distinction procedure, effect, and theory – jan de houwer - 09/06/2006 workshop implicit measures...

Distinction Procedure, Effect, and Theory – Jan De Houwer - 09/06/2006

WORKSHOP

Implicit Measures

Copy of slides available at : http://www.liplab.ugent.be/

Jan De Houwer

Ghent University, Belgium

Amsterdam ICPS - 13 March 2015


Overview

1. Examples of different implicit measures

2. Pros and cons of different implicit measures

3. Issues to consider when constructing your own implicit measure

- What do you want to measure?

- Select stimuli

- Program the task

- Collect the data

- Analyze the data

- Interpret the data

Amsterdam ICPS – 13 March 2015

Implicit measures are measurement outcomes that reflect the to-be-measured construct in an automatic manner (i.e., by virtue of processes that are uncontrolled, unintentional, goal-independent, purely-stimulus-driven, autonomous, unconscious, efficient, or fast). (DH & Moors, 2007)

Procedure – Person – Outcome

Automatic(in certain sense)


airplane

bad

Conscious Goal: go to ICPS

Controlled processing

Automatic Processing

Behavior



1. Examples of implicit measures

1.1. Implicit Association Test (IAT; Greenwald et al., 1998): => 2909 citations

- Procedure: Four categories assigned to two responses- Effect: Faster if associated categories are assigned to same response

=> see DEMO Task 1: POSITIVE + SMOKING - NEGATIVE + NOT-SMOKINGTask 2: POSITIVE + NOT-SMOKING - NEGATIVE + SMOKING



1.2. Evaluative priming (Fazio et al., 1986)

- Procedure: Respond to target preceded by prime

- Effect: Faster if prime and target share same valence

Congruent: CANCER - DIRTY

Incongruent: CANCER - HAPPY

1.3. AMP (Payne et al., 2005)

- Procedure: Evaluate Chinese characters preceded by prime

- Effect: valence of prime influence evaluation of character

=> see DEMO



1.4. R-SRC task (Mogg et al., 2003): See Demo

- Procedure: Move manikin toward or away from stimuli

- Effect: Faster to move to positive and away from negative

1.5. IRAP (Barnes-Holmes et al., 2010)

- Procedure: Respond “as if” you believe something

- Effect: Faster to respond “as if” if you actually believe it

1.6. RRT (De Houwer et al., in press)

- Same idea as IRAP but more user friendly

=> See talk on Saturday, 14:00, Veilingzaal


Many other measuresName letter effect (e.g., Koole, Dijksterhuis, & van Knippenberg, 2001; Nuttin, 1985)

Semantic priming (Wittenbrink, Judd, & Park, 1997)

Affective Simon effect (De Houwer & Eelen, 1998)

Go-NoGo Association Test (GNAT; Nosek & Banaji, 2001)

Stereotypic explanatory bias (Sekaquaptewa, Espinoza, Thompson, Vargas, & von Hippel, 2003)

Extrinsic Affective Simon Task (De Houwer, 2003)

Single-target IAT (Wigboldus, 2001; Karpinski & Steinman, 2006)

Implicit association procedure (Schnabel, Banse, & Asendorpf, 2006)

Single association test (Blanton, Jaccard, Gonzales, & Christie, 2006)

Approach-avoid task (Rinck & Becker, 2007)

Sorting paired features task (Bar-Anan, Nosek, & Vianello, 2008)

Brief IAT (Sriram, & Greenwald, 2008)

Recoding-free IAT (Rothermund et al., 2009)

...


2. Pros and cons of different implicit measures

2.1. IAT is still the standard

a) Pro: reliable, user friendly, evidence for added value

b) Contra:

- (sometimes) questions about validity: What is it measuring?

(e.g., associations? attitudes? salience? similarity?)

- relative (2 target concepts: e.g., alcohol vs soda)

=> ST-IAT, “not-alcohol”

2.2. Evaluative priming

a) Pro: more “personal” and implicit evaluative response?

b) Contra: Low reliability

Amsterdam ICPS - 13 March 2015 Amsterdam ICPS – 13 March 2015


2.3. AMP

a) Pro: reliable, user friendly, structurally very different from IAT

b) Contra: Automatic? See debate between Bar-Anan vs. Payne.

=> But still a good additional measure

2.4. R-SRC

a) Pro: reliable

b) Contra:

- not many studies (mostly addiction)

- relative (2 target concepts: e.g., alcohol vs soda)

=> “not-alcohol”

Amsterdam ICPS - 13 March 2015 Amsterdam ICPS – 13 March 2015


2.5. IRAP

a) Pro: can capture beliefs (e.g., I AM good vs I WANT TO BE good)

b) Contra: user unfriendly (e.g., 20% dropout in students)

2.6. RRT

a) More user friendly way to capture beliefs

b) Contra:

- not many studies

- how implicit is it + what is added value compared to explicit?

Þ Use measures that can capture the to-be-measured representations

Þ Use several measures!


3. Issues to consider when constructing your own implicit measure

3.1. What do I want to measure?a) Beliefs vs associations=> Most measures capture only associations: I – GOOD=> Some measures aspire to measure propositions: I AM GOOD

- IRAP: http://irapresearch.org/- “propositionalized” IAT: e.g., I like, I want, …

b) Relative vs “absolute” associations=> IAT captures relative associations (e.g., ME, OTHERS, GOOD, BAD)=> Other measures are less relative (e.g., ST-IAT, priming, EAST) but also less reliable => Is any association/belief “absolute”?

d



c) Personal vs societal views=> Measure personal views: Personalized IAT (Olson & Fazio, 2004; - other labels: I Like / I Dislike vs Good / Bad

- no error feedback (sometimes)=> Measure societal view: Normative IAT (Yoshida et al., 2012)

- other labels: Most people like / Most people dislike



3.2. Select stimuli (for the IAT)

a) Labels

- are crucial: IAT measures associations between category labels, not between items in a category (e.g. De Houwer, 2001: British vs. Foreign)

- you can use items and instructions to guide the way that participants interpret the labels

b) Items

- important because they determine interpretation of labels

- better fewer good items (e.g., 1, 2, 3, 4) then many bad ones

- avoid items that allow for an obvious recoding of labels / task

(e.g., perceptual features, familiarity, ...)



3.3 Write IAT program

Many programs available: Inquisit, E-prime, Direct-RT, Affect, …

a) Inquisit is easy to use and cheap (but perhaps a bit less flexible)

+ has version for internet research

www.millisecond.com

- 30-day trial version

- task library => screen shot

b) FREE IAT: http://www4.ncsu.edu/~awmeade/FreeIAT/FreeIAT.htm

Meade, A. W. (2009). FreeIAT: An open-source program to administer the implicit association test. Applied Psychological Measurement, 33, 643.



c) Affect 4.0: http://fac.ppw.kuleuven.be/clep/affect4/ - Spruyt et al. (2010, Exp Psy)- Free- programs and tutorials available- SCR (manikin) & RRT: http://www.liplab.ugent.be/ (downloads)



3.4. Collect data

a) With enough practice and clear instructions, almost everyone can do most implicit measures (e.g., depressed patients, 10 year olds)

b) More practice if young, old, low education, …

c) IAT typically has big effect size => no need for big samples (but not bad either)

d) Data collection via internet is possible with most implicit measures, also via Apps.



3.5. Analyze data

a) Technically:

- Inquisit output (.dat) file => ASCII

- Most basic analysis: Calculate mean RT for each of the two test block

- More sophisticated analysis: D600 (Greenwald et al., 2003), but not always the best results

=> conduct (and report) different analysis

=> R scripts available for calculating many scoring algorithms

(soon on http://www.liplab.ugent.be/ )

b) Item or category specific effects

Technically possible but (with IAT) rarely done because effect for one category depends on other category (but Researcher Df)



3.6. Interpret the data

a) IAT = Relative measure: always different associations involved (e.g., more positive self- than other-esteem)

b) Be careful with interpreting absolute direction of effect (zero is not necessarily neutral; Blanton & Jaccard, 2006)

c) Don’t make claims about unconscious, “true” knowledge=> point is “spontaneous thoughts / feelings”



Good luck!


distinction procedure, effect, and theory – jan de houwer - 09/06/2006 workshop implicit measures...

Documents