common methodologies include miniaturized or complete reviews, descriptive or integrated reviews, narrative reviews, theoretical reviews, methodological reviews and systematic reviews.

When navigating through the literature, researchers must try to answer their research question with the most current peer-reviewed research when finding relevant data for a research project. It is important to use the existing literature in at least two different databases and adapt the key concepts to amplify their search. Researchers also pay attention to the titles, summaries and references of each article. It is recommended to have a research diary for useful previous research as it could be the researcher´s go-to source when writing the final report.

A good research design involves data analysis methods suited to the research question, and where data collection generates appropriate data for the analysis method (Willig, 2001).

Designing a qualitative study is a critical step in the research process, serving as the blueprint for the research study. This phase is a fundamental part of the planning process, ensuring that the chosen research methods align perfectly with the research's purpose. During this stage, a researcher decides on a specific approach—such as narrative , phenomenological , grounded theory , ethnographic , or case study —tailoring the design to the unique research problem and needs of the research project. By carefully selecting the research method and planning how to approach the data, researchers can ensure that their work remains focused and relevant to the intended study area.

A well-constructed research design maintains the integrity and credibility of the study. It guides the researcher through the research process steps, from data collection to analysis, helping to manage and mitigate potential interpretations and errors. This detailed planning, particularly in qualitative studies, provides the depth of understanding and interpretive nature of analysis that can significantly influence outcomes.

The design of a qualitative study is a strategic component of the research that enhances the quality of the results. It requires thoughtful consideration of the research question, ensuring that every aspect of the methodology contributes effectively to the overarching goals of the project.

Collecting data

Gathering data can involve various methods tailored to the study's specific needs. To collect data , techniques may include interviews , focus groups , surveys and observations , each chosen for its ability to target a specific group relevant to the research population. For example, focus groups might explore attitudes within a specific age group, while observations might analyze behaviours in a community for population research projects. Data may also come from secondary sources with quantitative and qualitative approaches such as library resources, market research, customer feedback or employee evaluations.

Effective data management ensures that primary data from direct collection and secondary data from sources like public health records are organized and maintained properly. This step maintains the integrity of the data throughout the research process steps, supporting the overall goal of conducting thorough and coherent research.

Analyzing data

Once research data has been collected, the next critical step is to analyze the data. This phase transforms raw data into high-quality information for meaningful research findings.

Analyzing qualitative data often involves coding and thematic analysis , which helps identify patterns and themes within the data. While qualitative research typically does not focus on drawing statistical conclusions, integrating basic statistical methods can sometimes add depth to the data interpretation, especially in mixed-methods research where quantitative data complements qualitative insights.

In each of the research process steps, researchers utilize various research tools and techniques to conduct research and analyze the data systematically. This may include computer-assisted qualitative data analysis software (CAQDAS) such as ATLAS.ti, which assists in organizing, sorting, and coding the data efficiently. It can also host the research diary and apply analysis methods such as word frequencies and network visualizations.

Interpretation, conclusion and presentation of research findings

Interpreting research findings.

By meticulously following systematic procedures and working through the data, researchers can ensure that their interpretations are grounded in the actual data collected, enhancing the trustworthiness and credibility of the research findings.

The interpretation of data is not merely about extracting information but also involves making sense of the data in the context of the existing literature and research objectives. This step is not only about what the data is, but what it means in the broader context of the study, enabling researchers to draw insightful conclusions that contribute to the academic and practical understanding of the field.

Concluding and presenting research findings

The final step is concluding and presenting the research data which highlights how that data is analyzed for meaningful insights and credible findings.

The results are typically shared in a research report or academic paper, detailing the findings and contextualizing them within the broader field. This document outlines how the insights contribute to existing knowledge, suggests areas for future research, and may propose practical applications.

Effective presentation is key to ensuring that these findings reach and impact the intended audience. This involves articulating the conclusions clearly and using engaging formats and visual aids to enhance comprehension and engagement with the research.

The research process is characterized by a series of systematic research process steps designed to guide researchers successfully from inception to conclusion. Each step—from designing the study and collecting data to analyzing results and drawing conclusions—plays a critical role in ensuring the integrity and credibility of the research.

Qualitative research is guided by key principles designed to ensure the rigour and depth of the research study. Credibility is achieved through accurate representations of participant experiences, often verified by peer-review revision. Transferability is addressed by providing rich context, allowing others to evaluate the applicability of findings to similar settings. Dependability emphasizes the stability and consistency of data, maintained through detailed documentation of the research process (such as in a research diary), facilitating an audit trail. This aligns with confirmability, where the neutrality of the data is safeguarded by documenting researcher interpretations and decisions, ensuring findings are shaped by participants and not researcher predispositions.

Ethical integrity is critical, upholding standards like informed consent and confidentiality to protect participant rights throughout the research journey. Qualitative research also strives for a richness and depth of data that captures the complex nature of human experiences and interactions, often exploring these phenomena through an iterative learning process. This involves cycles of data collection and analysis, allowing for ongoing adjustments based on emerging insights. Lastly, a holistic perspective is adopted to view phenomena in their entirety, considering all aspects of the context and environment, which enriches the understanding and relevance of the research outcomes. Together, these principles ensure qualitative research is both profound and ethically conducted, yielding meaningful and applicable insights.

Daft, R. L. (1995). Organization Theory and Design. West Publishing Company.

Willig, C. (2001). Introducing Qualitative Research in Psychology: Adventures in Theory and Method. McGraw-Hill Companies, Incorporated.

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Research Process Steps: What they are + How To Follow

There are various approaches to conducting basic and applied research. This article explains the research process steps you should know. Whether you are doing basic research or applied research, there are many ways of doing it. In some ways, each research study is unique since it is conducted at a different time and place.

Conducting research might be difficult, but there are clear processes to follow. The research process starts with a broad idea for a topic. This article will assist you through the research process steps, helping you focus and develop your topic.

Research Process Steps

The research process consists of a series of systematic procedures that a researcher must go through in order to generate knowledge that will be considered valuable by the project and focus on the relevant topic.

To conduct effective research, you must understand the research process steps and follow them. Here are a few steps in the research process to make it easier for you:

Step 1: Identify the Problem

Finding an issue or formulating a research question is the first step. A well-defined research problem will guide the researcher through all stages of the research process, from setting objectives to choosing a technique. There are a number of approaches to get insight into a topic and gain a better understanding of it. Such as:

• A preliminary survey
• Case studies
• Interviews with a small group of people
• Observational survey

Step 2: Evaluate the Literature

A thorough examination of the relevant studies is essential to the research process . It enables the researcher to identify the precise aspects of the problem. Once a problem has been found, the investigator or researcher needs to find out more about it.

This stage gives problem-zone background. It teaches the investigator about previous research, how they were conducted, and its conclusions. The researcher can build consistency between his work and others through a literature review. Such a review exposes the researcher to a more significant body of knowledge and helps him follow the research process efficiently.

Step 3: Create Hypotheses

Formulating an original hypothesis is the next logical step after narrowing down the research topic and defining it. A belief solves logical relationships between variables. In order to establish a hypothesis, a researcher must have a certain amount of expertise in the field. 

It is important for researchers to keep in mind while formulating a hypothesis that it must be based on the research topic. Researchers are able to concentrate their efforts and stay committed to their objectives when they develop theories to guide their work.

Step 4: The Research Design

Research design is the plan for achieving objectives and answering research questions. It outlines how to get the relevant information. Its goal is to design research to test hypotheses, address the research questions, and provide decision-making insights.

The research design aims to minimize the time, money, and effort required to acquire meaningful evidence. This plan fits into four categories:

• Exploration and Surveys
• Data Analysis
• Observation

Step 5: Describe Population

Research projects usually look at a specific group of people, facilities, or how technology is used in the business. In research, the term population refers to this study group. The research topic and purpose help determine the study group.

Suppose a researcher wishes to investigate a certain group of people in the community. In that case, the research could target a specific age group, males or females, a geographic location, or an ethnic group. A final step in a study’s design is to specify its sample or population so that the results may be generalized.

Step 6: Data Collection

Data collection is important in obtaining the knowledge or information required to answer the research issue. Every research collected data, either from the literature or the people being studied. Data must be collected from the two categories of researchers. These sources may provide primary data.

• Questionnaire

Secondary data categories are:

• Literature survey
• Official, unofficial reports
• An approach based on library resources

Step 7: Data Analysis

During research design, the researcher plans data analysis. After collecting data, the researcher analyzes it. The data is examined based on the approach in this step. The research findings are reviewed and reported.

Data analysis involves a number of closely related stages, such as setting up categories, applying these categories to raw data through coding and tabulation, and then drawing statistical conclusions. The researcher can examine the acquired data using a variety of statistical methods.

Step 8: The Report-writing

After completing these steps, the researcher must prepare a report detailing his findings. The report must be carefully composed with the following in mind:

• The Layout: On the first page, the title, date, acknowledgments, and preface should be on the report. A table of contents should be followed by a list of tables, graphs, and charts if any.
• Introduction: It should state the research’s purpose and methods. This section should include the study’s scope and limits.
• Summary of Findings: A non-technical summary of findings and recommendations will follow the introduction. The findings should be summarized if they’re lengthy.
• Principal Report: The main body of the report should make sense and be broken up into sections that are easy to understand.
• Conclusion: The researcher should restate his findings at the end of the main text. It’s the final result.

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The research process involves several steps that make it easy to complete the research successfully. The steps in the research process described above depend on each other, and the order must be kept. So, if we want to do a research project, we should follow the research process steps.

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Research Process Steps: What Are They and How to Follow Them?

Scientific research plays a critical role in advancing our understanding of the environment and finding solutions to the increasingly complex problems that plague our world today. It requires researchers to identify knowledge gaps and undertake thorough investigations on the issues at hand. Consequently, scientific research calls for a systematic approach to acquiring and assessing new knowledge. However, because each study has its distinct objectives, variables, and potential problems, conducting scientific research can prove to be complex and challenging.  In this article, we will outline the fundamental steps to be followed when conducting research, which will benefit early career researchers.  

Table of Contents

Steps to conducting scientific research  

Some basic processes are common to all research studies. These steps help ensure that the research is conducted in a systematic and rigorous manner. 

Defining the research question

All scientific research must begin with a clearly defined research question that the research aims to address. A well-defined research question should be specific, relevant, and focused and must provide a clear direction to the study.  

Conducting a comprehensive literature review

Once the research question has been defined, the next step is to conduct a literature review. This will help researchers understand the current state of knowledge on their topic of research and enable them to identify gaps in the literature. This is crucial as it will allow them to determine the novelty and significance of their proposed research. It will also help researchers to refine their research questions, develop hypotheses, and select appropriate methodologies.  

Designing the research study

Designing the research study will help researchers to narrow down the methodologies to be used in research. A good research design allows researchers to select sampling techniques, data collection instruments, and data analysis methods. The research question, the nature of the data, and the resources available usually guide the choice of the research method. A well-designed methodology ensures the validity, reliability, and replicability of research findings. 

Collecting insights and data

   Once the research design has been finalized, the next step is to collect the data. The data collection phase involves gathering information or observations relevant to the research question. Depending on the research design, data can be collected through surveys, experiments, interviews, observations, or other appropriate methods. Researchers must ensure that data collection is conducted systematically and ethically, following established protocols.  

Interpret and analyze findings

Once the data is collected, the next step will be to interpret and analyze the findings using appropriate statistical or qualitative analysis techniques. This interpretation of research findings is a critical step in the research process as it aims to uncover patterns, relationships, and trends within the collected data, helping to answer the research question and test the proposed hypotheses or research objectives. 

Writing and presenting the research report

Once the research has been completed, it is essential to write a research report that will help researchers communicate their findings to wider audiences. Research reports must be clear, concise, objective, accurate, and well-presented. They must also be written in a simple, transparent way that allows reproducibility.  

Points to keep in mind when conducting scientific research  

Conducting scientific research can be a difficult and time-consuming process. However, it is essential to follow the research process steps mentioned above to ensure the validity and accuracy of the findings. It is also necessary to keep certain critical factors in mind when conducting scientific research. These include- 

• Watch for personal bias: One of the most important things to keep in mind when conducting scientific research is to be objective. This means that researchers must be vigilant and ensure that their personal biases and beliefs do not influence the results of their study.  
• Ensure that research is conducted ethically: Another critical consideration that researchers must focus on is the ethical implications of their research. Researchers must ensure that their work is moral in every way. For example, researchers must obtain informed consent from all participants and ensure that their research does not harm participants. 
• Avoid plagiarism: Early career researchers must understand what constitutes plagiarism in academic writing. Often, they inadvertently commit plagiarism, which could have serious consequences. Plagiarism is viewed as highly unethical in academia and can result in a loss of credibility and reputation for researchers. Therefore, when conducting scientific research, always ensure that your work is original, accurate, and well-presented.  

Following these research process steps and guidelines provided in this article will help early career researchers navigate the intricacies of the research process and maximize the quality of their investigations.

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