quant and qual data

Question 1

A01

Answer 1

Definition: Data that is numerical and can be measured, counted, and statistically analysed.

Definition: Data that is descriptive, non-numerical, and explores thoughts, feelings, and meanings in detail.

Question 2

quant strengths

Answer 2

Objective and Reliable (PEE)
Point: Quantitative data is less affected by bias.
Evidence: For example, scores from a memory test (e.g. words recalled out of 20).
Explanation: This makes results easier to replicate and increases reliability.
Easily Analysed Statistically
Evidence: Researchers can use statistical tests (e.g. t-tests) to find significant results.
Explanation: Helps identify patterns, test hypotheses, and generalise findings.
Large Sample Sizes
Evidence: Structured methods like online surveys can reach thousands of people.
Explanation: Increases population validity and generalisability.

Question 3

quant weaknesses

Answer 3

Lacks Depth and Detail
Evidence: A depression score of 7/10 doesn’t explain the person’s lived experience.
Explanation: Reduces the insight into the causes or meaning behind behaviour.
Over-simplifies Complex Behaviour
Evidence: A scale rating stress may not capture the quality or origin of stress.
Explanation: Limits the validity of conclusions drawn from numerical data.

Question 4

qual strengths

Answer 4

Rich, In-depth Insight (PEE)
Point: Allows detailed exploration of psychological experience.
Evidence: A patient interview about anxiety may reveal key life events.
Explanation: This increases the validity of the data by showing the full context.
High Ecological Validity
Evidence: Observing a child at home provides realistic behaviour.
Explanation: Findings reflect real-life situations better than artificial tasks.
Exploratory and Flexible
Evidence: Open-ended interviews may reveal unexpected themes.
Explanation: Useful when researching new or sensitive psychological topics.

Question 5

qual weaknesses

Answer 5

ubjectivity and Researcher Bias
Evidence: Researchers may interpret interview responses differently.
Explanation: Lowers reliability and makes results harder to replicate.
Difficult to Analyse
Evidence: A case study may contain 30 pages of descriptive notes.
Explanation: Hard to summarise, compare, or quantify for conclusions.
Limited Generalisability
Evidence: Small samples in qualitative research (e.g. 5 interviewees).
Explanation: Makes it hard to apply findings to wider populations.