Department Psychology • Matthias Ziegler

People fake! - So

what?

Matthias Ziegler 2

Contents• The BIG 5 – Knowldege and questions?

• Study design

• 3 questions

• General Conclusion

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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?

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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?

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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

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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

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C11

e

C12

e

C21

e

C22

e

C31

e

C32

e

C41

e

C42

e

C51

e

C52

e

C61

e

C62

e

State 1State 2(Fake)

State 1:CG = EG

+CG:

State 1 = State 2

State 2:EG > CG

LST Theory + ICE DesignC

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Results• 1st semester psychology students

- NCG = 94 NEG = 92, about 70% females in both groups

- demografics comparable

• What was faked?

CG EG

N -0.14* -2.36***

E -0.05 0.90***

O -0.08 0.23

A 0.12 1.05***

C -0.10* 2.23*** Cohen‘s d for repeated measurement designs

Except for O all means differ substantially (and significantly) from time 1 to time 2 in the EG but not in the CG.

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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!

N E A C

N-.40**

.08-.26**

E-.40**

-.05

.11

A-.33**

.14 .04

C -.20 .10 .15

N E A C

N-.42**

.13-.28**

E-.42**

-.06 .11

A-.39**

.32**

.03

C-.78**

.46**

.34**

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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!

dw/o sit dsit

N -1.31*** .42

E .99*** .01

A .60*** .25

C 1.44*** .71

state

1

- set equal

state

2

- 5.97***

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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

N E A C

N -.02-.43**

-.09

E-.42**

.76**

.05

A-.39**

.32**

.12

C-.78**

.46**

.34**

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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

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Next question• What predicts performance?

- trait or state (faking)

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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

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Results• What predicts performance?

• Dimension variance drops loadings only from 1 or 2 facets

EG

t1 t2 SEM

N -.20 -.16 -.15

E -.16 -.13 -.15

A -.22* .04 -.26*

C .14 .03 .09

state 2

- - .09

R² .09 .03 .13

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Results• What is faking?

- correlations between faking variance and other measures

CG EG

gf .07 -.08

gc .02 .13

SOE -.02 -.08

SEB .07 .31**

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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?

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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%)

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Differences between the classes• multinomial logistic regression with rr as reference categoy

• no differences in criterion validity (R² = .02)

χ² B (sf) B (ef) Wald χ2 (sf)

Wald χ2 (ef)

A 10.15* -.005 .037* .17 4.97

C 26.07*** .054*** .047* 21.12 10.10

gf 1.67 -.004 .014* .12 .68

gc .63 -.228 -.240 .58 .37

SOE 1.81 -.030 -.152 .11 1.55

SEB 12.04* -.081 .091 2.69 1.98

age 5.89 -.066 -.133 3.79 3.29

gender 2.25 .242 .876 .25 2.09

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Conclusion III• only important items are faked

• 2 different faking styles

• faking depends on trait, ability, age, and gender

• no differences in criterion validity

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General Conclusion

Model of Responding to Situational Demand

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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 Thank you for your

attention!attention!