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Using Implicit Measures in Attitude and Personality Research Wilhelm Hofmann University of Chicago Booth School of Business 1 SPSP 2012 GSC and Training Committee Innovative Methods preconference Overview I. What are implicit measures useful for? II. Conceptual approaches to the use of implicit measures III. The Implicit Measurement Zoo: Which procedure to pick? IV. Resources 2

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Page 1: san diego Implicit Measures distribute - Social Psychology · 2014-02-05 · Implicit Stereotypes MALE Career FEMALE Family Implicit Prejudice YOUNG good ELDERLY bad Brand Attitudes

Using Implicit Measures in Attitude and Personality Research

Wilhelm Hofmann

University of Chicago 

Booth School of Business

1

SPSP 2012 GSC and Training Committee Innovative Methods pre‐conference

Overview

I. What are implicit measures useful for?

II. Conceptual approaches to the use of implicit measures

III. The Implicit Measurement Zoo: Which procedure to pick?

IV. Resources

2

Page 2: san diego Implicit Measures distribute - Social Psychology · 2014-02-05 · Implicit Stereotypes MALE Career FEMALE Family Implicit Prejudice YOUNG good ELDERLY bad Brand Attitudes

Mental operations

I) What are implicit measures useful for? 

Aspects of attitudes and traits that are difficult to assess via self‐report due to

• Introspective limits

• Self‐presentational concerns

3

Mental experience 

The Idea behind Implicit Measurement

Mental Associations

Thinking, Feeling, Behavior

4

e.g., implicit stereotypes,Implicit prejudiceImplicit self‐esteem…

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Implicit Measurement

Measurement Procedure

Mental Associations

Task Responses

Measurement Outcome

Inference

Automatic Process

Person

5

De Houwer & Moors, 2010

Link to Dual‐System Theories

6

System 1Associative Processing

System 2 Propositional Reasoning

• Fast, intuitive, habitual response generation

• Schemas and scripts• Basic affective and motivational 

orientations

• Slow, deliberate, rule‐based• Judgments, decisions, intentions• Reasoned, planned behavior

More on dual‐systems/processes: Evans, 2008, ARP; Strack & Deutsch, 2004, PSPR; Gawronski & Bodenhausen, 2006, PB; Smith & DeCoster, 2000, PSPR; Sherman, Gawronski, & Trope, in prep., Guilford Press

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Example: Implicit Gender Stereotypes

• Are hiring decisions and salary offers influenced by implicit gender stereotypes?

• Procedure: Implicit Association Test (IAT)(Greenwald, McGhee, & Schwartz, 1998, JPSP)

• Demonstration (not included in this slideshow): Clear the desk in front of you and prepare to tap on your desk

7

The Traditional IAT(Greenwald et al., 1998)

Block N trials Task Left keyassignment

Right keyassignment

1 20 Target discrimination FEMALE MALE

2 20 Attribute discrimination Career Family

3 20 Initial combined block (p) FEMALE, Career MALE, family

4 40 Initial combined block (t) FEMALE, Career MALE, family

5 20 Reversed target discrimination MALE FEMALE

6 20 Reversed combined block (p) MALE, Career FEMALE, family

7 40 Reversed combined block (t) MALE, Career FEMALE, family

8

Note. p = originally denoted “practice” block; t = “test” block

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Career Family

Left Right

Career Family

Left Right

FEMALE MALE

MALE FEMALE

9

„Incompatible“ Block

„Compatible“ Block

Difference → IAT Effect

difficult & slow

easy & fast

IAT Effect

0

100

200

300

400

500

600

700

800

Female + Career& Male + Family

Male + Career& Female + Family

Average Block Reaction Tim

es

10

‐200

‐100

0

100

200

IAT Effect

Relative Preference

Female+Career

Male+Career

For a more sophisticated scoring algorithm (D‐Score), see Greenwald et al., 2003, JPSP

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Some Variations of Implicit Constructs (as measured with the IAT procedure)

11

Implicit Stereotypes

MALECareer

FEMALEFamily

Implicit Prejudice

YOUNGgood

ELDERLYbad

Brand Attitudes

COKEgood

PEPSIbad

Implicit Self‐Esteem

MEgood

OTHERSbad

Implicit Self‐Concept

MEangry

OTHERScalm

Political Attitudes

Al Goregood

Bushbad

Social Attitudes, Group Research

Consumer, Health, Self‐Regulation, Political etc.

Self‐Esteem, Personality Self‐Concept

II. Conceptual Approaches

• Implicit Measure as Outcome (DV)

– Universal attitudes

– Known groups approach

– Experimental manipulations

– Method‐specific effects 

• Implicit Measure as Predictor (IV)

– Relation between Implicit and Explicit Cognition

– Behavior Prediction

12

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Conceptual Approaches

Implicit Measure as DV

– Universal attitudes

– Known groups approach

– Experimental manipulations

– Method‐specific effects 

13

Research Example

• Greenwald et al., 1998, JPSP

ImplicitAttitude

‐200

‐100

0

100

200

IAT Effect

Implicit Attitude

Flower+ good

Insect+ good

mean IAT effects should not be over‐interpreted!

Conceptual Approaches

Implicit Measure as DV

– Universal attitudes

– Known groups approach

– Experimental manipulations

– Method‐specific effects 

14

Research Example

• Snowden et al., 2003, Archives of Sexual Behavior

ImplicitAttitude

‐200

‐100

0

100

200

HeterosexualMen

HomosexualMen

Implicit Attitude

Women+ attractive

Men+ attractive

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Conceptual Approaches

Implicit Measure as DV

– Universal attitudes

– Known groups approach

– Experimental manipulations/Interventions

– Method‐specific effects 

15

Research Examples

• Olson & Fazio, 2001, Psych Science

• Hollands et al., 2001, Health Psych

• Wittenbrink et al., 2001, JPSP

ImplicitAttitude

‐0.5

0

0.5

CS1+posUSCS2+negUS

CS1+negUSCS2+posUS

Implicit Attitude

CS1+ positive

CS2+ positive

‐Evaluative   Conditioning

‐Context Effects Olson & Fazio, 2001

Conceptual Approaches

Implicit Measure as DV

– Universal attitudes

– Known groups approach

– Experimental manipulations

– Method‐specific effects 

16

Research Examples

• Mierke & Klauer, 2003, JPSP

• Rothermund & Wentura, 2004, JEP:G

• Bluemke & Friese, 2006, JESP

• Steffens, 2004, Exp Psych

ImplicitConstruct

construct‐ related variance

method‐specific variance

error variance

Variance Decomposition

Cognitive abilitiesStimulus SalienceStimulus SelectionStrategies (e.g., faking)...

Measurement Process

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Conceptual ApproachesImplicit Measure as “IV”

– Relation between Implicit and Explicit Cognition

– Behavior Prediction

17

Research Examples

• Brown & Ryan, 2003, JPSP

• Ranganath et al., 2005, JESP

• Hofmann et al., 2005, PSPB

• Gawronski et al., 2007, JESP

• Koole et al., 2001, JPSP

ExplicitConstruct

ImplicitConstruct

Accessibility/StrengthIntuition/Affective focusValidation/EndorsementSpontaneity/Time pressure

Implicit‐Explicit Correlation

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Low implicitself‐esteem

High implicitself‐esteem

Explicit Self‐Esteem

No time pressure

Time pressure

Koole et al., 2001

Explicit report

Conceptual ApproachesImplicit Measure as “IV”

– Relation between Implicit and Explicit Cognition

– Behavior Prediction

18

Research Examples

• Dunton & Fazio, 1997, PSPB

• Nier, 2005, GPIR

• Payne et al., 2005, JPSP

• …

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Low AMP score‐

High AMP score+

Explicit Attitude

High concern with acting prejudiced

Low concern with acting prejudiced

+

Payne et al., 2005

ExplicitConstruct

ImplicitConstruct

Accessibility/StrengthIntuition/Affective focusValidation/EndorsementSpontaneity/Time pressure

Implicit‐Explicit Correlation

Self‐Presentation

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Conceptual ApproachesMore complex cases of 

Implicit & Explicit Attitude Change

19

• Gawronski & Bodenhausen, 2006, Psych Bull (review)

ExplicitAttitude

ImplicitAttitude

Influence

→APE‐Model

Conceptual ApproachesImplicit Measure as IV

– Relation between Implicit and Explicit Cognition

– Behavior Prediction

20

Research Examples

• Egloff & Schmukle, 2002, JPSP

• Payne et al., 2008, Cog. & Emo.

• Back et al., 2009, JPSP

• Greenwald et al., 2009, JPSP (meta‐analysis)Incremental Validity Approach

ExplicitConstruct

ImplicitConstruct

Behavioral Outcome

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Conceptual ApproachesImplicit Measure as IV

– Relation between Implicit and Explicit Cognition

– Behavior Prediction

21

Research Examples

• Egloff & Schmukle, 2002, JPSP

• Payne et al., 2008, Cog. & Emo.

• Back et al., 2009, JPSP

• Greenwald et al., 2009, JPSP (meta‐analysis)

Incremental Validity

Conceptual ApproachesImplicit Measure as IV

– Relation between Implicit and Explicit Cognition

– Behavior Prediction

22

Research Examples

• Dovidio et al., 1997, JPSP

• Asendorpf et al., 2001, JPSP

ControlledBehavior

AutomaticBehavior

ExplicitConstruct

ImplicitConstruct

Asendorpf et al., 2005

Double‐Dissociation Model

Page 12: san diego Implicit Measures distribute - Social Psychology · 2014-02-05 · Implicit Stereotypes MALE Career FEMALE Family Implicit Prejudice YOUNG good ELDERLY bad Brand Attitudes

Conceptual ApproachesImplicit Measure as IV

– Relation between Implicit and Explicit Cognition

– Behavior Prediction

23

Research Examples

• Hofmann et al., 2007, JESP

• Conner et al., 2007, PSPB

• Friese et al., 2008, ERSP (review)

ExplicitConstruct

ImplicitConstruct

Moderated Predictive Validity

Behavioral Outcome

Control ResourcesControl Motivation

Process Reliance...

Conceptual ApproachesImplicit Measure as IV

– Relation between Implicit and Explicit Cognition

– Behavior Prediction

24

Research Examples

• Hofmann et al., 2007, JESP

• Conner et al., 2007, PSPB

• Friese et al., 2008, ERSP (review)

Moderated Predictive Validity

-1.5

-1

-0.5

0

0.5

1

1.5

low (-1 SD) high (+1 SD)

Implicit Attitude Measure

Can

dy C

onsu

mpt

ion

Depletion No Depletion

-1.5

-1

-0.5

0

0.5

1

1.5

low (-1 SD) high (+1 SD)

Restraint Standards

Can

dy C

onsu

mpt

ion

Depletion No Depletion

*

*

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Conceptual ApproachesMore complex case:

Implicit‐Explicit Consistency as IV

25

Research Examples

Attitudes:

• Brinol et al., 2006, JPSP

Self‐esteem:

• Jordan et al., 2003, JPSP

• Zeigler‐Hill, 2006, JPSP

Intelligence self‐concept: 

• Dislich et al., 2012, EJPExplicit

Construct

ImplicitConstruct

DV

Inter‐action

Hot and under‐researched avenues

– Correspondence between different implicit procedures (e.g., Bosson et al., 2000, JPSP; Gawronski & Bodenhausen, 2005, JPSP)

– Development and long‐term change of implicit constructs (e.g., Dunham, Baron, & Banaji, 2008, TiCS)

– Neural and physiological correlates (e.g., Cunningham et al., 

2003, JPSP)

– Process‐dissociation approaches (Conrey et al., 2005, JPSP)

– Extensions to new applied areas such as law, politics, clinical & health, etc.   Era of Application

26

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III. The Implicit Measurement Zoo27

• Priming and related measures– Sequential Conceptual Priming (LDT)

– Sequential Evaluative Priming (EP)

– Affect Misattribution Procedure (AMP)

• IAT and variants– Implicit Association Test (IAT)

– Single‐Category IAT (SC‐IAT)

– Brief Implicit Association Test (BIAT)

– Recoding Free IAT (IAT‐RF)

– Single‐Block IAT (SB‐IAT)

– Go/No‐Go Association Task (GNAT)

• Extrinsic Affective Simon Task (EAST)

• Approach‐Avoidance Measures– Approach‐Avoidance Task (AAT)

– Implicit Association Procedure (IAP)

– Evaluative Movement Assessment (EMA)

– Stimulus Response Compatibility Task (SRCT); “Manikin‐Task”

• Paper and Pencil Measures– Name‐Letter Task (NLT)

– Linguistic Intergroup Bias (LIB)

– Breadth‐based Adjective Rating Task (BART)

– Stereotypic Explanatory Bias (SEB)

– Paper & Pencil IAT

...

The Implicit Measurement Zoo:Which procedure to pick?

• Depends a lot on what you want to measure & on your constraints

– Does it make theoretical sense to include an implicit measure in your research?

– Is focus on experimental manipulations/mean differences (“I as DV”) or on correlational/predictive research (“I as IV”)? 

• issue of reliability

– Does the procedure allow you to appropriately represent the construct you are interested in? 

• e.g., absolute vs. relative comparison

– Computer‐based or paper & pencil?

– Time constraints?• are briefer options available?

28

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Selected Procedures: Pros and Cons

• Priming

– Concept Priming and Evaluative Priming

– Affect Misattribution Procedure (AMP)

• Traditional Implicit Association Test

– Some problems with the traditional IAT and their suggested solutions

• Single‐Category IAT

• Personalized IAT

• Single‐Block IAT

29

Career

30

Word Nonword

75 ms

125 ms

response

Sequential Concept Priming(e.g., Banaji & Hardin, 1996; Wittenbrink et al., 1997)

Prime

Target

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31

Word           Nonword

Career

Word           Nonword

Career

Word           Nonword

Family

Word           Nonword

Family

Supposed mechanism: spreading activation in associative semantic network 

Sequential Concept Priming

32

Word           Nonword

Career

Word           Nonword

Career

Word           Nonword

Family

Word           Nonword

Family

Stereotyping index = (RT(malefamily) – RT(male career) + RT(femalecareer) – RT(femalefamily))/2

(For further details on different scoring indices, see Wittenbrink, 2007)

Sequential Concept Priming

slow slowfast fast

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Positive Negative

Peace

Evaluative Priming(e.g., Fazio et al., 1986; 1995)

33

Positive Negative

Peace

Main difference between concept and evaluative priming: Affective decision instead of lexical decision (→response competition) 

(For further details on priming measures, see Wentura & Degner, 2010; Wittenbrink, 2007)

Positive Negative

Illness

Positive Negative

Illness

Sequential Priming: Pros and Cons

Pros

• very unobtrusive

• optional subliminal prime presentation

• allows both absolute and relative comparisons

34

Cons

• time‐intensive

• complicated (especially with regard to indices)

• relatively small effects 

• very low reliability → not very suitable for correlational research (implicit as IV)

Good alternative to evaluative priming: AMP

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Affect Misattribution Procedure (AMP) (Payne, Cheng, Govorun, Stewart, 2005, JPSP)

35

unpleasant pleasant

75 ms125 ms

100 ms

response

NegativePrime

Target

Affect Misattribution Procedure36

unpleasant pleasant

75 ms125 ms

100 msresponse

PositivePrime

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Affect Misattribution Procedure37

unpleasant pleasant

75 ms125 ms

100 msresponse

Supposed Mechanism: misattribution of activated affect to judgment of ambivalent target

DV = percentage positive responses to target stimuli when preceded by prime of interest 

Prime of interest

AMP: Pros and Cons

38

Pros

• easy to implement

• allows both absolute and relative comparisons

• good reliability. Suitable for correlational research (“implicit as IV”)

• promising findings regarding incremental validity

Cons

• mechanism not well understood yet

• sometimes very large overlap with explicit measures (possibly more “explicit” than other implicit measures)

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The Traditional IAT(Greenwald et al., 1998)

Block N trials Task Left keyassignment

Right keyassignment

1 20 Target discrimination FEMALE MALE

2 20 Attribute discrimination Career Family

3 20 Initial combined block (p) FEMALE, Career MALE, family

4 40 Initial combined block (t) FEMALE, Career MALE, family

5 20 Reversed target discrimination MALE FEMALE

6 20 Reversed combined block (p) MALE, Career FEMALE, family

7 40 Reversed combined block (t) MALE, Career FEMALE, family

39

Note. p = originally denoted “practice” block; t = “test” block

The traditional IAT: Pros and Cons

Pros

• high reliability (both internal consistency and retest)

• ease of administration

• very well‐researched

• clear indication of incremental validity

40

Cons

• relative comparison measure

• various sources of unwanted method‐specific variance identified

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Some IAT Problems and Suggested Remedies

Problem D‐Score Algorithm

Single CategoryIAT

Personalized IAT

Single Block IAT

Cognitive Skill Confounds

x x

Order Effects x x

Relative Comparison

x

Extra‐personal Associations 

x

RecodingStrategies

x

41

D‐Score Algorithm(Greenwald et al., 2003, JPSP)

42

Substantially reduces:• Cognitive ability confounds (e.g., task‐switching; Klauer & Mierke, 2003)

• Compatibility order effects (compatible block first produces larger IAT scores than vice versa)

SPSS and SAS scripts available at: http://faculty.washington.edu/agg/iat_materials.htm

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Single‐Category IAT(Karpinski & Steinman, 2006, JPSP)

Addressed Problem: relative nature of IAT– Hard to know what specific association drives an IAT effect

– Sometimes a suitable comparison part is lacking

Solution: only one Target Category, balancing of number of trials

43

SPIDERnegative positive negative

SPIDERpositive

“compatible” block “incompatible” block

Personalized IAT(Olson & Fazio, 2004, JPSP)

Addressed Problem: is IAT influenced by extra‐personal associations?

Solution (Olson & Fazio, 2004): – exchange “positive” and “negative” attribute category labels with 

more personalized ones (“I like”; “I dislike”)

44

Candy-bars“I like”

Apples“I dislike”

Apples“I like”

Candy-bars“I dislike”

“compatible” block “incompatible” block

For further discussions, see Nosek & Hansen, 2008, EJPA; Gawronski et al., 2008, SPPC 

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Single‐Block IAT(Teige-Mocigemba et al., 2008, EJPA)

Eliminates block structure of the IAT:• thus, no compatibility order effects• reduces cognitive skill confounds• disables participant recoding strategies

45

Football

negative

Baseball

Baseball

positive

Football

Stimulus presentation

Outlook:Process Dissociation Approaches

• No implicit measure is process‐pure

• Process Dissociation (PD) approaches separate multiple processes that determine responses (e.g., error rates in the IAT)– Simple PD Approach (Jacoby, 1991; Payne, 2001): 

automatic and controlled process

– QUAD model (Conrey et al., 2005; Sherman et al., 2008) ‐ 4 parameters:Automatic Activation, Stimulus Discrimination, Overriding Bias, Guessing

• For more information, seeSherman, J. W., Klauer, K. C., & Allen, T. J. (2010). Mathematical modeling of implicit social cognition: The machine in the ghost. In B. Gawronski & B. K. Payne (Eds.), Handbook of implicit social cognition (pp. 156‐175). New York: Guilford.

Tutorial etc.: http://psychology.ucdavis.edu/labs/sherman/site/research.html

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IV. Resources

• Hardware 

• Programming Software

– Inquisit

– DirectRT

– Eprime

• Project Implicit®

• Recommended Books and Hands‐On Chapters

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Hardware and Software48

• Hardware: any modern computer will do

• Software– Inquisit (Millisecond): http://www.millisecond.com

– DirectRT (Empirisoft): http://www.empirisoft.com

– Eprime (Psychology Software Tools, Inc.): http://www.pstnet.com/eprime

– FreeIAT: http://www4.ncsu.edu/~awmeade/FreeIAT/FreeIAT.htm

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Project Implicit®(www. https://implicit.harvard.edu)

• IAT demonstrations

• Background information

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Resources: Books on Implicit Measures

Handbook of implicit social cognition: Measurement, theory, and applications 

Bertram Gawronski, & Keith Payne (Eds.)2010. Guilford Press.

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Implicit Measures of Attitudes

Bernd Wittenbrink & Norbert Schwarz (Eds.)2007. Guilford Press.

Attitudes: Insights from the new implicit measures 

Richard Petty, Russell Fazio, & Pablo Briñol (Eds.)2008. Psychology Press.

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Practical Hands‐On Chapters

IAT (and its variants)• Teige‐Mocigemba, S., Klauer, K. C., & Sherman, J. W. (2010). Practical guide to 

Implicit Association Test and related tasks. In B. Gawronski & B. K. Payne (Eds.), Handbook of Implicit Social Cognition (S. 117‐139). New York: Guilford.

• Lane, K. A., Banaji. M. R., Nosek. B. A., & Greenwald, A. G. (2007). Understanding and using the Implicit Association Test: IV: What we know (so far). In B. Wittenbrink & N. S. Schwarz (eds.) Implicit Measures of Attitudes: Procedures and Controversies (59‐102). New York: Guilford.

Priming and AMP• Wentura, D. & Degner, J. (2010). A Practical Guide to Sequential Priming and 

Related Tasks. In B. Gawronski, & B. K. Payne (Eds.), Handbook of implicit social cognition (pp. 95‐116). New York: Guilford.

Paper and Pencil• Sekaquaptewa, D., Vargas, P., & von Hippel, W. (2010). A practical guide to 

paper and pencil implicit measures of attitudes.  In B. Gawronski & B. K. Payne (Eds.), Handbook of Implicit Social Cognition (pp. 140‐155).  New York: Guilford.

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