Structural equation modeling of the medical outcome study: Short Form 36 (SF-36)

The factorial validity of the SF-36 was evaluated using confirmatory factor analysis (CFA) methods, structural equation modeling (SEM), and multigroup structural equation modeling (MSEM). First, the measurement and structural model of the hypothesized SF-36 was explicated. Second, the model was tested for the validity of a second-order factorial structure, upon evidence of model misfit, determined the best-fitting model, and tested the validity of the best-fitting model on a second random sample from the same population. Third, the best-fitting model was tested for invariance of the factorial structure across race, age, and educational subgroups using MSEM.^ The findings support the second-order factorial structure of the SF-36 as proposed by Ware and Sherbourne (1992). However, the results suggest that: (a) Mental Health and Physical Health covary; (b) general mental health cross-loads onto Physical Health; (c) general health perception loads onto Mental Health instead of Physical Health; (d) many of the error terms are correlated; and (e) the physical function scale is not reliable across these two samples. This hierarchical factor pattern was replicated across both samples of health care workers, suggesting that the post hoc model fitting was not data specific. Subgroup analysis suggests that the physical function scale is not reliable across the "age" or "education" subgroups and that the general mental health scale path from Mental Health is not reliable across the "white/nonwhite" or "education" subgroups.^ The importance of this study is in the use of SEM and MSEM in evaluating sample data from the use of the SF-36. These methods are uniquely suited to the analysis of latent variable structures and are widely used in other fields. The use of latent variable models for self reported outcome measures has become widespread, and should now be applied to medical outcomes research. Invariance testing is superior to mean scores or summary scores when evaluating differences between groups. From a practical, as well as, psychometric perspective, it seems imperative that construct validity research related to the SF-36 establish whether this same hierarchical structure and invariance holds for other populations.^ This project is presented as three articles to be submitted for publication. ^

Yoga off the mat: How far and to whom do the benefits extend?

At least 15 million American adults have participated in yoga at least once in their lifetime (Saper, Eisenberg, Davis, Culpepper, & Phillips, 2004). The field of yoga research is relatively new in the United States, and the majority of studies have concentrated on yoga's effect on measures of physiology (cardiovascular disease, diabetes, obesity) or psychological measures of stress and anxiety. This review attempted to identify studies that had been conducted measuring a different set of outcome measures, specifically violence, trauma, eating, and other behavioral disorders. In 9 of 10 studies reviewed, researchers observed statistically significant effects of yoga interventions. Effects were most evident within multifaceted studies that combined intensive yoga practice with group discussion and training. Only one group (Mitchell, Mazzeo, Rausch, & Cooke, 2007) failed to observe any significant differences between yoga practice groups and control groups. Effects were seen in both sexes, although a majority of the studies were aimed specifically at women. All studies were limited by small sample size and lack of follow-up data. Future research should seek to increase sample size, to diversify recruitment to allow for the randomization of treatment and control groups, and to allow for long-term follow-up.^