1. 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

2. Distinction Procedure, Effect, and Theory – Jan De Houwer - 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 Amsterdam ICPS – 13 March 2015

3. 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)

4. Distinction Procedure, Effect, and Theory – Jan De Houwer - 09/06/2006 airplane bad Conscious Goal: go to ICPS Controlled processing Automatic Processing Behavior Amsterdam ICPS - 13 March 2015

5. Distinction Procedure, Effect, and Theory – Jan De Houwer - 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 Amsterdam ICPS – 13 March 2015

6. Distinction Procedure, Effect, and Theory – Jan De Houwer - 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: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 Amsterdam ICPS – 13 March 2015

7. Distinction Procedure, Effect, and Theory – Jan De Houwer - 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 Amsterdam ICPS – 13 March 2015

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

9. 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

10. 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

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

12. Distinction Procedure, Effect, and Theory – Jan De Houwer - 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!

13. 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”? d Amsterdam ICPS - 13 March 2015

14. Distinction Procedure, Effect, and Theory – Jan De Houwer - 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 Amsterdam ICPS - 13 March 2015

15. Distinction Procedure, Effect, and Theory – Jan De Houwer - 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,...) Amsterdam ICPS - 13 March 2015

16. Distinction Procedure, Effect, and Theory – Jan De Houwer - 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. Amsterdam ICPS - 13 March 2015

17. Distinction Procedure, Effect, and Theory – Jan De Houwer - 09/06/2006 Amsterdam ICPS - 13 March 2015

18. Distinction Procedure, Effect, and Theory – Jan De Houwer - 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) Amsterdam ICPS - 13 March 2015

19. Distinction Procedure, Effect, and Theory – Jan De Houwer - 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. Amsterdam ICPS - 13 March 2015

20. Distinction Procedure, Effect, and Theory – Jan De Houwer - 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) Amsterdam ICPS - 13 March 2015

21. Distinction Procedure, Effect, and Theory – Jan De Houwer - 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” Amsterdam ICPS - 13 March 2015

22. Distinction Procedure, Effect, and Theory – Jan De Houwer - 09/06/2006 Good luck! Amsterdam ICPS - 13 March 2015