post hoc power analysis

Question

Hi

I tested the simple mediation model in the manual (page 287):

Initially, I defined the parameter estimates as follows:

a @ .39 b @ .39 and cp @ 0, but the program gave an error.

later

a? .39 b? .39 and cp? 0, but the program gave an error again.

I gathered data from a small group to determine the sample size for my study (n = 117). Firstly, I analyzed this data. I want to add the parameter estimates I obtained from this analysis as fixed values in the model, and I want to calculate power according to these values.

@ or ? instead of using *, the program runs, but the parameter estimates on the output page are not what I initially defined; Estimates close to 0.

What do you suggest in this case? I want to know if I should add to my sample by doing post hoc power analysis.

Thank you.

Zeynep

Answer 1

Using a?0.39 and b?0.39 should work. Please include a link to your analysis in the error output if something went wrong.

In general, post-hoc power analysis isn't recommended since it does not add anything new to your sample.

Comments

Dear Authorized,

unfortunately, it is not possible to calculate the power using the diagram, whichever of these (* or? or add @) is used.

With your support, the model I have constructed for my thesis work is working. but even when I enter the sample size of 1000 people, the power is very low.

In the mediation model, it doesn't matter to fix the a and b parameters to specific values because the estimate for these parameters gets 0 each time in the output. So very very large samples are required.

For example, the output is below 

whereas in the model I assigned values such as a1 and b1 and, etc.

Basic information: Estimation method ML Standard error standard Number of requested replications 1000 Number of successful replications 1000 Sample size 400 True Estimate MSE SD Power Power.se Coverage Regressions: M1 ~ X (a1) 0.000 0.002 0.050 0.051 0.057 0.007 0.943 Y ~ X (c1) 0.000 0.002 0.050 0.052 0.067 0.008 0.933 M1 (b1) 0.000 0.000 0.050 0.051 0.057 0.007 0.943 M2 (b4) 0.000 -0.005 0.050 0.052 0.057 0.007 0.943 M2 ~ X (a2) 0.000 -0.000 0.050 0.050 0.056 0.007 0.944 Y ~ V (b2) 0.000 -0.003 0.050 0.051 0.057 0.007 0.943 M1V (b3) 0.000 -0.001 0.050 0.050 0.052 0.007 0.948 M2V (b5) 0.000 -0.001 0.050 0.051 0.059 0.007 0.941 Intercepts: M1 0.000 -0.003 0.050 0.051 0.059 0.007 0.941 Y 0.000 -0.001 0.050 0.052 0.069 0.008 0.931 M2 0.000 -0.001 0.050 0.049 0.043 0.006 0.957 Variances: M1 1.000 0.991 0.070 0.069 1.000 0.000 0.942 Y 1.000 0.981 0.069 0.068 1.000 0.000 0.929 M2 1.000 0.993 0.070 0.070 1.000 0.000 0.934 Indirect/Mediation effects: a1b1 0.000 0.000 0.003 0.003 0.000 0.000 1.000 a1b3 0.000 0.000 0.003 0.003 0.000 0.000 1.000 a2b4 0.000 0.000 0.003 0.003 0.000 0.000 1.000 a2b5 0.000 0.000 0.003 0.002 0.000 0.000 1.000