methods-section-guide

name	methods-section-guide
description	Guide to writing clear and reproducible methodology sections
metadata	{"openclaw":{"emoji":"⚙️","category":"writing","subcategory":"composition","keywords":["methods writing","methodology section","reproducible methods"],"source":"wentor-research-plugins"}}

Methods Section Writing Guide

Write methodology sections that are clear, complete, and reproducible, following discipline-specific conventions and best practices.

Purpose of the Methods Section

The methods section answers: "How did you do this study, and can someone else replicate it?" A well-written methods section:

• Provides enough detail for replication by an independent researcher
• Justifies why each method was chosen
• Describes the study design, participants, materials, and procedures
• Specifies statistical or analytical approaches
• Addresses ethical considerations

Standard Structure

The methods section typically follows this order (adapt to your discipline):

Subsection	Contents
Study Design	Overall approach (experimental, observational, computational, qualitative)
Participants / Samples	Population, sampling strategy, inclusion/exclusion criteria, sample size justification
Materials / Instruments	Equipment, software, reagents, questionnaires, datasets
Procedure	Step-by-step protocol, chronological order of data collection
Data Analysis	Statistical tests, software, significance thresholds, model specifications
Ethical Considerations	IRB approval, informed consent, data privacy

Writing by Discipline

Experimental Sciences (Biology, Chemistry, Physics)

## Materials and Methods

### Cell Culture and Treatment
HeLa cells (ATCC CCL-2) were maintained in DMEM (Gibco, #11965092)
supplemented with 10% FBS (Gibco, #26140079) and 1% penicillin-
streptomycin (Gibco, #15140122) at 37C in 5% CO2. Cells were
seeded at 5 x 10^4 cells/well in 24-well plates and treated with
compound X (0.1, 1, 10 uM) for 24 hours.

### Western Blot Analysis
Total protein was extracted using RIPA buffer (Thermo, #89900)
with protease inhibitor cocktail (Roche, #04693116001). Proteins
(30 ug/lane) were separated on 10% SDS-PAGE gels and transferred
to PVDF membranes. Primary antibodies: anti-TargetProtein
(Cell Signaling, #1234, 1:1000), anti-beta-actin (Sigma, #A5441,
1:5000). Secondary antibodies: HRP-conjugated (1:10000).

Key conventions:

• Include catalog numbers for all reagents
• Specify concentrations, temperatures, durations, and instrument models
• Reference established protocols by citation rather than rewriting them in full
• Use past tense throughout

Computational / Machine Learning Studies

## Methods

### Dataset
We evaluated our method on three benchmark datasets:
- **ImageNet-1K** (Russakovsky et al., 2015): 1.28M training images,
  50K validation images across 1,000 classes
- **CIFAR-100** (Krizhevsky, 2009): 50K training, 10K test, 100 classes
- **Oxford Flowers-102** (Nilsback & Zisserman, 2008): 8,189 images, 102 classes

### Model Architecture
Our model extends the Vision Transformer (ViT-B/16) with the
following modifications:
1. Replaced standard self-attention with linear attention (Katharopoulos et al., 2020)
2. Added a learnable class-conditional normalization layer after each block
3. Used patch size 16x16 with input resolution 224x224

### Training Details
| Hyperparameter | Value |
|---------------|-------|
| Optimizer | AdamW (beta1=0.9, beta2=0.999) |
| Learning rate | 1e-3 with cosine decay |
| Weight decay | 0.05 |
| Batch size | 256 (across 4 A100 GPUs) |
| Training epochs | 300 |
| Warmup epochs | 10 |
| Data augmentation | RandAugment (N=2, M=9), Mixup (alpha=0.8) |
| Label smoothing | 0.1 |

All experiments were implemented in PyTorch 2.1 and run on 4x NVIDIA A100
80GB GPUs. Training took approximately 18 hours per run. Code is available
at [repository URL].

Social Science / Survey Research

## Methods

### Participants
A total of 412 participants (245 female, 162 male, 5 non-binary;
M_age = 34.2, SD = 11.8) were recruited via Prolific. Inclusion
criteria: (a) aged 18-65, (b) fluent in English, (c) resided in
the US. Exclusion criteria: (a) failed two or more attention checks,
(b) completed the survey in under 3 minutes. After exclusions,
387 participants remained (attrition: 6.1%).

Sample size was determined a priori using G*Power 3.1 (Faul et al., 2007).
For a medium effect size (f^2 = 0.15), alpha = .05, and power = .80
in a multiple regression with 5 predictors, the required sample was 92.
We oversampled to ensure adequate power for subgroup analyses.

### Measures

**Perceived Stress Scale (PSS-10)** (Cohen et al., 1983): 10 items,
5-point Likert scale (0 = never, 4 = very often). Cronbach's alpha
in the current sample: .87.

**Big Five Inventory (BFI-10)** (Rammstedt & John, 2007): 10 items,
5-point Likert scale. Subscale alphas ranged from .68 to .81.

### Procedure
After providing informed consent, participants completed measures in
the following fixed order: demographics, PSS-10, BFI-10, experimental
task, manipulation check, debriefing. Median completion time: 14 minutes.
Participants were compensated GBP 2.50.

### Ethical Approval
This study was approved by the [University] IRB (Protocol #2024-0123).
All participants provided informed consent.

Reproducibility Checklist

Use this checklist to ensure your methods section is complete:

For All Studies

• Study design and rationale clearly stated
• Sample/dataset described with inclusion/exclusion criteria
• Sample size justified (power analysis, saturation, or convention)
• All measures and instruments described with psychometric properties or specifications
• Procedure described in chronological order with enough detail for replication
• Statistical/analytical methods specified, including software and version
• Significance level (alpha) stated
• Missing data handling described
• Ethical approval and consent documented

For Computational Studies

• Hardware specifications (GPU model, memory, training time)
• Software framework and version (PyTorch 2.1, TensorFlow 2.15, etc.)
• All hyperparameters listed in a table
• Random seed policy described
• Code and data availability statement
• Evaluation metrics defined precisely
• Baseline methods described or cited

Common Pitfalls

Issue	Example	Fix
Vague descriptions	"Data was analyzed statistically"	Specify exact tests: "We used a two-tailed independent samples t-test"
Missing software versions	"Analysis done in R"	"Analysis conducted in R 4.3.1 using lme4 v1.1-35"
No sample size justification	Just reporting N	Include power analysis or justify based on conventions
Ambiguous order	Reader cannot tell what happened when	Use numbered steps or chronological narrative
Results in methods	Including p-values or outcomes	Save all results for the Results section
Over-referencing	Citing a protocol without summarizing key details	Provide enough detail to understand without reading the reference

Language and Tense

• Use past tense for what you did: "Participants completed a questionnaire..."
• Use present tense for established methods: "ANOVA tests for differences between group means..."
• Use passive voice when the agent is unimportant: "Samples were centrifuged at 12,000 rpm..."
• Use active voice when clarity is improved: "We excluded participants who..."