Doing Quantitative Research 26E02900, 6 ECTS Cr.

1. Doing Quantitative Research26E02900, 6 ECTS Cr. Olli-Pekka Kauppila Daria Volchek Lecture III - May 16, 2014

2. Today’slecture AM session • Moderation and mediation in regression analysis PM session • Logistic regression and analysis of variance

3. Learning objectives – AM session Deepen the knowledge of different analytical methods and testing hypotheses Gain an understanding of how independent variables can interact to influence the dependent variable Learn to calculate moderated (interactive) effects with SPSS Learn to interpret the coefficients in interaction Understand the meaning and applications of mediation Develop skills to test mediated effects with SPSS Understand the difference between partial and full mediation

4. Part I: Moderation

5. Are employee’s with high job performance more or less likely to leave the organization? Job performance Turnover Independent variable Dependent variable

6. It depends…It depends on what?

7. Perhaps it depends on their career prospects… Moderating variable Opportunities for upward mobility Job performance Turnover Independent variable Dependent variable

8. The rationale of moderation Moderation means that the relationship between independent and dependent variable is contingent upon another variable (i.e. moderating variable) • i.e. the relationship is different depending on the value of the moderating variable In management studies, moderating effects play a key role, because most relationships in social sciences are context-dependent

9. Important things to consider when estimating moderated models Moderation is technically an interactive effect between the independent variable and the moderating variable that is regressed on the dependent variable Avoid interactions between strongly correlated variables Standardize both variables (independent and moderator) that you use in the interaction When adding the interaction term to the model, make sure that you add also the standardized variables used in the interaction (independent and moderating variables)

10. Estimating moderating effects Job performance Job performance × Opportunities for upward mobility Turnover Opportunities for upward mobility

11. Classroom exercise: How risk aversion moderates the effect of perceived managerial support on affective organizational commitment? Moderating variable Risk aversion Perceived managerial support Affective organizational commitment Independent variable Dependent variable

12. Investigating moderating effects Risk aversion Perceived managerial support × Risk aversion Affective organizational commitment Perceived managerial support • Use the following variables as controls: • Firm 2 dummy • Firm 3 dummy • Employee age • Employee gender

13. Getting started… Compute affective organizational commitment variable Standardize ”perceived managerial support” and ”risk aversion” variables • Analyze → Descriptive statistics → Descriptives → Choose “Save standardized values as variables” Compute the interaction using standardized values • Transform → Compute variable • Do regression analysis

14. Correlationtable Significantrelationship: good Notoverlystrongcorrelation: good

15. The interaction effect is significant and positive Regression table The main effect of risk avoidance is not significant The positive main effect of perceive managerial support

16. Plottedinteraction

17. Part II: Mediation

18. The rationale of mediation Although an independent may have an effect on dependent variable, this effect is sometimes indirect; that is, mediated through another variable Whether there is a mediating variable in the relationship depends on theory (i.e. is it justified to expect that the effect is mediated?) In general, the mediating variable helps us understand the process through which the independent variable produces the outcome

19. Mediated model with mediated and direct effects Mediating variable Independent variable Dependent variable

20. Investigating mediation with SPSS Usually, researchers use structural equation modeling (our lecture on May 21) to examine the mediated effects, because this method enables using the same variable simultaneously as independent and dependent variable However, there are procedures for testing mediation also in ordinary regression • The best known is the procedure outlined by Baron & Kenny (1986)* * Baron, R., & Kenny, D. 1986. The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51: 1173-1182.

21. Conditions for mediation as outlined by Baron & Kenny (1986) The independent variable must affect the mediator in the first equation The independent variable must be shown to affect the dependent variable in the second equation The mediator must affect the dependent variable in the third equation “If these conditions all hold in the predicted direction, then the effect of the independent variable on the dependent variable must be less in the third equation than in the second. Perfect mediation holds if the independent variable has no effect when the mediator is controlled.”

22. 1st condition Mediating variable Tertius iungens orientation * Proactive personality Independent variable

23. 2nd condition * Job satisfaction Proactive personality Independent variable Dependent variable

24. 3rd condition Mediating variable Tertius iungens orientation * Job satisfaction Dependent variable

25. “4th condition” Mediating variable Tertius iungens orientation * Job satisfaction Proactive personality n.s. / significantlyweakenedeffect Independent variable Dependent variable

26. Full mediation Mediating variable Tertius iungens orientation * * Job satisfaction Proactive personality n.s. Independent variable Dependent variable

27. Partial mediation Mediating variable Tertius iungens orientation * * Job satisfaction Proactive personality * (But significantly weaker than when the mediating variable is not in the model) Independent variable Dependent variable