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Sophie Leib posted on Tuesday, January 15, 2019 - 12:27 pm
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I have count data that is not normal and does not fit a poisson distribution (tested with Kolmogorov-Smirnov). For a mediation analysis, would you recommend: 1. treating the data as count, comparing models (zero-inflated negative binomial, negative binomial) and running the regression with the best fitting model 2. Treating the data as normal and bootstrap the confidence intervals of the outcome? |
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This depends on the percentage at 0. With a high percentage, perhaps a mixture Poisson (or negative binomial) would work, although a mixture complicates the mediation analysis. Otherwise, a censored-from-below variable might work. With a low percentage, I would treat it as normal and simply use MLR with its robust SEs. I assume it is the Y variable that is of concern. |
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Sophie Leib posted on Wednesday, January 16, 2019 - 12:39 pm
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Thank you for the response. I am treating the data as normal and using MLR estimation. Do you recommend to analyze the standardized or unstandardized indirect effect? Results show different p-values for standardized vs. usstandardized indirect effect. Can you point me to a reference about the difference between the two? |
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Q1: Either one is fine. We discuss this in our RMA book. Q2: They have different sampling distributions. Use bootstrapped confidence intervals which allow a nonnormal sampling distribution for the indirect effect by forming a non-symmetric CI. CI's may agree better in terms of covering zero or not. |
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