Evaluating the dimensionality of perceived cognitive function

Abstract

Decrements in cognitive function are common in cancer patients and other clinical populations. As direct neuropsychological testing is often not feasible or affordable, there is potential utility in screening for deficits that may warrant a more comprehensive neuropsychological assessment. Furthermore, some evidence suggests that perceived cognitive function (PCF) is independently associated with structural and functional changes on neuroimagery, and may precede more overt deficits. To appropriately measure PCF, one must understand its components and the underlying dimensional structure. The purpose of this study was to examine the dimensionality of PCF in people with cancer. The sample included 393 cancer patients from four clinical trials who completed a questionnaire consisting of the prioritized areas of concerns identified by patients and clinicians: self-reported mental acuity, concentration, memory, verbal fluency, and functional interference. Each area contained both negatively worded (i.e., deficit) and positively worded (i.e., capability) items. Data were analyzed by using Cronbach's alpha, item-total correlations, one-factor confirmatory factor analysis, and a bi-factor analysis model. Results indicated that perceived cognitive problem items are distinct from cognitive capability items, supporting a two-factor structure of PCF. Scoring of PCF based on these two factors should lead to improved assessment of PCF for people with cancer.

Figures

Figure 1

Relationship between the general factor (overall perceived cognitive function) and local factors (cognitive deficits, consequences of cognitive deficits, and cognitive capabilities) Note: 1. Bi-Factor analysis results show that all items of Cognitive deficits and Consequences of cognitive deficits have higher loadings (solid lines) on the general factor Overall perceived cognitive function rather than on their own local factors (dashed lines). While all Cognitive capability items have higher loadings on its local factor (solid lines) than on the general factor (dashed lines). Comparisons of item loadings are shown on Table 3. 2. Model fit: CFI=0.92 and TLI=0.98; RMSEA=0.120