# Matching analysis to data type

> IB Psychology · IB Psychology 2027 First Assessment
> Source: https://www.owlsprep.com/study/ib-psychology-rm-matching-analysis-to-data-type/

Understand that data is represented and analysed in different forms based on the design of the study and the nature of the data.

## Learning objectives

- Understand that data is represented and analysed in different forms based on the design of the study and the nature of the data.

## Core concept

How data is presented and analysed depends on the study's design and on the nature of the data it produces. Quantitative designs (experiments, correlational studies) yield numbers, summarised with descriptive statistics, graphs and, at HL, inferential tests; qualitative designs (case studies, interviews, observations) yield words and meanings, analysed by methods such as thematic analysis, not by calculating an average. Data also come at different levels — nominal categories, ordinal ranks, or interval scores — and the level limits which summaries and tests are appropriate. The core skill is choosing the analysis to fit the design and data, rather than forcing numbers onto meanings.

> **info**
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> **Level note:** 2027 coverage places the whole Data-analysis cluster (RM_1-RM_6) at HL — level follows the _HL code suffix.

## Key studies

IB Psychology answers must be supported by named studies. Learn these to cite as evidence:

**Bandura, Ross & Ross (1961)** — Children who watched an aggressive adult model imitated both the modelled and novel aggressive acts toward a Bobo doll. — *An experiment with control conditions produces QUANTITATIVE data — counts of aggressive acts — suited to descriptive summary and statistical comparison between conditions.*

**Scoville & Milner (1957)** — The case study of H.M. found severe anterograde amnesia after hippocampal removal, with procedural memory spared. — *A case study produces rich QUALITATIVE data about a unique individual — described in depth, not reduced to a single average.*

## Evaluation (AO3)

**Strength — fit improves validity** — Matching the analysis to the design and data type means the conclusions actually reflect what was measured, instead of distorting it.

**Limitation — judgement required** — Design and level of measurement are not always clear-cut (e.g. combined Likert items are treated as interval-like), so the 'right' analysis can be a defensible judgement rather than a rule.

## Scope

> **note**
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> The umbrella idea that design + data nature drive analysis; the qualitative route is thematic analysis (RM_4) and the quantitative routes are descriptive (RM_5) and inferential (RM_6) statistics.

## Common pitfalls

- **Wrong:** Any data can be averaged or run through a statistical test.
  - Why it fails: A frequently penalised misconception in IB Psychology exams.
  - Correct: Statistics suit quantitative, interval-type data; qualitative data (transcripts, field notes) are analysed for themes and meaning. Averaging them, or treating a single ordinal rating as a precise score, misrepresents the data.

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