Tableplot A New Tool for Assessing Precise Predictions Ernest Kwan 1 , Irene R.R. Lu 1 , and Michael Friendly 2 1 Carleton University, Ottawa, Canada, 2 York University, Toronto, Canada DOI 10.1027/0044-3409.217.1.38 Abstract. In the debate over null hypothesis significance testing, Paul Meehl strongly advocated appraising theories through the gener- ation and evaluation of precise predictions (e.g., Meehl, 1978). The study of personality structure through the five-factor model (FFM; McCrae & John, 1992) is an important area of research where one encounters many precise predictions. Extant methods of assessing such predictions, however, do not allow researchers to examine the outcome of the predictions in great detail. That is, it may be difficult to determine how estimates fail to match predicted values. As Meehl argued, one must examine how a theory fails to predict in order to refine and improve the theory. To promote better theory appraisal in FFM research, we present a powerful new tool, called a tableplot (Kwan, 2008a), that can summarize and clarify factor-analytic results. Specifically, we illustrate how the tableplot enables detailed appraisal of precise predictions in the FFM. Keywords: precise predictions, theory appraisal, five-factor model, graphical display, tableplot In the debate over null hypothesis significance testing (NHST) Paul Meehl strongly criticized psychology’s use of NHST for theory testing (e.g., Meehl, 1967, 1978, 1986, 1990, 1997). Meehl’s main concern is that NHST does not require researchers to carefully generate or examine precise predictions derived from a substantive theory of interest. Meehl argued that unless one examines how empirical es- timates fail to match a theory’s precise predictions, one can do little to refine or improve the theory. Despite Meehl’s emphasis on precise predictions, he also noted that substantive theories in many areas of psychology cannot generate very precise predictions (e.g., Meehl, 1978). We consider an important exception: personality re- search through the use of factor-analytic techniques. Par- ticularly, we refer to the study of personality structure through the five-factor model (FFM; McCrae & John, 1992) and the associated five-factor theory (FFT; McCrae & Costa, 1996, 1999). In the FFM there are many parame- ters with corresponding predicted values (e.g., Rolland, 2002). However, researchers do not adequately examine the extent to which these estimates match their predicted values (e.g., McCrae, Zonderman, Costa, Bond, & Paunonen, 1996). According to Meehl, this neglect is detrimental be- cause it limits the advancement and refinement of FFT. One clear reason behind the unsatisfactory assessment of precise predictions in FFM research is the large number of parameter estimates involved. The FFM has 150 param- eters (e.g., Costa & McCrae, 1992). Extant methods of as- sessment include summary indices of fit and significance tests of such indices (e.g., McCrae et al., 1996; also see the next section of this paper). If one wishes to examine how parameter estimates deviate from predicted values, one must rely on a table of predicted and estimated values (see Table 1). As one can verify from Table 1, using a table to compare 300 values can be a challenge, even when supple- mented by summary statistics. Indeed, several researchers have discussed the inadequacies of employing tables to in- terpret quantitative information (e.g., Friendly & Kwan, 2003; Wainer, 1997). Thus, even if one intends to carefully examine the outcome of precise predictions in the FFM, one may still find it difficult to do so. The goal of this paper is to promote better assessment of precise predictions in FFM research. Recently Kwan (2008a) proposed a new graphical display, called a table- plot, for presenting factor analysis results. We illustratehow the tableplot can be used to help assess precise predictions. First we examine research on the FFM: We discuss the role of precise predictions, extant methods for assessing such predictions, and the limitations of these methods. We then introduce the tableplot and illustrate how the tableplot fa- cilitates better assessment of precise predictions in the FFM. We provide a discussion and conclusion in the last section. Precise Predictions in Studies of Personality The FFM is commonly operationalized by the Revised NEO Personality Inventory (NEO PI-R; Costa & McCrae, 1992). The NEO PI-R comprises 240 items that measure 30 facets. These facets relate to five domains (factors) of personality: Neuroticism, Extraversion, Openness, Agree- ableness, and Conscientiousness. Factor analysis (FA) esti- mates the relationship between a facet and a domain; these Zeitschrift für Psychologie / Journal of Psychology 2009; Vol. 217(1):38–48 © 2009 Hogrefe & Huber Publishers