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Headings: !!Resources for Courses on Race and Ethnicity !!!!Hot Topics !!!!Critical Thinking !!!!Data !!!!Using data in the classroom !!!!Previous Syllabi
Note: See also page on International Migration!
and 1990 data on the composition of population by state by race and Hispanic origin
. See also the Changing American Pie Data, 1999 and 2025.
are also freely available.
and download the data in SPSS, SPSS portable and Stata formats.
students look at data on the relationship between race and the death penalty. The assignment asks students to consider both the race of the defendant and the race of the victim, and the frequency of murder between and across different race groups. The goal is for students to learn the complex nature of racial bias. We also provide a more difficult version of the assignment, which does not include the contingency tables.
. Data Sets: derived from the 1990's FBI Uniform Crime Report and the US Census. In this module, the objective is to investigate how city size (by population), poverty rate and black population size might explain instrumental crimes, as measured by street robberies. The exercise asks students to hypothesize about the relationships before looking at the data. Note that, although the concepts of independent and dependent variables and causality are mentioned, these are not discussed. Similarly, the key exercise is to demonstrate the effect of controlling for a third variable, yet the text does not explain the motivation for doing so. Students may find this context helpful.
Data set: GSS and Bureau of Justice Statistics (BJS). In this module, the objective is to examine the effect of sex and race on a person's fear of crime and then to see how fear of crime is also affected by actual victimization. The text gives good step by step directions on using the GSS (see our notes) and navigating the Bureau of Justice Statistics (BJS). The main weakness in this exercise is that the data between the two sources are not linked. The GSS has information on sex and race and fear of crime and the BJS has information on race and actual victimization. Making inferences about the interaction of all these indicators is difficult.
Data sets:cen1990/work9-45.dat. In this module, the objective is to compare the income earnings of Blacks and Whites. Students are asked to consider all aspects of the problem including, how to define and measure income, whether to consider full or part-time status, and how to handle differences across time periods or differences by age. Students are asked to form hypotheses and then to test these by using contingency tables. The module provides instructions on how to read, analyze and "percentage" a contingency table. It also suggests ways to motivate the difference between observable numerical/statistical "significance" and actual racial discrimination or bias. It offers a good explanation of how to control for a third variable, in this case education, and provides instructions on how to do this using WebCHIP.
and Part 2 - Race and Ethnic Inequality. Data Sets: /cen1990/educimm9.dat, /centrend/educ5090.dat, /centrend/edoc5090.dat, /cen1990/lawyers9.dat and /cen1990/earn9.dat. In this module, the objective is to examine the relationship between education, occupation, earnings and race. Its codebook is well-done and has good instructions on how to use WebCHIP. However, instead of trying to determine whether these indicators affect the level of racial or ethnic inequality, the module uses these to measure inequality instead. This is problematic because it does not challenge the student to investigate or replicate the claim of racial inequality in the first place.
. In this module, the objective is to examine the changing demographics of households over time, and by race and household type (i.e. married, female headed family etc.). This exercise allows the student to explore the data both longitudinally and cross-sectionally. It also includes many methodological concepts. "The exercise is intended to provide students with an opportunity to discuss difficulties in defining variables, provide descriptive information, identify testable hypotheses relating independent and dependent variables, and construct a simple test of the identified hypothesis using cross-tabular procedures." The only drawback is that it is linked to information presented in lecture and is thus not a standalone module.