Methodological artifacts in dimensionality assessment of the Hospital Anxiety and Depression Scale (HADS)

Objective The Hospital Anxiety and Depression Scale (HADS) is a brief, self-administered questionnaire for the assessment of anxiety and depression in hospital patients. A recent review [7] discussed the disagreement among different studies with respect to the dimensionality of the HADS, leading Coyne and Van Sonderen [8] to conclude from this disagreement that the HADS must be abandoned. Our study argues that the disagreement is mainly due to a methodological artifact, and that the HADS needs revision rather than abandonment. Method We used Mokken scale analysis (MSA) to investigate the dimensionality of the HADS items in a representative sample from the Dutch non-clinical population (N = 3643) and compared the dimensionality structure with the results that Emons, Sijtsma, and Pedersen [11] obtained in a Dutch cardiac-patient sample. Results We demonstrated how MSA can retrieve either one scale, two subscales, or three subscales, and that the result not only depends on the data structure but also on choices that the researcher makes. Two 5-item HADS scales for anxiety and depression seemed adequate. Four HADS items constituted a weak scale and contributed little to reliable measurement. Conclusions We argued that several psychometric methods show only one level of a hierarchical dimensionality structure and that users of psychometric methods are often unaware of this phenomenon and miss information about other levels. In addition, we argued that a theory about the attribute may guide the researcher but that well-tested theories are often absent. Keywords: Anxiety, Depression, Dimensionality, HADS, Mokken scale analysi

Using conditional association to identify locally independent item sets

The ordinal, unidimensional monotone latent variable model assumes unidimensionality, local independence, and monotonicity, and implies the observable property of conditional association. We investigated three special cases of conditional association and implemented them in a new procedure that aims at identifying locally dependent items, removing these items from the initial item set, and producing an item subset that is locally independent. A simulation study showed that the new procedure correctly identified 89.5% of the model-consistent items and up to 90% of the model-inconsistent items. We recommend using this procedure for selecting locally independent item sets. The procedure may be used in combination with Mokken scale analysis. Keywords: conditional association, local independence, model-fit assessment, monotonicity, nonparametric item response theory, unidimensionalit

Comparing optimization algorithms for item selection in Mokken scale analysis

Mokken scale analysis uses an automated bottom-up stepwise item selection procedure that suffers from two problems. First, when selected during the procedure items satisfy the scaling conditions but they may fail to do so after the scale has been completed. Second, the procedure is approximate and thus may not produce the optimal item partitioning. This study investigates a variation on Mokken’s item selection procedure, which alleviates the first problem, and proposes a genetic algorithm, which alleviates both problems. The genetic algorithm is an approximation to checking all possible partitionings. A simulation study shows that the genetic algorithm leads to better scaling results than the other two procedures. Keywords: Item selection, Genetic algorithm, Mokken scaling, Test constructio

Minimum sample size requirements for Mokken scale analysis

An automated item selection procedure in Mokken scale analysis partitions a set of items into one or more Mokken scales, if the data allow. Two algorithms are available that pursue the same goal of selecting Mokken scales of maximum length: Mokken’s original automated item selection procedure (AISP) and a genetic algorithm (GA). Minimum sample size requirements for the two algorithms to obtain stable, replicable results have not yet been established. In practical scale construction reported in the literature, we found that researchers used sample sizes ranging from 133 to 15,022 respondents. We investigated the effect of sample size on the assignment of items to the correct scales. Using a misclassification of 5% as a criterion, we found that the AISP and the GA algorithms minimally required 250 to 500 respondents when item quality was high and 1,250 to 1,750 respondents when item quality was low