Sociology 202 Research Principles and Practice

Bogardus Social Distance Scale Let’s say you’re interested in the extent to which non-Muslim Canadian citizens are willing to associate with, say, Muslims. You might ask the following questions: Are you willing to permit Muslims to live in your country? Are you willing to permit Muslims to live in your community? Are you willing to permit Muslims to live in your neighbourhood? Would you be willing to let a Muslim live next door to you? Would you let your child marry a Muslim?

Objectives To introduce the logic and skills of social scientific research. To develop a critical understanding in consuming research. Note: The importance in formulating social policy (e.g., health care)

Overview of Course Human Inquiry vs. Relies on common sense Tradition Authority Faith Science Observation Logic Verifiable evidence Explicit procedures Replication

Common Errors in Human Inquiry Overgeneralization Inaccurate observation Ego involvement in understanding Premature closure of inquiry Example: Letters to the editor

Aim of Systematic Empirical Research To observe (data collection) Describe Explain Predict

The Research Process Research is an integrated process. It is made up of a series of sequential steps. It builds on previous research.

Steps in the Research Process Choose a topic or research interest. The research topic should be clear and focused. Review the literature. What have other researchers done on the topic? How did they do it? What did they find? Validity and reliability.

Steps in the Research Process Based upon the review of literature, refine/reformulate your research topic.

Steps in the Research Process Deductive or inductive logic.

Types of Research Descriptive Exploratory Explanatory What exists? Exploratory What is going on here? Explanatory Identify causes and effects of social phenomena Predict how one phenomenon will change or vary in response to variation in some other phenomenon. Example: Testing hypotheses and theories.

Variables and Attributes Variables are parts or aspects of reality that can be seen to change or vary. Example: The effect of socioeconomic status on health. SES Health (cause) (effect)

Variables and Attributes Example:

Types of Variables Independent and Dependent Independent Dependent Intervening Variables

Types of Variables Antecedent variable

Attributes Variables are sets of related values or attributes.

Hypotheses Probability statements testing the relationships between or among variables. There are two types of statements: null or research (alternative or hypothesis of interest) Null means no relationship.] Example: There is no relationship between SES and health. Research (alternative or hypothesis of interest) states that there is a relationship. Example: There is a positive relationship between SES and health, i.e., people with high SES are more likely to have better health.

Theories Explain why things are by linking cause and effect. Specify the circumstances or conditions under which things happen. Permit us to predict events. Generally, we do not test theories; theories are comprised of a number of hypotheses which are then tested as parts of a theory.

Relationship Between Theory and Research Theories function three ways in research: They prevent our being taken in by flukes. They can shape and direct research efforts. They makes sense of observed patterns.

Units of Analysis Units of analysis are the what or whom being studied. In social science research there are four types of units of analysis: Individual people

Units of Analysis Organizations (e.g., universities in Mclean’s polls); Groups (e.g., families or households) Artifacts (e.g., paintings, books).

Time Dimension Cross-sectional Studies – based on observations made during a single time period. Longitudinal Studies – involve observations at two or more time periods. Trend studies – study changes within some general population over time. Cohort studies – examine more specific subpopulations (cohorts) as they change over time. Panel studies -- similar to trend and cohort studies except that the same set of people is studied each time.

Qualitative and Quantitative Qualitative analysis is the nonnumerical examination and interpretation of observations, for the purpose of discovering underlying meanings and patterns of relationships. Quantitative analysis is the numerical representation and manipulation of observations for the purpose of describing and explaining the phenomena that those observations reflect.

Qualitative and Quantitative

Qualitative and Quantitative

Relationship Between Research Topic, Theory and Data Collection The research topic could influence the theoretical perspective and the type of data collection. It is also possible that the theoretical perspective could influence the research methods or data collection techniques. Question: How would one study socialization of children in the family?

Data Collection or Observation Surveys Field Research Content Analysis Experiments Comparative Historical Methods Analysis of Existing Statistics (Secondary Data Analysis)

Conceptualization and Measurement Conceptualization refers to nominal definitions (e.g., what is meant by better health or SES?) Operationalization refers to measurement of the variables (e.g., how do you measure better health or SES?)

Validity and Reliability There are two criteria in measurements: Validity – a term describing a measure that accurately reflects the concept it is intended to measure. Reliability – the degree to which a research instrument, such as a questionnaire or a coding system, produces consistent results with use. Example: Are students’ grade point averages a valid and reliable measure of their intelligence?

Operationalization (measurement) Every variable must have two important qualities: The attributes should be exhaustive. We must be able to classify every observation in terms of one of the attributes. (e.g., political party affiliation in Nova Scotia: Liberal, PC, NDP, other) They must be mutually-exclusive. Every observation must be able to be classified in terms of one and only one attribute. (e.g., income: under 5000; 5000-10,000; over 10,000)

Levels of Measurement There are four levels of measurement of variables: Nominal – variables whose attributes have only the characteristics of exhaustiveness and mutually-exclusiveness, e.g., gender. Ordinal – variables with attributes that are logically rank-ordered, e.g., SES – low, middle, high.

Levels of Measurement Interval – the distance between attributes can be expressed in meaningful standard intervals, e.g., temperature. Ratio – variables having all the above requirements plus are based on a true zero point, e.g., income. Question: What are the advantages of measuring income as an ordinal measure versus a ratio measure?

Multiple Indicators Some variables are better measured (greater validity and reliability) using multiple indicators. For example, liberalism. We can combine indicators to form indexes and scales. For example, job satisfaction. Indexes and scales are both ordinal measures.

Indexes An index is constructed by simply adding scores assigned to individual attributes, e.g., we might measure prejudice by adding up the number of statements each respondent agreed with.

Indexes

Scales A scale is constructed by assigning scores to patterns of responses. Some items reflect a relatively weak degree of the variable, while others reflect something stronger. For example, women are different from men (weak degree of sexism); women should not be allowed to vote (stronger evidence of sexism). A scale has an intensity structure among the attributes.

Scales

Ethics Ethical obligations to respondents: Voluntary participation and consent Statements informing participants about the goals of the study and consent forms would need to be prepared in advance. The right to privacy, the maintenance of dignity and protection against harm. Anonymity and confidentiality Question: What effects could the above have on the research in terms of, for example, reliability?

Quantitative Data Analysis Univariate Analysis Central tendency (averages) Mode (most frequent number) Mean (arithmetic average) Median (midpoint)

Quantitative Data Analysis

Quantitative Data Analysis Dispersion Range (from highest to lowest) Standard deviation

Quantitative Data Analysis Bivariate Analysis Looking for relationships between two variables. Independent Dependent (e.g., gender) (e.g., attitude toward legalization of marijuana)

Quantitative Data Analysis Constructing and reading bivariate tables Tables should have a title and number The independent variable should be at the top; the dependent variable at the side. NOTE: Insert example of a table.

Quantitative Data Analysis Multivariate Analysis and the Elaboration Model Multivariate analysis is the analysis of more than two variables simultaneously.

Quantitative Data Analysis The Elaboration Paradigm

Quantitative Data Analysis Elaboration Model Replication is when the partial relationships are essentially the same as the original relationship. Interpretation is when the original relationship is significantly weakened or disappears as a result of an intervening variable. Explanation is used to describe a spurious relationship, i.e., an original relationship shown to be false through the introduction of an antecedent test variable. An antecedent variable is one that precedes both the independent and dependent variables.

Quantitative Data Analysis Refinements to the Elaboration Model A suppressor variable affects the relationship between the independent and dependent variable such that no relationship seems to exist. However, when controlling for the suppressor variable, the relationship between the independent and dependent variable appears. A distorter variable reverses the true relationship between the independent and dependent variables.