Biomedical subjects

Michael W Browne

Publications and source records attributed to Michael W Browne.

5 recordsLinked to original sources

PubMed2006

Testing differences between nested covariance structure models: Power analysis and null hypotheses.

For comparing nested covariance structure models, the standard procedure is the likelihood ratio test of the difference in fit, where the null hypothesis is that the models fit identically in the population. A procedure for determining statistical power of this test is presented where effect size is based on a specified difference in overall fit of the models. A modification of the standard null hypothesis of zero difference in fit is proposed allowing for testing an interval hypothesis that the difference in fit between models is small, rather than zero. These developments are combined yielding a procedure for estimating power of a test of a null hypothesis of small difference in fit versus an alternative hypothesis of larger difference.

Analysis of Variance↗

PubMed2005

How can I connect with thee? Let me count the ways.

Two studies were conducted to examine mental representations of loneliness and social connectedness. In Study 1, young adults (N = 2,531) completed the revised UCLA Loneliness Scale (R-UCLA scale) and demographic questionnaires. An exploratory factor analysis of the R-UCLA scale on half the sample revealed a three-dimensional conceptual structure that generalized across gender. This mental representation consisted of correlated facets labeled Isolation, Relational Connectedness, and Collective Connectedness. A confirmatory factor analysis on the other half of the sample corroborated this three-factor solution. In Study 2, a population-based sample of 197 older males and females (M(age) = 57.5 years) completed the R-UCLA scale and measures of objective social circumstances. The confirmatory factor analysis supported the three-factor structure in this diverse and older adult sample. Each facet was uniquely predicted by theoretically related social circumstances. These findings suggest how humans make meaning of their social relationships in their mental representations of loneliness and connectedness.

Adult↗

PubMed2004

Assessing stress in cancer patients: a second-order factor analysis model for the Perceived Stress Scale.

Using the Perceived Stress Scale (PSS), perceptions of global stress were assessed in 111 women following breast cancer surgery and at 12 and 24 months later This is the first study to factor analyze the PSS. The PSS data were factor analyzed each time using exploratory factor analysis with oblique direct quartimin rotation. Goodness-of-fit indices (root mean square error of approximation [RMSEA]), magnitude and pattern of factor loadings, and confidence interval data revealed a two-factor solution of positive versus negative stress items. The findings, replicated across time, also indicate factor stability. Hierarchical factor analyses supported a second-order factor of "perceived stress." This alternative factor model of the PSS is presented along with observations regarding the measure's use in cancer research.

Adult↗

PubMed2002

When fit indices and residuals are incompatible.

Standard chi-square-based fit indices for factor analysis and related models have a little known property: They are more sensitive to misfit when unique variances are small than when they are large. Consequently, very small correlation residuals indicating excellent fit can be accompanied by indications of bad fit by the fit indices when unique variances are small. An empirical example of this incompatibility between residuals and fit indices is provided. For illustrative purposes, an artificial example is provided that yields exactly the same correlation residuals as the empirical example but has larger unique variances. For this example, the fit indices indicate excellent fit. A theoretical explanation for this phenomenon is provided using relationships between unique variances and eigenvalues of the fitted correlation matrix.

Humans↗

PubMed2002

Comments on the Meehl-Waller (2002) procedure for appraisal of path analysis models.

P. E. Meehl and N. G. Waller (2002) proposed an innovative method for assessing path analysis models wherein they subjected a given model, along with a set of alternatives, to risky tests using selected elements of a sample correlation matrix. Although the authors find much common ground with the perspective underlying the Meehl-Waller approach, they suggest that there are aspects of the proposed procedure that require close examination and further development. These include the selection of only one subset of correlations to estimate parameters when multiple solutions are generally available, the fact that the risky tests may test only a subset of parameters rather than the full model of interest, and the potential for different results to be obtained from analysis of equivalent models.

Humans↗