Chapter 3 Sociology: Methods of Research

Primary Data

Information collected personally by a researcher(surveys, interviews, or experiments)

Primary Data Strengths

• Complete control over data collection methods, purpose, and participants.

• Greater reliability and validity as the researcher designs the study.

• Tailored to research needs – specific questions can be addressed.

• High representativeness if sampling is well-structured.

Primary Data Weaknesses

• Time-consuming to design, conduct, and analyze.

• Expensive due to costs of surveys, interviews, or fieldwork.

• Access issues – some groups may be hard to reach or unwilling to participate.

• Non-response bias – some participants may refuse to engage.

• Historical limitations – if studying the past, key respondents may be unavailable.

Secondary Data

Data that already exists; data not personally generated by the researcher (census data, government reports, existing market research, and articles analyzing prior research)

Secondary Data Strengths

✅ Saves time & money – Uses existing data (e.g., government reports, historical records).
✅ Large-scale data available – Access to big datasets like census records.
✅ Historical & comparative use – Allows long-term trend analysis (e.g., Aries' study on childhood).
✅ High reliability (sometimes) – Official stats (crime, census) are standardized and consistent.
✅ Only option in some cases – Essential for studying past events (e.g., suicide statistics).

Secondary Data Weaknesses

❌ Not tailored to research needs – Definitions (e.g., poverty, crime) may differ from sociological ones.
❌ Potential bias – Official stats may underreport (e.g., unreported crimes).
❌ Unreliable sources – Personal documents (diaries, letters) can be subjective.
❌ Limited representativeness – Historical docs may reflect one person’s view, not society.
❌ Outdated or incomplete – Some data may be old or missing key details.

Quantitative Data

• Numerical data expressed as:

  1. Raw numbers (e.g., population counts).

  2. Percentages (e.g., 80% Hindus in India).

  3. Rates (e.g., birth rate per 1,000 people).

Quantitative Data Strengths

• Replicable: Standardized methods (Matveev, 2002).

• Objective: Reduces researcher bias.

• Comparable: Tracks trends over time (Kruger, 2003).

Quantitative Data Weaknesses

• Artificial settings: Lab environments distort behavior.

• Superficial: Misses "why" (Day, 1998).

• Rigid: Only measures pre-defined issues (McCullough, 1988).

Qualitative Data

• Non-numeric data exploring "why" (e.g., interviews, observations).

Qualitative Data Strengths

• Depth: Reveals motivations (Venkatesh, 2009).

• Flexible: Adapts to participant responses.

Qualitative Data Weaknesses

• Time-consuming: Hard to analyze.

• Subjective: Researcher bias possible.

Official Statistics Definition and Strengths

• Quantitative data collected by governments (e.g., crime rates, census). 

  -Reliability: Consistent and can be replicated.

• Availability: Often accessible and cover large populations.

Official Statistics Weaknsesses

• May lack validity (e.g., "dark figure" of unreported crime).

• Definitions change over time (e.g., unemployment metrics).

• Political bias in reporting.

Questionnaires/Surveys

 Postal questionnaires are normally completed

in private; respondents write their answers without

the presence of, or guidance from, the researcher.

Researcher-administered questionnaires are completed

in the presence of the researcher, with respondents

answering questions verbally.

Open-ended questionnaires use questions that allow respondents to answer in their own words, while closed-ende/ pre coded questionnaires provide a fixed set of answer options

- Matveev (2002) used surveys to compare cross-cultural communication. 

Questionnaires/Surveys Strength

• Cost-effective and time-efficient, allowing for large sample sizes.

• Anonymity increases honesty (e.g., sensitive topics)

Questionnaires/Surveys Weaknesses

- Superficial (can’t probe "why"). 

• May lack depth of understanding due to closed questions.

• low response rate, where only a small proportion of those receiving a questionnaire return it

• May not represent the entire population due to selection bias.

• The researcher has no way of knowing whether a

  respondent has understood a question properly.

Structured Interviews

are a type of interview with predetermined questions asked in a specific order, allowing for consistency in data collection.

- Rosenthal-Jacobson (1968) tested teacher expectations with fixed interview scripts. 

Structured Interviews Strengths

• Allow for in-depth data collection, providing detailed insights.

• Enable clarification of questions and probes for deeper understanding.

Structured Interviews Weaknesses

include potential for interviewer bias

limited flexibility for exploring answers

may lead to superficial responses without deeper insights.

Semi-Structured Interview

is a type of interview that combines predetermined questions with the flexibility to explore topics in more depth(with theme) allowing for a conversational approach.

- Hamid et al. (2010) studied Pakistani women’s views on marriage. 

Semi-Structured Interview Strengths

• Balance of structure and flexibility, promoting rich data collection.

• Interviewer can adapt follow-up questions based on responses.

Semi-Structured Interview Weaknesses

may result in inconsistent data collection, researcher bias due to flexibility, and difficulty in comparing responses across interviews; time-consuming

Unstructured Interviews

are interviews without a predetermined set of questions, allowing for spontaneous discussion and in-depth exploration of participant perspectives.

- Goffman’s (1961) informal chats with mental hospital staff. 

Unstructured Interviews Strengths

allow for deep exploration of participant perspectives, flexibility in questioning, and the ability to follow interesting topics as they arise.

Unstructured Interviews Weaknesses

may lead to lack of comparability, interviewer bias, potential for digression from key topics, and challenges in data analysis.

Group Interview

- Discussion among multiple participants (qualitative). 

- Kitzinger (1995) studied media influence via focus groups. 

Group Interview Strengths

- Natural interaction (e.g., peer pressure dynamics). 

- Efficient for gathering diverse views. 

Group Interview Weaknesses

- Dominant participants may skew data. 

- Hard to analyze (multiple voices). 

Participant Observation

- Researcher joins a group (overt/covert). 

- Whyte (1943) lived with Italian street gangs. 

Participant Observation Strengths

- High validity (real behavior observed). 

- Contextual understanding. 

Particicipant Observation Weaknesses

- Time-intensive (e.g., Venkatesh spent 8 years with gangs). 

- Ethical issues (e.g., deception in covert). 

• Observer bias may occur.

Non-Participant Observation

A research method where the observer does not engage with the group being studied, only observing their behavior without direct involvement.

- Parke & Griffiths (2002) observed gamblers in casinos

Non- Participant observation Strengths

• Less intrusive for subjects.

• Reduced observer bias.

Non participant observation weaknesses

- Limited insight into motivations. 

- Ethical concerns if covert. 

Experiments (Lab/Field)

Manipulating variables to test cause-effect (quantitative). Research conducted in controlled settings or natural environments to observe behaviors.

Bandura Bobo Doll 1963

 Experiments (Lab/Field) Strengths

- High control (lab) or realism (field). 

- Replicable. 

 Experiments (Lab/Field)Weaknesses

• Demand characteristics may bias results.

• Ethical issues with manipulation of variables.

• Artificiality (lab) or unpredictability (field). 

Content Analysis

Systematic analysis of texts/media

- Meehan (1983) analyzed gender stereotypes in TV. 

Content Analysis Strengths

• Allows for the study of communications in a non-intrusive way.

• Can analyze large amounts of data efficiently.

• No participant bias

Content Analysis Weaknesses

• May lack depth of understanding.

• Difficulties in quantifying qualitative data.

• - Subjective coding (reliability issues). 

  - Misses audience interpretation. 

Case Studies

In-depth study of a single group/event (qualitative)

Case Studies strength

• Provides detailed insights and rich qualitative data. '

• Allows for exploration of complex issues.

• Can generate hypotheses for further study.

Case Study Weakness

• Limited generalizability due to focus on a single case.

• Time-consuming and potentially biased.

Longitudinal StudieS

Research conducted over an extended period to observe changes and developments in a particular group or phenomenon.

Power et al. (2011) followed families for 10 years. 

Longtitudinal Studies Strengths

• Captures changes over time.

• Provides in-depth data on trends.

Longtiduanal Studies Weaknesses

• Often expensive and time-consuming.

• Attrition can affect validity.

Semiology

The study of signs, symbols, and their use or interpretation in communication and culture.- Hall (1980) decoded racial bias in news photos. 

Semiology Strengths

- Reveals hidden ideologies. 

- Combines well with content analysis. 

Semiology Weaknesses

• Can be subjective and open to interpretation.

• Requires high levels of expertise for accurate analysis.

• Over-interpretation risk

• Ignores production context

Pilot Study

A small-scale preliminary study conducted to test feasibility, time, cost, and adverse events involved in a larger-scale research project. It helps refine research design and identify potential problems before the main study.

Documentary Sources

Written materials such as reports, articles, and archival documents used for research, providing evidence and context in sociological studies.

Documentary Sources Strength

• Provide rich, detailed information from primary texts or records.

• Save money and time

Documentary Sources Weakness

• May lack current data or context

• Can be biased or limited in perspective

Sampling Frame

A list or database from which a sample is drawn for research. It must accurately represent the population to ensure valid results.

• Purpose:

  • Ensures sample accurately represents population characteristics

  • Allows researcher to contact selected participants

• Access Issues:

  • Legal restrictions (e.g., privacy laws)

  • Confidentiality (e.g., company payrolls)

  • Groups refusing participation

Random Sampling

• Simple Random:

  • Everyone has equal chance of selection (like a lottery)

  • Requires complete sampling frame

  • Example: Randomly picking 30 names from 100

• Systematic:

  • Selects every nth person from a list (e.g., every 4th name)

  • Not fully random but practical for large populations

Stratified Sampling

• Divides population into subgroups (e.g., age/gender)

• Randomly samples from each subgroup

• Example: Selecting 8 females and 2 males from 100 people (80F/20M)

• Stratified Quota:

  • Similar but selects participants opportunistically within subgroups

  • Stops once quotas are filled (e.g., first 2 males who agree)

Non-Representative Sampling

• Uses readily available participants

• Best Opportunity: Targets specific groups to test hypotheses (e.g., affluent workers)

• Snowball: Participants recruit others (e.g., studying drug users)

Oberg's Research Design Stages

1. Planning (hypothesis)

2. Information gathering (data collection)

3. Information processing (analysis)

4. Evaluation (reviewing findings)

Glaser & Strauss Design Process

1 analysing related research to discover common themes and trends in the data

2 reflecting on the research itself; does it, for example, support or disprove the hypothesis?

3 is it possible to discover patterns in the data?

4 does the research suggest ways the data can be linked to create an overall theory?

Covert

- Definition: Subjects unaware of study

- Best for: Deviant groups, sensitive topics

- Example: Studying criminal behavior

- Limitations: Ethical issues

Overt

- Definition: Subjects know they're studied

- Best for: Ethical studies, institutions

- Example: School classroom observations

- Limitations: Hawthorne effect

Bandura et al. (1963)

- Type: Laboratory experiment (Quantitative)

- What: Demonstrated social learning through Bobo doll aggression study

- Strengths: High control, clear cause-effect

- Limitations: Artificial setting, ethical concerns with children

Goffman (1961)

- Type: Covert participant observation (Qualitative)

- What: Studied mental institutions by posing as staff

- Strengths: Authentic behavior observed

- Limitations: Ethical issues with deception

Durkheim (1897)

- Type: Secondary data analysis (Quantitative)

- What: Used suicide stats to show social influences

- Strengths: Large-scale data

- Limitations: Potential missing/inaccurate data

Milgram (1963)

- Type: Controlled lab experiment (Quantitative)

- What: Obedience to authority study

- Strengths: Standardized procedures

- Limitations: Extreme deception used

Venkatesh (2009)

- Type: Participant observation (Qualitative)

- What: Studied Chicago gang culture

- Strengths: Rich, detailed data

- Limitations: Time-consuming, dangerous

Hawthorne effect

the phenomenon where people change their behavior simply because they are aware that they are being observed or studied, regardless of the actual experimental conditions