Effects of estimation methods on making trait-level inferences from ordered categorical items for assessing psychopathology.
In assessments of attitudes, personality, and psychopathology, unidimensional scale scores are commonly obtained from Likert scale items to make inferences about individuals' trait levels. This study approached the issue of how best to combine Likert scale items to estimate test scores from the practitioner's perspective: Does it really matter which method is used to estimate a trait? Analyses of 3 data sets indicated that commonly used methods could be classified into 2 groups: methods that explicitly take account of the ordered categorical item distributions (i.e., partial credit and graded response models of item response theory, factor analysis using an asymptotically distribution-free estimator) and methods that do not distinguish Likert-type items from continuously distributed items (i.e., total score, principal component analysis, maximum-likelihood factor analysis). Differences in trait estimates were found to be trivial within each group. Yet the results suggested that inferences about individuals' trait levels differ considerably between the 2 groups. One should therefore choose a method that explicitly takes account of item distributions in estimating unidimensional traits from ordered categorical response formats. Consequences of violating distributional assumptions were discussed.
Dumenci
Levent
L
Department of Social and Behavioral Health, Virginia Commonwealth University, Richmond, VA 23298, USA. ldumenci@vcu.edu
Achenbach
Thomas M
TM
D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D002648:Child; D005163:Factor Analysis, Statistical; D005260:Female; D006801:Humans; D008297:Male; D001523:Mental Disorders / Q000175:diagnosis* / Q000523:psychology; D008875:Middle Aged; D010552:Personality Assessment / Q000706:statistics & numerical data; D025341:Principal Component Analysis / Q000379:methods; D011594:Psychometrics / Q000379:methods / Q000706:statistics & numerical data; D013223:Statistics as Topic / Q000379:methods* / Q000592:standards