quantitative and qualitative data

quantitative data

• Definition: Numerical data (measurable, can be counted).

• Examples: Reaction time (seconds), number of words recalled, questionnaire ratings (1–10).

• Analysis: Statistical — e.g., mean, median, mode, range, standard deviation, percentages, correlations.

• Produces: Objective, numerical results that can show patterns and trends.

✅ Strengths

• Easy to analyse, compare and summarise.

• Objective and reliable.

• Allows identification of patterns, trends, cause & effect.

❌ Weaknesses

• Lacks detail/meaning (low validity).

• May ignore context.

• Can be reductionist (oversimplifies human behaviour).

qualitative data

• Definition: Descriptive, non-numerical data (words, meanings, experiences).

• Examples: Interview transcripts, diary entries, open-ended responses, observation notes.

• Analysis: Thematic or content analysis (finding patterns or themes).

• Produces: Rich, detailed accounts and insight into meanings.

✅ Strengths

• Provides depth, detail, and validity.

• Gives insight into thoughts, feelings, and motivations.

• Captures context and meaning.

❌ Weaknesses

• Harder to analyse statistically.

• Subjective (researcher bias possible).

• Harder to replicate or generalise findings.

difference between qualitative and quantitative data

• types of data: quantitative 

• numerical 

• collected through controlled methods ( experiments, controlled observations and closed questions)

• types of analysis - statistical

• objectivity - high

• depth - shallow

• approach - positivist

• qualitative

• type of data: descriptive/verbal

• collected through open methods ( interviews, case studies )

• types of analysis - thematic  

• objectivity - low ( subjective interpretation)

• depth - detailed , deep

• approach - interpretivist

data collection techniques

Experiments

• Usually collect quantitative data (e.g., reaction times, scores, number of correct answers).

• Data is measured objectively using operationalised variables.

• Sometimes may include qualitative data (e.g., asking participants to describe experiences post-experiment).

Observations

• Structured observation → Quantitative

  • Pre-determined behaviour categories.

  • Behaviour counted/tallied (frequency data).

• Unstructured observation → Qualitative

  • Researcher writes rich, detailed descriptions.

  • Data analysed for themes and patterns.

Self-Report Methods

Includes questionnaires and interviews.

Questionnaires

• Closed questions → Quantitative (e.g., yes/no, multiple choice, rating scales).

• Open questions → Qualitative (free-response answers).

• Many questionnaires combine both types.

Interviews

• Structured interview: Predetermined questions → quantitative (e.g., tick boxes, scales).

• Unstructured interview: Flexible, open discussion → qualitative (detailed answers).

• Semi-structured interview: Mix of both (fixed + open questions).

Case Studies

• Typically qualitative (in-depth investigation of one person or small group).

• Use multiple methods (interviews, diaries, observation).

• Can also include quantitative tests (e.g., IQ tests, memory scores).

Content Analysis

• A way to convert qualitative data → quantitative.

• Steps:

  1. Collect qualitative data (e.g., interview transcript, diary, video).

  2. Identify categories/themes.

  3. Count frequency of each theme (quantitative) or describe patterns (qualitative).

• Can produce both types of data depending on analysis.