College of Psychology: Faculty Proceedings, Presentations, Speeches and Lectures

Title

Rates of Apparently Abnormal WMS-4 Index Differences in the Normal Population

Event Location / Date(s)

San Diego, CA / October 16-19, 2013

Document Type

Conference Proceeding

Presentation Date

10-16-2013

Conference Name / Publication Title

33rd Annual Meeting of the National Academy of Neuropsychology

Description

Abstract

Objective: Interpretation of the WMS-IV involves examination of multiple Index score differences. For example, Immediate > Delayed or Auditory > Visual Index patterns may suggest memory impairment. Base rate data from the standardization sample suggest that 15-point differences between any specific pair of Index scores are relatively uncommon in normal individuals, but these data refer to comparisons between individual Index pairs rather than multiple possible comparisons among five Indexes. This paper provides normative data for the simultaneous occurrence of these Index score discrepancies.

Method: The normal incidence of Index score differences was calculated using Monte Carlo simulations and validated against standardization data. Correlations among Indexes from the standardization sample were used to recreate the distributions of expected score differences. The frequency of observed Index discrepancies was then determined. Results: Differences of 15 points between any two Indexes occurred in 60% of the normative sample, and at least one 20-point Index difference was present in 38% of the normal population. Forty-four percent had at least two Index pairs that were 15 points discrepant, and 23% had a 20-point difference between two or more Index pairs. The percentage of the population with differences between individual index pairs did not differ significantly when Monte Carlo results were compared with published standardization data.

Conclusion: WMS-IV Index score discrepancies are normally common when all possible such comparisons are made, and this reduces their clinical significance. Specific prior interpretive hypotheses are necessary to reduce the number of Index comparisons and associated false positive conclusions. Monte Carlo simulation accurately predicts these false-positive rates.

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