Keywords: Workshop in Methods; research methods; statistical methods; quantitative research methods. Date: Publisher: Indiana University Workshop in Methods.
Type: Presentation. Link s to data and video for this item. Size: Format: PDF. Description: Event flyer. Login Register. Connect and share knowledge within a single location that is structured and easy to search. I would like to produce a regression analysis model. I have ordinal categorical data.
I can use SPSS. I do not know what analysis to perform or what assumptions to check. When your dependent variable is ordinal, you want to do ordinal logistic regression. This can be done in SPSS. If you use OLR for your analysis, you can get tests of each variable with standard output. SPSS can output this for you as well. For ordinal categorical response data, the best thing is usually to use a cumulative multinomial logit model.
Those are somewhat non-trivial to implement, though I think SAS can do it and there is I think some R package that can do it.
I would have to check on which one though. The idea is that you're modeling the probability that the response is less than or equal to each category. For the dependent variables, you can keep those as categorical, or you can set them to be ordinal values and use a linear trend on your selected values. The latter is usually effective in practice even if the interpretation of the coefficients is challenging.
For a logistic model, the Mantel-Hansel test will let you test the linear trend. For a cumulative multinomial logistic model, there might be an analog that works as well. Sign up to join this community. The best answers are voted up and rise to the top. I hear this can be remedied by mean-centering anyway, so that should not be an issue? I am beyond frustrated with this dataset, going back and forth between deciding to a combine my dependent variables into two scales, in order to be able to consider my dependent variables continuous I have 6 dependent variables total, they actually are separate items on two scales - one has 3 items, the other has 2 items.
From what I gather now, a would mean losing information that could be gained from differentiating between separate items b would mean losing information by means of having to simplify my model but How would you go about analysing this data?
Improve this question. Mandarc Mandarc 47 8 8 bronze badges. Add a comment. Active Oldest Votes. The residuals have normal distribution 6. None of the predictors are correlated is incorrect, neither the DV nor the IVs need to be normal.
Is correct but too limited; there should be a lot more observations although exactly how many more is debated, some say 10 observations for every IV. Is sort of right, but modest correlations are OK and what is problematic is not correlations but colinearity, which is not quite the same. You have also misinterpreted "linear" in this context it means linear in the parameters.
Improve this answer. Peter Flom Peter Flom I got that list from a book about statistical analysis, so I guess I need to look for a good book now, with the emphasis on good. However, if you were to analyse your data using standard linear regression, there are problems that the CLT cannot amend: Your dependent variable y has a fixed value range, and chances are that observations, at least in some regions of the explanatory variables X, "X-space" , are not strongly concentrated in a safe distance from the limits of the value range.
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