1. Sample Description
For this assignment, I am working with the General Social Survey (GSS) dataset, a widely used sociological survey conducted by the National Opinion Research Center (NORC) at the University of Chicago. The GSS has been collecting data on American attitudes, behaviors, and demographics since 1972, making it a valuable resource for social science research.
The sample consists of nationally representative cross-sections of U.S. adults, selected through a stratified random sampling method to ensure diversity in age, gender, race, education, and geographic location. The survey typically includes around 1,500 to 3,000 respondents per wave, depending on the year.
2. Data Collection Procedure
The GSS primarily collects data through face-to-face interviews, though some recent waves have incorporated online surveys. Key aspects of the data collection process include:
Sampling Frame: Households are selected using a multi-stage probability sampling design to ensure representativeness.
Interview Method: Trained interviewers conduct in-person or virtual interviews, asking respondents about a wide range of topics, including politics, religion, work, and family life.
Periodicity: The survey was conducted annually from 1972 to 1994 (except 1979, 1981, and 1992) and biennially since 1994.
More details about the GSS methodology can be found on the official GSS website.
3. Measures and Data Management
Research Question:
"How does education level influence attitudes toward climate change among U.S. adults?"
Variables Used:
Dependent Variable:
Climate Change Concern (grnclimate): Measured on a Likert scale (1 = "Not at all concerned" to 5 = "Extremely concerned").
Independent Variable:
Education Level (degree): Categorized as:
Less than high school
High school graduate
Associate/Junior college
Bachelor’s degree
Graduate degree
Control Variables:
Age (age)
Political ideology (polviews)
Income (realinc, adjusted for inflation)
Data Management Steps:
Data Cleaning:
Removed missing values (NA) from key variables.
Recoded degree into a categorical variable for clearer interpretation.
Variable Transformation:
Standardized income (realinc) for better comparability.
Collapsed some response categories in polviews to simplify analysis.
Analytical Approach:
Conducted an ordinal logistic regression to assess the relationship between education and climate change concern, controlling for age, income, and political ideology.
Findings (Preliminary):
Higher education levels were associated with greater concern about climate change.
Political ideology was a stronger predictor than income or age.
This analysis helps illuminate how education shapes environmental attitudes, which could inform policy and advocacy strategies.
Would love to hear your thoughts—have you worked with the GSS before? What variables interest you the most?