Ann Del Bianco

Methodology is the part of the research process concerned with research design and the various considerations made when collecting, analyzing, and interpreting the data used to answer a research question.

Ann Del Bianco[1] is an Adjunct Professor in the Faculty of Environmental and Urban Change at York University in Toronto, Canada. Her research interests include alternative methodological approaches, holistic conceptual frameworks, the lifegrid, esophageal cancer, environmental and occupational cancer, cancer prevention, and environmental and ecosystem health.

Methodological considerations to researching children’s return to school during the COVID-19 pandemic

Methodology is an important part of the research process, providing a common language and toolkit necessary for researchers to conduct good studies needed to acquire knowledge. It provides a detailed roadmap that explains the different steps taken and types of decisions the researcher has made. In this way, it provides a level of transparency, which is important especially if another researcher wants to replicate the study results. This is possible for quantitative studies that are objective and use numerical data, but not for qualitative studies that are subjective and use other data, such as those that are text-based or images.

There are many different ways a research question can be posed and answered. Depending on the choice made, there is an impact on the research approach taken, the selection of research design, and the type of data collected (which needs to be done in an ethical way). Researchers also need to use a common language to describe how the data was analyzed and interpreted. For instance, in the social sciences, it is typical for qualitative researchers to use conceptual and/or theoretical frameworks to inform this process. The table below provides a simplified overview of what a methodology section addresses within a research study. The table is not meant to be exhaustive; it is meant as a guide.

All research begins with a topic. In this example, it is children and the COVID-19 pandemic. Prior to each new school year during the pandemic, schools request that parents/legal guardians decide the mode of delivery (remote only or in-person) for their children’s education, which is made with or without input from their children. Little is known on how children feel about returning to in-person learning during the pandemic and why. An example of a good research question about this issue that could be posed would be: In York Region, Ontario, to what extent does grade three, in-person school enrolment for the 2021–22 school year reflect how students going into grade three feel about the prospect of going back to in-person school during the COVID-19 pandemic and why? Note that the research question is framed to capture students from York Region and follows a mixed methodology because it uses both quantitative and qualitative research approaches. The first part is quantitative because it uses numbers to describe and make predictions about student enrolment. The second part is qualitative because it is about understanding how students going into grade three feel and why. The study can also be considered as cross-sectional because the data collected represents only one moment in time.

The research design for the first part of this question is descriptive and uses enrollment data gathered by school boards. This means that it will use secondary data, because the information used was already collected by someone else for other purposes. From this data, we might learn that 95% of grade three students are going back to in-person learning for the 2021–22 school year. Enrolment data is publicly available, but the most current data can only be obtained by contacting each school board serving York Region. These population-based findings might in turn be generalized to other populations. If you wanted to also obtain more sensitive information tied to enrolment data, such as race, gender, and household income, you would have to inquire if this data is collected by the school boards and then submit an official request and research proposal to each of the Research and Ethics Boards (REBs) of the involved institutions (including your own university). This is because such data is not publicly available or accessible, and the study also involves interviewing human participants. If approved, this additional data would allow you to examine, for example, if there is a relationship between in-person enrolment and household income, changing it to a correlational research design. You could use statistical analysis software to then analyze the data, and previous research from the literature to help interpret it.

The dataset from the school board would never include confidential information like the student’s name, address, or contact information. Therefore, you could not expect to contact and recruit a sample of this population to interview them for the second part of the research question. Before considering interviewing seven and eight year olds, there are several ethical issues that need to be considered, since these children are not of age to provide informed consent. Their parents or legal guardians would need to consent, ideally in writing, for their child to partake in the study, which would maintain anonymity and permit withdrawal from the study at any time. The study would have to be fully explained to all involved, including how data would be stored, and for how long, as well as how findings would be communicated.

Phenomenology is a qualitative research design you might choose to understand the feelings that these children are experiencing. It is an approach that evaluates how a phenomenon (e.g., in-person learning during a pandemic) is consciously experienced (e.g., by a student going into grade three). The primary data (collected solely for the purposes of the study) could be gathered using an interview guide that consists of demographics and a series of open-ended questions, and probes that would allow different themes to emerge during the interviews. To collect the primary data, you could use a technique called convenience sampling. This might mean that you live in York Region and directly ask parents in the community if their children could be interviewed for your study. As part of the methodology, you would have to position yourself, to try to avoid any researcher biases or conflicts of interest you may bring to the collection and interpretation of the data. For instance, if you are interested in gaining perspectives across York Region and you had an eight-year-old child, interviewing a sample of only their closest friends that go to the same school and using leading questions to do so would be problematic. Also, if you were funded to conduct the research, you would have to ensure that this was disclosed and that your funder was impartial.

Data collection would end once data saturation was reached, meaning that no new themes were emerging during your interviews. One example of a theme that might come up consistently would be household income. If the quantitative portion of your study already found a relationship between in-person enrollment and household income, your study is able to show what is called data triangulation. This means that you have cross-referenced different methodologies and data sources to verify and show the same results, increasing the validity and reliability of your work (for quantitative studies) and showing credibility (for qualitative studies).

Examples of some other data that might come out of the interview would be students who say they are “feeling excited” because they “cannot wait to see friends and teachers,” or perhaps students who are “feeling anxious” because of “wanting to stay home, but cannot because parents have to go to work,” or even “worried” because they “might contract COVID-19 by attending school.” Software programs such as NVivo can help you analyze data, and the subsequent interpretation or discussion should be couched in a theoretical framework informed by the current literature and/or drawn explicitly from your findings. Note that the example research question provided above explores just some of the research designs that may be selected, as well as some data, sampling, and ethical considerations that would need to be identified in the methodology section of a research study.


Discussion Questions

Use Table 1 to answer the following questions.

  • What should a methodology section of a research study include?
  • Come up with another research question that can be derived from the topic, children and COVID-19. What sort of research approach and design could you use to answer this question? Would you use primary or secondary data? How would you collect and/or obtain the data? What are the ethical considerations that need to be made?
  • Describe some of the kinds of data that might be generated. How might you analyze this data?

Table 1: Research methodologies and their components. The ways in which each method or process are applied also depend on broader methodological, ethical, and other considerations.

Research Approach Qualitative Quantitative Mixed



- Couched in an existing theory or one derived from findings
- Exploratory and/or investigative
- Interested in gaining a contextual or in-depth understanding
- Explores under-researched problems or previously researched ones from a different perspective or positionality
- Used to generate new ideas/concepts


- Couched in other literature
- Explanatory and/or empirical
- Descriptive
- Predictive
- Interested in relationships, causality or making comparisons
- Interested in studying phenomenon by controlling and/or isolating variables
- Tests hypotheses between variables

Both qualitative and quantitative

Research Design

Design can be cross-sectional or longitudinal

- Narrative
- Participant-action-research
- Case study
- Phenomenology
- Ethnography
- Interpretative Description
- General Qualitative Inquiry
- Grounded Theory

Design can be cross-sectional or longitudinal

- Experimental
- Quasi-experimental
- Correlational
- Descriptive

Uses designs and data from both approaches

Order of how quantitative and qualitative data is collected and analyzed is identified by one of the following:
- Convergent parallel
- Embedded
- Explanatory sequential
- Exploratory sequential

Sample Size and Sampling Techniques

Small sample size;
data collection ends when data saturation is reached

Sampling Techniques:
Non-Probability Sampling
- Convenience
- Snowball
- Purposive
- Voluntary response
- Theoretical

Large sample size predetermined by a calculation

Sampling Techniques:
- Population based
- Probability Sampling (based on a sample of the population):
- Simple random
- Systematic
- Stratified
- Cluster
- Multistage

Data Source & Data Collection

Primary data: Uses mostly open-ended questions

- Diaries
- Video, audio, texts, images
- Maps
- Narratives
- Focus groups
- Interviews
- Participant-observation
- Questionnaires

Secondary data:
- Documents (e.g., newspaper, social media, blogs)

Primary data: uses mostly closed-ended questions with categories and scales

- Polls
- Surveys
- Questionnaires
- Experiments

Secondary data:
- Government, industry, institutional data
- Census
- Enrolment data
- Geological data (i.e., air, soil, water)

Data Generation

Text-based: themes and/or patterns


Statistics - Numerical

Data Quality

- Credibility
- Transferability
- Dependability
- Confirmability
- Authenticity

- Rigour
- Reliability
- Validity

- Generalizability

Data Analysis

- Content
- Thematic
- Textual
- Discourse
- Narrative
- Visual

Software such as NVIVO

- Descriptive Statistics
- Inferential Statistics

Software such as SPSS, SAS

Additional Resources

Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed). California: Sage.

Del Balso, M. & Lewis, A.D. (2012). First steps: A guide to social research (5th ed). Canada: Nelson Thomson Learning.

Lahman, M.K.E. (2018). Ethics in social science research: Becoming culturally responsive. California: Sage.

Leung L. (2015). Validity, reliability, and generalizability in qualitative research. Journal of family medicine and primary care4(3), 324–327.

Loseke, D.R. (2016). Methodological thinking: basic principles of social research design (2nd ed). California: Sage.

Palys, T. & Atchison, C. (2021). Research methods in the social and health sciences: Making decisions. California: Sage.

Silverman, D. (2021). Doing qualitative research (6th ed). California: Sage.

Scribbr. (2020, June 12). How to write a research methodology in 4 Steps. YouTube.

Virginia Tech Libraries. (2011, May 31). Qualitative vs quantitative research. YouTube.

Yilmaz, K. (2013). Comparison of quantitative and qualitative research traditions: Epistemological, theoretical, and methodological differences. European Journal of Education, 48(2), 311-325. Retrieved August 25, 2021, from

References to Embedded Links in the Table

Bevans, R. (2021, August 13). A guide to experimental design. Scribbr.

Bhandari, P. (2020, July 30). An introduction to qualitative research. Scribbr.

Bhandari, P. (2021, February 15). An introduction to descriptive statistics. Scribbr.

Bhandari, P. (2021, March 2). An introduction to inferential statistics. Scribbr.

Bhandari, P. (2021, June 3). Population vs sample: what’s the difference? Scribbr.

Bhandari, P. (2021, July 7). An introduction to correlational research. Scribbr.

Bhandari, P. (2021, July 16). An introduction to quantitative research. Scribbr.

Bhandari, P. (2021, August 13). A step-by-step guide to data collection. Scribbr.

Bhandari, P. (2021, August 16). An introduction to multistage sampling. Scribbr.

Bhandari, P., & McCombes, S. (2021, July 26). How to create a research design. Scribbr.

Caulfield, J. (2020, June 19). A quick guide to textual analysis. Scribbr.

Caulfield, J. (2020, August 14). How to do thematic analysis. Scribbr.

Caulfield, J. (2021, January 6). A guide to ethnography. Scribbr.

George, T. (2021, August 13). An introduction to mixed methods research. Scribbr.

Luo, A. (2020, June 19). What is discourse analysis? Scribbr.

Luo, A. (2021, February 15). What is content analysis and how can you use it in your research. Scribbr. .

McCombes, S. (2020, June 19). How to do a case study. Scribbr.

McCombes, S. (2020, September 3). Descriptive research. Scribbr.

McCombes, S. (2021, July 16). How to do survey research. Scribbr.

McCombes, S. (2021, August 16). An introduction to sampling methods. Scribbr.

Thomas, L. (2020, May 8). What is a longitudinal study? Scribbr.

Thomas, L. (2020, June 5). What is a cross-sectional study? Scribbr.

Thomas, L. (2020, October 2). An introduction to simple random sampling. Scribbr.

Thomas, L. (2020, October 2). How to perform systematic sampling. Scribbr.

Thomas, L. (2020, October 12). How to use stratified sampling. Scribbr.

Thomas, L. (2021, March 8). An introduction to quasi-experimental designs. Scribbr.

Thomas, L. (2021, August 16). An introduction to cluster sampling. Scribbr.

  1. The author has previously published under the name Ann Novogradec.


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