I've conducted a survey measuring 4 variables.

VAR1 - VAR2 - VAR3 - VAR4

All questions loading on each variable were scored on a 1-7 Likert scale, and there were 46 total questions. There were no missing values.

I hypothesised that VAR2 and VAR3 would positively predict VAR4 while VAR1 would negatively predict VAR4.

Initial bivariate correlation analysis (using Pearson R) has failed to reject the null hypothesis - significant positive correlations were found between VAR2~VAR4 and VAR3~VAR4. A significant negative correlation was found between VAR1~VAR4. Further, VAR2~VAR3 were positively significantly correlated.

I then thought that VAR1 could be interacting with VAR2 and VAR3 to moderate the effects of VAR2 and VAR3 on VAR4. So I standardized the results and created interaction terms of:

zVAR1 * zVAR2 - and - zVAR1 * zVAR3

I then performed a linear regression with VAR4 as the dependent variable, VAR1/VAR2 in block 1, zVAR1 * zVAR2 in block 2 and again for VAR1/VAR3 and zVAR1 * zVAR3.

There was no significant beta score indicating that VAR1 does not significantly moderate the effects of VAR2/VAR3 on VAR4.

Now I am at a loss as to where to go with my data.

I originally hypothesised that VAR1 would affect the effects of VAR2/3 on VAR4, but having failed to find a significant interaction effect, I'm not sure where to go now.

What kind of statistical test would be useful to my data now? ANOVA? Multiple regression?

Thanks. My statistical knowlege is quite severely limited and I'm running out of time.

Your help is greatly appreciated.