Describing Participants and Sampling in Your Methods

The participants subsection is the first thing peer reviewers evaluate in the methods section, and it often determines the tone of the entire review. A well-written participants and sampling description signals that the researcher thought carefully about who was studied and why. A vague or incomplete description signals the opposite, and reviewers who lose trust here rarely regain it later in the manuscript.

This guide covers what to include when describing participants, how to write about sampling decisions, how to justify sample size, and the specific reporting patterns that satisfy reviewers across social science and health research. It focuses on the writing conventions rather than the underlying methodological choices; sampling design decisions are covered in depth in the research methodology series.

What Belongs in the Participants Subsection

The participants subsection answers three questions: who was studied, how did they end up in the study, and what does the final analytic sample look like. Every element in the subsection serves one of these three questions.

A complete participants subsection includes:

• The target population and the accessible population from which the sample was drawn
• The sampling method used
• The total sample size and how it was determined
• Inclusion and exclusion criteria
• Recruitment source and procedure
• Key demographic characteristics of the final sample
• Any incentives offered to participants
• Attrition rates and how missing participants were handled
• The final analytic sample size, if it differs from the recruited sample

The order in which these elements appear varies by discipline and design. A common sequence in the social sciences is: target population, sampling method, recruitment procedure, inclusion/exclusion criteria, sample size and justification, demographics, and attrition. Clinical trials often follow a CONSORT-style flow diagram that structures the information differently. Consult recent articles in your target journal for the convention that fits.

Naming the Population and the Sample

Reviewers want to see the distinction between the population you wanted to study, the population you were able to access, and the sample you actually recruited. These three layers are often collapsed in student writing, which invites reviewer questions about generalizability.

The target population is the group to which you want to generalize your findings ("adults in the United States who are actively saving for retirement"). The accessible population is the subset of the target population you could realistically reach ("clients of one financial planning firm in Ohio"). The sample is the group of individuals who actually participated ("142 clients who responded to the survey invitation").

Making these three layers explicit lets you address generalizability honestly and reduces the risk that a reviewer will raise it as an unaddressed threat.

Reporting Sample Size and How It Was Determined

Sample size justification is one of the most frequently criticized elements of methods sections. Reviewers want to know that the sample was large enough to detect meaningful effects and that the size was chosen for a principled reason, not by convenience.

Three common approaches to justifying sample size:

• A priori power analysis. The researcher used software (G*Power, R, or an equivalent) to calculate the sample size needed to detect a specified effect size with a specified power (usually 0.80) at a specified alpha level (usually 0.05). Report the software, the parameters used, and the resulting sample size.
• Precision-based justification. The researcher chose a sample size based on the desired width of a confidence interval around the primary estimate. This is common in prevalence studies and survey research.
• Convention or feasibility. The researcher used a sample size consistent with prior published research in the area or determined by resource constraints. This is the weakest justification but the most honest; blurring it into a false power analysis is worse.

Whichever approach applies, name it. A sentence such as "An a priori power analysis in G*Power indicated that a sample of 128 participants would provide 80% power to detect a medium effect size (Cohen's d = 0.5) at α = 0.05 for an independent samples t-test" satisfies reviewers far more than a bare statement of the sample size.

Writing About the Sampling Method

The sampling method should be named explicitly and described in enough detail that a reader can evaluate the design. The main families are probability sampling (simple random, stratified, cluster, systematic) and non-probability sampling (convenience, purposive, snowball, quota).

A common mistake in student writing is claiming a sampling method that does not match the recruitment procedure. If participants were recruited through social media posts, the sampling is convenience, not random. If participants were selected from a mailing list, that is systematic or possibly stratified. Reviewers will read the recruitment description and compare it to the claimed sampling method, and mismatches damage credibility.

Notice what this paragraph does: it names the sampling method (stratified random), describes the sampling frame, explains the stratification variables, gives the sample size at each stage, and reports the response rate. A reviewer reading this paragraph can evaluate the sampling design without asking any follow-up questions.

Describing Recruitment Procedures

The recruitment description covers how you contacted participants, what you told them, and how they enrolled. This information matters because recruitment procedures can introduce selection bias, and reviewers use it to evaluate whether the sample is representative of the population it claims to represent.

Report:

• The recruitment channel or channels (email, flyers, social media, Prolific, MTurk, clinic patient lists)
• The exact eligibility screening used, if any
• The dates or period of recruitment
• Any incentives offered, including amounts
• The consent procedure and whether it was written, oral, or implied
• The IRB or ethics board that approved the study

Online recruitment via crowdsourcing platforms (Prolific, MTurk, CloudResearch) requires additional detail. Report the platform, any qualification filters or prescreens used, the payment rate, and any attention-check or data-quality procedures. Reviewers scrutinize crowdsourced samples closely, and the writing needs to demonstrate that the researcher applied appropriate quality controls.

Reporting Inclusion and Exclusion Criteria

Inclusion criteria are the characteristics participants had to have to be eligible. Exclusion criteria are the characteristics that disqualified otherwise-eligible participants. Both should be stated explicitly and justified when the criteria could affect generalizability.

Common inclusion criteria in survey research include age (usually 18 or older), residence in a specific location, membership in a target group, and language fluency. Common exclusion criteria include prior participation in a related study, incomplete responses, and failed attention checks.

Report exclusions in two places: in the participants subsection (what criteria excluded people up front) and in the analysis or results section (how many participants were excluded from the analytic sample and why). A single flow statement often handles both efficiently: "Of the 342 respondents who began the survey, 18 were excluded for failing attention checks, 12 for completing the survey in less than one-third of the median completion time, and 9 for missing more than 20% of items on the primary outcome measure. The final analytic sample consisted of 303 participants."

Describing the Sample Demographically

Demographic description gives reviewers the information they need to judge whether the sample is representative of the population and whether the results might generalize to other populations. The specific variables to report depend on the research question, but common defaults in social science research include age, gender, race and ethnicity, education, and socioeconomic status.

For each variable, report the appropriate summary statistic. Continuous variables (age, income) call for mean and standard deviation, sometimes with range. Categorical variables (gender, race, education level) call for frequencies and percentages. When the sample is highly skewed on a demographic, mention it explicitly rather than letting a reviewer notice from the numbers.

Percentages and raw counts both appear because reviewers use each for different checks. Percentages let readers compare the sample to the population; raw counts let readers evaluate whether subgroup analyses will have adequate power.

Reporting Response Rates and Attrition

Response rates and attrition are among the most commonly missed elements in student writing, and reviewers ask about them almost every time. The response rate is the proportion of invited participants who agreed to participate. The completion rate is the proportion of those who started who finished. Attrition is the proportion lost between enrollment and the final analytic sample.

Report each rate separately when they differ. A study might have a 45% response rate (people who clicked the link out of those invited), a 92% completion rate (people who finished the survey out of those who started), and a 5% attrition due to data-quality exclusions.

If attrition was substantial or non-random, describe what you did about it. Compare completers to non-completers on baseline demographics when possible. Report whether the pattern of missingness affects the interpretation.

Writing About Sensitive Sample Characteristics

Some studies involve samples with characteristics that require careful language. Studies of clinical populations, sensitive behaviors, marginalized groups, or vulnerable populations should describe the sample in terms that are precise, respectful, and consistent with the terminology preferred by the community itself.

Practical guidance:

• Use person-first or identity-first language depending on community preference. Autism research is increasingly identity-first ("autistic adults"), while much clinical research remains person-first ("adults with schizophrenia").
• Avoid outdated diagnostic or racial terminology. Match current DSM or ICD language and current APA guidelines for demographic descriptions.
• Report the sensitive characteristic only when it is relevant to the research question.
• Describe the sample without pathologizing language when the study is not clinical.

When in doubt, look at how leading journals in the same subfield describe similar samples, and match that convention.

Common Reporting Mistakes

Claiming a random sample when the recruitment was convenience. The most common credibility-damaging error in student writing. If participants self-selected in response to an open invitation, the sample is convenience, not random, no matter how large it is.

Reporting only the final analytic N without accounting for attrition. Reviewers want to see the recruited sample, the excluded participants, and the analytic sample. Skipping the middle steps looks like an attempt to hide something.

Justifying sample size only by citing prior research. "Prior research used similar sample sizes" is not a justification; it is a description. A power analysis or precision-based justification is expected in most current publications.

Under-describing crowdsourced samples. Studies using MTurk or Prolific still need the same demographic detail and data-quality procedures as studies using conventional recruitment. Vague descriptions ("participants were recruited through MTurk") invite reviewer skepticism.

Omitting the ethics approval statement. The IRB or equivalent ethics body should be named, along with the approval date or protocol number. Missing statements can lead to desk rejection.

Cherry-picking demographics. Reporting only the demographics that make the sample look strong (educational attainment, age diversity) while omitting weaker points (all one race, all one region) reads as selective and damages credibility when the discussion has to address generalizability.

Reporting for Secondary Data

Studies using secondary data (national datasets, existing surveys, administrative records) have different reporting requirements. Rather than describing recruitment, describe the original data source, the year or wave used, the original sampling frame, and the subset used in the current analysis. Cite the original data collection documentation.

A secondary-data participants paragraph might read: "Data came from the 2019 Survey of Consumer Finances (SCF), a triennial cross-sectional survey sponsored by the Federal Reserve Board that collects detailed financial information from a representative sample of U.S. households. The 2019 SCF included 5,777 households drawn from a dual-frame sample designed to represent the U.S. population while oversampling wealthy households. The current analysis was restricted to households in which the primary respondent was between 25 and 65 years old and had a positive employment income, yielding an analytic sample of 3,412 households."

Before You Submit: A Self-Audit

The participants and sampling section carries disproportionate weight in peer review because it establishes whether the reviewer trusts the rest of the paper. If you have a manuscript in preparation and want a professional editor to review the methods section, results, or full paper before submission, Editor World offers journal article editing by editors with subject-matter backgrounds. Clients browse editor profiles and choose the editor whose expertise best matches their field. A free sample edit on the first 300 words is available for every project, and a certificate of editing is available as an optional add-on for publishers or committees that require one.

Frequently Asked Questions