2.2 Methodological Concepts


2026 Syllabus Objectives

By the end of this topic, you should be able to:

  • Understand aims, hypotheses, and variables in research
  • Explain how variables are controlled in studies
  • Distinguish between types of data
  • Describe and evaluate different sampling techniques
  • Explain ethical guidelines for research with humans and non-human animals
  • Understand validity, reliability, and replicability
  • Analyse data using measures of central tendency and spread
  • Interpret and select appropriate graphs

1. Aims, Hypotheses, and Variables

The Aim

The aim is a simple statement that explains why a researcher is doing a study and what they hope to find out. Think of it as the "goal" of the research.

Example: "To investigate whether caffeine affects how quickly people react."


The Hypothesis

A hypothesis is a specific prediction — the researcher states exactly what they think will happen before the study begins.

There are four types of hypothesis you need to know:

  • Directional (one-tailed) hypothesis — Predicts the direction of the result (e.g., says something will be higher, lower, more, or less).

    Example: "People who drink coffee will have faster reaction times than people who drink water."

  • Non-directional (two-tailed) hypothesis — Predicts that there will be a difference or relationship, but does NOT say which direction.

    Example: "There will be a difference in reaction times between people who drink coffee and people who drink water."

  • Experimental hypothesis — Used when the study has an independent variable (like a lab experiment). It predicts the effect of the IV on the DV.

  • Alternative hypothesis — Used in non-experimental studies like correlations. It predicts a relationship between two variables.

  • Null hypothesis — Predicts that the independent variable will have no effect on the dependent variable. It basically says "nothing significant will happen." Researchers try to disprove this.

    Example: "There will be no difference in reaction times between people who drink coffee and people who drink water."


Operationalisation

Before a study begins, the researcher must operationalise their variables. This means defining them clearly and specifically so they can actually be measured.

Think of it like a recipe — you can't just say "add some flour." You need to say "add 200g of plain flour."

Example of poor operationalisation: "Measure stress." Example of good operationalisation: "Measure stress using a score out of 100 on a self-report questionnaire."

Every operationalised variable should include:

  • What is being measured
  • How it is being measured (the tool or method)
  • Units of measurement (e.g., seconds, millimetres, beats per minute)

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