I'm doing a regression analysis of the effect of housing type on resident depression. When I included all samples in a single model, housing type had a significant effect on depression (p=0.000). But when I divided the sample into males and females, and performed regression analysis on the two separately, the analysis results of both males and females showed that housing type had no significant effect on depression (p=0.1-0.2). I wonder how to explain this result
It could be a power problem. When your samples get smaller, you reduce the statistical power to detect an effect. A better way to do this would be to include gender as a binary predictor into the overall regression analysis (and also potentially the interaction/product term housing*gender). That way, you can examine a potential gender (and interaction) effect without splitting your sample or running separate regression analyses.
Also, I would examine the data graphically using histograms, scatterplots etc. to see whether there are any peculiarities in the scores and their distributions (e.g., outliers, non-linear effects, etc.).
The result of regression analysis being significant when analyzing the whole sample but not significant after dividing the sample could be due to several reasons:
It is important to consider these factors when dividing the sample and conducting regression analysis. It may also be useful to examine the residual plots of the regression models and conduct residual tests to check the assumptions of the regression model and the validity of the results.
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