Modal profiles for the WISC-III.

Abstract

The present study presents a normative typology for classifying the Wechsler Intelligence Scale for Children-Third Edition (WISC-III) factor index profiles according to profile shape. Q-type principal components analysis of the WISC-III factor index scores identified four profile shapes that were replicated in independent samples. The typology provides good coverage with between 80-85% (depending on classification rule) of participants with significant profile scatter being assigned to a profile type. An initial attempt to establish external validity indicated that the classified participants display dissimilar patterns of performance on independent variables. The current analyses indicate that overall profile level accounted for a majority of the variance in WISC-III index scores, but a considerable proportion of the variance was because of differences in profile shape. As a result, factor index profiles appear to present a viable level of analysis for research and clinical use. The present typology is compared with typologies developed with other clustering procedures. Although the profile types described in this study are likely to generalize to comparable samples of normal children, it is likely that additional profile types exist that characterize clinical populations.

Cognitive and motivational profile shape predicts mathematical skills over and above profile level

“…Furthermore, this profile shape accounted for an additional 8.8% variance in the prediction of Mathematical Skills over and above general profile level, which supports our hypothesis and is consistent with previous research (e.g., Kim et al, ; Naglieri, ; Pritchard et al, ). The more closely children matched this profile shape, the lower their performance in Mathematical Skills when profile level was controlled.…”

“…Critics of the interpretation of intraindividual differences or profile shape analysis have pointed to the finding that profile shape or pattern does not add incremental validity to the prediction of educational outcomes, once profile level is accounted for (e.g., Kahana et al, 2002;Watkins & Glutting, 2000). Other studies showed that profile shape contributes to the prediction of educational outcomes, over and above the effect of profile level (Kim et al, 2004;Naglieri, 2000;Pritchard et al, 2000;Rohde & Thompson, 2007). In the present study, profile level was expected to be the strongest predictor of Mathematical Skills, but profile shape was hypothesized to add incremental validity, especially when meaningful subtest variation is present.…”

Estimating Cognitive Profiles Using Profile Analysis via Multidimensional Scaling (PAMS)

“…MPA yields clusters that vary in terms of profile shape. MPA identifies the most frequently occurring profile patterns in a dataset, which are then compiled to create the "modal" profiles (Pritchard, Livingston, Reynolds, & Moses, 2000;Skinner & Lei, 1980).…”

A Review of Multidimensional Scaling (MDS) and its Utility in Various Psychological Domains

“…Modal profile analysis relies on standardized scores to yield clusters that vary in terms of profile shape. It identifies the most frequently occurring profile patterns in the data and compiles them to create normative profiles (Pritchard et al, 2000). One limitation of cluster analysis in comparison to MDS is that the clusters describe individual differences in overall profile level, rather than individual differences in profile patterns (Kim et al, 2004).…”