WORKSHOP
Implicit Measures
Copy of slides available at : http://www.liplab.ugent.be/
Jan De Houwer
Ghent University, Belgium
Procedure, Effect, and Theory – Jan De Houwer Amsterdam ICPS - Distinction
13 March 2015
09/06/2006
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
Procedure, Effect, and Theory – Jan De Houwer Amsterdam ICPS –Distinction
13 March 2015
09/06/2006
Implicit measures are measurement outcomes that reflect the to-bemeasured construct in an automatic manner (i.e., by virtue of processes
that are uncontrolled, unintentional, goal-independent, purely-stimulusdriven, autonomous, unconscious, efficient, or fast). (DH & Moors, 2007)
Procedure – Person – Outcome
Automatic
(in certain sense)
Controlled processing
Behavior
Conscious Goal:
go to ICPS
bad
airplane
Automatic Processing
Procedure, Effect, and Theory – Jan De Houwer Amsterdam ICPS - Distinction
13 March 2015
09/06/2006
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-SMOKING
Task 2: POSITIVE + NOT-SMOKING - NEGATIVE + SMOKING
Procedure, Effect, and Theory – Jan De Houwer Amsterdam ICPS –Distinction
13 March 2015
09/06/2006
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:
Incongruent:
CANCER
CANCER
-
DIRTY
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
Procedure, Effect, and Theory – Jan De Houwer Amsterdam ICPS –Distinction
13 March 2015
09/06/2006
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
Procedure, Effect, and Theory – Jan De Houwer Amsterdam ICPS –Distinction
13 March 2015
09/06/2006
Distinction Procedure, Effect, and Theory – Jan De Houwer 09/06/2006
Many other measures
Name 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)
...
Amsterdam ICPS – 13 March 2015
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
Amsterdam ICPS
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”
Procedure, Effect, and Theory – Jan De Houwer Amsterdam
Amsterdam ICPS
ICPS -–Distinction
13
13March
March2015
2015
09/06/2006
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?
 Use measures that can capture the
to-be-measured representations
 Use several measures!
Distinction Procedure, Effect, and Theory – Jan De Houwer 09/06/2006
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”?
Procedure, Effect, and Theory – Jan De Houwer Amsterdam ICPS - Distinction
13 March 2015
09/06/2006
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
Procedure, Effect, and Theory – Jan De Houwer Amsterdam ICPS - Distinction
13 March 2015
09/06/2006
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, ...)
Procedure, Effect, and Theory – Jan De Houwer Amsterdam ICPS - Distinction
13 March 2015
09/06/2006
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.
Procedure, Effect, and Theory – Jan De Houwer Amsterdam ICPS - Distinction
13 March 2015
09/06/2006
Procedure, Effect, and Theory – Jan De Houwer Amsterdam ICPS - Distinction
13 March 2015
09/06/2006
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)
Procedure, Effect, and Theory – Jan De Houwer Amsterdam ICPS - Distinction
13 March 2015
09/06/2006
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.
Procedure, Effect, and Theory – Jan De Houwer Amsterdam ICPS - Distinction
13 March 2015
09/06/2006
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)
Procedure, Effect, and Theory – Jan De Houwer Amsterdam ICPS - Distinction
13 March 2015
09/06/2006
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”
Procedure, Effect, and Theory – Jan De Houwer Amsterdam ICPS - Distinction
13 March 2015
09/06/2006
Good luck!
Procedure, Effect, and Theory – Jan De Houwer Amsterdam ICPS - Distinction
13 March 2015
09/06/2006