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The article is behind a paywall so I cannot read it. I don’t know of any good way to convert dichotomous items into Likert ones.

Hi Dr Peter,

Can you give me some suggestion what should i interpret where my p-value is 1.248 which is more than 0.05 and my coefficients is -1.0003?

Hope to see your replied soon.

Thank you,

Regards.

Hi Steffenie

The p value cannot be 1.248. P values are always between 0 and 1.

Peter

Dear Peter,

Can to please help me out with this statistical problem:

An independent variable “X”(construct) has 4 factors in it A, B, C, D which are significantly positively correlated with each other. There are two dependent variables ( “Y” and “Z”) which are correlated with these factors. A, B, C, D significantly positively correlated with “Y”. Except for A , rest are uncorrelated with “Z”. “Y” and “Z” are unrelated. The correlation matrix is as follows:

A B C D Y Z

A 1

B 0.67** 1

C 0.58** 0.64** 1

D 0.56** 0.68** 0.67** 1

Y 0.48** 0.41** 0.39** 0.38** 1

Z 0.21** 0.05 0.07 -0.04 0.10 1

Now on moving to the regression model, Y is significantly positively predicted by A.

For Z, A was a significant predictor as well as D (uncorrelated bivariate relation) also predicted it significantly but in opposite direction.

A–>Z (beta= 0.34, p=0.001)

B–>Z (beta= -0.11, p=0.241)

C–>Z(beta= 0.10, p=0.616)

D–>Z(beta= -0.40, p=0.015)

Please help me to clarify how come a nonsignificant bivariate correlation becomes so significant in regression model?

When you do the regression you are controlling for the other variables. When you do the correlations, you are not.

Peter