Objective and measurement
For each objective, identify the measure and how it was obtained. Describe measurement conditions in experimental work. In humanities research, define material selection rules and the unit of analysis. A term should not describe different operations in different chapters.
Reproducibility
Check the procedure description. Another specialist should understand inclusion criteria, collection procedures and analysis. For equipment, provide details needed to understand accuracy and operating conditions. State software versions and procedures when they affect the result.
Why this approach was chosen
Prepare an answer about suitable alternatives. Explain your choice through the material, aim and study limitations. The University of Manchester’s Academic Phrasebank treats procedure, material and the basis for selecting a method as distinct parts of academic writing.
Self-check questions
Check that every stated method is actually used in the results. Clarify the role of any method mentioned in chapter two without corresponding results. Prepare explanations for missing observations, protocol changes and measurement quality checks before the discussion.
Connecting methods to objectives
Map each objective to material, action and result. It should be clear which data addresses each objective and what counts as an output. Clarify the role of any method that answers no research question.
Interviews may explain participants’ experiences, while tests assess changes in a measure. They provide different information. Mixed-methods research must explain how that information complements each other and when it is combined. Listing methods alone does not explain the study design.
What the material description includes
State the data source, selection conditions and inclusion and exclusion criteria. Readers must understand what the result applies to. Surveys need participant details and collection procedures; text analysis needs corpus selection principles and the unit of analysis.
Explain and record any exclusions after initial selection. Do not combine dataset versions without checking them. In a multistage study, identify which observations belong to each stage. This prevents confusion about different sample sizes across tables.
Discussing alternative approaches
Explain why this method was selected and compare it with one or two realistic alternatives. Discuss suitability, available material and interpretation limits. An alternative is not wrong simply because it answers a different question.
If limited data access shaped the choice, say so and explain its effect on the conclusions. A convincing justification reflects actual conditions. A list of familiar method names without application details usually raises more questions than a concise explanation of one method.
Preparing for methodological questions
Collect backup material: a selection diagram, an instrument sample, a variable dictionary or an annotation example. Record where each decision is described. This helps move quickly from a general answer to specific evidence.
Can a colleague understand the procedure without further spoken explanation? If not, clarify it in the text. The methods section must match the work actually performed, including changes made during the study, before defence materials are submitted.
