Chapter 2 Studying Social Life: Sociological Research Methods - Vocabulary Flashcards
Chapter 2 Notes: Studying Social Life – Sociological Research Methods
Purpose of the chapter
Introduces methodological tools to understand social life and to apply sociological theories in research.
A practical, how-to guide for Data Workshops and real-world research conducted by sociologists.
Emphasizes both understanding and applying methods; chapter serves as a reference for future research.
Learning objectives (summary)
Differentiate quantitative vs. qualitative research with examples.
Outline the steps of the scientific method.
Examine six methods: ethnography/participant observation, interviews, surveys, existing sources, experiments, and social network analysis.
Assess strengths and weaknesses of each method.
Identify pitfalls and ethical issues in sociological research.
Overview of research methods (key ideas)
Sociologists use both quantitative and qualitative methods to study the social world.
Examples:
Quantitative: U.S. Census; statistics, rates, and percentages.
Qualitative: ethnography and participant observation.
Quantitative data are numerical and often seek to identify patterns and cause-effect relationships; qualitative data are non-numerical (texts, field notes, transcripts, photos, videos) and aim to understand meanings, experiences, and social processes.
Correlation does not imply causation; an intervening variable may produce changes in both variables (spurious correlations).
Quantitative vs. Qualitative research (definitions and contrasts)
Quantitative research
Translates social life into numbers; uses mathematical manipulation to identify patterns and relationships among variables.
Examples: any social statistic, rates, percentages, graphs.
Qualitative research
Works with nonnumerical data (texts, field notes, transcripts, photos, audio).
Aims to describe cases in depth and understand meanings from the perspective of the studied people.
Methods: participant observation, in-depth interviews, analysis of transcripts, historical sources, social media/text messages.
Ethnographers study diverse worlds (truck drivers, fashion models, low-income students) to reveal meanings from insiders’ perspectives.
The scientific approach and the scientific method
Scientific method: a procedure for acquiring knowledge emphasizing observation and experimentation; aims to verify empirical knowledge and build testable theory.
General steps (Fig. 2.1):
Identify a problem or ask a question.
Conduct a literature review.
Form a hypothesis; give operational definitions to variables.
Choose a research design or method.
Collect data.
Analyze data.
Disseminate findings.
Not all sociologists follow steps in lockstep; replicability is a key feature of scientific results.
Operational definitions: precise definitions of variables to ensure clear measurement.
Literature review helps avoid duplication and provides background for new study.
Hypothesis: theoretical statement about relationships between phenomena (variables).
Example: Watching violence on TV (variable V) and acting violently (variable A).
Hypothesis: If V increases, A increases. Observation requires explicit operational definitions for V and A (e.g., types/levels of violence, definitions of aggressive behavior).
Correlation vs. causation; intervening variables; spurious correlations
Correlation: two variables change together but one does not necessarily cause the other.
Causation: a change in one variable directly produces a change in another.
Intervening variable: a third variable that explains the relationship between two other variables.
Spurious correlation example: ice cream sales and violent crime both rise with weather; weather is the intervening variable.
Importance: distinguishing correlation from causation is essential for valid conclusions.
Inductive vs. deductive approaches; paradigm shifts
Deductive approach: start with theory -> generate hypotheses -> test with data.
Inductive approach: collect data -> formulate theory to fit data (grounded theory).
Both are systematic, scientific ways to link data with theory; the order differs.
Philosophical note: Thomas Kuhn argued truth is relative to paradigms; paradigm shifts occur when new data force new ways of looking at the world.
Which method to use? practical considerations
Different methods have distinct advantages and limitations; researchers choose methods based on goals, competence, time, funding, and access.
Woodstock example: to study attendees’ experiences, ethnography/participant observation might be ideal, but access and timing constrain feasibility; alternatives include interviews, surveys, existing sources, or experiments.
All methodological choices involve trade-offs in what information is gained vs. what is sacrificed.
Ethnography / Participant observation (qualitative)
Ethnography: study people in their natural environments; fieldwork is central.
Participant observation: researcher becomes a participant in the group while observing.
Field site access is essential; gaining entry and establishing rapport are critical first steps.
Data collection via detailed field notes; can include photos/videos; focus on thick description.
Thick description (Geertz): detailed, context-rich descriptions of interactions and meanings within a cultural context.
Reflexivity: researchers’ own identities and emotions influence the research; researcher's presence may affect interactions (expected and acknowledged in analysis).
Overt vs covert research: overt (transparent about aims) is preferred for ethics; covert may be necessary in some cases, but raises ethical concerns.
Examples: Edin & Kefalas (Promises I Can Keep) studied single mothers in East Camden; their approach involved deep community immersion.
Advantages
Rich, detailed storytelling that challenges stereotypes and informs policy.
Can reveal underrepresented or nontraditional life trajectories.
Disadvantages
Limited representativeness; hard to generalize from small, context-specific samples.
Resource-intensive and difficult to replicate; authors often disclose methods and data to support validity.
Data Workshop: Analyzing Everyday Life (ethnography practice)
Emphasis on thick description in field notes; practice focusing first on listening, then on watching.
Activity: write extremely detailed descriptions of conversations observed or overheard; attach descriptive details to support conclusions.
Options for completing the workshop:
PREP-PAIR-SHARE: partner exchange field notes; annotate for clarity and evaluative language; discuss as a class to establish standards of descriptive detail.
DO-IT-YOURSELF: write a 2–3 page essay discussing fieldwork experience and include thick descriptions from field notes.
Interviews
Interviews are face-to-face conversations used to gather qualitative data; may be combined with other methods.
Researchers identify a target population and then select a representative sample for interview.
Focus groups can be used to increase sample size and allow interaction among participants.
Informed consent is essential; interviews are typically audio/video recorded.
Examples: Dawn Marie Dow’s study of Black middle- and upper-middle-class moms; Hochschild’s The Second Shift on two-career families.
Question design: open-ended questions are preferred; avoid bias and leading questions; avoid double-barreled questions; minimize ambiguity.
Coding: after transcription, data are coded into recurring categories; qualitative data can be quantified (as Hochschild did by coding household labor divisions).
Advantages
Allows respondents to express thoughts and feelings in their own words; captures subjective experiences.
Can reveal issues not anticipated by researchers.
Disadvantages
Generalizability is limited due to small samples; risk of biased responding; social desirability effects.
Surveys (quantitative)
Surveys use questionnaires administered to a sample from a target population.
Closed-ended questions dominate; Likert scales are common; open-ended questions can supplement.
Important design considerations: clarity, lack of ambiguity, avoidance of bias, order effects, pretesting (pilot studies).
Sampling: sampling technique is crucial; probability sampling (randomization) helps ensure representativeness; simple random sample is a basic form.
Cross-sectional vs longitudinal designs:
Cross-sectional: data collected at one point in time.
Longitudinal: data collected at multiple points in time; includes repeated cross-sectional surveys and panel surveys.
Response rate matters for validity; higher rates improve generalizability, but even low rates can be acceptable with proper sampling.
Data analysis: responses are coded into numerical form; statistical software (e.g., SPSS, Stata, R) assists in examining relationships between variables.
Online surveys present sampling challenges; tools like SurveyMonkey/Qualtrics increase accessibility but require careful design to maintain reliability and validity.
Advantages
Efficient for studying large populations; generalizable findings through proper sampling.
Quick and cost-effective, especially online surveys; large data sets enable robust statistical analysis.
Disadvantage
May fail to capture depth and context; limited ability to measure complex social realities; self-report biases; sampling bias if self-selection occurs.
Overreliance on closed-ended questions may miss nuanced meanings; need for pilot testing and potential inclusion of write-in responses.
Existing sources (secondary data, unobtrusive measures)
Definition: data produced for other purposes but usable for social research (archival records, newspapers, books, websites, films, etc.).
Approaches: qualitative or quantitative; examples include demographic data from government agencies (e.g., U.S. Census), social archaeology (studying artifacts like garbage), comparative historical research using cultural artifacts.
Content analysis: identify and study themes or variables (words, visual elements) in texts, images, or media; quantify appearances or frequencies and analyze relations between them.
Advantages
Access to data beyond what a researcher could collect firsthand; enables replication and pooling of datasets; broad temporal and geographic scope.
Allows study of social worlds and time periods inaccessible to the researcher (e.g., frontier women).
Disadvantages
Data may not perfectly fit the research question; content analysis shows messages but not how they are interpreted by audiences; limitations in inferring causality.
Examples in chapter: Stearns’ comparative historical research on helicopter parents; Sabrina Strings' content analysis linking Black women’s body images to anti-Blackness; U.S. Census data usage.
Experiments
Key setup: random assignment to experimental vs. control groups; manipulation of an independent variable; measurement of a dependent variable.
Examples:
Divorce study: assign couples to receive marriage counseling vs. no counseling; measure likelihood of staying married (dependent variable).
Gender-role socialization study (pink vs. blue baby): same baby presented with different color cues to trigger different perceptions and behaviors by participants.
In criminology, housing, employment, or policing experiments test discrimination by race or gender (e.g., job application audits).
Data tended to be quantitative due to the aim of isolating variables and testing specific hypotheses.
Advantages
Strongest method for establishing causality; ability to control for extraneous factors; replicability of experiments.
Disadvantages
Limited to questions that can be ethically and practically manipulated in controlled settings; lab artificiality may limit external validity; deception can raise ethical concerns and requires debriefing.
Ethics and deception: post-participation debriefings; ethical guidelines govern transparency; deception is sometimes used but must be justified and minimized.
Replicability concerns have emerged in some subfields (replication crisis).
Social network analysis (SNA) and GIS (emerging tools)
Social Network Analysis (SNA)
Measures relationships and structure of social ties among individuals or groups; data often collected via name-generating questions.
Outputs: network diagrams, centrality, bridges, structural holes, degrees of separation (e.g., six degrees of separation, Milgram 1969).
Examples: network diagrams of friendships in a class; identification of central individuals and bridging ties; applications to diffusion of information, risk behaviors, and interventions.
Advantages: traces diffusion of ideas, diseases, or rumors; useful for epidemiology and organizational studies; can leverage big