Department Psychology • Matthias Ziegler

1. Department Psychology • Matthias Ziegler People fake! - So what?

2. Contents • The BIG 5 – Knowldege and questions? • Study design • 3 questions • General Conclusion

3. The BIG 5 – Knowldege and questions? • Latent State Trait Theory (LST) Steyer, Ferring & Schmitt (1992) • up to 20% of variance in a questionnaire state or interaction (Deinzer et al. 1995) • Correlations between personality dimensions increase due to faking • Schmit & Ryan, 1993; Pauls & Crost, 2005 • Meta-analytical evidence for correlated dimensions • Mount, Barrick, Scullen & Rounds (2005) • true correlations up to ρ = .52 between N and C • Higher order personality factors (α&β, Digman, 1997)  There is a situational influence when measuring personality  How does that influence impact construct validity?

4. The BIG 5 – Knowldege and questions? • BIG 5 prediciting job performance • C  r = .31 • Meta analysis Barrick, Mount & Judge (2001) • BIG 5 prediciting academic performance • Furnham & Chamorro Premuzic 12 % incremental validity to IQ  BIG 5 predict performance  Where does the predictive power come from? Trait or fake?

5. The BIG 5 – Knowldege and questions? • What happens when people fake? • Models for faking from McFraland and Ryan (2001), Snell et al. (1999) • little empirical support/research • new model from Mueller-Hanson, Heggestad & Thornton (2006) • published after my project  • faking regarded equal between people (but Zickar, Gibby & Robie, 2004) • Study idea • qualitative analysis using cognitive interviews • Mixed Rasch Model (C) to detect different answer styles • explore differences between the classes

6. Study design • Integration of LST Theory and ICE Design (Steyer, 2005) • 2 measurement times (LST) • NEO – PI – R twice • variance can be split into faking and personality • 2 groups (ICE) • CG  normal instructions twice • EG  2nd time concrete situation (test for student selection, good result, expert) • causal interpretation possible • Hypotheses • H1: A specific faking takes place in the EG  correlations between faked dimensions increase • H2: Controlling situational demand strongly diminishes correlations

7. State 2: EG > CG State 1: CG = EG + CG: State 1 = State 2 State 1 State 2 (Fake) C11 C12 C21 C22 C31 C32 C41 C42 C51 C52 C61 C62 e e e e e e e e e e e e C LST Theory + ICE Design

8. Results • 1st semester psychology students • NCG = 94 NEG = 92, about 70% females in both groups • demografics comparable • What was faked? Except for O all means differ substantially (and significantly) from time 1 to time 2 in the EG but not in the CG. Cohen‘s d for repeated measurement designs

9. Results • What happened to the correlations? Time 1 Time 2 Above the diagonal are the correlations within the control group and beneath the diagonal within the faking group. * p < .05 ** p < .01 Correlations increase despite diminished variance!

10. Results • fit indices of SEM • χ² = 3768.03 (2051), Bollen Stine p = .33 • CFI = .81; RMSEA = .067 (.064 - .071); SRMR = .138 • means and latent means between groups Groups differ significantly only in their amount of faking after controlling for situational demand!

11. Results • What happened to the correlations? • Not part of the model  not necessary; inclusion does not improve model (neither does a higher order factor!) Correlations diminish drastically (E and A!) • significant state and trait variance (E!) • mostly substantial trait and state paths

12. Conclusion I • faking had a causal effect on structure and means of the BIG 5 • specific faking took place causing highly inflated correlations (H1) and mean differences (except for Openness) • controlling the situational demand (H2) • both groups have the same means in personality dimensions • correlations diminished  uncorrelated BIG 5 structure in both groups • Extraversion and Agreeableness still share a lot of variance  explains troublesome SRMR • replication in larger and different (applicant) samples

13. Next question • What predicts performance? • trait or state (faking)

14. Very complex design • only faked facors were used • Pauls & Crost (2005) • within the CG loadings on the dimensions were set equal for each dimension • Allik & McCrae (2004) • Model fit • χ² = 3977.98 (2175), Bollen Stine p = .38 • CFI = .80; RMSEA = .067 (.064 - .070); SRMR = .137

15. Results • What predicts performance? • Dimension variance drops  loadings only from 1 or 2 facets

16. Results • What is faking? • correlations between faking variance and other measures

17. Conclusion II • criterion validity as effect size remains stable • faking variance adds only little to the prediction • but positively • faking does influence construct validity • only few facets predict performance • faking is related with self efficacy beliefs • Question • What happens when people fake?

18. Question 3 – What happens when people fake? • Qualitative analysis • N = 50 • 2 different faking strategies were used • slight faking and extreme faking • only relevant items were faked • unimportant items were answered honestly or neutrally Mixed Rasch Model • 3 class solution had best fit • regular respondents (4%), slight faker (69%), and extreme faker (27%)

19. Differences between the classes • multinomial logistic regression with rr as reference categoy • no differences in criterion validity (R² = .02)

20. Conclusion III • only important items are faked • 2 different faking styles • faking depends on trait, ability, age, and gender • no differences in criterion validity

21. General Conclusion Model of Responding to Situational Demand

22. Contact • Matthias Ziegler • Ludwig-Maximilians-University Munich • Department Psychology • Leopoldstraße 13 • 80802 München • phone: +49 89 / 2180 6066 • fax: +49 89 / 2180 3000 • Email: ziegler@psy.uni-muenchen.de Thank you for your attention!