This study examined the factor structure and invariance of the Quality of Life in Alzheimer’s Disease (QoL-AD) assessment scale in a large, normal, and well-characterized sample using state-of-the-art statistical procedures. Identification of invariance in this investigation allows researchers to assume that the measurement of these constructs remains invariant when comparing age groups and genders. In addition, establishing the factor structure and invariance of this QoL measure provides a baseline for understanding how ratings of QoL may change as community-dwelling older adults experience changes in functioning leading to cognitive impairment and resulting alterations to their quality of life or for the comparison of impaired individuals and their caregivers.
Prior to establishing factorial invariance, clear factor structures were needed. In the exploratory factor analyses, one-, two-, and three-factor solutions were generated and compared. The three-factor solution had a clearer interpretation and explained more variance in QoL-AD responses than the other factor solutions. Confirmatory factor analyses revealed that the three-factor model had a better fit, based on the absolute and relative fit indices, than the one-factor and two-factor models. For the three-factor solution, the factors were labeled Physical Well-being, Social Well-being, and Psychological Well-being. After model modification, the three-factor confirmatory factor model was then tested for factorial invariance. We were able to accept weak factorial invariance across both age group and gender for the current status ratings. This finding indicated that the pattern of items to these underlying domains as well as the actual level of relation of the items to the factors were constant across these subgroups.
In contrast to our three-factor solution, Thorgrimsen et al. (2003)
may have found a one-factor solution in part due to the sample used, specifically involving 201 participants with a mean MMSE of 14.4 (SD
= 3.8, range = 7–24), of whom 86.4% were in residential homes and day centers. Our sample was comprised of healthy, community-dwelling older adults, with a mean MMSE of 28.6 (SD
= 1.5, range = 20–30). In addition, Thorgrimsen et al.’s use of principal components analysis (PCA) differed from our use of principal axis factor exploratory factor analysis (EFA) in that PCA explains the total variance in the data whereas EFA explains the common variance shared by the items (Bryant & Yarnold, 1995
). Further, the confirmatory analysis results did not show a good fit for a single-factor model, a finding that is supported by the literature. The majority of studies on various QoL measures, including the current study, contain support for a multidimensional model, as Lawton (1983
had originally proposed. Similar to our multi-factor model, the World Health Organization’s Quality of Life Group (WHOQOL Group, 1998
) found four dimensions for the 26-item WHOQOL-Bref measure of quality-of-life through confirmatory factor analysis. The actual items of the WHOQOL-Bref, which are in full sentence question format, are different than those of the QoL-AD, yet the content of several items is very similar to the QoL-AD. Two of our three factors, those tapping psychological and physical aspects of quality of life, were also identified by the WHOQOL four-dimension model, and our third factor (Social Well-being) shares many of the areas tapped by the remaining two factors of the WHOQOL model, labeled Social Relations and Environment. Likewise, Kane et al. (2003)
found 10 distinct factors through confirmatory factor analysis for a 42-item short scale, whereas other research (Brod, Steward, Sands, & Walton, 1999
; Rabins, Kasper, Kleinman, Black, & Patrick, 1999
) determined five dimensions were necessary to evaluate the quality of life for dementia patients.
When compared across males and females and across age groups, the interrelations among the three factors found in the current study were similar both in level of statistical significance and in pattern of correlation strength. In all groups, the correlation between Psychological Well-being and Social Well-being was higher than the correlation of Psychological Well-being with Physical Well-being, and both of these correlations were stronger than the correlation between Social Well-being and Physical Well-being. A pattern was also observed that the correlation between Physical Well-being and Social Well-being was stronger for women than for men and stronger for old-old than for young-old. This finding is supported by research by Almeida, Wethington, and Kessler (2002)
indicating the relation between daily health symptoms and both network and interpersonal stressors, measured using the Daily Inventory of Stress Events (Almeida, 1998
), was significantly stronger for women than for men.
The relatively lower standardized loadings for the items assessing memory (Item 5), ability to do things for fun (Item 11), and money (Item 12) in both the age and gender invariance models could be due to variations in participants’ perceptions of item content. Other studies using this measure have reported that the item on “money” may have been endorsed less for those who saw themselves as self-sufficient and needing privacy (Thorgrimsen et al, 2003
), whereas another study suggested that participants may have been unable to interpret the context of this item in relation to their quality of life (Selai, Vaughan, Harvey, & Logsdon, 2001
). Similar to our item of “ability to do things for fun,” low loadings for the WHOQOL-Bref item of “how much do you enjoy life?” were found in a factor analytic model of the WHOQOL-Bref (Ohaeri, Olusina, & Al-Abassi, 2004
). Similar issues may have influenced the frequency to which the items were endorsed and thus how the items loaded on the associated factors in our sample.
As noted in the description of this study’s sample, our sample was uniformly Caucasian with a high educational level; thus, our results may not be generalizable to a more diverse population. Given the lack of sufficient numbers of participants with a second time point of data on the QoL-AD in our sample, longitudinal stability of the factors was not assessed in this investigation and will be warranted in future research, particularly if participants in this sample progress to early dementia. Future research directions also include replicating these findings in more diverse populations and in those with chronic health conditions.
In conclusion, the QoL-AD was found to represent three dimensions of quality of life for our sample of nondemented, community-dwelling older adults: Physical, Social, and Psychological Well-being. The findings will enable clinicians and other researchers to use this measure to assess these dimensions in clinically nondemented samples that are similar to the SLS sample assessed in this study of healthy, community-dwelling older adults. The results will also enable research investigators to utilize this scale and the latent constructs to obtain indications of treatment efficacy and patient satisfaction through repeated use of this scale. In the nondemented or preclinical individual, these findings could be useful for establishing a baseline of quality of life before disease onset. This baseline level could then be compared at a later time in diseased states when the individual’s living situation or environment may change and thus alter quality of life.