Thank you for your response. I have used bootstrapping method in my mediation. I will discuss the small effect size that you pointed out with my professor. Thanks once again! |
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The short answer is that if you are using a method such as bootstrapping to
test the Indirect Effect, then you need to only find a significant ab-path. If you are using a method like The Test of Joint Significance (TJS), then you would look to see if both a and b are different from 0. In your example - The test of the indirect effect is under "Total, Direct, and Indirect Effects". The CI for the Indirect Effect is [.0009,.1240] which doesn't include zero, so you would conclude that it is statistically significant (using bootstrapping criterion). So, you could have an "a" or "b" path that is non-significant, but the "ab" path is significant. However, the Total Effect is non-significant, indicating that there isn't an effect to mediate, to begin with. Do you expect a Total Effect (should X predict Y)? The Direct and Indirect Effects have the same sign, so you aren't dealing with "Inconsistent Mediation" (a suppression effect). So, you could talk about having an Indirect Effect but not Mediation. Also, you should look at the size of the Indirect Effect. It seems small - is this an important effect or is it trivial? pl -----Original Message----- From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of psy_vm Sent: Thursday, July 24, 2014 12:17 AM To: [hidden email] Subject: [SPSSX-L] Interpreting Mediation results Hello, I have a few questions regarding interpretation of my results for Mediation using Hayes' Process macro. The key point for finding significance is to see that the Class Interval does not include zero. However, in my mediation model, although the indirect effect CI doesn't include zero, the initial Models's CI (for bcope_ma, under Duration) includes zero. Should I still count my indirect effects as significant even though initial model for mediation is not significant? Results pasted below. Please Help! Thanks in advance! Model = 4 Y = qoltot X = duration M = bcope_ma Sample size 70 ************************************************************************** Outcome: bcope_ma Model Summary R R-sq F df1 df2 p .1830 .0335 2.3550 1.0000 68.0000 .1295 Model coeff se t p LLCI ULCI constant 22.5169 1.3151 17.1223 .0000 19.8927 25.1411 duration -.0388 .0253 -1.5346 .1295 -.0893 .0117 ************************************************************************** Outcome: qoltot Model Summary R R-sq F df1 df2 p .3234 .1046 3.9140 2.0000 67.0000 .0247 Model coeff se t p LLCI ULCI constant 91.2951 9.5536 9.5560 .0000 72.2258 110.3643 bcope_ma -1.0460 .3823 -2.7364 .0079 -1.8091 -.2830 duration .0059 .0811 .0725 .9425 -.1559 .1677 ************************** TOTAL EFFECT MODEL **************************** Outcome: qoltot Model Summary R R-sq F df1 df2 p .0674 .0045 .3102 1.0000 68.0000 .5794 Model coeff se t p LLCI ULCI constant 67.7413 4.3387 15.6134 .0000 59.0836 76.3990 duration .0465 .0834 .5570 .5794 -.1200 .2129 ***************** TOTAL, DIRECT, AND INDIRECT EFFECTS ******************** Total effect of X on Y Effect SE t p LLCI ULCI .0465 .0834 .5570 .5794 -.1200 .2129 Direct effect of X on Y Effect SE t p LLCI ULCI .0059 .0811 .0725 .9425 -.1559 .1677 Indirect effect of X on Y Effect Boot SE BootLLCI BootULCI bcope_ma .0406 .0311 .0009 .1240 Partially standardized indirect effect of X on Y Effect Boot SE BootLLCI BootULCI bcope_ma .0022 .0016 .0000 .0068 Completely standardized indirect effect of X on Y Effect Boot SE BootLLCI BootULCI bcope_ma .0589 .0411 .0017 .1650 Ratio of indirect to total effect of X on Y Effect Boot SE BootLLCI BootULCI bcope_ma .8736 149.0933 .1603 53.5001 Ratio of indirect to direct effect of X on Y Effect Boot SE BootLLCI BootULCI bcope_ma 6.9093 17.9156 3.4423 275.8767 R-squared mediation effect size (R-sq_med) Effect Boot SE BootLLCI BootULCI bcope_ma .0045 .0173 -.0184 .0543 Preacher and Kelley (2011) Kappa-squared Effect Boot SE BootLLCI BootULCI bcope_ma .0601 .0401 .0066 .1692 Normal theory tests for indirect effect Effect se Z p .0406 .0318 1.2753 .2022 ******************** ANALYSIS NOTES AND WARNINGS ************************* Number of bootstrap samples for bias corrected bootstrap confidence intervals: 1000 Level of confidence for all confidence intervals in output: 95.00 NOTE: Some cases were deleted due to missing data. 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