Respondents are the people (or units) who actually supply data to your study—the subset who complete your instruments or provide observations. As one methods primer puts it, “in research a population is the entire group that you want to draw conclusions about, while sample is the smaller group of individuals you actually collect data from” 
Research.... Use respondents for survey- and questionnaire-based designs; reserve participants for qualitative or interactive designs, where individuals engage more actively in data generation Participa....
This subsection belongs in Methods/Chapter 3 to document who provided data, why they were appropriate, and how they were accessed. Report who they are and key demographics, because readers assess fit-to-question and potential biases from this description Responden.... State inclusion and exclusion criteria to show relevance and safeguard validity Steps in.... Align your sampling approach with design: probability sampling (random selection) is typical in quantitative studies and supports representativeness, whereas non-probability approaches are easier to implement but carry greater bias risk Research.... A clearly justified method strengthens the credibility and generalizability of your findings Steps in....
Define the target population precisely—who, where, and when—then distinguish it from the smaller group that will actually provide data (the sample). A clear statement such as “all Grade 11 students enrolled in public schools in City X during SY 2024–2025” sets scope and anchors generalization, while reminding readers that only a subset will be measured Research...Writing t.... Specify the unit of analysis (individuals, classes, clinics) and the study setting to keep your design coherent with the research questions Writing t....
State inclusion and exclusion criteria to ensure relevance and reduce noise (e.g., enrollment status, tenure, language ability), and justify each criterion briefly to protect validity Steps in.... Identify the sampling frame—the accessible list or source from which you’ll draw cases (registries, rosters, platforms)—and note its coverage limits, since frame gaps can bias who enters your sample Research.... Your frame and criteria should foreshadow the sampling strategy: probability methods (random selection) aid representativeness in quantitative studies, whereas non-probability approaches are easier to implement but carry greater bias risk Research...Steps in....
Match your sampling technique to the research design: probability methods support representativeness in quantitative studies, while non-probability methods are easier to implement but carry greater bias risk Research...Writing t.... A sampling method is the technique used to select a sample that represents the group as a whole, so define it plainly and justify why it fits your question and frame Responden.... For example, in simple random sampling every population member has an equal chance of selection, helping curb selection bias when a good frame exists Responden.... Non-probability options (e.g., purposive) prioritize relevance over inference breadth; state the trade-offs explicitly Research....
Anchor your choice with concrete details. A purposive design might target “select 10 Grade 12 PM students” meeting defined criteria; explain why these cases are information-rich for your aims Responden.... If the population is small and accessible (≤100), universal sampling—inviting all members (e.g., “20 out of 20 teachers”)—can eliminate selection bias within the frame Responden.... Describe the selection steps (frame, randomization or criteria, invitations) and provide a brief rationale tying method to generalizability or depth, since a well-justified sampling method strengthens the credibility of findings Steps in....
Justify sample size by balancing multiple considerations—there is rarely a single decisive factor; study aims, population size/diversity, variables, and constraints all interact How to De.... Probability sampling can sustain smaller samples for the same inference quality compared with many non-probability designs, because random selection supports representativeness How to De...Research.... When frames are strong and simple random sampling is feasible, equal selection chances reduce selection bias and clarify generalizability claims Responden...Research....
Use defensible rules: for surveys without power analysis, Slovin’s formula n = N / (1 + N e²) can set a baseline tied to error tolerance, while acknowledging its simplicity Responden.... Align rules-of-thumb to design: for structural equation models, many aim for ≈150 as a minimum plus ≈15 cases per additional observed variable, scaling with model complexity How to De.... State expected nonresponse and attrition and pad recruitment accordingly; then report the achieved sample and its implications for bias and precision. A transparent, method-aligned justification strengthens credibility and the scope of inference Steps in...Research....
Lead with a clear structure that shows who provided data and why they fit your aims. Start by stating the population and setting, then define the respondents via inclusion/exclusion criteria and the accessible sampling frame; this clarifies scope and potential coverage gaps Research...Writing t...Steps in.... Name and justify the sampling technique (probability vs non-probability) and explain how cases are selected in practice; define the method plainly to show how it represents the group Research...Responden.... Conclude with sample size and justification (multi-factor rationale or a simple rule such as Slovin), plus recruitment steps and timeframe; report achieved sample, response rate, and key demographics to support appraisal of bias and fit How to De...Responden....
Model phrasing you can adapt: “The target population comprises all Grade 11 students in City X during SY 2024–2025, while the respondents are those sampled from the division’s enrollment roster” Research.... “Inclusion criteria were current enrollment and age 15–17; exclusion criteria included transfer status or incomplete consent” Steps in.... “Simple random sampling was used from the official roster; each student had an equal chance of selection” Responden.... “The intended sample size was computed using n = N/(1 + Ne²) with e = 0.05; allowances were made for 20% nonresponse” Responden...How to De.... For qualitative or targeted designs: “Purposive sampling selected 10 information-rich cases meeting ; recruitment proceeded via advisor referrals over four weeks” Responden.... Close with a concise demographic profile and response rate to complete the picture Responden....
Protecting respondents centers on informed consent, confidentiality, and fair inclusion. Define inclusion and exclusion criteria upfront to ensure relevance and reduce undue burden or risk to ineligible individuals Steps in.... Use accurate terminology—“respondents” for structured surveys and “participants” for more interactive qualitative roles—so expectations and consent language match what contributors will actually do Participa.... When reporting the sample, summarize demographics at an aggregate level and avoid unnecessary identifiers, since the respondents subsection typically presents percentages by categories rather than traceable details Responden....
Recruitment should minimize coercion and be transparent: document access procedures, the sampling frame, and selection rationale; a well-justified method supports fairness and credible inference Steps in.... Disclose your sampling approach and its implications—non-probability sampling is often easier to implement but carries higher bias risk, which readers must weigh when interpreting findings Research.... Small populations (e.g., universal sampling of all members) can heighten re-identification risks; take extra care with anonymization and reporting, especially when N is very small Responden.... Secure data handling, limited retention, and ethics/IRB approval (where required) complete a defensible protection plan.
Template (quantitative survey): “The target population comprises in during , while the sample is the smaller group from whom data will be collected; the sampling frame is ” Research.... “Inclusion criteria were ; exclusion criteria were to ensure relevance and protect validity” Steps in.... “We used , giving each eligible unit an equal selection chance” Responden.... “Planned sample size was determined via with , inflated for nonresponse; recruitment proceeded via over ” Responden.... “The achieved sample was (response rate ); key demographics are summarized as percentages by category” Responden.... Template (qualitative interviews/focus groups): “Participants were selected purposively to provide information-rich cases meeting within ; selection prioritized depth and relevance over statistical representativeness” Responden...Research.... “Use ‘participants’ (not ‘respondents’) for qualitative roles; terminology should reflect the nature of engagement” Participa....
Mini samples you can emulate: (1) Simple random survey of students: “The population is all Grade 11 students in City X (SY 2024–2025); using the official enrollment roster as the sampling frame, we selected students by simple random sampling; each had equal selection probability; n was computed with Slovin’s formula n = N/(1 + Ne²) at e = 0.05 and inflated by 15% for nonresponse; we report achieved n, response rate, and demographic percentages” Responden...Research.... (2) Purposive/complete enumeration examples: “A purposive sample of 10 Grade 12 PM students meeting predefined criteria was recruited to align cases with the study aims,” and “For a small, accessible population, universal sampling invited all 20 teachers in the school” Responden.... When choosing between probability and non-probability approaches, state the trade-off: random selection supports representativeness for quantitative inference, whereas non-probability methods are easier but carry higher bias risk Research....
