Can anyone help me in Interpretation of the results of Moderation analysis using Process SPSS?

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Belal Edries

Dear Sara Khan

Your results shows the following:

1. The moderator variable (W: CAM) does not influence the (Y: GEL) since it is not significant (T-test=1.0631, P-test=0.2888).

2. The interaction variable (Int_1) which is the moderation relation you want to study. The results have show that it has a very slightly negative impact on (Y: GEL) as beta was (-0.0082). This interaction moderation effect is valid since it is significant as (T-test= - 2.0013, P-test=0.0464)

Sara Khan

Thanks Belal Edries. it helped me alot. one thing which i want to clear is that t test is used for comparing two groups, in moderation analysis what its(t-test) role?

Regarding the T-test, it is a measure of the significance of the relationship between the two variables similar to the P-Values.

Generally speaking, if you are interested to compare two groups you can run a multi-group analysis to study if the inclusion of the moderator variable has a significant effect or not.

Dear Belal ,there was one sample in my study and not group which i intended to compare. in this case what the purpose of t test would be?

secondly,is there any specific value which determines the significance of the t value?like p value

To run a muli-group analysis, you should split the data on a particular criterion for example (men group, women group, young group, old group and son on). Then you can run the multi-group analysis.

Critical t values for a two-tailed test are 1.65 (significance level 10%),1.96 (significance level = 5%), and 2.57 (significance level = 1%). Examine the p value, which should be lower than 0.10 (significance level = 10%), 0.05 (significance level = 5%), or 0.01 (significance level = 1%)

Thanks a lot Belal Edries

Karin Schermelleh-Engel

Sara, a regression coefficient divided by its standard error is a measure of significance of this value. This ratio follows a t-distribution for small sample sizes and a z-distribution for larger sample sizes.

Take, e.g., the regression coefficient of CAS(X). Dividing this coefficient by its standard error results in .4463/.1322 = 3.3759. The small difference to the value reported in the output ( 3.3779) is due to rounding errors. Therefore, the t-value is 3.3779 and this value is compared to the critical value of the t-distribution.

Does this help?

Kind regards, Karin