When writing an APA-style paper, particularly in the social and natural sciences, the methods section is crucial in providing comprehensive details about how the study was conducted. This section allows others to understand and replicate the research procedures.
The primary purpose of the APA methods section is to offer an in-depth, transparent account of the methodology employed. This includes specific information regarding the sample of participants, the measures and instruments utilized for data collection, and a thorough explanation of the procedures that guided the research process.
Structuring an APA methods section
The methods section begins with the primary heading “Method,” centralized, boldfaced, and fully capitalized. Relevant subheadings should be applied under this main heading to delineate the specific components.
These subheadings, such as “Participants,” “Materials,” and “Procedure,” are left-aligned with the margin and formatted in bold using title case capitalization. However, these particular subheadings are not mandatory – authors should utilize headings that logically organize and represent the methodological elements of their specific study.
Further content division can be achieved within each subheading through additional lower-level headings if necessary. For example, under “Participants,” there could be two subsections with Level 3 headings: one titled “Sample Characteristics” and another “Sampling Procedures.” These subsection headings would be indented, in boldface, with only the first word capitalized, followed by a period.
| Heading | What to Include |
| Participants | Participant or subject characteristics (age, gender, demographics, etc.) Sampling procedures (how participants were recruited/selected) Sample size and rationale (power analysis, effect size, etc.) |
| Materials | Primary measures, tests, or instruments used Secondary or additional measures Evidence of measurement quality (reliability, validity) |
| Procedure | Methods of data collection (online, in-person, etc.) Research design type (experimental, correlational, descriptive) Data processing steps (outlier removal, cleaning, etc.) Statistical analyses and tests conducted Justification for analysis plan (comparisons, regressions, etc.) |
Not every methodological element will necessarily be applicable or require detailed reporting for your particular research study. The content you choose to include under headings like Participants, Materials, and Procedure should directly align with the core components of your design and approach.
For instance, if your study did not involve separating participants into different experimental conditions or implementing outlier removal techniques during analysis, there is no need to provide reporting on those specific procedures. The level of detail provided should correspond to the actual methodological steps taken.
The APA Publication Manual provides tailored reporting guidelines customized for various research designs, such as longitudinal studies, replications, and experimental models. Consulting these specialized guidelines ensures you include all relevant procedural details expected for the precise structure of your study. If your work involves a combined or mixed methods approach, refer to the guidelines that cater to reporting those unique integration designs.
Additionally, any extensive methodological details, materials, or supplemental information that does not fit into the primary methods narrative can be included as appendices or supplemental documents. Common examples are full instructions/stimuli given to participants, complete statistical code for analysis, and extra data figures/tables.
Participants
The methods section should begin by concisely reporting the total number of participants, key demographic characteristics, recruitment and sampling procedures used, and the justification for the final sample size.
Participant or subject characteristics
When reporting details about the participants or subjects in your study, it is important to clearly and comprehensively describe their key demographic and background characteristics. This allows readers to understand the sample’s composition and assess the findings’ potential generalizability.
Aim to include the total number of participants and specific details about their age, gender, ethnicity/race, and other relevant characteristics pertinent to the research question or hypotheses being investigated.
For age, it is recommended to report both the age range (minimum and maximum values) and the mean age and standard deviation, as this provides a more complete picture of the age distribution within the sample.
Regarding gender, you should indicate the proportions or percentages of participants identifying as male, female, or other gender identities, as applicable. Similarly, for ethnicity and race, provide a breakdown of the different groups represented in the sample, ideally using both numerical values and percentages.
If additional demographic variables are particularly relevant to your study, such as educational attainment, socioeconomic status, or specific population characteristics (e.g., clinical diagnoses, occupational roles), include and describe them.
Example: Reporting participant characteristics
The final sample consisted of 125 college students (60% female, 40% male) from a large public university in the Southwestern United States. Participants ranged in age from 18 to 25 years (M = 20.3, SD = 1.7). The ethnic composition was 52% White, 25% Hispanic/Latino, 15% Asian, 5% Black, and 3% Other.
Sampling procedures
Provide a detailed account of the procedures used to recruit and select the participants for your study. Begin by describing the sources from which participants were drawn. Outline these criteria if specific inclusion or exclusion criteria were applied during recruitment.
Explain the specific methods used to recruit participants. These should also be mentioned if incentives or compensation were offered to participants. Provide information about the sampling strategy employed, whether a convenience sample, a random sample, a stratified sample, or any other relevant sampling technique.
Example: Reporting sampling procedures
Participants were recruited from the university’s paid research participant pool. The study opportunity was advertised via a regularly updated online platform, and interested students signed up for available timeslots. Inclusion required being a currently enrolled student between 18 and 26 years old and able to provide informed consent.
Sample size and power
Justify the sample size used in your study by providing information about the expected effect size, any a priori power analyses conducted, and the desired level of statistical power.
Begin by stating the target sample size and the rationale for this choice. If a power analysis was performed before data collection, describe the parameters used in this analysis, such as the anticipated effect size, the desired level of statistical power (typically set at 0.80 or higher), and the alpha level (commonly 0.05).
If relevant, discuss any adjustments made to the target sample size to account for potential attrition or other factors that could impact the final sample size. If your study involved multiple analyses or comparisons, please provide information about the power considerations for each analysis separately.
Example: Reporting sample size and power
Based on a priori power calculations using G*Power for the proposed multiple regression analysis, a minimum sample of 115 was required to detect a medium effect size (f² = .15) with 80% power and an alpha of .05 for up to 5 predictors. We oversampled to account for potential data attrition.
Materials
The materials section provides a comprehensive and transparent account of all measures, instruments, and tools used to assess the variables of interest. It includes details on their structure, psychometric properties, and any adaptations or novel techniques employed.
Primary and secondary measures
Provide detailed descriptions of all the measures, instruments, and materials utilized in your study. This includes both the primary measures that were central to addressing your research questions or hypotheses, as well as any secondary or supplementary measures that were included.
For each measure, provide the following information:
- The full name of the measure or instrument.
- A brief description of what the measure is designed to assess or measure.
- The number of items or questions included in the measure.
- The response scale or format used (e.g., Likert-type, true/false, open-ended).
- Any relevant subscales or dimensions within the measure.
- Scoring procedures or algorithms derive scores or indices from the measure.
If you adapted or modified an existing measure for your study, describe the specific changes or alterations and the rationale for doing so. Describe the content, structure, and development process thoroughly if you developed or created new measures or materials specifically for your research.
Example: Reporting materials
The primary measure used in this study was the Beck Depression Inventory-II (BDI-II; Beck, Steer, & Brown, 1996), a 21-item self-report instrument designed to assess the severity of depressive symptoms in adults and adolescents. Participants rate each item on a 4-point scale ranging from 0 (not present) to 3 (severe), with higher total scores indicating more severe depression. The BDI-II consists of two subscales: the Somatic-Affective and Cognitive dimensions.
Quality of measurements
For any established or previously published measures included in your study, it is important to report information about the reliability and validity of these instruments. This information helps to demonstrate the psychometric properties and overall quality of the measures used, which is essential for ensuring the rigor and trustworthiness of your findings.
Regarding reliability, you should report any available data on the internal consistency (e.g., Cronbach’s alpha coefficients), test-retest reliability, or inter-rater reliability of the measure, depending on the type of instrument and the context in which it was used in your study.
For validity, you should provide information on the various types of validity evidence established for the measure, such as content validity, construct validity (including convergent and discriminant validity), criterion-related validity, and so on.
If you used multiple measures or instruments in your study, report the reliability and validity information for each one separately.
Example: Reporting Quality of Measurements
The BDI-II has demonstrated good internal consistency, with Cronbach’s alpha coefficients ranging from .84 to .92 in previous studies (Beck et al., 1996; Whisman et al., 2000). It has also shown strong convergent validity with other well-established measures of depression, such as the Hamilton Rating Scale for Depression (r = .71; Segal et al., 2008).
Procedure
Describe the steps undertaken, including the data collection process, research design specifics, any data processing diagnostics or cleaning methods, and the planned statistical analyses or analytic techniques employed to address the research questions/hypotheses.
Data collection methods and research design
Describe the procedures followed during the data collection process and the overall research design employed in your study. This information is crucial for ensuring the replicability and transparency of your research methods.
Begin by outlining the specific steps involved in the data collection process. This may include details such as:
- The setting or context in which data were collected (e.g., laboratory, online, field study)
- Instructions or prompts provided to participants
- The sequence and timing of any experimental manipulations or interventions
- Measures taken to ensure participant confidentiality and ethical treatment
Specify the type of research design utilized in your study. For example, was it an experimental design (if so, specify the type, such as between-subjects, within-subjects, etc.), a correlational design, a longitudinal design, or a qualitative design? Provide a clear rationale for your chosen design and how it aligns with your research questions or hypotheses.
If your study involved randomly assigning participants to different conditions or groups, describe the procedures used for randomization and any techniques employed to counterbalance or control for potential confounding variables.
Example: Reporting data collection methods and research design
Participants were randomly assigned to either a mindfulness meditation or control condition. The study utilized a 2×2 mixed factorial design with online administration of all materials. Pre-test measures of anxiety, mood, and emotion regulation were collected from all participants.
Those in the mindfulness group completed a 10-minute guided meditation exercise, while controls engaged in a neutral filler task for the same duration. Then, post-test assessments of the same measures were administered.
After data screening, a 2×2 mixed ANOVA was conducted to examine the effects of condition over time on anxiety, mood, and emotion regulation scores. Significant interactions were followed up with simple effects tests using adjusted alpha levels.
Data diagnostics
If your study involved specific data processing or diagnostic steps before the main analyses, it is important to report these procedures in detail. This may include steps such as:
- Screening for outliers or influential data points
- Checking for violations of statistical assumptions (e.g., normality, homogeneity of variance)
- Handling missing data using techniques like imputation or case deletion
- Transformations applied to variables (e.g., centering, standardization)
Providing transparency about these data preparation and cleaning steps allows readers to evaluate the appropriateness of your analytical approach and the validity of your findings.
Analytic strategies
Outline the statistical analyses or analytical techniques to address your research questions or hypotheses. This includes:
- The names and descriptions of the statistical tests or models used (e.g., t-tests, ANOVA, regression, SEM)
- The rationale for choosing these particular analyses
- Any software packages or programs utilized for conducting the analyses
- The alpha level or criteria for determining statistical significance
If your study involves multiple stages of analysis or a complex analytical strategy, it may be helpful to present this information in a step-by-step or hierarchical manner, guiding the reader through the logical progression of your analytical approach.
Example: Reporting analytical strategy
To examine the effects of mindfulness induction on self-reported anxiety levels, a 2 (condition: experimental, control) x 2 (time: pre-intervention, post-intervention) mixed-model ANOVA was conducted using SPSS version 26. The between-subjects factor was a condition, and the within-subjects factor was time. Significant interactions were followed up with simple effects analyses and pairwise comparisons using the Bonferroni correction to control for familywise error rate.
Example of an APA methods section
Here is an example of a methodology section using the APA guidelines:
Methods
Participants
The study included 205 adults (63% female) recruited from a primary care clinic in an urban setting. Participants ranged in age from 30 to 65 years (M = 48.2, SD = 9.8). The sample was ethnically diverse, with 42% White, 32% Black, 18% Hispanic/Latino, and 8% Other. Key inclusion criteria were being overweight (BMI > 25) and having one or more metabolic risk factors (hypertension, high cholesterol, impaired fasting glucose). Exclusion criteria included the presence of diabetes, cardiovascular disease, or other serious chronic illnesses. The target sample size of 200 was determined via an a priori power analysis using G*Power to detect a medium effect size (f = 0.25) with 80% power and alpha = 0.05 in the primary analyses.
Materials
The primary outcome was weight loss over the 12-week intervention period, measured as a change in body mass index (BMI) from baseline to final assessment. Secondary outcomes included changes in blood pressure, cholesterol, glucose, self-reported quality of life, and eating behaviors.
The Impact of Weight on Quality of Life-Lite Questionnaire (IWQOL-Lite) assessed weight-related quality of life across five domains: physical function, self-esteem, sexual life, public distress, and work. It demonstrates good reliability (a = 0.94) and validity.
The Three-Factor Eating Questionnaire (TFEQ) measures three dimensions of eating behavior: cognitive restraint, emotional eating, and uncontrolled eating. It comprises 51 items rated on a 4-point scale and has acceptable psychometric properties.
Procedure
After an initial screening, eligible participants attended an orientation session. They provided informed consent and completed baseline assessments of height, weight, blood pressure, fasting glucose, cholesterol, and the IWQOL-Lite and TFEQ questionnaires. They were then randomly assigned via a random number generator to one of two conditions: a standard behavioral weight loss program involving calorie restriction and exercise (SBP) or an acceptance-based behavioral treatment focused on mindfulness strategies (ABT).
Both interventions involved 12 weekly 90-minute group sessions led by trained health educators, with content tailored to the specific treatment approach. Participants also attended monthly individual coaching sessions. Weight, vitals, and psychosocial assessments were repeated at 12 weeks post-baseline.
Primary analyses utilized intent-to-treat principles with baseline observation carried forward for participants missing data at 12 weeks (15% attrition rate). 2 (condition) x 2 (time) mixed ANOVAs were conducted to evaluate intervention effects on BMI, blood pressure, cholesterol, glucose, quality of life, and eating behaviors over time. Significant interactions were probed using tests of simple effects.
In this example:
- Participants and sample size details are provided
- Primary and secondary outcome measures are described
- The procedure outlines the study design, randomization, interventions, assessments, and primary analysis plan
- Key methodological components like participants, materials, procedures, and analysis strategy are covered