Step by Step of Mediating Effect Testing

Baron and Kenny’s (1986) procedure is adopted to test the mediating effects. This method is

very useful to test the mediating effects by using hierarchical regression analysis (Mathieu,

Maynard, Taylor, Gilson, & Ruddy, 2007; Mathieu & Taylor, 2007). There are 4 conditions to

test the mediating effects. First, independent variable (X) must relate significantly to dependent

variable (Y). Second, independent variable (X) must relate significantly to mediating variable

(M). Third, mediating variable (M) must relate to significantly to dependent variable (Y).

Finally, if independent variable (X) fail to account for significant dependent variable (Y) after

mediating variable (M) has been controlled, then the evidence is considered as a full mediation.

Alternatively, if independent variable (X) and mediating variable (M) account for significant

dependent variable (Y), the evidence is considered to be a partial mediation.

In our group homework assignment, Hypotheses H11 and H12 need to be tested by hierarchical

regression. We will only practice H11. The following instructions are to show you how to get the

results of mediating effects.

1. Compute mean score of independent, mediating, and dependent variables.

2. Above 4 conditions need to apply to produce the results of mediating effects.

3. Let see step by step of mediating effects.

Analyze >> Regression >> Linear >> Select a factor of Dependent variables from the

left panel to “Dependent” >> Select all the factors of independent variables from the

left panel to “Independent” >> Click on “Statistics” >> Select “R squared change”,

“Collinearity Diagnostics”, “Durbin-Watson”, and “Covariance Matrix” >> Click on

“Continue.” For “Method”, you may need to select “Stepwise” >> Click on “OK”

As recommended above, you need to conduct the mediating effects with 4 steps of regression analysis.

a. SC (X)TI (Y)

b. SC (X)CU (M)

c. CU (M) TI (Y)

d. [SC (X) + CU (M)]TI (Y)

Note: SC = Social capital; CU = Competence upgrading; TI = Technology innovation.

X = Independent variable; M = Mediating variable; Y = Dependent variable.

Step1 : SC (X) TI (Y)

Step 2: SC (X) CU (M)

Step3 : CU (M) TI(Y)

Step 4 : [SC (X) + CU (M)] TI (Y)

The results of mediating effects

Table 5: The results of mediating effects of “Competence Upgrading”

Independent variables

Dependent variables

Technology

Innovation

(TIMean)

Competence

Upgrading

(CUOverallMean)

Technology Innovation (TIMean)

Step 1 Step 2 Step 3 Step 4

Model 1 Model 2 Model 3 Model 3

Beta (β) Beta (β) Beta (β) Beta (β)

Social capital

(SCOverallMean) 0.266 0.393

*** - 0.066

Competence Upgrading

(CUOverallMean) - - 0.536

*** 0.510***

R2 0.071 0.154 0.287 0.291

Adj-R2 0.066 0.150 0.283 0.283

F-value 15.072 36.175 79.680 40.346

P-value 0.000 0.000 0.000 0.316/0.000

D-W 1.682 1.224 1.694 1.688

VIF Range 1.000 1.000 1.000 1.183/1.183

t-value 3.882 6.015 8.926 1.004/7.813

Method Stepwise Stepwise Stepwise Enter

Note: ***

p < .001, **

p<.01, * p< .05,

+ p < 0.1

Conclusion: Hypothesis 12 is fully mediated and supported

References:

Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social

psychological research: Conceptual, strategic, and statistical considerations. Journal of

Personality and Social Psychology, 55(6), 1173-1182.

Mathieu, J. E., Maynard, M. T., Taylor, S. R., Gilson, L. L., & Ruddy, T. M. (2007). An

examination of the effects of organizational district and team contexts on team processes

and performance: A meso-mediational model. Journal of Organizational Behavior,

28(7), 891-910.

Mathieu, J. E., & Taylor, S. R. (2007). A framework for testing meso-mediational relationships

in organizational behavior. Journal of Organizational Behavior, 28(2), 141-172.