I have matched two groups using the MatchIt package matching 2:1 nearest neighbor with replacement. After matching, I want to compare the difference in a test score (range: 0-100) between the two groups - however these scores are not normally distributed. I don't think I can use a weighted t-test (using the weights created by the matching program) since the data isn't normal. What should I use instead to analyze this continuous variable after matching?
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1 Answers
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If you have a large sample and the groups have similar variances, it doesn't matter whether the variable is normally distributed; the t-test p-value will be approximate but very close to accurate. Otherwise, you can try using a robust standard error to correct for heteroscedasticity; this is recommended with weights (including matching weights) in general. You might also think about how scores are generated. If you see floor or ceiling effects, maybe a tobit model would make sense. If it's very hard to get values close to 0 or 100, maybe a fractional logit model would make sense.
