Measuring Trauma: Workshop Summary by unknow

Author:unknow
Language: eng
Format: epub
Tags: Health and Medicine: Mental Health and Behavior, Health and Medicine: Policy, Reviews and Evaluations, Behavioral and Social Sciences: Policy
Publisher: The National Academies Press
Published: 2016-07-21T00:00:00+00:00

CHARACTERISTICS OF GOOD MEASURES OF TRAUMA

Schell discussed procedures for developing, scoring, and evaluating the performance of a trauma scale. He began his presentation by saying that measures of trauma exposure are very different from scales that one might develop on other topics and that applying psychometric theory and techniques to trauma scales can be counterproductive.

As background, Schell provided an overview of the psychometric theory of reflexive, or effect-indicated, measures, which are measures with items that are theorized to share a common cause. The common cause is the construct to be measured, and the items reflect the influence of the construct or are the effect of the construct. The items are correlated with each other because they have the same cause, but they may otherwise be very dissimilar. For example, weight loss and suicidal thoughts are sometimes included on the same depression scale because they are both considered to be manifestations of a problem in a person’s brain, but they are otherwise dissimilar. Most standard psychological measures are reflexive, and causal assumptions of this type are the basis of most psychometric analyses in general, including classical test theory, factor analysis, and item response theory.

Items in a reflexive measure are correlated due to their shared cause, and the quality of the measurement can be inferred from the correlation between the items. Schell noted that summing correlated items converges on an error-free measure of the common cause as the number of items goes to infinity, and the correlation between items approaches 1. This occurs because the interest is in the covariance term, not the variance. Schell said that for most scales, adding items leads to a better measure. In other words, one gets a better measure of the common cause if more items are averaged because as more items are added, the covariance of the items has increasingly bigger effect on the variance of the scale. He noted that reviewers of journal articles often ask authors to discuss Cronbach’s alpha, which is a measure of the extent to which the covariance terms dominate in the variance of the sum, which is a function of the number of items and the average correlation between them.

Advanced psychometric methods can enable a measure to converge to being error free more quickly than a simple sum of items: for example, one can give more weight in the sum to items that are more correlated with the other items or by subtracting out of the scale the portion of variance that appears to be unique to an item, in other words, the one that is not caused by the common cause. However, Schell pointed out that an error-free measure of the common cause of the items is not necessarily an error-free measure of the intended construct. For example, the causal model could be wrong, or the measure may be reliable but not valid. There may be multiple shared causes, some of which the researcher did not intend to measure. An example of this is response bias, such as order effects.

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