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Research Methodology – Types, Examples and writing Guide

Table of Contents

Research Methodology

Definition:

Research Methodology refers to the systematic and scientific approach used to conduct research, investigate problems, and gather data and information for a specific purpose. It involves the techniques and procedures used to identify, collect , analyze , and interpret data to answer research questions or solve research problems . Moreover, They are philosophical and theoretical frameworks that guide the research process.

Structure of Research Methodology

Research methodology formats can vary depending on the specific requirements of the research project, but the following is a basic example of a structure for a research methodology section:

I. Introduction

• Provide an overview of the research problem and the need for a research methodology section
• Outline the main research questions and objectives

II. Research Design

• Explain the research design chosen and why it is appropriate for the research question(s) and objectives
• Discuss any alternative research designs considered and why they were not chosen
• Describe the research setting and participants (if applicable)

III. Data Collection Methods

• Describe the methods used to collect data (e.g., surveys, interviews, observations)
• Explain how the data collection methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or instruments used for data collection

IV. Data Analysis Methods

• Describe the methods used to analyze the data (e.g., statistical analysis, content analysis )
• Explain how the data analysis methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or software used for data analysis

V. Ethical Considerations

• Discuss any ethical issues that may arise from the research and how they were addressed
• Explain how informed consent was obtained (if applicable)
• Detail any measures taken to ensure confidentiality and anonymity

VI. Limitations

• Identify any potential limitations of the research methodology and how they may impact the results and conclusions

VII. Conclusion

• Summarize the key aspects of the research methodology section
• Explain how the research methodology addresses the research question(s) and objectives

Research Methodology Types

Types of Research Methodology are as follows:

Quantitative Research Methodology

This is a research methodology that involves the collection and analysis of numerical data using statistical methods. This type of research is often used to study cause-and-effect relationships and to make predictions.

Qualitative Research Methodology

This is a research methodology that involves the collection and analysis of non-numerical data such as words, images, and observations. This type of research is often used to explore complex phenomena, to gain an in-depth understanding of a particular topic, and to generate hypotheses.

Mixed-Methods Research Methodology

This is a research methodology that combines elements of both quantitative and qualitative research. This approach can be particularly useful for studies that aim to explore complex phenomena and to provide a more comprehensive understanding of a particular topic.

Case Study Research Methodology

This is a research methodology that involves in-depth examination of a single case or a small number of cases. Case studies are often used in psychology, sociology, and anthropology to gain a detailed understanding of a particular individual or group.

Action Research Methodology

This is a research methodology that involves a collaborative process between researchers and practitioners to identify and solve real-world problems. Action research is often used in education, healthcare, and social work.

Experimental Research Methodology

This is a research methodology that involves the manipulation of one or more independent variables to observe their effects on a dependent variable. Experimental research is often used to study cause-and-effect relationships and to make predictions.

Survey Research Methodology

This is a research methodology that involves the collection of data from a sample of individuals using questionnaires or interviews. Survey research is often used to study attitudes, opinions, and behaviors.

Grounded Theory Research Methodology

This is a research methodology that involves the development of theories based on the data collected during the research process. Grounded theory is often used in sociology and anthropology to generate theories about social phenomena.

Research Methodology Example

An Example of Research Methodology could be the following:

Research Methodology for Investigating the Effectiveness of Cognitive Behavioral Therapy in Reducing Symptoms of Depression in Adults

Introduction:

The aim of this research is to investigate the effectiveness of cognitive-behavioral therapy (CBT) in reducing symptoms of depression in adults. To achieve this objective, a randomized controlled trial (RCT) will be conducted using a mixed-methods approach.

Research Design:

The study will follow a pre-test and post-test design with two groups: an experimental group receiving CBT and a control group receiving no intervention. The study will also include a qualitative component, in which semi-structured interviews will be conducted with a subset of participants to explore their experiences of receiving CBT.

Participants:

Participants will be recruited from community mental health clinics in the local area. The sample will consist of 100 adults aged 18-65 years old who meet the diagnostic criteria for major depressive disorder. Participants will be randomly assigned to either the experimental group or the control group.

Intervention :

The experimental group will receive 12 weekly sessions of CBT, each lasting 60 minutes. The intervention will be delivered by licensed mental health professionals who have been trained in CBT. The control group will receive no intervention during the study period.

Data Collection:

Quantitative data will be collected through the use of standardized measures such as the Beck Depression Inventory-II (BDI-II) and the Generalized Anxiety Disorder-7 (GAD-7). Data will be collected at baseline, immediately after the intervention, and at a 3-month follow-up. Qualitative data will be collected through semi-structured interviews with a subset of participants from the experimental group. The interviews will be conducted at the end of the intervention period, and will explore participants’ experiences of receiving CBT.

Data Analysis:

Quantitative data will be analyzed using descriptive statistics, t-tests, and mixed-model analyses of variance (ANOVA) to assess the effectiveness of the intervention. Qualitative data will be analyzed using thematic analysis to identify common themes and patterns in participants’ experiences of receiving CBT.

Ethical Considerations:

This study will comply with ethical guidelines for research involving human subjects. Participants will provide informed consent before participating in the study, and their privacy and confidentiality will be protected throughout the study. Any adverse events or reactions will be reported and managed appropriately.

Data Management:

All data collected will be kept confidential and stored securely using password-protected databases. Identifying information will be removed from qualitative data transcripts to ensure participants’ anonymity.

Limitations:

One potential limitation of this study is that it only focuses on one type of psychotherapy, CBT, and may not generalize to other types of therapy or interventions. Another limitation is that the study will only include participants from community mental health clinics, which may not be representative of the general population.

Conclusion:

This research aims to investigate the effectiveness of CBT in reducing symptoms of depression in adults. By using a randomized controlled trial and a mixed-methods approach, the study will provide valuable insights into the mechanisms underlying the relationship between CBT and depression. The results of this study will have important implications for the development of effective treatments for depression in clinical settings.

How to Write Research Methodology

Writing a research methodology involves explaining the methods and techniques you used to conduct research, collect data, and analyze results. It’s an essential section of any research paper or thesis, as it helps readers understand the validity and reliability of your findings. Here are the steps to write a research methodology:

• Start by explaining your research question: Begin the methodology section by restating your research question and explaining why it’s important. This helps readers understand the purpose of your research and the rationale behind your methods.
• Describe your research design: Explain the overall approach you used to conduct research. This could be a qualitative or quantitative research design, experimental or non-experimental, case study or survey, etc. Discuss the advantages and limitations of the chosen design.
• Discuss your sample: Describe the participants or subjects you included in your study. Include details such as their demographics, sampling method, sample size, and any exclusion criteria used.
• Describe your data collection methods : Explain how you collected data from your participants. This could include surveys, interviews, observations, questionnaires, or experiments. Include details on how you obtained informed consent, how you administered the tools, and how you minimized the risk of bias.
• Explain your data analysis techniques: Describe the methods you used to analyze the data you collected. This could include statistical analysis, content analysis, thematic analysis, or discourse analysis. Explain how you dealt with missing data, outliers, and any other issues that arose during the analysis.
• Discuss the validity and reliability of your research : Explain how you ensured the validity and reliability of your study. This could include measures such as triangulation, member checking, peer review, or inter-coder reliability.
• Acknowledge any limitations of your research: Discuss any limitations of your study, including any potential threats to validity or generalizability. This helps readers understand the scope of your findings and how they might apply to other contexts.
• Provide a summary: End the methodology section by summarizing the methods and techniques you used to conduct your research. This provides a clear overview of your research methodology and helps readers understand the process you followed to arrive at your findings.

When to Write Research Methodology

Research methodology is typically written after the research proposal has been approved and before the actual research is conducted. It should be written prior to data collection and analysis, as it provides a clear roadmap for the research project.

The research methodology is an important section of any research paper or thesis, as it describes the methods and procedures that will be used to conduct the research. It should include details about the research design, data collection methods, data analysis techniques, and any ethical considerations.

The methodology should be written in a clear and concise manner, and it should be based on established research practices and standards. It is important to provide enough detail so that the reader can understand how the research was conducted and evaluate the validity of the results.

Applications of Research Methodology

Here are some of the applications of research methodology:

• To identify the research problem: Research methodology is used to identify the research problem, which is the first step in conducting any research.
• To design the research: Research methodology helps in designing the research by selecting the appropriate research method, research design, and sampling technique.
• To collect data: Research methodology provides a systematic approach to collect data from primary and secondary sources.
• To analyze data: Research methodology helps in analyzing the collected data using various statistical and non-statistical techniques.
• To test hypotheses: Research methodology provides a framework for testing hypotheses and drawing conclusions based on the analysis of data.
• To generalize findings: Research methodology helps in generalizing the findings of the research to the target population.
• To develop theories : Research methodology is used to develop new theories and modify existing theories based on the findings of the research.
• To evaluate programs and policies : Research methodology is used to evaluate the effectiveness of programs and policies by collecting data and analyzing it.
• To improve decision-making: Research methodology helps in making informed decisions by providing reliable and valid data.

Purpose of Research Methodology

Research methodology serves several important purposes, including:

• To guide the research process: Research methodology provides a systematic framework for conducting research. It helps researchers to plan their research, define their research questions, and select appropriate methods and techniques for collecting and analyzing data.
• To ensure research quality: Research methodology helps researchers to ensure that their research is rigorous, reliable, and valid. It provides guidelines for minimizing bias and error in data collection and analysis, and for ensuring that research findings are accurate and trustworthy.
• To replicate research: Research methodology provides a clear and detailed account of the research process, making it possible for other researchers to replicate the study and verify its findings.
• To advance knowledge: Research methodology enables researchers to generate new knowledge and to contribute to the body of knowledge in their field. It provides a means for testing hypotheses, exploring new ideas, and discovering new insights.
• To inform decision-making: Research methodology provides evidence-based information that can inform policy and decision-making in a variety of fields, including medicine, public health, education, and business.

Advantages of Research Methodology

Research methodology has several advantages that make it a valuable tool for conducting research in various fields. Here are some of the key advantages of research methodology:

• Systematic and structured approach : Research methodology provides a systematic and structured approach to conducting research, which ensures that the research is conducted in a rigorous and comprehensive manner.
• Objectivity : Research methodology aims to ensure objectivity in the research process, which means that the research findings are based on evidence and not influenced by personal bias or subjective opinions.
• Replicability : Research methodology ensures that research can be replicated by other researchers, which is essential for validating research findings and ensuring their accuracy.
• Reliability : Research methodology aims to ensure that the research findings are reliable, which means that they are consistent and can be depended upon.
• Validity : Research methodology ensures that the research findings are valid, which means that they accurately reflect the research question or hypothesis being tested.
• Efficiency : Research methodology provides a structured and efficient way of conducting research, which helps to save time and resources.
• Flexibility : Research methodology allows researchers to choose the most appropriate research methods and techniques based on the research question, data availability, and other relevant factors.
• Scope for innovation: Research methodology provides scope for innovation and creativity in designing research studies and developing new research techniques.

Research Methodology Vs Research Methods

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What is Research Methodology? Definition, Types, and Examples

Research methodology 1,2 is a structured and scientific approach used to collect, analyze, and interpret quantitative or qualitative data to answer research questions or test hypotheses. A research methodology is like a plan for carrying out research and helps keep researchers on track by limiting the scope of the research. Several aspects must be considered before selecting an appropriate research methodology, such as research limitations and ethical concerns that may affect your research.

The research methodology section in a scientific paper describes the different methodological choices made, such as the data collection and analysis methods, and why these choices were selected. The reasons should explain why the methods chosen are the most appropriate to answer the research question. A good research methodology also helps ensure the reliability and validity of the research findings. There are three types of research methodology—quantitative, qualitative, and mixed-method, which can be chosen based on the research objectives.

What is research methodology ?

A research methodology describes the techniques and procedures used to identify and analyze information regarding a specific research topic. It is a process by which researchers design their study so that they can achieve their objectives using the selected research instruments. It includes all the important aspects of research, including research design, data collection methods, data analysis methods, and the overall framework within which the research is conducted. While these points can help you understand what is research methodology, you also need to know why it is important to pick the right methodology.

Why is research methodology important?

Having a good research methodology in place has the following advantages: 3

• Helps other researchers who may want to replicate your research; the explanations will be of benefit to them.
• You can easily answer any questions about your research if they arise at a later stage.
• A research methodology provides a framework and guidelines for researchers to clearly define research questions, hypotheses, and objectives.
• It helps researchers identify the most appropriate research design, sampling technique, and data collection and analysis methods.
• A sound research methodology helps researchers ensure that their findings are valid and reliable and free from biases and errors.
• It also helps ensure that ethical guidelines are followed while conducting research.
• A good research methodology helps researchers in planning their research efficiently, by ensuring optimum usage of their time and resources.

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Types of research methodology.

There are three types of research methodology based on the type of research and the data required. 1

• Quantitative research methodology focuses on measuring and testing numerical data. This approach is good for reaching a large number of people in a short amount of time. This type of research helps in testing the causal relationships between variables, making predictions, and generalizing results to wider populations.
• Qualitative research methodology examines the opinions, behaviors, and experiences of people. It collects and analyzes words and textual data. This research methodology requires fewer participants but is still more time consuming because the time spent per participant is quite large. This method is used in exploratory research where the research problem being investigated is not clearly defined.
• Mixed-method research methodology uses the characteristics of both quantitative and qualitative research methodologies in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method.

What are the types of sampling designs in research methodology?

Sampling 4 is an important part of a research methodology and involves selecting a representative sample of the population to conduct the study, making statistical inferences about them, and estimating the characteristics of the whole population based on these inferences. There are two types of sampling designs in research methodology—probability and nonprobability.

• Probability sampling

In this type of sampling design, a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are:

• Systematic —sample members are chosen at regular intervals. It requires selecting a starting point for the sample and sample size determination that can be repeated at regular intervals. This type of sampling method has a predefined range; hence, it is the least time consuming.
• Stratified —researchers divide the population into smaller groups that don’t overlap but represent the entire population. While sampling, these groups can be organized, and then a sample can be drawn from each group separately.
• Cluster —the population is divided into clusters based on demographic parameters like age, sex, location, etc.
• Convenience —selects participants who are most easily accessible to researchers due to geographical proximity, availability at a particular time, etc.
• Purposive —participants are selected at the researcher’s discretion. Researchers consider the purpose of the study and the understanding of the target audience.
• Snowball —already selected participants use their social networks to refer the researcher to other potential participants.
• Quota —while designing the study, the researchers decide how many people with which characteristics to include as participants. The characteristics help in choosing people most likely to provide insights into the subject.

What are data collection methods?

During research, data are collected using various methods depending on the research methodology being followed and the research methods being undertaken. Both qualitative and quantitative research have different data collection methods, as listed below.

Qualitative research 5

• One-on-one interviews: Helps the interviewers understand a respondent’s subjective opinion and experience pertaining to a specific topic or event
• Document study/literature review/record keeping: Researchers’ review of already existing written materials such as archives, annual reports, research articles, guidelines, policy documents, etc.
• Focus groups: Constructive discussions that usually include a small sample of about 6-10 people and a moderator, to understand the participants’ opinion on a given topic.
• Qualitative observation : Researchers collect data using their five senses (sight, smell, touch, taste, and hearing).

Quantitative research 6

• Sampling: The most common type is probability sampling.
• Interviews: Commonly telephonic or done in-person.
• Observations: Structured observations are most commonly used in quantitative research. In this method, researchers make observations about specific behaviors of individuals in a structured setting.
• Document review: Reviewing existing research or documents to collect evidence for supporting the research.
• Surveys and questionnaires. Surveys can be administered both online and offline depending on the requirement and sample size.

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What are data analysis methods.

The data collected using the various methods for qualitative and quantitative research need to be analyzed to generate meaningful conclusions. These data analysis methods 7 also differ between quantitative and qualitative research.

Quantitative research involves a deductive method for data analysis where hypotheses are developed at the beginning of the research and precise measurement is required. The methods include statistical analysis applications to analyze numerical data and are grouped into two categories—descriptive and inferential.

Descriptive analysis is used to describe the basic features of different types of data to present it in a way that ensures the patterns become meaningful. The different types of descriptive analysis methods are:

• Measures of frequency (count, percent, frequency)
• Measures of central tendency (mean, median, mode)
• Measures of dispersion or variation (range, variance, standard deviation)
• Measure of position (percentile ranks, quartile ranks)

Inferential analysis is used to make predictions about a larger population based on the analysis of the data collected from a smaller population. This analysis is used to study the relationships between different variables. Some commonly used inferential data analysis methods are:

• Correlation: To understand the relationship between two or more variables.
• Cross-tabulation: Analyze the relationship between multiple variables.
• Regression analysis: Study the impact of independent variables on the dependent variable.
• Frequency tables: To understand the frequency of data.
• Analysis of variance: To test the degree to which two or more variables differ in an experiment.

Qualitative research involves an inductive method for data analysis where hypotheses are developed after data collection. The methods include:

• Content analysis: For analyzing documented information from text and images by determining the presence of certain words or concepts in texts.
• Narrative analysis: For analyzing content obtained from sources such as interviews, field observations, and surveys. The stories and opinions shared by people are used to answer research questions.
• Discourse analysis: For analyzing interactions with people considering the social context, that is, the lifestyle and environment, under which the interaction occurs.
• Grounded theory: Involves hypothesis creation by data collection and analysis to explain why a phenomenon occurred.
• Thematic analysis: To identify important themes or patterns in data and use these to address an issue.

How to choose a research methodology?

Here are some important factors to consider when choosing a research methodology: 8

• Research objectives, aims, and questions —these would help structure the research design.
• Review existing literature to identify any gaps in knowledge.
• Check the statistical requirements —if data-driven or statistical results are needed then quantitative research is the best. If the research questions can be answered based on people’s opinions and perceptions, then qualitative research is most suitable.
• Sample size —sample size can often determine the feasibility of a research methodology. For a large sample, less effort- and time-intensive methods are appropriate.
• Constraints —constraints of time, geography, and resources can help define the appropriate methodology.

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How to write a research methodology .

A research methodology should include the following components: 3,9

• Research design —should be selected based on the research question and the data required. Common research designs include experimental, quasi-experimental, correlational, descriptive, and exploratory.
• Research method —this can be quantitative, qualitative, or mixed-method.
• Reason for selecting a specific methodology —explain why this methodology is the most suitable to answer your research problem.
• Research instruments —explain the research instruments you plan to use, mainly referring to the data collection methods such as interviews, surveys, etc. Here as well, a reason should be mentioned for selecting the particular instrument.
• Sampling —this involves selecting a representative subset of the population being studied.
• Data collection —involves gathering data using several data collection methods, such as surveys, interviews, etc.
• Data analysis —describe the data analysis methods you will use once you’ve collected the data.
• Research limitations —mention any limitations you foresee while conducting your research.
• Validity and reliability —validity helps identify the accuracy and truthfulness of the findings; reliability refers to the consistency and stability of the results over time and across different conditions.
• Ethical considerations —research should be conducted ethically. The considerations include obtaining consent from participants, maintaining confidentiality, and addressing conflicts of interest.

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Frequently Asked Questions

Q1. What are the key components of research methodology?

A1. A good research methodology has the following key components:

• Research design
• Data collection procedures
• Data analysis methods
• Ethical considerations

Q2. Why is ethical consideration important in research methodology?

A2. Ethical consideration is important in research methodology to ensure the readers of the reliability and validity of the study. Researchers must clearly mention the ethical norms and standards followed during the conduct of the research and also mention if the research has been cleared by any institutional board. The following 10 points are the important principles related to ethical considerations: 10

• Participants should not be subjected to harm.
• Respect for the dignity of participants should be prioritized.
• Full consent should be obtained from participants before the study.
• Participants’ privacy should be ensured.
• Confidentiality of the research data should be ensured.
• Anonymity of individuals and organizations participating in the research should be maintained.
• The aims and objectives of the research should not be exaggerated.
• Affiliations, sources of funding, and any possible conflicts of interest should be declared.
• Communication in relation to the research should be honest and transparent.
• Misleading information and biased representation of primary data findings should be avoided.

Q3. What is the difference between methodology and method?

A3. Research methodology is different from a research method, although both terms are often confused. Research methods are the tools used to gather data, while the research methodology provides a framework for how research is planned, conducted, and analyzed. The latter guides researchers in making decisions about the most appropriate methods for their research. Research methods refer to the specific techniques, procedures, and tools used by researchers to collect, analyze, and interpret data, for instance surveys, questionnaires, interviews, etc.

Research methodology is, thus, an integral part of a research study. It helps ensure that you stay on track to meet your research objectives and answer your research questions using the most appropriate data collection and analysis tools based on your research design.

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• Research methodologies. Pfeiffer Library website. Accessed August 15, 2023. https://library.tiffin.edu/researchmethodologies/whatareresearchmethodologies
• Types of research methodology. Eduvoice website. Accessed August 16, 2023. https://eduvoice.in/types-research-methodology/
• The basics of research methodology: A key to quality research. Voxco. Accessed August 16, 2023. https://www.voxco.com/blog/what-is-research-methodology/
• Sampling methods: Types with examples. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/types-of-sampling-for-social-research/
• What is qualitative research? Methods, types, approaches, examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-qualitative-research-methods-types-examples/
• What is quantitative research? Definition, methods, types, and examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-quantitative-research-types-and-examples/
• Data analysis in research: Types & methods. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/data-analysis-in-research/#Data_analysis_in_qualitative_research
• Factors to consider while choosing the right research methodology. PhD Monster website. Accessed August 17, 2023. https://www.phdmonster.com/factors-to-consider-while-choosing-the-right-research-methodology/
• What is research methodology? Research and writing guides. Accessed August 14, 2023. https://paperpile.com/g/what-is-research-methodology/
• Ethical considerations. Business research methodology website. Accessed August 17, 2023. https://research-methodology.net/research-methodology/ethical-considerations/

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The Ultimate Guide To Research Methodology

Research methodology is a crucial aspect of any investigative process, serving as the blueprint for the entire research journey. If you are stuck in the methodology section of your research paper , then this blog will guide you on what is a research methodology, its types and how to successfully conduct one. 

Table of Contents

What Is Research Methodology?

Research methodology can be defined as the systematic framework that guides researchers in designing, conducting, and analyzing their investigations. It encompasses a structured set of processes, techniques, and tools employed to gather and interpret data, ensuring the reliability and validity of the research findings. 

Research methodology is not confined to a singular approach; rather, it encapsulates a diverse range of methods tailored to the specific requirements of the research objectives.

Here is why Research methodology is important in academic and professional settings.

Facilitating Rigorous Inquiry

Research methodology forms the backbone of rigorous inquiry. It provides a structured approach that aids researchers in formulating precise thesis statements , selecting appropriate methodologies, and executing systematic investigations. This, in turn, enhances the quality and credibility of the research outcomes.

Ensuring Reproducibility And Reliability

In both academic and professional contexts, the ability to reproduce research outcomes is paramount. A well-defined research methodology establishes clear procedures, making it possible for others to replicate the study. This not only validates the findings but also contributes to the cumulative nature of knowledge.

Guiding Decision-Making Processes

In professional settings, decisions often hinge on reliable data and insights. Research methodology equips professionals with the tools to gather pertinent information, analyze it rigorously, and derive meaningful conclusions.

This informed decision-making is instrumental in achieving organizational goals and staying ahead in competitive environments.

Contributing To Academic Excellence

For academic researchers, adherence to robust research methodology is a hallmark of excellence. Institutions value research that adheres to high standards of methodology, fostering a culture of academic rigour and intellectual integrity. Furthermore, it prepares students with critical skills applicable beyond academia.

Enhancing Problem-Solving Abilities

Research methodology instills a problem-solving mindset by encouraging researchers to approach challenges systematically. It equips individuals with the skills to dissect complex issues, formulate hypotheses , and devise effective strategies for investigation.

Understanding Research Methodology

In the pursuit of knowledge and discovery, understanding the fundamentals of research methodology is paramount. 

Basics Of Research

Research, in its essence, is a systematic and organized process of inquiry aimed at expanding our understanding of a particular subject or phenomenon. It involves the exploration of existing knowledge, the formulation of hypotheses, and the collection and analysis of data to draw meaningful conclusions. 

Research is a dynamic and iterative process that contributes to the continuous evolution of knowledge in various disciplines.

Types of Research

Research takes on various forms, each tailored to the nature of the inquiry. Broadly classified, research can be categorized into two main types:

• Quantitative Research: This type involves the collection and analysis of numerical data to identify patterns, relationships, and statistical significance. It is particularly useful for testing hypotheses and making predictions.
• Qualitative Research: Qualitative research focuses on understanding the depth and details of a phenomenon through non-numerical data. It often involves methods such as interviews, focus groups, and content analysis, providing rich insights into complex issues.

Components Of Research Methodology

To conduct effective research, one must go through the different components of research methodology. These components form the scaffolding that supports the entire research process, ensuring its coherence and validity.

Research Design

Research design serves as the blueprint for the entire research project. It outlines the overall structure and strategy for conducting the study. The three primary types of research design are:

• Exploratory Research: Aimed at gaining insights and familiarity with the topic, often used in the early stages of research.
• Descriptive Research: Involves portraying an accurate profile of a situation or phenomenon, answering the ‘what,’ ‘who,’ ‘where,’ and ‘when’ questions.
• Explanatory Research: Seeks to identify the causes and effects of a phenomenon, explaining the ‘why’ and ‘how.’

Data Collection Methods

Choosing the right data collection methods is crucial for obtaining reliable and relevant information. Common methods include:

• Surveys and Questionnaires: Employed to gather information from a large number of respondents through standardized questions.
• Interviews: In-depth conversations with participants, offering qualitative insights.
• Observation: Systematic watching and recording of behaviour, events, or processes in their natural setting.

Data Analysis Techniques

Once data is collected, analysis becomes imperative to derive meaningful conclusions. Different methodologies exist for quantitative and qualitative data:

• Quantitative Data Analysis: Involves statistical techniques such as descriptive statistics, inferential statistics, and regression analysis to interpret numerical data.
• Qualitative Data Analysis: Methods like content analysis, thematic analysis, and grounded theory are employed to extract patterns, themes, and meanings from non-numerical data.

The research paper we write have:

• Precision and Clarity
• Zero Plagiarism
• High-level Encryption
• Authentic Sources

Choosing a Research Method

Selecting an appropriate research method is a critical decision in the research process. It determines the approach, tools, and techniques that will be used to answer the research questions. 

Quantitative Research Methods

Quantitative research involves the collection and analysis of numerical data, providing a structured and objective approach to understanding and explaining phenomena.

Experimental Research

Experimental research involves manipulating variables to observe the effect on another variable under controlled conditions. It aims to establish cause-and-effect relationships.

Key Characteristics:

• Controlled Environment: Experiments are conducted in a controlled setting to minimize external influences.
• Random Assignment: Participants are randomly assigned to different experimental conditions.
• Quantitative Data: Data collected is numerical, allowing for statistical analysis.

Applications: Commonly used in scientific studies and psychology to test hypotheses and identify causal relationships.

Survey Research

Survey research gathers information from a sample of individuals through standardized questionnaires or interviews. It aims to collect data on opinions, attitudes, and behaviours.

• Structured Instruments: Surveys use structured instruments, such as questionnaires, to collect data.
• Large Sample Size: Surveys often target a large and diverse group of participants.
• Quantitative Data Analysis: Responses are quantified for statistical analysis.

Applications: Widely employed in social sciences, marketing, and public opinion research to understand trends and preferences.

Descriptive Research

Descriptive research seeks to portray an accurate profile of a situation or phenomenon. It focuses on answering the ‘what,’ ‘who,’ ‘where,’ and ‘when’ questions.

• Observation and Data Collection: This involves observing and documenting without manipulating variables.
• Objective Description: Aim to provide an unbiased and factual account of the subject.
• Quantitative or Qualitative Data: T his can include both types of data, depending on the research focus.

Applications: Useful in situations where researchers want to understand and describe a phenomenon without altering it, common in social sciences and education.

Qualitative Research Methods

Qualitative research emphasizes exploring and understanding the depth and complexity of phenomena through non-numerical data.

A case study is an in-depth exploration of a particular person, group, event, or situation. It involves detailed, context-rich analysis.

• Rich Data Collection: Uses various data sources, such as interviews, observations, and documents.
• Contextual Understanding: Aims to understand the context and unique characteristics of the case.
• Holistic Approach: Examines the case in its entirety.

Applications: Common in social sciences, psychology, and business to investigate complex and specific instances.

Ethnography

Ethnography involves immersing the researcher in the culture or community being studied to gain a deep understanding of their behaviours, beliefs, and practices.

• Participant Observation: Researchers actively participate in the community or setting.
• Holistic Perspective: Focuses on the interconnectedness of cultural elements.
• Qualitative Data: In-depth narratives and descriptions are central to ethnographic studies.

Applications: Widely used in anthropology, sociology, and cultural studies to explore and document cultural practices.

Grounded Theory

Grounded theory aims to develop theories grounded in the data itself. It involves systematic data collection and analysis to construct theories from the ground up.

• Constant Comparison: Data is continually compared and analyzed during the research process.
• Inductive Reasoning: Theories emerge from the data rather than being imposed on it.
• Iterative Process: The research design evolves as the study progresses.

Applications: Commonly applied in sociology, nursing, and management studies to generate theories from empirical data.

Research design is the structural framework that outlines the systematic process and plan for conducting a study. It serves as the blueprint, guiding researchers on how to collect, analyze, and interpret data.

Exploratory, Descriptive, And Explanatory Designs

Exploratory design.

Exploratory research design is employed when a researcher aims to explore a relatively unknown subject or gain insights into a complex phenomenon.

• Flexibility: Allows for flexibility in data collection and analysis.
• Open-Ended Questions: Uses open-ended questions to gather a broad range of information.
• Preliminary Nature: Often used in the initial stages of research to formulate hypotheses.

Applications: Valuable in the early stages of investigation, especially when the researcher seeks a deeper understanding of a subject before formalizing research questions.

Descriptive Design

Descriptive research design focuses on portraying an accurate profile of a situation, group, or phenomenon.

• Structured Data Collection: Involves systematic and structured data collection methods.
• Objective Presentation: Aims to provide an unbiased and factual account of the subject.
• Quantitative or Qualitative Data: Can incorporate both types of data, depending on the research objectives.

Applications: Widely used in social sciences, marketing, and educational research to provide detailed and objective descriptions.

Explanatory Design

Explanatory research design aims to identify the causes and effects of a phenomenon, explaining the ‘why’ and ‘how’ behind observed relationships.

• Causal Relationships: Seeks to establish causal relationships between variables.
• Controlled Variables : Often involves controlling certain variables to isolate causal factors.
• Quantitative Analysis: Primarily relies on quantitative data analysis techniques.

Applications: Commonly employed in scientific studies and social sciences to delve into the underlying reasons behind observed patterns.

Cross-Sectional Vs. Longitudinal Designs

Cross-sectional design.

Cross-sectional designs collect data from participants at a single point in time.

• Snapshot View: Provides a snapshot of a population at a specific moment.
• Efficiency: More efficient in terms of time and resources.
• Limited Temporal Insights: Offers limited insights into changes over time.

Applications: Suitable for studying characteristics or behaviours that are stable or not expected to change rapidly.

Longitudinal Design

Longitudinal designs involve the collection of data from the same participants over an extended period.

• Temporal Sequence: Allows for the examination of changes over time.
• Causality Assessment: Facilitates the assessment of cause-and-effect relationships.
• Resource-Intensive: Requires more time and resources compared to cross-sectional designs.

Applications: Ideal for studying developmental processes, trends, or the impact of interventions over time.

Experimental Vs Non-experimental Designs

Experimental design.

Experimental designs involve manipulating variables under controlled conditions to observe the effect on another variable.

• Causality Inference: Enables the inference of cause-and-effect relationships.
• Quantitative Data: Primarily involves the collection and analysis of numerical data.

Applications: Commonly used in scientific studies, psychology, and medical research to establish causal relationships.

Non-Experimental Design

Non-experimental designs observe and describe phenomena without manipulating variables.

• Natural Settings: Data is often collected in natural settings without intervention.
• Descriptive or Correlational: Focuses on describing relationships or correlations between variables.
• Quantitative or Qualitative Data: This can involve either type of data, depending on the research approach.

Applications: Suitable for studying complex phenomena in real-world settings where manipulation may not be ethical or feasible.

Effective data collection is fundamental to the success of any research endeavour. 

Designing Effective Surveys

Objective Design:

• Clearly define the research objectives to guide the survey design.
• Craft questions that align with the study’s goals and avoid ambiguity.

Structured Format:

• Use a structured format with standardized questions for consistency.
• Include a mix of closed-ended and open-ended questions for detailed insights.

Pilot Testing:

• Conduct pilot tests to identify and rectify potential issues with survey design.
• Ensure clarity, relevance, and appropriateness of questions.

Sampling Strategy:

• Develop a robust sampling strategy to ensure a representative participant group.
• Consider random sampling or stratified sampling based on the research goals.

Conducting Interviews

Establishing Rapport:

• Build rapport with participants to create a comfortable and open environment.
• Clearly communicate the purpose of the interview and the value of participants’ input.

Open-Ended Questions:

• Frame open-ended questions to encourage detailed responses.
• Allow participants to express their thoughts and perspectives freely.

Active Listening:

• Practice active listening to understand areas and gather rich data.
• Avoid interrupting and maintain a non-judgmental stance during the interview.

Ethical Considerations:

• Obtain informed consent and assure participants of confidentiality.
• Be transparent about the study’s purpose and potential implications.

Observation

1. participant observation.

Immersive Participation:

• Actively immerse yourself in the setting or group being observed.
• Develop a deep understanding of behaviours, interactions, and context.

Field Notes:

• Maintain detailed and reflective field notes during observations.
• Document observed patterns, unexpected events, and participant reactions.

Ethical Awareness:

• Be conscious of ethical considerations, ensuring respect for participants.
• Balance the role of observer and participant to minimize bias.

2. Non-participant Observation

Objective Observation:

• Maintain a more detached and objective stance during non-participant observation.
• Focus on recording behaviours, events, and patterns without direct involvement.

Data Reliability:

• Enhance the reliability of data by reducing observer bias.
• Develop clear observation protocols and guidelines.

Contextual Understanding:

• Strive for a thorough understanding of the observed context.
• Consider combining non-participant observation with other methods for triangulation.

Archival Research

1. using existing data.

Identifying Relevant Archives:

• Locate and access archives relevant to the research topic.
• Collaborate with institutions or repositories holding valuable data.

Data Verification:

• Verify the accuracy and reliability of archived data.
• Cross-reference with other sources to ensure data integrity.

Ethical Use:

• Adhere to ethical guidelines when using existing data.
• Respect copyright and intellectual property rights.

2. Challenges and Considerations

Incomplete or Inaccurate Archives:

• Address the possibility of incomplete or inaccurate archival records.
• Acknowledge limitations and uncertainties in the data.

Temporal Bias:

• Recognize potential temporal biases in archived data.
• Consider the historical context and changes that may impact interpretation.

Access Limitations:

• Address potential limitations in accessing certain archives.
• Seek alternative sources or collaborate with institutions to overcome barriers.

Common Challenges in Research Methodology

Conducting research is a complex and dynamic process, often accompanied by a myriad of challenges. Addressing these challenges is crucial to ensure the reliability and validity of research findings.

Sampling Issues

Sampling bias:.

• The presence of sampling bias can lead to an unrepresentative sample, affecting the generalizability of findings.
• Employ random sampling methods and ensure the inclusion of diverse participants to reduce bias.

Sample Size Determination:

• Determining an appropriate sample size is a delicate balance. Too small a sample may lack statistical power, while an excessively large sample may strain resources.
• Conduct a power analysis to determine the optimal sample size based on the research objectives and expected effect size.

Data Quality And Validity

Measurement error:.

• Inaccuracies in measurement tools or data collection methods can introduce measurement errors, impacting the validity of results.
• Pilot test instruments, calibrate equipment, and use standardized measures to enhance the reliability of data.

Construct Validity:

• Ensuring that the chosen measures accurately capture the intended constructs is a persistent challenge.
• Use established measurement instruments and employ multiple measures to assess the same construct for triangulation.

Time And Resource Constraints

Timeline pressures:.

• Limited timeframes can compromise the depth and thoroughness of the research process.
• Develop a realistic timeline, prioritize tasks, and communicate expectations with stakeholders to manage time constraints effectively.

Resource Availability:

• Inadequate resources, whether financial or human, can impede the execution of research activities.
• Seek external funding, collaborate with other researchers, and explore alternative methods that require fewer resources.

Managing Bias in Research

Selection bias:.

• Selecting participants in a way that systematically skews the sample can introduce selection bias.
• Employ randomization techniques, use stratified sampling, and transparently report participant recruitment methods.

Confirmation Bias:

• Researchers may unintentionally favour information that confirms their preconceived beliefs or hypotheses.
• Adopt a systematic and open-minded approach, use blinded study designs, and engage in peer review to mitigate confirmation bias.

Tips On How To Write A Research Methodology

Conducting successful research relies not only on the application of sound methodologies but also on strategic planning and effective collaboration. Here are some tips to enhance the success of your research methodology:

Tip 1. Clear Research Objectives

Well-defined research objectives guide the entire research process. Clearly articulate the purpose of your study, outlining specific research questions or hypotheses.

Tip 2. Comprehensive Literature Review

A thorough literature review provides a foundation for understanding existing knowledge and identifying gaps. Invest time in reviewing relevant literature to inform your research design and methodology.

Tip 3. Detailed Research Plan

A detailed plan serves as a roadmap, ensuring all aspects of the research are systematically addressed. Develop a detailed research plan outlining timelines, milestones, and tasks.

Tip 4. Ethical Considerations

Ethical practices are fundamental to maintaining the integrity of research. Address ethical considerations early, obtain necessary approvals, and ensure participant rights are safeguarded.

Tip 5. Stay Updated On Methodologies

Research methodologies evolve, and staying updated is essential for employing the most effective techniques. Engage in continuous learning by attending workshops, conferences, and reading recent publications.

Tip 6. Adaptability In Methods

Unforeseen challenges may arise during research, necessitating adaptability in methods. Be flexible and willing to modify your approach when needed, ensuring the integrity of the study.

Tip 7. Iterative Approach

Research is often an iterative process, and refining methods based on ongoing findings enhance the study’s robustness. Regularly review and refine your research design and methods as the study progresses.

Frequently Asked Questions

What is the research methodology.

Research methodology is the systematic process of planning, executing, and evaluating scientific investigation. It encompasses the techniques, tools, and procedures used to collect, analyze, and interpret data, ensuring the reliability and validity of research findings.

What are the methodologies in research?

Research methodologies include qualitative and quantitative approaches. Qualitative methods involve in-depth exploration of non-numerical data, while quantitative methods use statistical analysis to examine numerical data. Mixed methods combine both approaches for a comprehensive understanding of research questions.

How to write research methodology?

To write a research methodology, clearly outline the study’s design, data collection, and analysis procedures. Specify research tools, participants, and sampling methods. Justify choices and discuss limitations. Ensure clarity, coherence, and alignment with research objectives for a robust methodology section.

How to write the methodology section of a research paper?

In the methodology section of a research paper, describe the study’s design, data collection, and analysis methods. Detail procedures, tools, participants, and sampling. Justify choices, address ethical considerations, and explain how the methodology aligns with research objectives, ensuring clarity and rigour.

What is mixed research methodology?

Mixed research methodology combines both qualitative and quantitative research approaches within a single study. This approach aims to enhance the details and depth of research findings by providing a more comprehensive understanding of the research problem or question.

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The Research Methodology Test! Trivia Quiz

Welcome to another quiz on The Research Methodology. Information for a given study can either acquired through observational, experimental, simulation, and derived means. This quiz will test how much you know about gaining information and the importance of getting the best method. Do give it a try and get a deeper understanding of all of them. All the best, and check out other quizzes just like it.

Research is

Searching again and again

Finding solution to any problem

Working in a scientific way to search for truth of any problem

None of the above

Rate this question:

Which of the following is the first step in starting the research process?

Searching sources of information to locate problem.

Survey of related literature

Identification of problem

Searching for solutions to the problem

A common test in research demands much priority on

Reliability

Objectivity

 All of the above

In the process of conducting research ‘Formulation of Hypothesis” is followed by

 Statement of Objectives

Analysis of Data

Selection of Research Tools

Collection of Data

A research paper is a brief report of research work based on

Primary Data only

Secondary Data only

Both Primary and Secondary Data

Information is…..

 Raw Data    

Processed Data

 Input data  

Organized data

Conference proceedings are considered as..................documents.

Conventional

Questionnaire is a :

Research method

 Measurement technique

Tool for data collection

Data analysis technique

“Controlled Group” is a term used in.............. .

Survey research

Historical research

Experimental research

Descriptive research

Which of the following is not covered under Intellectual Property Rights ?

Trade Marks

Which of the following is not true about e journals ?

They are distributed through digital methods

They also have editors or editorial boards

 They are publications of serial nature

 They are always free of cost  

The main purpose of research in education is to _________

Increase social status of an individual 

Increase job prospects of an individual

Help in the personal growth of an individual

Help the candidate become an eminent educationist

The data of research is ______

Qualitative only

Quantitative only

Both (a) and (b)

Neither (a) nor (b)

Survey research studies _________

Populations

Circumstances

Evaluation research is concerned with ___________

Why are we doing?

What are we doing?

How well are we doing?

A college wants to give training in use of Statistical Package for Social Sciences (SPSS) to researchers. For this the college should organize:

Which of the following is not the characteristic of a research.

Research is systematic

Research is not passive

Research is not a process

Research is problem oriented

One of the following is not a quality of researcher:

Keenness in enquiry

He must be of alert mind

His assertion to outstrip the evidence

Unison with that of which he is in search

Books and records are the primary sources of data in:

Clinical research

Laboratory research

Participatory research

Which of the following options are the main tasks of research in modern society?

To discover new things

To keep pace with the advancement in knowledge

To systematically examine and critically analyse the investigations/sources with objectivity

All of the above

What do you consider as the main aim of inter disciplinary research?

To over simplify the problem of research

To bring out holistic approach to research

O create a new trend in research methodology

To reduce the emphasis of single subject in research domain

One of the aims of the scientific method in research is to:

Confirm triangulation

Introduce new variables

Improve data interpretation

Eliminate spurious relations

The depth of any research can be judged by:

Title of the research

Duration of the research

Objectives of the research

Total expenditure on the research

Which of the following is not the Method of Research?

Observation

Philosophical

Research can be classified as:

Basic, Applied and Action Research

Quantitative and Qualitative Research

Philosophical, Historical, Survey and Experimental Research

All the above

The first step of research is:

Finding a problem

Selecting a problem

Searching a problem

Identifying a problem

A research problem is feasible only when:

It is researchable

It has utility and relevance

It is new and adds something to knowledge

Bibliography given in a research report:

Has no relevance to research

Shows vast knowledge of the researcher

Helps those interested in further research

Fundamental research reflects the ability to:

Expound new principles

Synthesize new ideals

Evaluate the existing material concerning research

Study the existing literature regarding various topics

The study in which the investigators attempt to trace an effect is known as:

Survey Research

Historical Research

Summative Research

'Ex-post Facto' Research

The experimental study is based on:

Survey of literature

Conceptual parameters

Replication of research

The manipulation of variables

The main characteristic of scientific research is:

Theoretical

Experimental

Research problem is selected from the stand point of:

Social relevance

Financial support

Researcher's interest

Availability of relevant literature

The research is always 

Exploring new knowledge

Verifying the old knowledge

Filling the gap between knowledge

All of these

How can the objectivity of the research be enhanced?

Through its validity

Through its reliability

Through its impartiality

Action-research is:

An applied research

A longitudinal research

A research carried out to solve immediate problems

If a researcher conducts research on finding out which administrative style contributes more to institutional effectiveness? This will be an example of

Applied Research

Action Research

Basic Research

Which of the following variables cannot be expressed in quantitative terms?

Marital Status

Numerical Aptitude

Professional Attitude

Socio-economic Status

A doctor studies the relative effectiveness of two drugs of dengue fever. His research would be classified as

Ethnography

Descriptive Survey

Experimental Research

None of the above 

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How To Choose Your Research Methodology

Qualitative vs quantitative vs mixed methods.

By: Derek Jansen (MBA). Expert Reviewed By: Dr Eunice Rautenbach | June 2021

Without a doubt, one of the most common questions we receive at Grad Coach is “ How do I choose the right methodology for my research? ”. It’s easy to see why – with so many options on the research design table, it’s easy to get intimidated, especially with all the complex lingo!

In this post, we’ll explain the three overarching types of research – qualitative, quantitative and mixed methods – and how you can go about choosing the best methodological approach for your research.

Overview: Choosing Your Methodology

Understanding the options – Qualitative research – Quantitative research – Mixed methods-based research

Choosing a research methodology – Nature of the research – Research area norms – Practicalities

1. Understanding the options

Before we jump into the question of how to choose a research methodology, it’s useful to take a step back to understand the three overarching types of research – qualitative , quantitative and mixed methods -based research. Each of these options takes a different methodological approach.

Qualitative research utilises data that is not numbers-based. In other words, qualitative research focuses on words , descriptions , concepts or ideas – while quantitative research makes use of numbers and statistics. Qualitative research investigates the “softer side” of things to explore and describe, while quantitative research focuses on the “hard numbers”, to measure differences between variables and the relationships between them.

Importantly, qualitative research methods are typically used to explore and gain a deeper understanding of the complexity of a situation – to draw a rich picture . In contrast to this, quantitative methods are usually used to confirm or test hypotheses . In other words, they have distinctly different purposes. The table below highlights a few of the key differences between qualitative and quantitative research – you can learn more about the differences here.

• Uses an inductive approach
• Is used to build theories
• Takes a subjective approach
• Adopts an open and flexible approach
• The researcher is close to the respondents
• Interviews and focus groups are oftentimes used to collect word-based data.
• Generally, draws on small sample sizes
• Uses qualitative data analysis techniques (e.g. content analysis , thematic analysis , etc)
• Uses a deductive approach
• Is used to test theories
• Takes an objective approach
• Adopts a closed, highly planned approach
• The research is disconnected from respondents
• Surveys or laboratory equipment are often used to collect number-based data.
• Generally, requires large sample sizes
• Uses statistical analysis techniques to make sense of the data

Mixed methods -based research, as you’d expect, attempts to bring these two types of research together, drawing on both qualitative and quantitative data. Quite often, mixed methods-based studies will use qualitative research to explore a situation and develop a potential model of understanding (this is called a conceptual framework), and then go on to use quantitative methods to test that model empirically.

In other words, while qualitative and quantitative methods (and the philosophies that underpin them) are completely different, they are not at odds with each other. It’s not a competition of qualitative vs quantitative. On the contrary, they can be used together to develop a high-quality piece of research. Of course, this is easier said than done, so we usually recommend that first-time researchers stick to a single approach , unless the nature of their study truly warrants a mixed-methods approach.

The key takeaway here, and the reason we started by looking at the three options, is that it’s important to understand that each methodological approach has a different purpose – for example, to explore and understand situations (qualitative), to test and measure (quantitative) or to do both. They’re not simply alternative tools for the same job. 

Right – now that we’ve got that out of the way, let’s look at how you can go about choosing the right methodology for your research.

2. How to choose a research methodology

To choose the right research methodology for your dissertation or thesis, you need to consider three important factors . Based on these three factors, you can decide on your overarching approach – qualitative, quantitative or mixed methods. Once you’ve made that decision, you can flesh out the finer details of your methodology, such as the sampling , data collection methods and analysis techniques (we discuss these separately in other posts ).

The three factors you need to consider are:

• The nature of your research aims, objectives and research questions
• The methodological approaches taken in the existing literature
• Practicalities and constraints

Let’s take a look at each of these.

Factor #1: The nature of your research

As I mentioned earlier, each type of research (and therefore, research methodology), whether qualitative, quantitative or mixed, has a different purpose and helps solve a different type of question. So, it’s logical that the key deciding factor in terms of which research methodology you adopt is the nature of your research aims, objectives and research questions .

But, what types of research exist?

Broadly speaking, research can fall into one of three categories:

• Exploratory – getting a better understanding of an issue and potentially developing a theory regarding it
• Confirmatory – confirming a potential theory or hypothesis by testing it empirically
• A mix of both – building a potential theory or hypothesis and then testing it

As a rule of thumb, exploratory research tends to adopt a qualitative approach , whereas confirmatory research tends to use quantitative methods . This isn’t set in stone, but it’s a very useful heuristic. Naturally then, research that combines a mix of both, or is seeking to develop a theory from the ground up and then test that theory, would utilize a mixed-methods approach.

Let’s look at an example in action.

If your research aims were to understand the perspectives of war veterans regarding certain political matters, you’d likely adopt a qualitative methodology, making use of interviews to collect data and one or more qualitative data analysis methods to make sense of the data.

If, on the other hand, your research aims involved testing a set of hypotheses regarding the link between political leaning and income levels, you’d likely adopt a quantitative methodology, using numbers-based data from a survey to measure the links between variables and/or constructs .

So, the first (and most important thing) thing you need to consider when deciding which methodological approach to use for your research project is the nature of your research aims , objectives and research questions. Specifically, you need to assess whether your research leans in an exploratory or confirmatory direction or involves a mix of both.

The importance of achieving solid alignment between these three factors and your methodology can’t be overstated. If they’re misaligned, you’re going to be forcing a square peg into a round hole. In other words, you’ll be using the wrong tool for the job, and your research will become a disjointed mess.

If your research is a mix of both exploratory and confirmatory, but you have a tight word count limit, you may need to consider trimming down the scope a little and focusing on one or the other. One methodology executed well has a far better chance of earning marks than a poorly executed mixed methods approach. So, don’t try to be a hero, unless there is a very strong underpinning logic.

Need a helping hand?

Factor #2: The disciplinary norms

Choosing the right methodology for your research also involves looking at the approaches used by other researchers in the field, and studies with similar research aims and objectives to yours. Oftentimes, within a discipline, there is a common methodological approach (or set of approaches) used in studies. While this doesn’t mean you should follow the herd “just because”, you should at least consider these approaches and evaluate their merit within your context.

A major benefit of reviewing the research methodologies used by similar studies in your field is that you can often piggyback on the data collection techniques that other (more experienced) researchers have developed. For example, if you’re undertaking a quantitative study, you can often find tried and tested survey scales with high Cronbach’s alphas. These are usually included in the appendices of journal articles, so you don’t even have to contact the original authors. By using these, you’ll save a lot of time and ensure that your study stands on the proverbial “shoulders of giants” by using high-quality measurement instruments .

Of course, when reviewing existing literature, keep point #1 front of mind. In other words, your methodology needs to align with your research aims, objectives and questions. Don’t fall into the trap of adopting the methodological “norm” of other studies just because it’s popular. Only adopt that which is relevant to your research.

Factor #3: Practicalities

When choosing a research methodology, there will always be a tension between doing what’s theoretically best (i.e., the most scientifically rigorous research design ) and doing what’s practical , given your constraints . This is the nature of doing research and there are always trade-offs, as with anything else.

But what constraints, you ask?

When you’re evaluating your methodological options, you need to consider the following constraints:

• Data access
• Equipment and software
• Your knowledge and skills

Let’s look at each of these.

Constraint #1: Data access

The first practical constraint you need to consider is your access to data . If you’re going to be undertaking primary research , you need to think critically about the sample of respondents you realistically have access to. For example, if you plan to use in-person interviews , you need to ask yourself how many people you’ll need to interview, whether they’ll be agreeable to being interviewed, where they’re located, and so on.

If you’re wanting to undertake a quantitative approach using surveys to collect data, you’ll need to consider how many responses you’ll require to achieve statistically significant results. For many statistical tests, a sample of a few hundred respondents is typically needed to develop convincing conclusions.

So, think carefully about what data you’ll need access to, how much data you’ll need and how you’ll collect it. The last thing you want is to spend a huge amount of time on your research only to find that you can’t get access to the required data.

Constraint #2: Time

The next constraint is time. If you’re undertaking research as part of a PhD, you may have a fairly open-ended time limit, but this is unlikely to be the case for undergrad and Masters-level projects. So, pay attention to your timeline, as the data collection and analysis components of different methodologies have a major impact on time requirements . Also, keep in mind that these stages of the research often take a lot longer than originally anticipated.

Another practical implication of time limits is that it will directly impact which time horizon you can use – i.e. longitudinal vs cross-sectional . For example, if you’ve got a 6-month limit for your entire research project, it’s quite unlikely that you’ll be able to adopt a longitudinal time horizon. 

Constraint #3: Money

As with so many things, money is another important constraint you’ll need to consider when deciding on your research methodology. While some research designs will cost near zero to execute, others may require a substantial budget .

Some of the costs that may arise include:

• Software costs – e.g. survey hosting services, analysis software, etc.
• Promotion costs – e.g. advertising a survey to attract respondents
• Incentive costs – e.g. providing a prize or cash payment incentive to attract respondents
• Equipment rental costs – e.g. recording equipment, lab equipment, etc.
• Travel costs
• Food & beverages

These are just a handful of costs that can creep into your research budget. Like most projects, the actual costs tend to be higher than the estimates, so be sure to err on the conservative side and expect the unexpected. It’s critically important that you’re honest with yourself about these costs, or you could end up getting stuck midway through your project because you’ve run out of money.

Constraint #4: Equipment & software

Another practical consideration is the hardware and/or software you’ll need in order to undertake your research. Of course, this variable will depend on the type of data you’re collecting and analysing. For example, you may need lab equipment to analyse substances, or you may need specific analysis software to analyse statistical data. So, be sure to think about what hardware and/or software you’ll need for each potential methodological approach, and whether you have access to these.

Constraint #5: Your knowledge and skillset

The final practical constraint is a big one. Naturally, the research process involves a lot of learning and development along the way, so you will accrue knowledge and skills as you progress. However, when considering your methodological options, you should still consider your current position on the ladder.

Some of the questions you should ask yourself are:

• Am I more of a “numbers person” or a “words person”?
• How much do I know about the analysis methods I’ll potentially use (e.g. statistical analysis)?
• How much do I know about the software and/or hardware that I’ll potentially use?
• How excited am I to learn new research skills and gain new knowledge?
• How much time do I have to learn the things I need to learn?

Answering these questions honestly will provide you with another set of criteria against which you can evaluate the research methodology options you’ve shortlisted.

So, as you can see, there is a wide range of practicalities and constraints that you need to take into account when you’re deciding on a research methodology. These practicalities create a tension between the “ideal” methodology and the methodology that you can realistically pull off. This is perfectly normal, and it’s your job to find the option that presents the best set of trade-offs.

Recap: Choosing a methodology

In this post, we’ve discussed how to go about choosing a research methodology. The three major deciding factors we looked at were:

• Exploratory
• Confirmatory
• Combination
• Research area norms
• Hardware and software
• Your knowledge and skillset

If you have any questions, feel free to leave a comment below. If you’d like a helping hand with your research methodology, check out our 1-on-1 research coaching service , or book a free consultation with a friendly Grad Coach.

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Very useful and informative especially for beginners

Nice article! I’m a beginner in the field of cybersecurity research. I am a Telecom and Network Engineer and Also aiming for PhD scholarship.

I find the article very informative especially for my decitation it has been helpful and an eye opener.

Hi I am Anna ,

I am a PHD candidate in the area of cyber security, maybe we can link up

The Examples shows by you, for sure they are really direct me and others to knows and practices the Research Design and prepration.

I found the post very informative and practical.

I struggle so much with designs of the research for sure!

I’m the process of constructing my research design and I want to know if the data analysis I plan to present in my thesis defense proposal possibly change especially after I gathered the data already.

Thank you so much this site is such a life saver. How I wish 1-1 coaching is available in our country but sadly it’s not.

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What is research methodology?

The basics of research methodology

Why do you need a research methodology, what needs to be included, why do you need to document your research method, what are the different types of research instruments, qualitative / quantitative / mixed research methodologies, how do you choose the best research methodology for you, frequently asked questions about research methodology, related articles.

When you’re working on your first piece of academic research, there are many different things to focus on, and it can be overwhelming to stay on top of everything. This is especially true of budding or inexperienced researchers.

If you’ve never put together a research proposal before or find yourself in a position where you need to explain your research methodology decisions, there are a few things you need to be aware of.

Once you understand the ins and outs, handling academic research in the future will be less intimidating. We break down the basics below:

A research methodology encompasses the way in which you intend to carry out your research. This includes how you plan to tackle things like collection methods, statistical analysis, participant observations, and more.

You can think of your research methodology as being a formula. One part will be how you plan on putting your research into practice, and another will be why you feel this is the best way to approach it. Your research methodology is ultimately a methodological and systematic plan to resolve your research problem.

In short, you are explaining how you will take your idea and turn it into a study, which in turn will produce valid and reliable results that are in accordance with the aims and objectives of your research. This is true whether your paper plans to make use of qualitative methods or quantitative methods.

The purpose of a research methodology is to explain the reasoning behind your approach to your research - you'll need to support your collection methods, methods of analysis, and other key points of your work.

Think of it like writing a plan or an outline for you what you intend to do.

When carrying out research, it can be easy to go off-track or depart from your standard methodology.

Tip: Having a methodology keeps you accountable and on track with your original aims and objectives, and gives you a suitable and sound plan to keep your project manageable, smooth, and effective.

With all that said, how do you write out your standard approach to a research methodology?

As a general plan, your methodology should include the following information:

• Your research method.  You need to state whether you plan to use quantitative analysis, qualitative analysis, or mixed-method research methods. This will often be determined by what you hope to achieve with your research.
• Explain your reasoning. Why are you taking this methodological approach? Why is this particular methodology the best way to answer your research problem and achieve your objectives?
• Explain your instruments.  This will mainly be about your collection methods. There are varying instruments to use such as interviews, physical surveys, questionnaires, for example. Your methodology will need to detail your reasoning in choosing a particular instrument for your research.
• What will you do with your results?  How are you going to analyze the data once you have gathered it?
• Advise your reader.  If there is anything in your research methodology that your reader might be unfamiliar with, you should explain it in more detail. For example, you should give any background information to your methods that might be relevant or provide your reasoning if you are conducting your research in a non-standard way.
• How will your sampling process go?  What will your sampling procedure be and why? For example, if you will collect data by carrying out semi-structured or unstructured interviews, how will you choose your interviewees and how will you conduct the interviews themselves?
• Any practical limitations?  You should discuss any limitations you foresee being an issue when you’re carrying out your research.

In any dissertation, thesis, or academic journal, you will always find a chapter dedicated to explaining the research methodology of the person who carried out the study, also referred to as the methodology section of the work.

A good research methodology will explain what you are going to do and why, while a poor methodology will lead to a messy or disorganized approach.

You should also be able to justify in this section your reasoning for why you intend to carry out your research in a particular way, especially if it might be a particularly unique method.

Having a sound methodology in place can also help you with the following:

• When another researcher at a later date wishes to try and replicate your research, they will need your explanations and guidelines.
• In the event that you receive any criticism or questioning on the research you carried out at a later point, you will be able to refer back to it and succinctly explain the how and why of your approach.
• It provides you with a plan to follow throughout your research. When you are drafting your methodology approach, you need to be sure that the method you are using is the right one for your goal. This will help you with both explaining and understanding your method.
• It affords you the opportunity to document from the outset what you intend to achieve with your research, from start to finish.

A research instrument is a tool you will use to help you collect, measure and analyze the data you use as part of your research.

The choice of research instrument will usually be yours to make as the researcher and will be whichever best suits your methodology.

There are many different research instruments you can use in collecting data for your research.

Generally, they can be grouped as follows:

• Interviews (either as a group or one-on-one). You can carry out interviews in many different ways. For example, your interview can be structured, semi-structured, or unstructured. The difference between them is how formal the set of questions is that is asked of the interviewee. In a group interview, you may choose to ask the interviewees to give you their opinions or perceptions on certain topics.
• Surveys (online or in-person). In survey research, you are posing questions in which you ask for a response from the person taking the survey. You may wish to have either free-answer questions such as essay-style questions, or you may wish to use closed questions such as multiple choice. You may even wish to make the survey a mixture of both.
• Focus Groups.  Similar to the group interview above, you may wish to ask a focus group to discuss a particular topic or opinion while you make a note of the answers given.
• Observations.  This is a good research instrument to use if you are looking into human behaviors. Different ways of researching this include studying the spontaneous behavior of participants in their everyday life, or something more structured. A structured observation is research conducted at a set time and place where researchers observe behavior as planned and agreed upon with participants.

These are the most common ways of carrying out research, but it is really dependent on your needs as a researcher and what approach you think is best to take.

It is also possible to combine a number of research instruments if this is necessary and appropriate in answering your research problem.

There are three different types of methodologies, and they are distinguished by whether they focus on words, numbers, or both.

➡️ Want to learn more about the differences between qualitative and quantitative research, and how to use both methods? Check out our guide for that!

If you've done your due diligence, you'll have an idea of which methodology approach is best suited to your research.

It’s likely that you will have carried out considerable reading and homework before you reach this point and you may have taken inspiration from other similar studies that have yielded good results.

Still, it is important to consider different options before setting your research in stone. Exploring different options available will help you to explain why the choice you ultimately make is preferable to other methods.

If proving your research problem requires you to gather large volumes of numerical data to test hypotheses, a quantitative research method is likely to provide you with the most usable results.

If instead you’re looking to try and learn more about people, and their perception of events, your methodology is more exploratory in nature and would therefore probably be better served using a qualitative research methodology.

It helps to always bring things back to the question: what do I want to achieve with my research?

Once you have conducted your research, you need to analyze it. Here are some helpful guides for qualitative data analysis:

➡️  How to do a content analysis

➡️  How to do a thematic analysis

➡️  How to do a rhetorical analysis

Research methodology refers to the techniques used to find and analyze information for a study, ensuring that the results are valid, reliable and that they address the research objective.

Data can typically be organized into four different categories or methods: observational, experimental, simulation, and derived.

Writing a methodology section is a process of introducing your methods and instruments, discussing your analysis, providing more background information, addressing your research limitations, and more.

Your research methodology section will need a clear research question and proposed research approach. You'll need to add a background, introduce your research question, write your methodology and add the works you cited during your data collecting phase.

The research methodology section of your study will indicate how valid your findings are and how well-informed your paper is. It also assists future researchers planning to use the same methodology, who want to cite your study or replicate it.

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Research Methods Quiz

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Questions and Answers

Explain the difference between basic and applied research..

Basic research is focused on testing theories, while applied research generates knowledge that improves practice.

______ generates knowledge that improves practice.

Applied research

How do concepts relate to theories?

• Concepts are the building blocks of theories.

Concepts are the building blocks of ______.

<p>Theories</p> Signup and view all the answers

What is the purpose of a control group in an experiment?

<p>A control group does not receive treatment in experiments and is used as a baseline for comparison.</p> Signup and view all the answers

Control groups do not receive ______ in experiments.

<p>Treatment</p> Signup and view all the answers

What do correlational studies show?

<p>Correlational studies show relationships between variables.</p> Signup and view all the answers

______ studies show relationships between variables.

<p>Correlational</p> Signup and view all the answers

Why are dependent variables important in research?

<p>Dependent variables are the main focus of research, as they are the outcomes being measured.</p> Signup and view all the answers

Dependent variables are the main focus of ______.

<p>Research</p> Signup and view all the answers

What is internal validity and why is it important?

<p>Internal validity measures accuracy within a study, ensuring that the results are a true representation of the study's design.</p> Signup and view all the answers

Internal validity measures accuracy within ______.

<p>A study</p> Signup and view all the answers

What is random sampling and why is it used?

<p>Random sampling gives everyone an equal chance of being assigned to a group and is used to ensure that the sample is representative of the population being studied.</p> Signup and view all the answers

Random sampling gives everyone an equal chance of being assigned to ______.

<p>A group</p> Signup and view all the answers

Study Notes

• Technical terms used in research are important to understand.
• Applied research generates knowledge that improves practice.
• Basic research tests theories.
• Control groups do not receive treatment in experiments.
• Correlational studies show relationships between variables.
• Dependent variables are the main focus of research.
• Internal validity measures accuracy within a study.
• Random sampling gives everyone an equal chance of being assigned to a group.
• Research is a systematic way to generate facts.

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Test your knowledge of research basics with this quiz! From technical terms to the purpose of control groups, this quiz covers a range of topics related to research methodology. See how much you know about the building blocks of theories, the importance of internal validity, and the different types of studies used in research. With each question, you'll learn more about the systematic way that researchers generate facts and improve our understanding of the world. So, put your research skills to the test and take this quiz today!

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Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

• Qualitative vs. quantitative : Will your data take the form of words or numbers?
• Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
• Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

• For quantitative data, you can use statistical analysis methods to test relationships between variables.
• For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Table of contents

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

• From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
• Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

• During an experiment .
• Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square test of independence
• Statistical power
• Descriptive statistics
• Degrees of freedom
• Pearson correlation
• Null hypothesis
• Double-blind study
• Case-control study
• Research ethics
• Data collection
• Hypothesis testing
• Structured interviews

Research bias

• Hawthorne effect
• Unconscious bias
• Recall bias
• Halo effect
• Self-serving bias
• Information bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

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.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

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 analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is 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.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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Choosing the Right Research Methodology: A Guide for Researchers

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Choosing an optimal research methodology is crucial for the success of any research project. The methodology you select will determine the type of data you collect, how you collect it, and how you analyse it. Understanding the different types of research methods available along with their strengths and weaknesses, is thus imperative to make an informed decision.

Understanding different research methods:

There are several research methods available depending on the type of study you are conducting, i.e., whether it is laboratory-based, clinical, epidemiological, or survey based . Some common methodologies include qualitative research, quantitative research, experimental research, survey-based research, and action research. Each method can be opted for and modified, depending on the type of research hypotheses and objectives.

Qualitative vs quantitative research:

When deciding on a research methodology, one of the key factors to consider is whether your research will be qualitative or quantitative. Qualitative research is used to understand people’s experiences, concepts, thoughts, or behaviours . Quantitative research, on the contrary, deals with numbers, graphs, and charts, and is used to test or confirm hypotheses, assumptions, and theories. 

Qualitative research methodology:

Qualitative research is often used to examine issues that are not well understood, and to gather additional insights on these topics. Qualitative research methods include open-ended survey questions, observations of behaviours described through words, and reviews of literature that has explored similar theories and ideas. These methods are used to understand how language is used in real-world situations, identify common themes or overarching ideas, and describe and interpret various texts. Data analysis for qualitative research typically includes discourse analysis, thematic analysis, and textual analysis. 

Quantitative research methodology:

The goal of quantitative research is to test hypotheses, confirm assumptions and theories, and determine cause-and-effect relationships. Quantitative research methods include experiments, close-ended survey questions, and countable and numbered observations. Data analysis for quantitative research relies heavily on statistical methods.

Analysing qualitative vs quantitative data:

The methods used for data analysis also differ for qualitative and quantitative research. As mentioned earlier, quantitative data is generally analysed using statistical methods and does not leave much room for speculation. It is more structured and follows a predetermined plan. In quantitative research, the researcher starts with a hypothesis and uses statistical methods to test it. Contrarily, methods used for qualitative data analysis can identify patterns and themes within the data, rather than provide statistical measures of the data. It is an iterative process, where the researcher goes back and forth trying to gauge the larger implications of the data through different perspectives and revising the analysis if required.

When to use qualitative vs quantitative research:

The choice between qualitative and quantitative research will depend on the gap that the research project aims to address, and specific objectives of the study. If the goal is to establish facts about a subject or topic, quantitative research is an appropriate choice. However, if the goal is to understand people’s experiences or perspectives, qualitative research may be more suitable. 

Conclusion:

In conclusion, an understanding of the different research methods available, their applicability, advantages, and disadvantages is essential for making an informed decision on the best methodology for your project. If you need any additional guidance on which research methodology to opt for, you can head over to Elsevier Author Services (EAS). EAS experts will guide you throughout the process and help you choose the perfect methodology for your research goals.

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Qualitative vs. quantitative data analysis: How do they differ?

Learning analytics have become the cornerstone for personalizing student experiences and enhancing learning outcomes. In this data-informed approach to education there are two distinct methodologies: qualitative and quantitative analytics. These methods, which are typical to data analytics in general, are crucial to the interpretation of learning behaviors and outcomes. This blog will explore the nuances that distinguish qualitative and quantitative research, while uncovering their shared roles in learning analytics, program design and instruction.

What is qualitative data?

Qualitative data is descriptive and includes information that is non numerical. Qualitative research is used to gather in-depth insights that can't be easily measured on a scale like opinions, anecdotes and emotions. In learning analytics qualitative data could include in depth interviews, text responses to a prompt, or a video of a class period. 1

What is quantitative data?

Quantitative data is information that has a numerical value. Quantitative research is conducted to gather measurable data used in statistical analysis. Researchers can use quantitative studies to identify patterns and trends. In learning analytics quantitative data could include test scores, student demographics, or amount of time spent in a lesson. 2

Key difference between qualitative and quantitative data

It's important to understand the differences between qualitative and quantitative data to both determine the appropriate research methods for studies and to gain insights that you can be confident in sharing.

Data Types and Nature

Examples of qualitative data types in learning analytics:

• Observational data of human behavior from classroom settings such as student engagement, teacher-student interactions, and classroom dynamics
• Textual data from open-ended survey responses, reflective journals, and written assignments
• Feedback and discussions from focus groups or interviews
• Content analysis from various media

Examples of quantitative data types:

• Standardized test, assessment, and quiz scores
• Grades and grade point averages
• Attendance records
• Time spent on learning tasks
• Data gathered from learning management systems (LMS), including login frequency, online participation, and completion rates of assignments

Methods of Collection

Qualitative and quantitative research methods for data collection can occasionally seem similar so it's important to note the differences to make sure you're creating a consistent data set and will be able to reliably draw conclusions from your data.

Qualitative research methods

Because of the nature of qualitative data (complex, detailed information), the research methods used to collect it are more involved. Qualitative researchers might do the following to collect data:

• Conduct interviews to learn about subjective experiences
• Host focus groups to gather feedback and personal accounts
• Observe in-person or use audio or video recordings to record nuances of human behavior in a natural setting
• Distribute surveys with open-ended questions

Quantitative research methods

Quantitative data collection methods are more diverse and more likely to be automated because of the objective nature of the data. A quantitative researcher could employ methods such as:

• Surveys with close-ended questions that gather numerical data like birthdates or preferences
• Observational research and record measurable information like the number of students in a classroom
• Automated numerical data collection like information collected on the backend of a computer system like button clicks and page views

Analysis techniques

Qualitative and quantitative data can both be very informative. However, research studies require critical thinking for productive analysis.

Qualitative data analysis methods

Analyzing qualitative data takes a number of steps. When you first get all your data in one place you can do a review and take notes of trends you think you're seeing or your initial reactions. Next, you'll want to organize all the qualitative data you've collected by assigning it categories. Your central research question will guide your data categorization whether it's by date, location, type of collection method (interview vs focus group, etc), the specific question asked or something else. Next, you'll code your data. Whereas categorizing data is focused on the method of collection, coding is the process of identifying and labeling themes within the data collected to get closer to answering your research questions. Finally comes data interpretation. To interpret the data you'll take a look at the information gathered including your coding labels and see what results are occurring frequently or what other conclusions you can make. 3

Quantitative analysis techniques

The process to analyze quantitative data can be time-consuming due to the large volume of data possible to collect. When approaching a quantitative data set, start by focusing in on the purpose of your evaluation. Without making a conclusion, determine how you will use the information gained from analysis; for example: The answers of this survey about study habits will help determine what type of exam review session will be most useful to a class. 4

Next, you need to decide who is analyzing the data and set parameters for analysis. For example, if two different researchers are evaluating survey responses that rank preferences on a scale from 1 to 5, they need to be operating with the same understanding of the rankings. You wouldn't want one researcher to classify the value of 3 to be a positive preference while the other considers it a negative preference. It's also ideal to have some type of data management system to store and organize your data, such as a spreadsheet or database. Within the database, or via an export to data analysis software, the collected data needs to be cleaned of things like responses left blank, duplicate answers from respondents, and questions that are no longer considered relevant. Finally, you can use statistical software to analyze data (or complete a manual analysis) to find patterns and summarize your findings. 4

Qualitative and quantitative research tools

From the nuanced, thematic exploration enabled by tools like NVivo and ATLAS.ti, to the statistical precision of SPSS and R for quantitative analysis, each suite of data analysis tools offers tailored functionalities that cater to the distinct natures of different data types.

Qualitative research software:

NVivo: NVivo is qualitative data analysis software that can do everything from transcribe recordings to create word clouds and evaluate uploads for different sentiments and themes. NVivo is just one tool from the company Lumivero, which offers whole suites of data processing software. 5

ATLAS.ti: Similar to NVivo, ATLAS.ti allows researchers to upload and import data from a variety of sources to be tagged and refined using machine learning and presented with visualizations and ready for insert into reports. 6

SPSS: SPSS is a statistical analysis tool for quantitative research, appreciated for its user-friendly interface and comprehensive statistical tests, which makes it ideal for educators and researchers. With SPSS researchers can manage and analyze large quantitative data sets, use advanced statistical procedures and modeling techniques, predict customer behaviors, forecast market trends and more. 7

R: R is a versatile and dynamic open-source tool for quantitative analysis. With a vast repository of packages tailored to specific statistical methods, researchers can perform anything from basic descriptive statistics to complex predictive modeling. R is especially useful for its ability to handle large datasets, making it ideal for educational institutions that generate substantial amounts of data. The programming language offers flexibility in customizing analysis and creating publication-quality visualizations to effectively communicate results. 8

Applications in Educational Research

Both quantitative and qualitative data can be employed in learning analytics to drive informed decision-making and pedagogical enhancements. In the classroom, quantitative data like standardized test scores and online course analytics create a foundation for assessing and benchmarking student performance and engagement. Qualitative insights gathered from surveys, focus group discussions, and reflective student journals offer a more nuanced understanding of learners' experiences and contextual factors influencing their education. Additionally feedback and practical engagement metrics blend these data types, providing a holistic view that informs curriculum development, instructional strategies, and personalized learning pathways. Through these varied data sets and uses, educators can piece together a more complete narrative of student success and the impacts of educational interventions.

Master Data Analysis with an M.S. in Learning Sciences From SMU

Whether it is the detailed narratives unearthed through qualitative data or the informative patterns derived from quantitative analysis, both qualitative and quantitative data can provide crucial information for educators and researchers to better understand and improve learning. Dive deeper into the art and science of learning analytics with SMU's online Master of Science in the Learning Sciences program . At SMU, innovation and inquiry converge to empower the next generation of educators and researchers. Choose the Learning Analytics Specialization to learn how to harness the power of data science to illuminate learning trends, devise impactful strategies, and drive educational innovation. You could also find out how advanced technologies like augmented reality (AR), virtual reality (VR), and artificial intelligence (AI) can revolutionize education, and develop the insight to apply embodied cognition principles to enhance learning experiences in the Learning and Technology Design Specialization , or choose your own electives to build a specialization unique to your interests and career goals.

For more information on our curriculum and to become part of a community where data drives discovery, visit SMU's MSLS program website or schedule a call with our admissions outreach advisors for any queries or further discussion. Take the first step towards transforming education with data today.

• Retrieved on August 8, 2024, from nnlm.gov/guides/data-glossary/qualitative-data
• Retrieved on August 8, 2024, from nnlm.gov/guides/data-glossary/quantitative-data
• Retrieved on August 8, 2024, from cdc.gov/healthyyouth/evaluation/pdf/brief19.pdf
• Retrieved on August 8, 2024, from cdc.gov/healthyyouth/evaluation/pdf/brief20.pdf
• Retrieved on August 8, 2024, from lumivero.com/solutions/
• Retrieved on August 8, 2024, from atlasti.com/
• Retrieved on August 8, 2024, from ibm.com/products/spss-statistics
• Retrieved on August 8, 2024, from cran.r-project.org/doc/manuals/r-release/R-intro.html#Introduction-and-preliminaries

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Research on the measurement and estimation method of wheel resistance on a soil runway.

1. Introduction

2. theory and methodology, 2.1. characterization method of wheel resistance on soil road surfaces, 2.1.1. driving resistance characterization method based on the mechanical model, 2.1.2. kinematic model-based driving resistance characterization method, 2.2. field testing of driving resistance based on the mechanical model, 2.2.1. basic conditions of the test site, 2.2.2. test method, 2.3. field test of driving resistance based on the kinematic model, 2.3.1. the test principle of speed and distance, 2.3.2. test vehicle selection, 2.3.3. test procedure, 3. results and discussion, 3.1. test result analysis of the mechanical model, 3.2. test data analysis of the kinematic model, 3.3. estimation method of aircraft running resistance on the soil road surface, 3.3.1. determination of the calculation parameters of road surface driving resistance, 3.3.2. estimation of road surface driving resistance, 3.3.3. accuracy analysis of the estimation method, 3.3.4. the process of estimating the driving resistance of soil pavement, 4. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Wang, Z.; Chong, X.; Wang, G.; Liu, C.; Zhang, J. Research on the Measurement and Estimation Method of Wheel Resistance on a Soil Runway. Coatings 2024 , 14 , 1062. https://doi.org/10.3390/coatings14081062

Wang Z, Chong X, Wang G, Liu C, Zhang J. Research on the Measurement and Estimation Method of Wheel Resistance on a Soil Runway. Coatings . 2024; 14(8):1062. https://doi.org/10.3390/coatings14081062

Wang, Zihan, Xiaolei Chong, Guanhu Wang, Chaojia Liu, and Jichao Zhang. 2024. "Research on the Measurement and Estimation Method of Wheel Resistance on a Soil Runway" Coatings 14, no. 8: 1062. https://doi.org/10.3390/coatings14081062

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Testing the feasibility of a blood test to assess suicide risk: Innovative study will serve as precedent in suicide prevention research

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“RNA editing patterns as blood biomarker for predicting suicide risk” is an ambitious new study led by an interdisciplinary team of Kent State researchers. Principal investigator Helen Piontkivska, Ph.D., is a professor in the Department of Biological Sciences and an affiliate of the Brain Health Research Institute and the Healthy Communities Research Institute. Mark Dalman, Ph.D., associate professor in the College of Podiatric Medicine, serves as a co-investigator alongside Deric Kenne, Ph.D., director of the Center for Public Policy and Health and professor in the College of Public Health. A two-year grant funded by the American Foundation for Suicide Prevention will allow the team to explore the use of blood-based RNA editing biomarkers as potential predictors of suicidal ideation, a method never explored before. If successful, such biomarkers may later be used to inform targeted interventions and monitoring. 

The study addresses the question, "How can we better or more optimally detect and predict risk?”, which is a top priority of the National Action Alliance for Suicide Prevention’s Research Prioritization Task Force. When attempting to identify a person at risk of suicide, the current and most relied-upon approach is to assess physical appearance, actions, and verbalized intentions. However, Dr. Piontkivska’s study will go beyond this current methodology to use gene expression-based identifiers. 

The study will collect blood samples from participants receiving mental health care who have reported recent thoughts of suicide. These samples will be used to infer RNA editing profiles using computational approaches developed in Piontkivska’s lab. These profiles will then be analyzed to determine whether dynamic RNA editing dysregulation is associated with suicidal ideation risk and whether changes in RNA editing patterns can serve as biomarkers of suicidal ideation and/or recovery. Dr. Piontkivska, Dr. Kenne, Dr. Dalman, and the rest of the research team at Kent State University are hoping that results from this novel study – that combines existing approach of accessing physical identifiers with biological markers - could lead to improvements in suicide prevention and treatment, and ultimately, to better mental health outcomes. 

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• Open access
• Published: 19 August 2024

The relationship between moral reasoning and nurses’ professional values in undergraduate nursing students: a descriptive-correlational study

• Amir Mohamad Nazari 1 ,
• Fariba Borhani   ORCID: orcid.org/0000-0002-8937-2649 1 ,
• Akbar Zare-Kaseb   ORCID: orcid.org/0000-0002-0014-7104 1 ,
• Vahid Yousofvand 2 &
• Abbas Abbaszadeh 1  

BMC Medical Education volume  24 , Article number:  889 ( 2024 ) Cite this article

122 Accesses

Metrics details

Moral reasoning in nursing is crucial in delivering high-quality patient care and fostering increased job satisfaction among nurses. Adhering to professional values is vital to this profession, and nurses must modify their actions to align with these values.

This study aimed to examine the correlation between moral reasoning and professional values among undergraduate nursing students.

Research design

A descriptive correlational design was recruited.

Participants and research context

The research was conducted at three nursing schools located in Tehran, Iran. The sample was recruited through random stratified sampling, specifically targeting undergraduate nursing students. The data collection tool comprised a three-part questionnaire, including a demographic information form, the Nursing Dilemma Test, and the Nurses Professional Values Scale Revised Questionnaire. The distribution of questionnaires encompassed both face-to-face and electronic methods. The analysis of data was conducted using SPSS 16 software. The data was analyzed using the independent samples t-test, Pearson’s correlation coefficient, and linear regression analysis. The P value of 0.05 was considered significant.

Ethical considerations

The Ethics Research Center of Shahid Beheshti University of Medical Sciences approved the study.

Data analysis showed that moral reasoning was directly correlated to professional values ( r  = 0.528, p  < 0.001). The mean scores of Principled Thinking (P.T.), Practical Consideration (P.C.), and Familiarity with similar moral dilemmas of the NDT scale were 42.55 (SD = 12.95), 15.72 (SD = 6.85), 16.08 (SD = 6.67), respectively. Also, the total score of professional values of students was 90.63 (SD = 28.80).

The findings indicated that moral reasoning and interest in nursing predict students’ professional identity. Thus, any effort to enhance interest in the profession can contribute to developing students’ professional identity. This can involve incentivizing, enhancing the professional reputation at the community and university levels, and valuing student preferences and necessities.

Peer Review reports

The medical environment of nursing practice has been constantly evolving and becoming more complex. The continuous renewal of science and technology, the change in the disease spectrum, and the aging population have specifically impacted traditional nursing work [ 1 ]. On the other hand, the shortage of nursing human resources and the different disease cognition between nurses and patients have often led nurses into an ethical dilemma due to the confusion of roles, moral conflicts, and value conflicts [ 2 , 3 , 4 ].

Research findings in various nursing schools suggest a growing disregard among nursing students for ethical considerations in their everyday nursing practices. The nursing curriculum has recently been exposed to its lack of emphasis on ethics. Nursing schools do not explicitly cover the concept of professional ethics in any of their theoretical or clinical courses [ 5 ]. So, it seems that compared to professional nurses, nursing students are inexperienced and possess inadequate aptitude and the courage to confront and manage ethical dilemmas in clinical settings [ 6 ].

Nursing students face ethical problems in clinical settings, ranging from violating patient’s rights and dignity to insecure care delivery [ 7 ]. Despite learning about different moral theories and principles during their education, nursing students may find it daunting to apply those principles due to minimal support and guidance in clinical settings [ 8 , 9 ]. Encountering dilemmas in clinical settings can lead to student emotional distress, affecting their clinical learning and professional development [ 6 , 10 ]. Thus, strategies are needed to improve nurses’ and nursing students’ moral reasoning abilities to minimize the likelihood of these problems [ 7 ].

Moral reasoning refers to the cognitive process of recognizing an ethical dilemma and selecting the suitable course of action, enabling nurses to make informed decisions [ 11 ]. Moral reasoning in nursing necessitates nurses to evaluate and make appropriate decisions to tackle the daily challenges they face in the clinical environment [ 12 ].

Kohlberg’s research serves as the foundation for most studies on moral reasoning [ 13 ]. A classification of ethical development into six stages was suggested to assess advancement in attaining absolute universal justice. Some suggest that individuals’ moral reasoning aligns with their advancement in each stage [ 14 ].

Kohlberg’s model classifies moral reasoning into three levels: pre-conventional, conventional, and post-conventional, each comprising two stages. During the pre-conventional phase, individuals prioritize their interests and engage in actions focused on self-gratification or avoiding unfavorable outcomes. Within the conventional stage, individuals frequently use accepted social rules and principles to make decisions. At the post-conventional stage, individuals consciously align their actions with established ethical principles and prioritize ethical and compassionate decision-making [ 15 ].

Professional values are the performance standards accepted by the professional and specialist groups [ 16 ]. These values are the basis of nursing performance, the director of the nurses’ interaction with the patients, colleagues, other professionals, and the public, and as a guideline for ethical behavior to provide secure and humanitarian care [ 17 , 18 ]. Values are goals and beliefs that create behaviors and are a basis for decision-making and practice [ 19 , 20 ]. The acquisition and internalization of professional values are necessary in care settings for professional development, and they provide a common framework for meeting professional expectations and standards [ 21 , 22 ].

Considering that nursing involves scientific knowledge in addition to human and professional values, students must thoroughly understand these values to provide ethical care and engage in moral reasoning across various scenarios [ 23 ]. Professional nursing values play an essential role in shaping nursing professionals’ competence. They encompass human dignity, integrity, altruism, and justice and guide nursing standards, practice, and evaluation [ 24 , 25 ]. Therefore, developing nursing professional values can promote care quality, increase patient understanding, and increase job satisfaction and retention of nursing staff; it also helps the professional socialization process [ 21 , 26 ].

Prior research has been conducted in this specific domain, and it is worth noting that these studies possess certain limitations. The investigation conducted by Hajilo et al. [ 27 ] explored the association between ethical reasoning and professional values in nursing students. The results of their study revealed no significant correlation between the two factors. The researchers highlighted certain limitations in their research conducted amidst the coronavirus pandemic. Hence, they propose that the study be replicated in clinical settings with a larger sample size and random sampling to ensure better generalizability. Our study seeks to fill the gaps in this research by utilizing a larger sample size and examining and comparing these variables in nursing students from various semesters.

Moral reasoning and professional identity have been the subjects of only a limited number of Iranian studies, which have not fully addressed this topic’s various dimensions. Considering the importance placed on moral reasoning and the professional values of nursing students, building upon the hypothesis, if there is a relationship between these variables, promoting moral reasoning can be considered a significant factor in cultivating nursing students’ professional values. Thus, this study examined the relationship between moral reasoning and professional identity among nursing students.

Study design

The present study employed a descriptive-correlational design.

Sample and setting

The research was conducted at three nursing schools located in Tehran, Iran. The sample was recruited through random stratified sampling, specifically targeting undergraduate nursing students. The G*power software was utilized to determine the minimum sample size. The criteria for determining sample size were alpha = 0.05, power = 0.80, and a correlation coefficient of 0.2 with a 10% drop-out rate. The number of samples allocated to each faculty was computed based on the student population at the time of data collection [ 28 , 29 ].

C = 0.5*ln[(1 + r)/(1-r)]

N=[(z α +z β )/C] 2 +3.

Inclusion and exclusion criteria

The inclusion criteria were: (1) undergraduate nursing students who were studying in all semesters and (2) willingness and consent to participate in the study, and the exclusion criteria were: (1) returning incomplete questionnaires and (2) being students of other universities which was not in our inclusion criteria for setting and was transferred to this school.

Measurements

The research methodology involved the utilization of a demographic questionnaire, the Nursing Dilemma Test (NDT) [ 30 ], and the Nurses Professional Values Scale-Revised (NPVS-R) [ 31 ] as assessment tools. The demographic factors under investigation were determined through a comprehensive review of related studies and by consulting experts in the field.

Nursing dilemma test (NDT)

The NDT was established in 1981 at the University of Minnesota by Patricia Crisham [ 14 ]. NDT further examines nurses’ moral reasoning, decision-making capabilities, practical considerations, and familiarity with moral dilemmas. The NDT comprises six scenarios that specifically address ethical dilemmas in healthcare. These scenarios cover a range of situations, including (1) dealing with a newborn who has anomalies, (2) the issue of administering medication forcefully, (3) handling an adult’s request for assisted death, (4) orienting new nurses, (5) addressing medication errors, and (6) managing the treatment of an uninformed terminally ill adult [ 32 ].

Part A of the NDT focuses on the assessment of moral decision-making. In each of the six scenarios, participants are tasked with envisioning themselves as the nurse, and it is recommended that they respond to the question: “What actions should the nurse undertake? The choices for the participants include “Should act,” “Cannot decide,” or “Should not act.” The nurse who acts has made a moral decision. The ethical decision-making of the nurses is shown by the percentage of their chosen actions in each scenario.

The second section provides six statements for consideration when approaching the scenarios, encompassing the ethical dilemma. The participants must select the most significant statement from these six and arrange them according to personal importance. The aim of the responses given in this section of the test is to evaluate the levels of “Principled Thinking” (P.T.) and “Practical Consideration” (P.C.). The possible minimum P.T. score on the test is 18, while the maximum P.T. score is 66. The minimum possible P.C. score achievable on the test is 6, while the maximum P.C. score is 36. The P.T. demonstrates the significance of including moral principles in making ethical decisions within the nursing field. When making ethical decisions, the P.C. considers environmental factors like patient load, resource availability, institutional policies, nurses’ perception of administrative support, and doctors’ decision-making authority [ 33 ].

The assessment tool NDT - Part C measures nurses’ Familiarity (F) level with comparable moral dilemmas in each scenario, employing a 5-point scale. Items include: “I have decided in a similar dilemma” (score = 1), “I know someone else in a similar dilemma” (score = 2), “I do not know anyone in a similar dilemma, but the dilemma is conceivable” (score = 3), “It is difficult to imagine the dilemma as it seems remote” (score = 4), and “It is difficult to take the dilemma seriously as it seems unreal” (score = 5). The scoring system for moral dilemmas in NDT categorizes familiarity as a total score between 6 and 17 and unfamiliarity as a score between 18 and 30 (Table  1 ).

The reliability and validity of the questionnaire have been verified by its author, and it has been widely used by researchers [ 14 , 34 , 35 , 36 ]. Borhani et al. [ 37 ] and Mogadasian et al. [ 38 ] reported Cronbach’s alpha coefficients of 0.82 and 0.95, respectively, for the Persian version of the NDT.

Nurses professional values scale-revised (NPVS-R)

Weiss and Shank formulated the questionnaire utilized in this study in 2009 [ 39 ]. It comprises 26 elements from established nursing professional ethics codes, encompassing caring, trust, professionalism, justice, and activism.

The dimension of caring consists of 9 items, specifically items 16, 17, 18, 20, 21, 22, 23, 24, and 25. In the context of trust, there are five items to consider: 1, 2, 9, 14, and 15. The dimension of professionalism comprises four items, namely 5, 6, 7, and 8. The dimension of justice consists of three items, specifically items 3, 12, and 13. Finally, the dimension of activism encompasses items 4, 10, 11, 19, and 26.

The scoring is established on a five-point Likert scale, encompassing the spectrum from “unimportant” to “most important.” The scoring system assigns a score of 1 to the option “unimportant,” a score of 2 to “slightly important,” a score of 3 to “moderately important,” a score of 4 to “important,” and a score of 5 to “most important.” The range of scores for this questionnaire spans from 26 to 130, and a higher score signifies a higher level of familiarity among nurses with professional values. When scoring, a score below 43 signifies low-level professional values, between 43 and 86 indicates medium-level professional values, and above 86 represents high-level professional values.

By employing Cronbach’s alpha method, Weiss and Shank evaluated the tool’s total reliability, revealing a favorable coefficient of 0.92 for the tool [ 40 ]. In a study conducted by Parvan et al. 2012, the Persian version of the questionnaire was evaluated for its validity. The validity of the Persian version of the questionnaire was examined by Parvan et al. after its translation, with the results indicating good face and content validity. Moreover, the Persian adaptation of the questionnaire displayed a Cronbach’s alpha coefficient of 0.91 [ 41 ].

Data gathering

Data gathering lasted from June 1 to November 30, 2023. After securing ethical approval, the researchers presented in three nursing faculties of medical sciences universities. Participants were briefed about the research objectives face-to-face and online and filled out an informed written consent form. Then, participants were asked to complete the paper-based questionnaires. Once filled, researchers collected the questionnaires. Also, to increase the students’ participation, an electronic link to the questionnaires was provided to them. Students did not feel coerced into completing the questionnaires because the researchers were not among their teachers.

Statistical analysis

Data analysis was performed using IBM SPSS Statistics 16.0. We used descriptive statistics (frequency distribution, mean, and standard deviation) and analytical statistics, including the analysis of variance (ANOVA), independent t-test, Pearson’s correlation coefficient, and linear regression analysis. After screening the data for the assumptions of various parametric tests, correlations between moral reasoning and professional values were explored. Also, Multiple regression analysis was conducted to examine the best explanatory variables of professional values. The maximum alpha bias level for testing the hypotheses was fixed at 0.05.

Participants

Two hundred (83.3%) of the 240 distributed questionnaires were returned from study subjects. The Tehran University of Medical Sciences accounted for 40% of the total sample size, while Shahid Beheshti and Iran universities each held a 30% share.

Sample profile

The mean age of participants in this study was 21.34 (SD = 2.01). Most of the participants (55.5%) were male, were single (93.0%), and were interested in nursing (71.5%). Regarding the academic semester, the fourth semester had the highest percentage of participants, at 18.5%, whereas the seventh semester had the lowest rate, at 8% (Table  2 ).

Moral reasoning and professional values

The study’s findings showed that the mean scores of Principled Thinking (P.T.), Practical Consideration (P.C.), and Familiarity with similar moral dilemmas of the NDT scale were 42.55 (SD = 12.95), 15.72 (SD = 6.85), 16.08 (SD = 6.67), respectively. Also, the total score of professional values of students was 90.63 (SD = 28.80). The scores of professional values dimensions are presented in detail in Table  3 .

According to nursing students’ answers to the ethical scenarios of the NDT test, difficulty in decision-making regarding the resuscitation of a child with an anomaly was observed in 18.5% of cases among nursing students. In terms of mandatory drug prescription and providing honest answers to people’s questions at the end of life, the corresponding figures were 18% and 19%. The issue with the least amount of uncertainty pertains to the request of an adult to end their life, a situation which students overwhelmingly oppose, with a rate of 86%. In this particular scenario, a mere 9.5% of individuals remained undecided. The details are presented in Table  4 .

Correlation between major study variables

Principled thinking (P.C.) was directly correlated to professional value’s total ( r  = 0.528), caring ( r  = 0.504), activism ( r  = 0.531), trust ( r  = 0.515), professionalism ( r  = 0.496), and justice ( r  = 0.452), scores. This correlation was all significant ( p  < 0.01) and moderate (0.4 <  r  < 0.59).

Practical consideration (P.C.) had significant, negative, and moderate correlation with professional value’s total ( r  = − 0.539), caring ( r  = − 0.521), activism ( r  = − 0.527), trust ( r  = − 0.546), professionalism ( r  = − 0.490), and justice ( r  = − 0.450) scores.

Familiarity (F) with moral dilemmas didn’t correlate with the total score of professional value or any other subscales of NPVS-R (Table  5 ).

Factors influencing professional identity

Multivariate regression (enter method) determined that the practical consideration (P.C.) of the NDT scale is the most effective dimension in predicting the level of professional value of nursing students (β = -0.356, p  < 0.001). The overall predictive value of the P.T., P.C., and F scales to professional identity was 33.9% (R2 = 0.339, Adjusted R2 = 0.329) (Table  6 ).

Among the socio-demographic variables, multivariate regression showed that being interested in nursing (β = -0.120, p  = 0.045), marital Status (β = 0.090, p  = 0.139), and sex (β = 0.035, p  = 0.564) respectively, have had the most significant effect on the professional value of nursing students. Of the abovementioned factors, only being interested in nursing significantly impacted professional values. This relationship was indirect, as higher interest was associated with weaker professional values. Together, these factors (P.T., P.C., F, marital Status, interest in nursing, and sex) explained about 36.2% of the variance in the professional value of nursing students (Table  7 ).

Moral decision-making

The initial component of each scenario in the NDT questionnaire assesses students’ moral decision-making capacity. Based on our study, it is evident that students demonstrated indecisiveness in fewer than 20% of cases for all six scenarios. The greatest challenge arose in determining the appropriate and truthful approach towards end-of-life patients, with the least complexity encountered when addressing the request for euthanasia by an adult patient. This implies that student nurses can readily decide about euthanasia. As per our results, nursing students rejected the patient’s request in 86% of cases. This may pertain to the cultural aspects of euthanasia within the society under examination.

The primary factors linked to positivity and supportiveness stemmed from (a) the patient’s experience of extreme and uncontrollable pain, unbearable suffering, or other distressing situations, (b) the legal aspects of euthanasia, and (c) the patient’s right to choose their death. The negative and unsupportive attitude of nurses was influenced by various factors, such as religion, moral dilemmas, the role of gender in healthcare, and poor palliative care [ 42 ]. The findings of our study in this field have been validated by a recent study conducted in Iran. Additionally, the researchers discovered that nurses with elevated ethical reasoning exhibit a more unfavorable stance on euthanasia [ 43 ].

• Moral reasoning

Moreover, the outcomes of our study demonstrated that the students possess a remarkable level of moral reasoning, enabling them to effectively navigate and resolve moral difficulties frequently encountered in clinical settings. These findings align with the results of a comparable study in this particular domain. The study revealed that the students’ moral reasoning skills exceeded the average level [ 44 ]. Similar results were observed in another study on nurses [ 45 ].

Our study indicates a significant connection between students’ moral reasoning and professional values. Higher professional values were linked to a more favorable level of professional reasoning. This is entirely consistent with the outcomes of comparable research [ 46 , 47 ]. Nursing students who possess elevated professional values exhibit higher confidence when faced with ethical decision-making [ 48 ]. Considering the positive relationship between these two constructs, nursing education can help improve the other by strengthening each. The study’s findings prove that moral reasoning can be a significant stimulus for enhancing professional values. Ethical reasoning exercises, such as simulating scenarios in a simulated environment, can help improve professional values. Conversely, there was a study that did not observe any substantial link between moral reasoning and the professional values ​​of students [ 27 ].

The average score of practical considerations was at the average level, which indicates the importance of environmental factors and organizational climate for students’ ethical decision-making and clinical activities. One of the factors that can contribute to the influence of the work environment on the ethical decision-making and clinical performance of students is their limited exposure and incomplete familiarity with the work environment’s rules. This aligns with the findings of the study conducted by Sari et al. According to their statement, students exhibit less susceptibility to environmental regulations when making ethical decisions than nurses or students of higher semesters [ 44 ]. However, it is worth noting that in two separate studies, students’ practical considerations were higher than average, presenting a slight disparity with the results obtained in the present study [ 27 , 49 ].

The current study revealed a significant inverse correlation between practical considerations and students’ professional values. Thus, students who can decide and engage in moral thinking independently of the influence of environmental rules and organizational atmosphere uphold higher professional values. This is consistent with the results of a similar study [ 27 ].

Familiarity with moral dilemmas

The mean score of students’ familiarity with situations shows that students are slightly familiar with different moral challenges. Students’ lack of clinical experience and inadequate preparation for ethical dilemmas contribute to this issue. Findings from related research in this field yielded similar outcomes [ 27 ]. The findings indicated that students require extensive work experience to comprehensively understand ethical issues in clinical settings, which were inadequately addressed during academic semesters [ 44 ].

• Professional values

The results obtained from the present study show that the average score of students’ professional values was significantly high. Students show a heightened focus on the dimension of patient care and assign considerable importance to it, as per the reported priorities of professional values. This issue highlights the significance of cautiousness in nursing education programs within college and clinical settings. The results of similar studies have been the same [ 50 , 51 , 52 ]. Concerning the dimensions, the research conducted by Poorchangizi et al. emphasized the significance of the caring dimension, aligning with our study. However, their study also highlighted the importance of the justice dimension, which contradicts our findings [ 51 ]. Moreover, this study also revealed that students exhibited a notably more positive perception of the significance of professional values than nurses [ 51 ].

Predictors of professional values

The main variables, P.C. and P.T., demonstrated acceptable predictive efficacy in predicting the professional value. However, demographic variables make a modest contribution to the prediction. When considering the demographic variables, it is evident that only interest in nursing plays a significant role in predicting professional values. The order of effect is as follows: P.C., P.T., interest in nursing, F, marital status, and sex. This means that the professional values ​​of students are more influenced by the rules of the environment and organizational climate than by their decision-making and moral thinking. One of the notable points in this study is the negative relationship between interest in nursing and professional values. This could be because students who entered this field with interest had more expectations from this field. They wavered about professional values while entering the clinical environment and distancing themselves from the ideals. The findings of previous studies exploring the correlation between main variables and demographics have aligned with the findings of our research [ 51 , 53 , 54 ]. Among all the studies conducted, only Pourchengizi et al.‘s research shows a notable association between age and the professional values of students [ 51 ].

A limitation in correlational studies like this is the inability to demonstrate causation. Future research should be conducted with a larger sample size of nursing students from various faculties and a design that investigates cause-and-effect relationships.

A further restriction of this study concerns the scenarios posed in the NDT questionnaire. The responses and associated interpretations can be subjected to the impact of the cultural context. Generalizing the results of these scenarios to other communities can be challenging. A blended approach was adopted to collect students’ data to optimize time and minimize time wastage. This can contribute to the potential response differences between these modes and impact the results.

Despite an in-depth examination of the current literature and consultation with experts in the field, no specific confounding factors could be determined due to the limited number of relevant studies. Consequently, we analyzed the correlation between demographic characteristics and the main variables of the study. In the regression model, we included only those cases that demonstrated a substantial relationship with the primary variable below the level of 0.02. It is recommended that future researchers strive to identify confounding factors and mitigate their impact on the relationship between the main variables, thereby enhancing the generalizability of the results.

Implications for nursing education

Limited research has been conducted on the influence of educational factors, particularly curriculum, on students’ professional identity and moral reasoning. It appears that, given the unique circumstances of the nursing profession, particularly in light of the COVID-19 pandemic, modifying the student curriculum would enhance their circumstances. Nursing educators must be qualified to successfully guide students in cultivating a suitable professional identity.

The findings from our study suggest that nurses showed a high level of moral reasoning and uphold professional values. Also, results revealed a noteworthy correlation between students’ moral reasoning and professional values. The predictive value of moral reasoning in determining professional value was satisfactory. When considering socio-demographic variables, an interest in nursing was found to have a significant effect on professional values. Our research findings indicate that enhancing professional identity and moral reasoning can improve students’ circumstances. Furthermore, generating interest in the nursing profession can impact the professional identity of these students.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Amir Mohamad Nazari, Fariba Borhani, Akbar Zare-Kaseb & Abbas Abbaszadeh

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A.N. and A.Z. wrote the main manuscript text and A.Z. prepared Tables 1, 2, 3, 4, 5, 6 and 7. Statistical analysis was done by V.Y. All authors reviewed the manuscript.

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Nazari, A.M., Borhani, F., Zare-Kaseb, A. et al. The relationship between moral reasoning and nurses’ professional values in undergraduate nursing students: a descriptive-correlational study. BMC Med Educ 24 , 889 (2024). https://doi.org/10.1186/s12909-024-05888-z

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Purpose: In this study, we examine the joint effects of stunting and socioeconomic status (SES) on COVID-19 serostatus in Brazil and whether these patterns vary by COVID-19 vaccine status. Methods: We use data from birth through the 7-year follow-up of the Pelotas 2015 birth cohort, including anthropometry, demographics, vaccination status, and COVID-19 antibody test results. We use linear regression models to examine the associations between SES (exposure) and stunting (effect modifier) on COVID-19 antibody titer among children who had not been vaccinated for COVID-19 (n=1,103) and among children who had been vaccinated (n=1,875). Results: All SES strata had a statistically identical probability of testing positive. While not statistically significant, stunted children who were unvaccinated tended to have lower COVID-19 titers (difference -0.10, 95% CI: -0.21,0.004) compared with children who were not stunted, and this difference was attenuated among stunted children who were vaccinated. Duration of school enrollment was associated with increased antibody titer, with each month being associated with a 0.17 unit higher increase in OD titer (95% CI: 0.15, 0.18). Conclusions: This result may suggest that stunted children have poorer immune responses to natural infection and thus lower immunity, but vaccination can overcome this deficit. Future recommendations include population-wide follow-up vaccination, particularly for stunted children. Given increasing antibody titer with a longer duration of school attendance, primary COVID-19 vaccination and potential boosters may be useful for children prior to school entry to reduce the risk of natural infection.

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The study protocol was approved by the Research Ethics Committee at the Federal University of Pelotas (School of Physical Education: 0-4 years, #26746414.5.0000.5313. Medical School: 6-7 years follow-up, #51789921.1.0000.5317). The childrens parents or legal representatives signed an informed consent form before each interview, including a statement authorizing the investigators to check their data on official health information systems, such as National Immunization Program Information System (NIP-IS) of the Brazilian Unified Health System (SUS for short, in Portuguese).

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• Urology (165)