What goes in the methods section of a research paper?

The methods section describes participants and sampling, the materials or measures with their reliability and validity, the procedure step by step, and the data analysis plan naming each statistical test. It is written in the past tense with enough detail that another researcher could replicate the study.

How long should a methods section be?

There is no fixed length; the methods section should be exactly as long as it takes to make the study reproducible. For a typical empirical paper that is often two to four pages, but a complex procedure or several measures may need more. Detail that does not help replication should be cut.

What tense should the methods section be written in?

Write the methods section in the past tense, because you are describing what you already did. A common error is carrying the future tense over from a research proposal, where the same content was written as what you would do.

What is the most important part of the methods section?

The data analysis subsection is where reviewers and committees focus most. Naming the specific statistical test for each hypothesis, justifying it, and stating how assumptions and missing data were handled is what separates a methods section that passes review from one that is sent back.

Methods Section of a Research Paper: Guide + Example

To write the methods section of a research paper, you describe exactly what you did in enough detail that another researcher could replicate the study, written in the past tense and organized under standard subheadings: participants, materials or measures, procedure, and data analysis. The section that passes review is judged on whether it is reproducible and whether the analysis plan matches the questions, not on length. This guide walks each subsection, shows what a strong example contains, and covers the parts students most often get wrong.

What the methods section has to achieve

The methods section earns its place by making the study transparent and repeatable. A reader should be able to judge whether your design can answer your question and, in principle, run the study again and get comparable results. That single test, replicability, decides almost every choice about what to include: enough detail to reproduce the work, and no more. It sits between your introduction and your results section, and the two must align, every analysis you report later has to be named here first.

Participants and sampling

Describe who took part and how they were recruited: the sample size, the demographics that matter to your question, the sampling method, and the inclusion and exclusion criteria. State how you arrived at the sample size, ideally from an a priori power analysis, because a committee will ask whether the study was adequately powered to detect the effect you sought. Note any ethics approval and consent here.

Materials, measures, and variables

Specify every instrument, scale, or apparatus, with the published reliability and validity evidence for each measure and a citation. Define your independent and dependent variables precisely, including how each was operationalized and scored. If you used an established questionnaire, report its response format and a known internal consistency figure such as Cronbach's alpha; if you built your own, explain how it was developed and validated.

Procedure

Walk through what happened from a participant's point of view, in order: the setting, what they were asked to do, the sequence and timing of conditions, and any randomization or counterbalancing. This is the subsection that most directly delivers replicability, so be concrete about steps a reader could not guess. Write it in the past tense and the passive or active voice your target journal prefers, consistently.

The data analysis subsection, where most marks are lost

The data analysis subsection is where reviewers and committees concentrate, and where weak methods sections fail. Name the specific statistical test for each hypothesis, justify why it fits your data, state the software, the alpha level, and how you handled missing data and assumption checks. Do not just write "data were analysed in SPSS"; write that a one-way ANOVA tested the group difference, with Tukey post hoc comparisons, after checking normality and homogeneity of variance. If you are unsure which test belongs with which question, work through our guide to selecting a statistical test before writing this paragraph. A correct, pre-specified analysis plan is the difference between a methods section that survives and one that gets sent back.

A short methods section example

A strong methods section example reads, in miniature, like this: "Participants were 150 undergraduates (M age = 20.4, SD = 1.8; 68% women) recruited through a university participant pool; a power analysis indicated 128 were needed to detect a medium effect at 80% power. Academic motivation was measured with the Academic Motivation Scale (28 items, Cronbach's alpha = .89). Participants completed the scale online, then a graded assessment two weeks later. Exam scores were regressed on motivation and prior GPA in R; assumptions of linearity, homoscedasticity, and residual normality were checked through diagnostic plots, and alpha was set at .05." Every later result maps back to a sentence in that paragraph, which is the test of a finished methods section.

APA formatting and common mistakes

In APA style, the methods section uses the heading Method with bold subheadings, the past tense throughout, and numerals for numbers. The recurring mistakes are easy to avoid once named: reporting results inside the methods, omitting the sample-size justification, naming software but not the actual test, and writing in the future tense carried over from a proposal. Keep statistics out of this section; they follow APA guidance on reporting statistics and belong in your results. Get the analysis plan right here and the rest of the paper has a foundation it can stand on.

How to write the methods section of a research paper