11/3/2022 0 Comments Nibrs codebook![]() Simultaneously we can specify particular contrasts to test whether the income coefficient is different for the two outcomes. In SPSS we can use the GLM procedure to estimate the model. ![]() These models with multiple dependent variables have different names, economists call them seemingly unrelated regression, psychologists will often just call them multivariate models, those familiar with structural equation modeling can get the same results by allowing residual covariances between the two outcomes - they will all result in the same coefficient estimates in the end. I pasted the codebook for all of the items at the end of the post. ![]() Race is of course nominal, and income and age are binned as well, but I treat the income bins as a linear effect. The fear of crime variables are coded as Likert items with a scale of 1-5, (higher values are more safe) but I predict them using linear regression (see the Stata code at the end though for combining ordinal logistic equations using suest). #Nibrs codebook how to#The dataset has missing data, so I illustrate how to select out for complete case analysis, then I estimate the model. Here I also control for the race and the age of the respondent. People in more poverty tend to be at higher risk of victimization, but you may also expect people with fewer items to steal to be less worried. Here I want to see if the effect of income is the same between the two. ![]() This is taken from Dallas survey data (original data link, survey instrument link), and they asked about fear of crime, and split up the questions between fear of property victimization and violent victimization. Here we have different dependent variables, but the same independent variables. As promised earlier, here is one example of testing coefficient equalities in SPSS, Stata, and R. ![]()
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