Faculty of Psychology and Educational Sciences Beyond our means? A multivariate perspective on implicit measures Tom Everaert Master dissertation submitted to obtain the degree of Master of Statistical Data Analysis Promotor: Prof. Dr. Jan De Neve Dept. of Data Analysis Academic year 2015-2016 The author and the promoter give permission to consult this master dissertation and to copy it or parts of it for personal use. Each other use falls under the restrictions of the copyright, in particular concerning the obligation to mention explicitly the source when using results of this master dissertation. Tom Everaert September 5, 2016 Foreword In this master dissertation, a study is described that was conducted as part of my post-doctoral research at the Faculty of Psychology and Educational Sciences of Ghent University. Because research in social psychology is often performed in a quick and dirty fashion, peculiarities in the data are frequently overlooked. I therefore set out to demonstrate some methods that would allow for easy and insightful overviews of the complex data that are often associated with psychological studies. I approached my promotor, Prof. Dr. Jan De Neve, with this research question and he agreed to guide me for this master dissertation. All data were gathered by myself and were obtained in accordance with the ethical guidelines of Ghent University and the Faculty of Psychology and Educational Sciences. The data were anonymized and stored on the server of the Learning and Implicit Processes Lab of the Department of Experimental-Clinical and Health Psychology according to the data management plan of the faculty. Table of Contents Introduction ................................................................................................................................................... 1 1.1. Implicit measures .............................................................................................................................. 1 1.2. A multivariate perspective ................................................................................................................. 3 1.3 Principal component analysis ............................................................................................................ 5 1.4 Multidimensional scaling .................................................................................................................. 9 Study 1 ......................................................................................................................................................... 15 2.1. Method ............................................................................................................................................ 15 2.1.1. Participants ............................................................................................................................... 15 2.1.2. Materials ................................................................................................................................... 15 2.1.3. Procedure .................................................................................................................................. 16 2.2. Data analysis.................................................................................................................................... 17 2.2.1. Conventional analyses ............................................................................................................... 17 2.2.2. Principal component analysis ................................................................................................... 18 2.2.3. Multidimensional unfolding ....................................................................................................... 18 2.3. Results ............................................................................................................................................. 19 2.3.1. Conventional analyses ............................................................................................................... 19 2.3.2. Principal component analysis ................................................................................................... 21 2.3.3. Multidimensional unfolding ....................................................................................................... 25 2.4. Discussion ....................................................................................................................................... 27 Study 2 ......................................................................................................................................................... 31 3.1. Method ............................................................................................................................................ 32 3.1.1. Participants and materials ........................................................................................................ 32 3.1.2. Procedure .................................................................................................................................. 32 3.2. Data analysis.................................................................................................................................... 32 3.2.1. Conventional analyses ............................................................................................................... 32 3.2.2. Principal component analysis ................................................................................................... 33 3.2.3. Multidimensional unfolding ....................................................................................................... 33 3.3. Results ............................................................................................................................................. 33 3.3.1. Conventional analyses ............................................................................................................... 33 3.3.2. Principal component analysis ................................................................................................... 34 3.3.3. Multidimensional unfolding ....................................................................................................... 37 3.4. Discussion ....................................................................................................................................... 39 General Discussion ...................................................................................................................................... 42 4.1. Findings ........................................................................................................................................... 42 4.2. PCA or MDS ................................................................................................................................... 43 4.3. Other techniques .............................................................................................................................. 44 4.3.1. Non-linear decompositions of proportions ................................................................................ 44 4.3.2. Correspondence analysis........................................................................................................... 45 4.3.3. Factor analysis .......................................................................................................................... 46 4.5. Other measures: multi-way data ...................................................................................................... 47 4.5. Conclusions ..................................................................................................................................... 50 References ................................................................................................................................................... 51 Appendix ..................................................................................................................................................... 54 6.1. Checking assumptions different age AMP scores across race ........................................................ 54 6.2. Correspondence analysis biplot ....................................................................................................... 55 Abstract The liking of a person, stimulus, or entity is an essential predictor of behavior towards it. Conventional measures of liking through self-report, however, are often biased because of the tendency to give socially desirable answers or the failure to remember one’s likings. New measures have therefore been developed that are sensitive to somebody’s automatic evaluations and are purported to be free of the biases mentioned above. These implicit measures generally assess automatic evaluations through their effects on performance on an unrelated task. The data obtained from these measures are rather complex compared to simple self-report and require some calculation to obtain a score for an automatic evaluation. In a research tradition where quick and dirty analyses are often preferred to accomplish a fast publication, researchers often proceed blindly with calculating such scores instead of carefully scrutinizing the data first. The current report provides a demonstration of exploratory techniques that can be used to yield quick but insightful summaries of implicit measures data. As such data are multivariate in nature, multivariate decomposition techniques such as principal component analysis (PCA) and multidimensional