Data Handling and Analysis

What Data Handling and Analysis covers

Data Handling and Analysis covers what happens to data once it has been collected: summarising it, displaying it, judging the strength of a relationship, and identifying the level of measurement that governs which test can be used. It sits within research methods and can be assessed on any of the three papers.

The 7 subtopics

Each one has its own page.

• Primary and secondary dataData collected first-hand against data drawn from existing sources, what a meta-analysis combines, and what weakens its conclusions.Open →
• Descriptive statisticsCalculating the mean, median and mode, choosing between them for skewed data or categories, and what the range and standard deviation each measure.Open →
• Presentation and display of quantitative dataBar charts, histograms, scattergrams, tables and line graphs, and what each is appropriate for.Open →
• DistributionsNormal, positively skewed and negatively skewed distributions, and where the mean, median and mode fall in each.Open →
• Analysis and interpretation of correlationThe correlation coefficient, how its strength is banded, why correlation does not establish cause, and why a real relationship can still show no straight-line correlation.Open →
• Levels of measurementNominal, ordinal and interval data, what distinguishes them, and why the level has to be identified before a test can be chosen.Open →
• Coding in content analysisHow categories are built for content analysis, the properties they must have, inter-rater reliability, and the strengths and limitations of coding.Open →

What you need to know

Answer each one before you reveal it.

Take Data Handling and Analysis further

The same content, four other ways.

Commonly confused

Pairs that lose marks when they are blurred together.

• Mean vs Median

  Mean: the total of all values divided by how many values there are. Median: the middle value once the scores are placed in order.

• Nominal data vs Ordinal data

  Nominal data: separate categories with no inherent order, such as eye colour. Ordinal data: ranked positions where the gaps between ranks are not necessarily equal.

• Bar chart vs Histogram

  Bar chart: a display with gaps between the columns, used for discrete categories. Histogram: a display with no gaps between the columns, used for continuous data.

Questions from this topic

Each one with the model answer underneath.

1. 1

   What is primary data?

   Model answer

   Information collected directly by the researcher for the specific purpose of their current investigation.

2. 2

   Define secondary data in psychological research.

   Model answer

   Information that was collected by someone else for a different purpose and is then used by the researcher in their own investigation.

3. 3

   What advantage does collecting primary data give researchers regarding the research process?

   Model answer

   It gives the researcher control over the design, variables, and procedures used to gather the information.

4. 4

   If a researcher wants complete control over how variables are operationalised, should they use primary or secondary data? Explain why.

   Model answer

   Primary data, because collecting it themselves allows them to control the design, variables, and procedures.

5. 5

   Why does using secondary data limit a researcher's control?

   Model answer

   Because the data has already been collected, so the researcher has no control over how it was gathered or what variables were measured.

6. 6

   Explain why a researcher using census data cannot change what information is available to them.

   Model answer

   Census data is secondary data collected by others for a different purpose, so the researcher has no control over what variables were measured or how data was gathered.

What is inside Levo

Six ways to work the same material.

Data Handling and Analysis revision questions

What does Data Handling and Analysis cover in AQA A-Level Psychology?

7 subtopics: primary and secondary data, descriptive statistics, presentation and display of quantitative data, distributions, analysis and interpretation of correlation, levels of measurement and coding in content analysis. Each one has its own page with questions and model answers.

How is Data Handling and Analysis organised?

By the AQA 7182 specification, subtopic by subtopic, so it matches the order you are taught it in.