RU SOC 202 Researching the Social World: Methodology Notes

Researching the social world is not just a technical task—it is a disciplined way of producing trustworthy knowledge about people, institutions, and everyday life. In RU SOC 202, methodology matters because it connects your research question to your methods, your ethics, and your analysis. This study guide focuses on practical, South African–relevant thinking: how sociology students learn to plan studies, choose designs, sample responsibly, collect data rigorously, and interpret findings in ways that are ethically grounded and analytically sound.

Across these notes, you will find: conceptual foundations (epistemology, methodology, and design), step-by-step research processes, detailed guidance on common approaches in sociology (qualitative, quantitative, mixed methods), and concrete examples that reflect the realities of conducting research within South African universities, colleges, and TVETs. You will also see how to justify methodological choices, deal with bias and limitations, and meet ethical requirements—crucial for both coursework assignments and honours-level trajectories.

Methodology Foundations: From Sociological Questions to Research Designs

Sociology’s “Method Problem”: What counts as social knowledge?

In SOC 202, “methodology” usually refers to the logic of how you know—the principles that guide what kind of evidence you collect and how you interpret it. Sociology studies the social world as patterned and meaningful: social life is not random, but shaped by structures (class, race, gender, institutions, labour markets) and by meanings people assign to their experiences. This creates a distinctive methodological challenge:

• People are meaning-makers. Your data often comes from accounts, perceptions, and practices, not only measurable outcomes.
• Social life is structured. Your results cannot be understood without context: histories, inequalities, and institutions.
• Research changes what it studies. In qualitative work especially, participants interpret you as well as you interpret them.

A sound methodology therefore answers at least three questions:

1. How should the research question be framed so it is sociological?
   Example: Instead of “Why do students drop out?” (psychological phrasing), the sociological version asks: “How do institutional support systems, financial precarity, and academic cultures shape dropout decisions and trajectories?”
2. What evidence is appropriate for that question?
   For “how” and “why” mechanisms, qualitative interviews, ethnography, or mixed methods can be relevant; for “how much” comparisons or prevalence, surveys or administrative datasets may fit better.
3. How will you justify your claims?
   Methodology includes strategies for validity, reliability, credibility, and transparency.

Epistemology, ontology, and method: aligning your choices

Your methodological decisions are not arbitrary. They should follow from assumptions about:

• Ontology (what kind of reality social life is): Are social patterns real as structures? Are meanings and categories socially constructed?
• Epistemology (how knowledge is possible): Can you know social reality through observation, interpretation, measurement, or dialogue?

In practice, SOC 202 students often adopt a pragmatic position: you can use multiple methods because social phenomena have multiple dimensions. For example, educational inequality may include:

• Structured constraints (fees, accommodation, transport costs)
• Institutional practices (advising, assessment policies, learning support)
• Meaning and agency (how students interpret support, feel belonging, decide to persist or withdraw)

A pragmatic, sociology-friendly methodology might therefore combine:

• survey measures of prevalence (how many students report particular barriers),
• with qualitative interviews exploring mechanisms (how barriers translate into decisions).

Methodology vs method: avoiding confusion

It’s common to confuse:

• Methodology = the theory/logic of research choices (design, reasoning, epistemic commitments).
• Method = specific techniques (interviews, focus groups, surveys, observation, document analysis, experiments).

In SOC 202, marker expectations usually include how you link the two. You can use a technique (e.g., interviews) without a methodology—and that tends to produce work that is “descriptive” rather than “analytical.” The best responses show:

• what each method contributes,
• why alternatives were less suitable,
• how analysis will move from data to sociological claims.

Designing a study: research question, objectives, and hypotheses

A strong study design begins with a tight chain of reasoning:

1. Define the research problem
   Example: Persistent inequalities in student success across South African higher education and TVET contexts.
2. Ask a sociologically precise research question
   Example: “How do institutional support structures influence how first-year students understand and negotiate academic risk?”
3. Set objectives
   • identify key forms of institutional support students access,
   • explore student interpretations of support and feedback,
   • examine how support intersects with socio-economic constraints.
4. Select a design
   • exploratory (few prior studies),
   • descriptive/comparative,
   • explanatory/mechanism-focused.
5. Plan analysis
   Will you code themes? Test relationships? Triangulate across data sources?

Hypotheses: when they help and when they don’t

Hypotheses are not automatically required. In quantitative studies, hypotheses clarify expected relationships; in qualitative research, researchers typically use research questions and sensitising concepts rather than rigid hypotheses. However, even qualitative researchers can work with provisional expectations.

Examples:

• Quantitative hypothesis: “Students reporting higher financial stress will show lower academic persistence intentions, controlling for prior academic performance.”
• Qualitative expectation (not a strict hypothesis): “Financial stress is likely to be discussed alongside feelings of belonging and institutional trust, shaping decisions about attending tutorials or seeking help.”

The marker signal to watch: Your methodology should match your question. If your question asks for mechanisms and meaning, a purely prevalence survey may be insufficient on its own.

Research Approaches in SOC 202: Qualitative, Quantitative, and Mixed Methods

Qualitative research: understanding meaning, process, and lived experience

Qualitative research aims to produce depth: how social reality is interpreted, negotiated, and enacted. Typical qualitative methods include:

• Semi-structured interviews
• Focus groups
• Participant observation / ethnographic fieldwork
• Document analysis (policy texts, institutional reports, public discourse)
• Case studies (single institution, cohort, programme, or locality)

Strengths

Qualitative approaches are particularly strong when you need to understand:

• mechanisms (how support leads to change),
• processes (how decisions evolve across time),
• interpretive frameworks (how students make sense of assessment, belonging, or institutional culture).

For example, a study on student success might discover not only that support is lacking, but what students believe support is, how they judge its usefulness, and what social barriers make help-seeking difficult (e.g., shame, peer norms, language confidence).

Risks and limitations

Qualitative work can be vulnerable to:

• selection bias (who agrees to participate),
• researcher influence (participants respond to perceived expectations),
• interpretive bias (your theoretical lens shapes what you notice),
• limited generalisability (depth ≠ representativeness).

SOC 202 students must show strategies to manage these risks, such as purposive sampling rationale, reflexivity, and analytic transparency.

Quantitative research: measurement, patterns, and comparative claims

Quantitative research focuses on measurement and patterns across larger samples. Common techniques include:

• Surveys (questionnaires)
• Structured interviews (less common in student work, but possible)
• Administrative data analysis (where accessible: retention statistics, attendance logs, progression rates)
• Content analysis with quantification (e.g., coding frequency in documents)

Strengths

Quantitative approaches help you answer questions like:

• How widespread is a barrier?
• Are there statistically significant differences between groups?
• What correlations exist between variables?

For example:

• You could measure the relationship between perceived access to academic support and reported study hours.
• You could compare persistence intentions by socio-economic status categories or accommodation types (where ethically permissible and correctly operationalised).

Risks and limitations

Quantitative work can fail if:

• variables are poorly operationalised (e.g., “support” measured as one item rather than a construct),
• surveys oversimplify complex experiences into numbers,
• analysis becomes detached from context.

A common SOC 202 exam critique is “quantification without explanation.” You might report that “support correlates with persistence intentions,” but without qualitative insight, you may not explain why or how that relationship operates.

Mixed methods: triangulation and complementary explanation

Mixed methods combine qualitative and quantitative approaches. It is not “doing both” casually; it requires a design logic. Typical reasons to use mixed methods include:

• Complementarity: qualitative explains survey patterns.
• Triangulation: you verify findings across different data sources.
• Expansion: one method explores aspects the other cannot capture.
• Development: results from one phase inform the next phase.

Example mixed-method pathway (education research)

1. Phase 1: Survey
   • Measure prevalence of perceived barriers and persistence intentions.
2. Phase 2: Interviews or focus groups
   • Select participants showing contrasting patterns (high barrier/high persistence vs high barrier/low persistence).
3. Integration
   • Compare qualitative mechanisms to survey associations.
   • Use joint displays to connect the two datasets.

Integration is where many students lose marks if they don’t explicitly show how datasets inform each other.

A key decision: exploratory vs explanatory vs descriptive designs

SOC 202 often expects you to distinguish designs. Consider:

• Exploratory designs: useful when the topic is under-researched or when concepts need refinement.
• Descriptive designs: map features of a population or programme (who experiences what and how often).
• Explanatory / analytic designs: identify mechanisms or factors behind outcomes.

Example:
For student dropout research:

• Exploratory: discover what students identify as causes.
• Descriptive: estimate how common each factor is.
• Explanatory: test relationships and develop mechanism narratives.

You should be able to justify how your selected design matches your learning outcomes and research question.

Sampling, Data Collection, and Analysis: Rigour in Practice

Sampling strategies: who you study and why it matters

Sampling determines how evidence represents social reality. In SOC 202, both qualitative and quantitative sampling require ethical and methodological care.

Qualitative sampling: purposive logic

Common qualitative sampling strategies include:

• Purposive sampling: select participants with relevant experience.
• Maximum variation sampling: capture diverse perspectives (different faculties, accommodation types, genders, programme levels).
• Homogeneous sampling: focus on a relatively similar group to explore shared experiences deeply.
• Snowball sampling: useful for hard-to-reach populations, but it requires caution about bias.

Example: selecting students for dropout mechanisms

If your research asks how institutional support shapes persistence decisions, you might purposely select:

• students who are continuing despite barriers,
• students who have disengaged or are at risk,
• student support staff (tutors, advisers).

A strong write-up explains:

• how participants were approached,
• what inclusion criteria were used,
• why sample diversity helps your analysis.

Quantitative sampling: probability logic and representativeness

Quantitative sampling often uses:

• simple random sampling
• stratified sampling (e.g., by programme year)
• cluster sampling (e.g., by campus or classroom group)

In many student contexts, full probability sampling is difficult due to access constraints. When that happens, SOC 202 work should be transparent about the limitations and avoid claiming broad generalisability.

Sample size: depth vs breadth

Sample size differs across paradigms:

• Qualitative studies often focus on theoretical saturation (new interviews stop adding major themes) rather than numeric thresholds.
• Quantitative studies require power and precision considerations, depending on analysis type.

In exams, it’s usually enough to show that you understand:

• what drives sample size decisions,
• how you will justify your chosen number.

If you provide a sample size in assignments, it must link to your design and analytic plan. (For example, a small survey sample cannot credibly support complex multivariate modeling without strong justification.)

Data collection: building trustworthy evidence

Interviews: crafting questions and managing interaction

Semi-structured interviews combine consistency with flexibility. A rigorous interview guide includes:

• opening questions to establish context,
• core questions aligned directly with research objectives,
• probes (“Can you tell me more about…?” “What made that difficult?”),
• closing questions to invite additional perspectives.

Key practices:

1. Pilot testing: test the interview guide with a few people to refine wording.
2. Language considerations: in South Africa, multilingual contexts are common. You must plan how you will handle interviews in English, isiXhosa, Afrikaans, Sesotho, or other languages. Your plan should include translation approach and potential meaning shifts.
3. Consent and comfort: remind participants they can skip questions or stop.

Example interview topic alignment

Research question: “How do students interpret institutional support?”
Interview sections:

• first-year expectations,
• experiences of academic support (tutorials, advising),
• perceptions of institutional responsiveness,
• barriers to help-seeking (time, costs, stigma),
• what “support” means in their own words.

If your interview guide drifts away from the research question, your analysis will suffer, and exam markers will notice.

Observation: seeing practices, not only hearing accounts

Observation captures social interaction and routines. In student research, observation is often:

• structured (using a checklist),
• semi-structured (with guiding themes),
• participant observation (more interpretive, requires ethical reflexivity).

Observation is particularly useful when:

• people may not articulate practices clearly in interviews,
• institutional routines shape outcomes (e.g., tutorial attendance cultures, classroom participation norms).

However, observation raises questions:

• your presence may change behaviour,
• your role must be clearly defined to participants.

Surveys: designing and operationalising variables

Survey design requires careful thinking about constructs:

• Operationalisation: turning abstract ideas (“academic support,” “belonging,” “financial stress”) into measurable items.
• Question wording: avoid leading questions.
• Response options: ensure they are appropriate for participants’ contexts.
• Pilot testing: check comprehension and time burden.

A survey with ambiguous items produces unreliable data. If you use scales (e.g., Likert items), you should explain:

• how many items,
• what response anchors mean (e.g., “strongly disagree” to “strongly agree”),
• how reliability will be assessed (e.g., internal consistency if relevant).

Document analysis: using policy texts and institutional artefacts

Document analysis is common in sociology because institutions communicate rules, values, and definitions through texts. Examples include:

• student support policy documents,
• retention strategies,
• codes of conduct,
• programme prospectuses,
• public institutional reports.

A document analysis method typically includes:

1. selecting documents based on relevance to the research question,
2. coding content systematically (qualitative coding or quantification),
3. interpreting how institutional language frames problems and solutions.

Markers often reward students who connect documents to lived experiences from interviews. That creates a bridge between institutional discourse and student meanings.

Data analysis: turning raw material into sociological explanation

Qualitative analysis: coding, theme development, and interpretation

A standard qualitative workflow:

1. Data familiarisation
   • read transcripts closely,
   • note initial impressions.
2. Open coding
   • code line-by-line or segment-by-segment.
3. Focused coding
   • refine codes into more analytic categories.
4. Theme development
   • group codes into themes aligned with objectives.
5. Interpretation
   • connect themes to sociological concepts (inequality, institutional power, cultural meanings).
6. Verification
   • check alternative explanations,
   • ensure consistency between data and claims.

Example analytic chain for education research

Theme: “Support as conditional belonging”

• Codes: “only helped when I was struggling publicly,”
• “advisers were too busy,”
• “I felt judged,”
• “I asked later when I trusted someone.”

Interpretation links to sociology:

• support is not neutral; it is shaped by institutional resources and cultural norms about who is “deserving” of help.

Reflexivity and transparency

Reflexivity is not optional. It means acknowledging how your position, assumptions, and interactions may influence data production. In practice, you should include:

• how you approached participants,
• how you managed your role during interviews,
• how you handled conflicting interpretations in analysis.

Transparency includes describing your analytic steps clearly enough that another researcher could understand what you did—even if they wouldn’t replicate your exact conclusions.

Quantitative analysis: relationships, differences, and careful interpretation

Quantitative analysis depends on the research question:

• Descriptive statistics: frequencies, means, medians—what respondents report.
• Bivariate analysis: relationships between two variables.
• Multivariate analysis: controls for confounding variables (requires careful planning).
• Comparative analysis: differences across groups.

Even without performing advanced statistics in a course, a methodological exam can test whether you understand:

• what correlation means (and what it does not),
• why causality requires more than a cross-sectional design,
• how confounding affects interpretation.

A common mistake: claiming “support causes persistence” from a single survey snapshot. If your design is cross-sectional, appropriate language is “associated with,” or “linked to,” unless you design something that supports causal inference.

Integrating mixed methods in analysis

Integration methods can include:

• Joint displays: tables that align survey patterns with interview themes.
• Case-based integration: selecting cases showing particular quantitative profiles and exploring them qualitatively.
• Sequential linking: survey results guide interview questions and interpretive focus.

Exams often reward students who show integration explicitly rather than listing methods separately.

Ethics, Trustworthiness, and Research Quality in South African Contexts

Ethical research principles: respect, beneficence, and justice

Ethics in SOC 202 is not only about avoiding harm. It is about legitimacy: participants should have reason to trust that research is conducted fairly and respectfully.

Key ethical principles:

• Informed consent: participants understand what the study is, what participation involves, and their right to withdraw.
• Confidentiality and anonymity: protect identities in reports and transcripts.
• Minimising harm: avoid questions that could expose participants to risk or distress without support.
• Beneficence: research should aim to contribute knowledge that can improve social understanding.
• Justice: recruitment should not exploit vulnerable groups.

In South Africa, ethical issues may include language access, historical mistrust of institutions, and the dynamics of power in university settings.

Informed consent in real life: what “informed” really means

Consent is a process, not a signature. A robust consent process includes:

1. Plain-language explanation of the study purpose.
2. Opportunity to ask questions before agreeing.
3. Clarification of confidentiality limits (e.g., what happens if someone discloses harm).
4. Right to withdraw without penalty.
5. Language accessibility (translation or bilingual support).

For example, students conducting interviews in a multilingual context must plan how consent information is delivered. If consent is provided only in English to participants more comfortable in isiXhosa, comprehension problems can undermine ethical validity.

Confidentiality and data protection: handling transcripts, recordings, and notes

Data protection procedures typically include:

• secure storage of audio files and transcripts (password-protected devices),
• anonymisation strategies (pseudonyms; removing identifying details),
• controlled access to raw data (only the research team),
• clear retention policy (how long you keep data and when you destroy it).

A method note in an exam answer can be strengthened by specifying:

• how identifying information is removed,
• how pseudonyms are assigned,
• whether transcripts are shared with supervisors under secure conditions.

Vulnerability and power relations: when students study other students

In South African higher education and TVET contexts, power relations can be subtle. A student researcher may be seen as affiliated with the institution. Ethical risks include:

• perceived pressure to participate,
• fear that participation may affect academic standing,
• discomfort in discussing institutional support, fees, or academic failure.

Mitigation strategies:

• recruit participants who are not directly dependent on you (avoid “your classmates” if possible),
• make recruitment through neutral channels,
• emphasise voluntary participation and withdrawal.

Trustworthiness in qualitative research: credibility, transferability, dependability, confirmability

SOC 202 typically expects “rigour” beyond sample size and statistics. For qualitative work, trustworthiness is often evaluated using criteria such as:

• Credibility: whether findings plausibly reflect participants’ realities.
• Transferability: whether findings may be relevant to other contexts (not generalisable statistically, but conceptually useful).
• Dependability: whether the research process is consistent and documented.
• Confirmability: whether findings are shaped by the data rather than researcher preferences.

Practical strategies:

• triangulation (data sources or methods),
• member checking (where feasible),
• peer debriefing (discussing interpretations with supervisors/peers),
• keeping an audit trail (notes about coding decisions).

Reliability and validity in quantitative work: reducing error and bias

In quantitative studies, quality includes:

• instrument validity (does the survey measure what it claims?),
• reliability (are measures consistent?),
• measurement bias (social desirability effects, misunderstanding items),
• non-response bias (those who don’t respond differ systematically).

Even if students don’t compute reliability coefficients in exams, they should demonstrate the methodological reasoning:

• use piloting to refine items,
• ensure clarity,
• avoid sensitive wording that invites social desirability bias unless you plan ways to manage it.

Research quality through reflexive design

A mature methodology note includes quality control at multiple stages:

• research question alignment,
• sampling rationale,
• ethical recruitment,
• clear data collection procedures,
• systematic analysis,
• transparent reporting of limitations.

Limitations should be treated as methodological, not excuses. For example:

• “The study used purposive sampling in one department; therefore findings may not capture experiences in other faculties.”
• “Cross-sectional survey design limits causal inference.”

Institutional Methodology in South Africa: Applying SOC 202 Thinking to RU-Linked Education Settings

Cluster focus: Rhodes University (RU) — studying the social world in a South African university setting

SOC 202 methodology often becomes concrete when you think about field access and institutional environments. Rhodes University (RU) offers a useful anchor for thinking about how sociologists research within South African higher education institutions. Even when your specific assignment does not involve RU directly, the RU context provides familiar institutional logic: student support systems, departmental differences, language diversity, and structured academic routines.

In a RU-linked education research design, you would typically attend to:

• how academic support structures are organised (tutorials, mentoring, advising),
• how student identity and belonging are negotiated within campus cultures,
• how class, race, and language shape access to resources,
• how institutional policies translate into lived student experiences.

The key methodological point: when you study “the social world” in a university, you study institutional power in everyday practice. Policies and resources matter, but so do perceptions, interpretations, and micro-interactions.

Course-specific methodological considerations: aligning methods with sociology of education themes

SOC 202 includes coursework that often touches education, work, institutions, and inequality. To apply this within RU-like settings, it helps to map themes to method choices.

Theme 1: Institutional support and academic risk

Research question example:
“How do students who report low access to academic support interpret the role of the university in managing academic risk?”

Suitable methods:

• Qualitative interviews to explore meaning and mechanisms
• Document analysis of support policies and academic rules
• Optional survey measuring prevalence of reported support access

Why this works methodologically:

• interviews access interpretation and decision-making,
• documents reveal institutional framing,
• triangulation prevents “single-source truth.”

Theme 2: Belonging, language, and participation

Research question example:
“How do language confidence and experiences of belonging influence student participation in tutorials and peer learning groups?”

Suitable methods:

• Focus groups for peer dynamics,
• Observation in tutorial spaces (if ethically permissible),
• Qualitative coding of narratives and interaction patterns.

Quantitative option:

• a survey measuring language confidence and perceived belonging, combined with qualitative explanation of mechanisms.

Building an RU-informed research plan: step-by-step operational process

Below is a coherent research workflow that fits many SOC 202 education-context projects, written in an “exam answer ready” style.

Step 1: Define a narrow sociological question

Instead of “How does RU support students?”, choose something measurable and sociological:

• “How do first-year students describe experiences of academic advising, and how do these experiences shape their strategies for handling assessment pressures?”

Step 2: Choose design and justify it

If the question focuses on meaning and strategies, a qualitative design is defensible. If you need broader patterns, use mixed methods.

Step 3: Determine sampling

A purposive strategy could include:

• first-year students who report using advising,
• first-year students who report not using advising,
• academic support staff or tutors (depending on access).

Step 4: Prepare instruments

• interview guide aligned to objectives,
• document list for policy analysis,
• consent forms in appropriate languages or with translation.

Step 5: Collect data ethically and rigorously

• secure recordings and notes,
• conduct interviews in accessible language,
• create an audit trail.

Step 6: Analyse systematically

• qualitative coding with theme development,
• document coding for institutional framing,
• if mixed methods: integrate survey patterns with interview mechanisms.

Step 7: Report with methodological honesty

• explain how you managed bias and limitations,
• show how evidence supports claims,
• connect to broader sociological concepts (inequality, institutional power, cultural meanings).

Counter-arguments and methodological debates: what markers look for

High-scoring SOC 202 answers often acknowledge debates rather than ignoring them. Common debates include:

Debate 1: “Qualitative research can’t be generalised”

Counter-argument:

• generalisation is not always the goal; sociology often aims for analytic generalisation—identifying mechanisms that may operate in other contexts.
• credibility and transferability depend on context description and concept-level claims.

Debate 2: “Quantitative research is more objective”

Counter-argument:

• objectivity claims can hide measurement bias, sampling limitations, and interpretive choices.
• survey instruments embed assumptions; “neutral” measures are not automatically neutral in practice.

Debate 3: “Mixed methods are always better”

Counter-argument:

• mixed methods can deepen understanding, but it also increases time, complexity, and integration demands.
• the real criterion is design fit: mixed methods are justified when integration solves a specific research problem.

When writing exam notes, it’s beneficial to briefly state why each approach has merit and why your chosen method is the best match to your question.

Methodology in the South African education ecosystem: practical realities that shape research

Even if your study is based at (or inspired by) RU, South African education research often confronts:

• access constraints: timetable differences, institutional gatekeeping, limited permission for observation,
• language diversity: bilingual interview processes and translation validity,
• unequal participation incentives: participants may prioritise time differently due to financial or transport pressures,
• historical context: experiences of inequality may influence willingness to discuss challenges.

These realities influence methodology choices. A method is not only “correct” in theory; it must be feasible and ethically viable.

A concrete mini-case (exam-style): academic advising experiences and persistence strategies

Research problem: students experience academic pressure differently; institutional advising may function unevenly.

Research question:
“How do students at RU who experience assessment stress describe the role of academic advising in shaping persistence strategies?”

Design: qualitative with document support (triangulation).

Sampling: purposive selection:

• students who sought advising when stressed,
• students who did not seek advising.

Data collection:

• semi-structured interviews exploring:
  • onset of assessment stress,
  • awareness of advising services,
  • perceived quality and accessibility of advising,
  • strategies chosen (self-study plans, peer support, avoidance),
  • what “good advising” means in participants’ terms.
• document analysis:
  • advising service descriptions,
  • academic regulations affecting students under pressure.

Analysis:

• coding for themes like “access barriers,” “trust in staff,” “advising as recognition,” “time and workload.”
• compare student accounts with institutional framing in documents.

Ethical considerations:

• confidentiality because students may discuss failure or personal difficulties,
• voluntary participation and no academic consequences.

Limitations:

• findings contextual to RU’s institutional culture and may not generalise to other institutions,
• reliance on participants’ retrospective narratives.

This mini-case demonstrates methodology alignment: the question about meaning and strategy informs the method choices, and triangulation strengthens credibility.

Writing high-scoring methodology exam answers: what to include

To score well in an exam on “Methodology Notes,” you typically need a complete package:

1. Clear research question/objectives linked to method logic
2. Justified design choice (qualitative/quantitative/mixed)
3. Sampling plan with ethical recruitment logic
4. Data collection procedures aligned to objectives
5. Analysis approach with transparent steps
6. Trustworthiness/validity measures appropriate to the paradigm
7. Ethics plan (consent, confidentiality, vulnerability, harm minimisation)
8. Limitations expressed methodologically, not defensively
9. Coherent reporting that uses sociological language (structures + meanings + institutions)

If your answer contains these elements, it will generally read as rigorous and well-grounded in SOC 202 methodology expectations.

Summary of key methodological skills for SOC 202

• Translating a sociological question into an appropriate research design.
• Choosing sampling strategies that fit the purpose of the study.
• Collecting data with systematic, ethical procedures.
• Analysing data using a clear logic of coding, measurement, or integration.
• Demonstrating research quality through trustworthiness (qualitative) or validity/reliability (quantitative).
• Communicating limitations responsibly and maintaining ethical legitimacy.

Researching the social world is a craft that requires methodical reasoning and moral responsibility. In SOC 202, your highest-level achievement is not simply using techniques, but showing why those techniques are suitable—and how your evidence supports sociological explanations within South African educational and institutional realities.