Methods for Computing Missing Item Response in Psychometric Scale Construction

Ohidul Islam Siddiqui

doi:10.3844/amjbsp.2015.1.6

Abstract

Therapeutic potential of a new antidepressant drug is evaluated frequently based on multi-item psychometric scales. The total score of a psychometric scale is calculated based on the responses of multiple-items, in which each item is scored on a likert scale. Missing responses in some of the items are inevitable and hence it is a problem in calculating the total score of a scale. Different approaches can be used to handle the missing item responses in constructing the total scores of a psychometric scale. One approach is that if a patient has missing responses in one or more items, his/her total score will be missing; another approach is that the missing item response will be imputed before calculating the scale total score. For the imputation, different methods can be used. Each of the methods has some drawbacks. This paper compares six methods, commonly used in imputing the missing item responses when there are missing responses at one or more items, but not missing more than 50% items of the scale. Simulation studies indicate that substituting the mean of the completed items of a scale for a given patient is generally the most desirable method for imputing both the random and non-random missing items in the psychometric scale construction.

Highlights

In longitudinal clinical trials of antidepressant drug development, self-rating or clinician-rating multi-item psychometric scales are frequently used to evaluate treatment efficacy
Dropping the patients with missing data from the analyses reduce both the power and accuracy of the analyses (Madow et al, 1983)
When missing items are missing completely at random, the estimate and its significance level at each of the imputed methods are very close to the corresponding reference values

Summary

Introduction

In longitudinal clinical trials of antidepressant drug development, self-rating or clinician-rating multi-item psychometric scales are frequently used to evaluate treatment efficacy. If any item of a scale at postbaseline period is missing, the value for that item is imputed using LOCF approach based on the corresponding item response measured at the previous time point. If a patient has more than certain percentages (20 or 25%) missing items at baseline, the total score for the scale at baseline will be missing and in the change score analysis, this patient will be dropped out.

Results

Conclusion