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Understanding Units of Analysis and Avoiding Fallacies

Having the research design, mode of data collection, and method of data analysis all in alignment with each other and especially with the original research questions is crucial in any scientific study. One common pitfall that often traps students of social sciences into misalignment between them is failure to identify an appropriate unit of analysis or using inconsistent units of analysis through the stages of research, which can easily distort the hypothesis or results with ecological or individualistic fallacies. Based on Babbie (2014), this assignment is designed to help students in the introductory research methods course: (1) be familiar with the concept of units of analysis with simple examples; (2) practice identifying the unit of analysis in a hypothesis, data, and a research statement to check for consistency across them; and (3) practice examining common research statements for ecological and individualistic fallacies.

Be a Wise Consumer of Information

 “Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write” (H.G. Wells). While society is more and more filled with quantitative information, not all statements based on numerical results are scientific. Without applying solid logic in reading empirical data, we can be easily fooled by news stories, advertisements, or flashy pseudo-research findings only to make erroneous conclusions and decisions. This assignment is designed to help students (1) understand difference between correlation and causation; (2) use careful reasoning to fully digest empirical information; and finally (3) develop an alternative explanation to a simplistic causal statement by taking sociological variables (e.g., social class, years of education, age, gender) into account.

Metropolitan Area Characteristics and Residential Segregation Using American FactFinder

This paper assignment teaches students how to use American FactFinder from the U.S. Census Bureau to examine data on two comparably sized metropolitan areas. Students extract data from the 2014 5-year American Community Survey (ACS). Students also apply urban theories and recent research from course readings to theorize about residential segregation similarities and differences between their metropolitan areas using sociodemographic data from FactFinder. Dissimilarity scores are provided to students from the Longitudinal Tract Database (LTDB). Urban sociologists commonly study residential segregation within and between metropolitan areas by race/ethnicity, socioeconomic status, and nativity. This assignment offers students a chance to practice applying these findings and discussing other possible place-based explanations for segregation.