Estimating correlations and reliabilities of implicit and explicit tests using a latent variable approach

Estimating Correlations and Reliabilities of Implicit and Explicit Tests Using a Latent Variable Approach Ingmar Visser email: i.visser@uva.nl and Ton Blaazer Department of Psychology, University of Amsterdam Roetersstraat 15 1018 WB Amsterdam phone: +31 (20) 5256735 fax: +31 (20) 6390279 Abstract In many ﬁelds in psychology implicit tests are used to measure some construct of interest, such as priming measures to test implicit memory and the implicit asso- ciation test to measure implicit attitudes. In sequence learning, reaction times are regarded as an implicit test of sequence knowledge. The validity and interpretation of implicit tests hinges on its relationship with similar explicit tests. Using diﬀerent explicit measures, both large associations and dissociations have been reported between measures of sequence knowledge. Part of this inconclusiveness of the results may be due to diﬀerent reliabilities and sensitivities of the measures being used. In this paper, a latent variable model approach is used to assess diﬀerences in reliability and sensitivity of reac- tion times and online prediction performance in a typical sequence learning task, with the aim of arriving at pre- cise estimates of the correlations between implicit and explicit measures of sequence knowledge. Introduction In many ﬁelds in psychology implicit test procedures are used, for testing self-esteem, for testing attitudes, and in learning and memory research. Many of these pro- cedures rely on reaction times (RTs). That is, when someone has a negative attitude towards caucasians, one would not necessarily expect them to express this in an explicit questionnaire. However, it may very well be pos- sible to detect such attitudes in an implicit association test by detecting diﬀerential responding to, say, pictures of black and white people (Fazio & Olson, 2003). Simi- larly, in memory research, priming eﬀects may occur in the absence of explicit recollection of the presented ma- terial. In implicit learning research, a speed-up in RTs is observed in the absence of verbally reportable knowledge (Cleeremans & McClelland, 1991). The use of implicit measures diﬀers considerably in these ﬁelds, and so does the interpretation of associa- tions and dissociations that exist between implicit and explicit measures. However, there is one important sim- ilarity: the validity and interpretation of implicit tests depends crucially on precise knowledge about its rela- tionship with corresponding explicit tests. This relation- ship, be it an association or a dissociation, is greatly in- ﬂuenced by the reliability of the measures involved. As a consequence of diﬀering reliabilities and sensitivities of implicit and explicit measures, the true relationships between these measures may be obscured (Buchner & Wippich, 2000; Meier & Perrig, 2000). Many debates about the relationships between implicit and explicit measures center on the type of knowledge representations that underly participants’ responses to these measures. There are two extreme possibilities for these knowledge representations. The ﬁrst possibility is that there are two kinds of knowledge, implicit and ex- plicit. One is measured by RTs, as in priming eﬀects or in the implicit association test, and the other is mea- sured by explicit tasks such as questionnaires (in atti- tude research) or recognition or recall tasks (in memory research). The assumption of two kinds of knowledge implies that it should be possible to dissociate them. The second possibility is that there is only one kind of knowledge but there are diﬀerent measures, implicit or explicit (see e.g. work by Shanks & colleagues). Fazio & Olson (2003) state something similar about attitudes: “. it is more appropriate to view the measure as im- plicit or explicit, not the attitude” (emphasis added). In taking this position, researchers implicitly take on the challenge of ﬁnding plausible explanations of dissocia- tions that are nonetheless found between implicit and explicit measures. The focus of this paper is on implicit sequence learn- ing, although the proposed method of using factor analy- sis to arrive at estimates of correlations between implicit and explicit measures of a construct can be applied more generally, and indeed has been recently in the area of at- titude research (Blanton, Jaccard, Gonzalez & Christie 2006). In typical sequence learning experiments, partici- pants are presented with a sequence of stimuli that they have to respond to by pressing an appropriate response button. Unbeknownst to participants, the sequence of stimuli contains regularities that make upcoming stim- uli predictable by previously seen stimuli. As a result of this, a decrease in RTs is observed relative to a con- trol condition with diﬀerently or randomly structured stimuli. To establish that the learning process that un- derlies the speed-up in RTs is indeed implicit, a test of explicit knowledge is administered after the RT phase of the experiment, and the correlation between implicit and explicit test is computed. The results of such studies however, are mixed, with some studies pointing to lim- ited reportable knowledge (Reber, 1967; Cleeremans & McClelland, 1991) and others pointing to high correla- tions between implicit and explicit measures (Perruchet & Amorim, 1992; Shanks & Johnstone, 1999). These dif- ferences may be partly due to the diﬀerent reliabilities