Uses numerical data, surveys, and experiments to test hypotheses. Focuses on statistical accuracy and measurable relationships.
Explores experiences, meanings, and perceptions using interviews, focus groups, and thematic analysis.
Combines quantitative and qualitative techniques for both depth and generalizability in understanding a research problem.
This study adopted a quantitative, cross-sectional survey design to examine undergraduate students’ perceptions of their university’s dress code. A descriptive approach was chosen to provide a detailed overview of student attitudes during the Fall 2025 semester.
Rationale:
Quantitative research enables the measurement of opinions and attitudes across large populations, offering statistical evidence that can be generalized.
(Creswell & Creswell, 2018)
The target population consisted of undergraduate students enrolled at a large state university. A total of 250 participants were recruited through campus-wide email invitations and flyers displayed in high-traffic areas such as libraries and student unions.
A quota sampling method ensured equal representation from each academic year: first-year students, sophomores, juniors, and seniors. This approach improved sample diversity and minimized bias from overrepresentation of any single cohort.
Example:
Of the 250 participants, 140 identified as female (56%), 108 as male (43%), and two as non-binary (1%).
(Etikan & Bala, 2017)
Data were collected via a self-administered online survey built in Google Forms.
Example Question:
“The university’s dress code promotes a sense of professionalism among students.”
The data collected were first exported from Google Forms into Microsoft Excel for cleaning (removal of incomplete responses). The dataset was then imported into SPSS Version 29 for statistical analysis.
Example:
The results showed that senior students (M = 3.8, SD = 0.9) viewed the dress code more positively than first-year students (M = 3.1, SD = 1.1), t(248) = 3.22, p < .01.
(Field, 2018)
Participants were shown an electronic consent form before accessing the survey. The form explained:
The study protocol received approval from the University Institutional Review Board (IRB #25-108). All responses were stored in encrypted files, and identifying information was not collected.
This quantitative methodology demonstrates how structured sampling, controlled variables, and standardized analysis ensure statistical validity and reliability in behavioral research.
This study adopted a qualitative phenomenological design to explore the experiences of employees who have been working remotely for over a year. The aim was to uncover the essence of remote work as perceived by professionals across industries.
Rationale:
A phenomenological approach allows researchers to interpret lived experiences and reveal underlying meanings beyond what can be measured numerically.
(Silverman, 2020)
Eight professionals were selected using purposive sampling, ensuring diversity in gender, age, and occupation. All participants had at least 12 months of continuous remote work experience. Recruitment occurred via LinkedIn and personal referrals.
Example:
Participants represented fields such as education, marketing, IT, and finance, providing multi-sector insights into work-from-home dynamics.
Data were collected through semi-structured Zoom interviews lasting 45–90 minutes each. Interviews were audio-recorded with consent and transcribed verbatim.
Sample interview questions included:
The open-ended format encouraged participants to narrate their experiences freely, promoting rich data collection.
Thematic analysis (Braun & Clarke, 2006) was used to identify patterns within the transcribed data.
Steps included:
Example:
Three core themes emerged: autonomy and flexibility, technological fatigue, and blurring of work-life boundaries.
Informed consent was obtained before interviews began. Participants were reminded they could withdraw at any point without consequence.
Recordings and transcripts were stored on a password-protected drive, and pseudonyms replaced real names in the report.
This qualitative methodology illustrates how interviews, coding, and thematic analysis help reveal deep insights into human experiences — ideal for studies emphasizing context and perception.
This study used a sequential explanatory mixed-methods design (quantitative → qualitative)—first, a broad cross-sectional survey measured student outcomes and predictors of perceived remote-learning effectiveness. Second, in-depth interviews explained and elaborated key statistical findings (for example, why students with high self-regulation scored differently).
The sequential explanatory design allows quantitative results to guide purposive selection for qualitative follow-up, producing complementary insights that neither approach could yield alone. This design supports both generalizability (from the survey) and interpretive depth (from interviews). (Creswell & Creswell, 2018; Fetters, Curry & Creswell, 2013).
Example statement for a paper:
“We adopted a sequential explanatory mixed-methods design: an initial survey established prevalence and correlates of perceived remote-learning effectiveness, followed by semi-structured interviews to explain the mechanisms behind key quantitative patterns.”
Quantitative phase (survey): The sampling frame comprised all undergraduate students at a mid-sized university (N ≈ 8,000). Using stratified random sampling by faculty and year, 400 students were invited to participate; 352 usable responses were retained after data cleaning. Stratification ensured representativeness across disciplines and academic levels.
Qualitative phase (interviews): Based on survey results, 20 students were purposively selected to maximize variation on critical variables (high vs low perceived effectiveness, different faculties, and differing self-regulation scores). Purposive sampling targeted information-rich cases that could explain quantitative contrasts. Participant demographics for the interview sample (gender, age range, discipline) are reported to demonstrate diversity.
Example sentence:
“The survey used stratified random sampling (n = 352 final), and interview participants (n = 20) were purposively selected based on extreme-case and maximum-variation criteria drawn from the survey results.”
Quantitative phase: A 35-item online questionnaire (validated/adapted from prior studies) measured perceived learning effectiveness, engagement, self-regulation, and demographic variables. Items used 5-point Likert scales; the survey was distributed via university email and remained open for 3 weeks, with two reminders. A pilot test (n = 30) checked clarity and timing.
Qualitative phase: Semi-structured interview guides were developed from the survey outcomes and literature review. Interviews (45–60 minutes) were conducted via secure video conferencing, audio recorded with consent, and transcribed verbatim. Interview questions probed how course design, instructor presence, and personal strategies influenced students’ learning experiences—especially in cases where survey scores deviated from expectations.
Example note:
“Survey items were adapted from validated scales and piloted; interviews were scheduled within four weeks of survey completion to preserve temporal relevance to participants’ experiences.”
Separate analyses:
Integration (mixing): Integration occurred at two points: (a) participant selection for interviews (quant → qual sampling), and (b) interpretation using joint displays that juxtaposed quantitative results (e.g., regression coefficients) with illustrative qualitative quotations explaining the effect mechanisms. The joint-display technique facilitated meta-inferences—synthesized conclusions supported by both data strands. Where strands diverged, the analysis reported and interpreted discrepancies (contradictory evidence), increasing explanatory power. (Fetters et al., 2013)
Example paragraph to include:
“We performed statistical modeling to identify significant predictors, then used thematic analysis to explain underlying processes. Joint displays integrated coefficients with participant narratives, producing convergent and, where present, divergent meta-inferences.”
Mixed-methods projects require careful attention to consent, confidentiality, and data linkage:
Example clause for your methods section:
“All participants provided informed consent. Contact identifiers used for interview recruitment were stored separately and removed after selection; anonymized datasets were retained on encrypted servers in accordance with institutional policy (IRB #25-178).”
Provide explicit data-security and consent procedures for linked data.
Selecting the correct methodology is one of the most critical decisions in any research paper. Your choice depends on your research objectives, the nature of your data, and the research questions you aim to answer.
Quantitative methods are ideal when you seek measurable, generalizable findings, whereas qualitative methods uncover deeper meanings and contextual insights.
The following table highlights the key quantitative and qualitative research differences to help you determine which approach best aligns with your research design.
| Criteria | Quantitative Methodology | Qualitative Methodology |
|---|---|---|
| Primary Focus | Measures variables numerically and tests hypotheses using statistical analysis. | Explores meanings, perceptions, and lived experiences through non-numerical data. |
| Research Design | Structured, predetermined (e.g., experimental, correlational, survey). | Flexible, evolving (e.g., phenomenological, case study, ethnographic). |
| Data Collection | Surveys, questionnaires, standardized tests, experiments. | Interviews, focus groups, participant observation, document review. |
| Data Type | Numeric (quantifiable and measurable). | Textual, visual, or narrative (rich descriptive data). |
| Analysis Technique | Statistical analysis (e.g., t-tests, ANOVA, regression). | Thematic, content, or discourse analysis using coding frameworks. |
| Outcome | Generates measurable, generalizable results and identifies trends. | Provides deep contextual understanding of complex social or personal phenomena. |
| Sample Size | Large; aims for representativeness and statistical significance. | Small; focuses on information-rich cases rather than representativeness. |
| Example | A survey of 300 students measuring the impact of online learning on GPA. | Interviews with 10 teachers exploring challenges in online teaching. |
A good methodology clearly describes the research design, data collection tools, analysis process, and ethical measures—with enough detail for replication.
Quantitative methods measure variables numerically and rely on statistics, while qualitative methods explore experiences through language, meaning, and interpretation.
In a research paper, the methodology section typically ranges from 500 to 1,000 words, depending on the study’s complexity and the journal’s requirements.
A transparent, well-documented methodology is the single most crucial element that converts an interesting research idea into a credible scholarly contribution. The two worked examples in this article — a quantitative, survey-based methodology and a qualitative, interview-based phenomenological methodology — illustrate how method choices must flow directly from the research question, not the other way around. The quantitative example showed how structured sampling, standardized instruments, and statistical tests produce measurable, generalizable findings; the qualitative example demonstrated how purposive sampling, in-depth interviews, and thematic analysis uncover meanings, contexts, and nuance (Creswell & Creswell, 2018; Braun & Clarke, 2006).
For authors preparing methodology sections, prioritize the reader’s ability to judge and replicate your work. Use subheadings (Design; Participants; Data Collection; Analysis; Ethics; Limitations), include concrete examples (sample items, interview prompts, analytic code, or a codebook), and state software, thresholds, and decision rules explicitly. When reporting quantitative results, include reliability statistics (e.g., Cronbach’s α) and effect sizes alongside p-values; when reporting qualitative findings, include verbatim quotations to support theme interpretation and show how codes map to themes (Braun & Clarke, 2006; Field, 2018).
Finally, think of the methodology as an argument rather than a recipe. You are asking readers to accept that your approach was the best available way to answer your question, given the constraints. Justify your choices with references to established methodological literature, show you followed ethical best practice, and provide enough procedural detail to make replication feasible. Doing so not only improves the immediate credibility of a single paper but also contributes to the cumulative reliability of scholarship in your field.
Quick checklist before you finalize your methodology:
When these elements are present and well-argued, your methodology becomes more than a section of the paper — it becomes evidence that your conclusions rest on sound, transparent, and ethically conducted research.
Quantitative methodologies use numbers and statistics for measurable results, while qualitative methodologies uncover experiences and meanings. Choose your method based on the nature of your research question.
