Solved – How to set up a single continuous independent variable for a repeated measures linear model

I'm doing an education study and I'm trying to see the effect of website usage on quiz scores in a class of college students. There were 30 students in the class and they took 10 weekly quizzes over the course of the semester. They also had a website where they could log in and use a study tool. The website kept track of the time they were online.

So my dependent variable is quiz score (continuous) and my predictor variable is website usage over the previous seven days (also continuous).

My first instinct was to make a 300-line database, matching up each quiz score with the amount of website usage in the preceding week, and then run a regression. But I can't really do that, can I? Instead of 300 independent measurements, I really have 10 repeated measurements on 30 subjects.

So I tried using "General Linear Model > Repeated Measures" in SPSS, but I can't figure out how to tell the program that all those columns for website usage are a single, continuous predictor variable.

Any guidance? Am I on the right track here? Or should I be using a different analysis altogether?

The tests are repeated, but they're not really repeated measures, because you're not (I don't think) interested in the differences between the tests.

You're interested in the relationship between website usage and test scores, but the tests are non-independent, because the same people did them. So if you set your data up in one long column, something like:

ID Test Web  Y 1   1   10   8 1   2    7  15 ... 30  10   8   6 

You can test the relationship with mixed (multilevel) model. The SPSS menus are completely non-intuitive (IMHO), but the syntax is relatively straightforward.

I believe your syntax will then be something like:

mixed Y by test with web   /print=solution   /fixed = test web   /random intercept | subject(ID) . 

You're saying that Y is predicted by the test itself (some will be harder, some easier) and the web time score. Test and web are fixed effects, and people will have varying (random) intercepts, which you'd also like to take into account.

You might also run:

mixed Y by test with web   /print=solution   /fixed = test web   /random intercept web | subject(ID) . 

Which adds a random factor for web – that is, it allows the relationship between web and Y to vary between individuals.

It's a while since I've used SPSS, so this code is untested and probably not error-free. Andy Field's book "Discovering Statistics Using SPSS" has a nice chapter on multilevel models in SPSS – it might be worth looking at that, or at any other sources you like – you can find lots of examples with your favorite web search engine.

Edit: Also, I wonder if it's worth thinking about fixed effects regression. Comparing random effects and fixed effects is straightforward in Stata, using xtreg, not sure about SPSS.

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