Chapter 2 Notes: Graphs, Tables, and Research Methods in Sociology
Ethnography
A qualitative method for studying people or a social setting that uses observation, interaction, and sometimes formal interviewing to document behaviors, customs, experiences, social ties, and related phenomena. It’s like “walking in people’s shoes” and writing a detailed account of their social world.
Example: A sociologist wants to study how high school students form cliques.
They spend months inside a high school, sitting in classrooms, eating lunch with students, and observing how groups form.
They notice things like how clothing styles, sports, or shared interests affect group membership.
The ethnography produces a detailed picture of youth culture, peer pressure, and identity.
The Scientific Method
A procedure involving the formulation, testing, and modification of hypotheses based on systematic observation, measurement, and/or experience.
Process flow: Observe the world → form a theory about an aspect of it → generate hypotheses → design an experiment or systematic observations to test hypotheses → analyze results → accept/reject hypothesis and revise theory if needed → rinse and repeat.
Theory
A broad explanation of how and why the world works the way it does.
Built on lots of evidence, research, and observations.
Research Methods (Overview)
Two general categories for gathering sociological data: quantitative and qualitative.
Qualitative methods: seek information about the social world that is not readily converted to numeric form; document meanings of actions, gender, social participants; describe mechanisms by which social processes occur.
Quantitative methods: obtain information about the social world that can be converted to numeric form and analyzed statistically to describe the social world that those data represent.
Causality and Association
Correlation or association: when two variables tend to track each other (+ or -); positive or negative relationship.
Correlation does not imply causation by itself.
Natural experiment: an event/change in the real world that affects people in a way unrelated to preexisting factors or characteristics, approximating random assignment to treatment/control groups.
Causality: a change in one factor results in a corresponding change in another.
Reverse causality: when a researcher believes A causes B, but in fact B causes A; time order is crucial to establish direction.
Designing a Study: Variables
Dependent variable (DV): the outcome the researcher aims to explain.
Independent variable (IV): a measured factor hypothesized to have a causal impact on the DV.
Relationship: DV depends on IV.
Example framing: determine how IV changes DV.
Hypotheses and Operationalization
Hypothesis: a proposed relationship between two variables, usually with a stated direction.
A specific, testable statement about what you expect to find in a particular study.
Direction of relationship: positive (same direction) or negative (opposite directions).
Operationalization: the process of defining a concept in a way that can be measured or tested in research. Example: Concept: Happiness
Operationalization: Ask people to rate their happiness on a scale from 1–10, or measure frequency of smiling/laughing.
Validity, Reliability, and Generalizability
Validity: the extent to which an instrument measures what it intends to measure.
Reliability: the likelihood of obtaining consistent results using the same measure multiple times.
Generalizability: the extent to which findings inform about a group beyond the studied sample; ability to apply findings to a larger population.
Choosing Your Method: Data Collection Focus
Data collection methods include:
Participant observation: a qualitative method to uncover meanings people attach to their actions by observing behavior and practice in context, rather than asking after the fact.
Interviews: ask people to explain why they do something and how; provides in-depth understanding of attitudes and experiences.
Survey research: structured questionnaires to elicit information from responders; powerful for large samples.
Historical methods: collect data from written records, newspapers, journals, transcripts, diaries, artwork, and other artifacts dating from the period under study.
Comparative research: compare two or more entities (e.g., countries) that are similar in many dimensions but differ on the dimension of interest to learn what differs.
Content analysis: systematic analysis of the content (not just the structure) of communication, such as written works, speeches, or films.
Experimentation: controlled interventions in a social setting, often with a control group; used when ethical and feasible to test causal effects.
Population, Sample, Case Study, and Census
Population: the entire group of individuals, objects, or items from which samples may be drawn.
Sample: a subset of the population from which data are actually collected.
Census: data collected from the entire population.
Case study: intensive investigation of one unit of analysis to describe or uncover mechanisms; often focuses on one person or a small number of cases for in-depth analysis.
Representative sample: a sample that captures the essential characteristics of the larger population; random sampling helps ensure representativeness.
Example: studying OSU students; population = all OSU students; representative sample = randomly selected OSU students.
Connections to Foundational Principles and Real-World Relevance
Deductive vs. inductive approaches link theory to data in complementary ways:
Deductive: theory → hypothesis → data → analysis.
Inductive: data → theory.
Reflexivity and the role of the researcher (including experimenter effects) affect data collection and interpretation; acknowledging and managing these effects is part of ethical research practice.
Feminist methodology emphasizes treating women's experiences as legitimate empirical resources and foregrounding the researcher’s role and positionality in social inquiry.