Approaches to sociological research

Paper 1 – Methods of Research (Cambridge A‑Level Sociology 9699)

This set of notes covers every requirement for the “Approaches to sociological research” and “Research issues” sections of the Cambridge A‑Level Sociology syllabus. It is organised to match the syllabus wording, includes concise definitions, key examples and evaluation points that are directly usable in exam answers.

1. Approaches to Sociological Research

• Positivist (quantitative) – Objectivist paradigm; assumes an external, measurable reality.
• Interpretivist (qualitative) – Subjectivist paradigm; reality is socially constructed and multiple.
• Critical / Transformative – Value‑laden paradigm; research is a tool to expose power relations and promote emancipation (e.g., Marxist, feminist, post‑colonial perspectives).
• Pragmatic (mixed‑methods) – Chooses the method that best answers the research question, combining positivist and interpretivist techniques where appropriate.

2. Types of Data

2.1 Primary vs. Secondary Data

• Primary data: Collected directly for the specific study (questionnaire responses, interview transcripts, field notes, photographs, video recordings).
• Secondary data: Already existing and reused (census tables, government statistics, newspaper archives, existing survey datasets such as the British Social Attitudes Survey).
• When to use which? Primary data give fresh, context‑specific insight; secondary data are ideal for large‑scale, longitudinal or comparative work when time and resources are limited.

2.2 Quantitative vs. Qualitative Data

• Quantitative data – Numerical; can be categorical (e.g., gender, social class) or continuous (e.g., age, income). Enables statistical analysis.
• Qualitative data – Non‑numerical; includes textual (interview transcripts, field notes), visual (photographs, video) and audio material. Enables thematic, discourse or narrative analysis.

3. Core Research Designs (Eight Designs Required by the Syllabus)

Design	Key Features	Typical Strength(s)	Typical Limitation(s)
Survey	Structured questionnaire or interview; large‑scale quantitative data; probability sampling.	Statistical generalisation; clear hypothesis testing.	Limited depth; risk of superficial answers.
Interview	Semi‑structured or unstructured conversation; purposive sampling; textual/visual data.	Rich, in‑depth insight; flexibility to probe.	Small, non‑representative samples; time‑consuming.
Observation	Systematic watching of behaviour – structured or participant; can be covert or overt.	Direct evidence of actual behaviour; naturalistic setting.	Observer bias; limited access to private settings.
Experiment	Manipulation of an independent variable in a controlled setting; random allocation.	High internal validity; ability to infer causality.	Artificial environment; ethical constraints.
Case Study	Intensive investigation of one (or a few) case(s); multiple data sources.	Detailed contextual insight; theory‑building potential.	Limited generalisability; can be time‑intensive.
Content Analysis	Systematic coding of textual, visual or audio material (e.g., newspapers, TV programmes); can be quantitative or qualitative.	Unobtrusive; handles large corpora.	May miss deeper meanings; coding reliability issues.
Secondary‑Data Analysis	Re‑analysis of existing datasets (census, longitudinal surveys, archival records).	Cost‑effective; large sample sizes; longitudinal possibilities.	No control over data collection; possible mismatch with research question.
Longitudinal vs. Cross‑sectional	Longitudinal: same respondents followed over time. Cross‑sectional: data collected at one point.	Longitudinal – tracks change, causation clues. Cross‑sectional – quicker, cheaper snapshot.	Longitudinal – attrition, time‑consuming. Cross‑sectional – cannot infer change.

4. Quantitative (Positivist) Research

Key Features

• Large, statistically representative (probability) samples.
• Structured instruments – questionnaires, surveys, structured observation schedules.
• Statistical analysis – descriptive (means, frequencies) and inferential (correlation, regression, chi‑square).
• Emphasis on reliability, validity, replicability and objectivity.

Advantages

• Statistical generalisation to a wider population.
• Clear hypothesis testing and theory verification.
• Data can be compared across studies and over time.

Disadvantages

• May overlook context, meaning and nuance.
• Risk of reductionism – complex social life reduced to numbers.
• Limited flexibility once the instrument is fixed.

5. Qualitative (Interpretivist) Research

Key Features

• Small, purposively selected (non‑probability) samples.
• Unstructured or semi‑structured techniques – interviews, participant observation, focus groups, document/visual analysis.
• Thematic, discourse or narrative analysis.
• Emphasis on depth, context, reflexivity and researcher positionality.

Advantages

• Rich, detailed insight into social processes.
• Flexibility to follow unexpected leads.
• Captures participants’ own language and perspectives.

Disadvantages

• Findings are not easily generalisable.
• Subjectivity can affect reliability.
• Time‑consuming data collection and analysis.

6. Critical / Transformative Approach

Research is never value‑free. This paradigm draws on Marxist, feminist, post‑colonial, queer or other critical perspectives to expose power relations, challenge dominant ideologies and promote social change. Methods are often qualitative (in‑depth interviews with marginalised groups, documentary analysis) but can incorporate quantitative data to illustrate structural inequalities.

7. Mixed‑Methods (Pragmatic) Research

Combines quantitative and qualitative techniques within a single study to reap the benefits of both.

Design Types

• Sequential explanatory – Quantitative phase first; qualitative phase follows to explain the numbers.
• Sequential exploratory – Qualitative phase first; quantitative phase follows to test emerging hypotheses.
• Concurrent triangulation – Both strands collected simultaneously and compared during analysis.

Advantages

• Comprehensive understanding of the research question.
• Triangulation enhances credibility and validity.
• Addresses both breadth (breadth) and depth.

Disadvantages

• Requires expertise in both methodological traditions.
• More resource‑intensive (time, funding, skills).
• Complexity in integrating quantitative and qualitative datasets.

8. Research Issues (Syllabus Requirement)

8.1 Sampling & Sampling Bias

• Probability sampling – random, stratified, cluster; ensures representativeness.
• Non‑probability sampling – purposive, snowball, quota; targets specific groups.
• Sampling bias – systematic error that makes the sample unrepresentative (e.g., over‑sampling university students).
• Non‑response bias – when certain types of people fail to respond, potentially skewing results.

8.2 Operationalisation of Concepts

• Turning abstract sociological concepts into measurable variables (e.g., “social cohesion” operationalised as frequency of neighbourly interactions).
• Requires clear definition, indicator selection and pilot testing.

8.3 Reliability & Validity (Quantitative) / Credibility & Transferability (Qualitative)

• Reliability – Consistency of measurement (test‑retest, internal consistency, inter‑rater reliability).
• Validity – Extent to which the instrument measures what it claims (construct, criterion, face validity).
• Credibility – Trustworthiness of qualitative findings (triangulation, member checking, prolonged engagement).
• Transferability – Applicability of qualitative findings to other contexts (thick description).

8.4 Measurement Error & Data Quality

• Measurement error – Random error (reduces reliability) or systematic error (threatens validity). Examples: poorly worded questionnaire items, faulty coding.
• Data quality threats – Social desirability bias, recall bias, researcher bias, Hawthorne effect.
• Mitigation strategies: pilot studies, anonymous data collection, reflexive field notes, double‑coding.

8.5 Ethics

• Informed consent – participants must understand the purpose, procedures and their rights.
• Confidentiality & anonymity – protect identities, especially with sensitive topics.
• Right to withdraw – at any stage without penalty.
• Minimising harm – physical, psychological, social.
• Ethical review – obtain approval from a recognised ethics committee.
• Special considerations for vulnerable groups (e.g., children, prisoners) – parental consent, safeguarding procedures.

9. Comparative Summary of the Four Paradigms

Aspect	Positivist (quantitative)	Interpretivist (qualitative)	Critical / Transformative	Pragmatic (mixed‑methods)
Philosophical stance	Objectivist – reality is measurable	Subjectivist – reality is socially constructed	Value‑laden – research as a tool for emancipation	Pragmatic – choose the method that best answers the question
Typical sample size	Large, statistically representative	Small, purposively selected	Often small, targeted groups (e.g., marginalised communities)	Can combine large quantitative and small qualitative samples
Data collection methods	Surveys, questionnaires, structured observation	Interviews, participant observation, focus groups, document/visual analysis	Interviews, focus groups, documentary analysis with a critical lens	Combination of the above, depending on design
Data type	Numerical (categorical or continuous)	Textual, visual, audio	Textual/visual with emphasis on power relations	Both numerical and textual
Analysis technique	Statistical (descriptive & inferential)	Thematic, discourse, narrative analysis	Thematic/critical discourse analysis, triangulation with theory	Statistical + thematic; integration through triangulation
Strengths	Generalisation, hypothesis testing, replicability	Depth, context, participant voice	Highlights inequality, can inform activism/policy	Comprehensive insight, methodological triangulation
Limitations	May miss nuance, limited flexibility	Limited generalisability, time‑consuming	May be seen as biased; findings less easily generalisable	Resource intensive; requires expertise in both traditions

10. Suggested Diagram – The Research Process

11. Sample Exam Question & Mark Scheme Guidance

Question: Compare the strengths and limitations of using a questionnaire survey and semi‑structured interviews to investigate young people’s attitudes towards social media.

Key points to address (6‑8 marks):

1. Nature of data – numeric (survey) vs. textual (interview).
2. Sample size & representativeness – probability (survey) vs. purposive (interview).
3. Depth of insight – ability to probe follow‑up questions in interviews.
4. Reliability & validity – statistical reliability & construct validity for surveys; credibility & transferability for interviews.
5. Practical considerations – cost, time, anonymity, ease of administration.
6. Ethical issues specific to young participants – parental consent, safeguarding, protecting privacy.
7. Potential bias – social desirability in self‑report questionnaires; researcher bias in interviews.

12. Revision Checklist

• Define the four paradigms using the exact syllabus terminology.
• Distinguish primary vs. secondary data and give at least two examples of each.
• Identify three quantitative and three qualitative data‑collection methods, noting their typical sample size.
• Explain reliability, validity, credibility and transferability and how each is assessed.
• List and briefly describe the eight core research designs.
• State the main ethical principles and special considerations for vulnerable groups.
• Recall the additional research‑issues: sampling bias, non‑response, operationalisation, measurement error, data‑quality threats.
• Be able to construct a comparative table of the four approaches (use the table above as a model).
• Know when each mixed‑methods design (explanatory, exploratory, concurrent) is appropriate.
• Practice answering past‑paper questions that ask you to evaluate a design or compare two methods.