This article is an empirical evaluation of the choice of fixed cutoff points in assessing the root mean square error of approximation (RMSEA) test statistic as a measure of goodness-of-fit in Structural Equation Models. Using simulation data, the authors first examine whether there is any empirical evidence for the use of a universal cutoff, and then compare the practice of using the point estimate of the RMSEA alone versus that of using it jointly with its related confidence interval. The results of the study demonstrate that there is little empirical support for the use of .05 or any other value as universal cutoff values to determine adequate model fit, regardless of whether the point estimate is used alone or jointly with the confidence interval. The authors' analyses suggest that to achieve a certain level of power or Type I error rate, the choice of cutoff values depends on model specifications, degrees of freedom, and sample size. KeywordsRMSEA; SEM; goodness-of-fit; computer simulations Structural Equation Modeling (SEM) has been widely used in sociological, psychological, and social science research. One of the appealing attributes of SEM is that it allows for tests of theoretically derived models against empirical data. For researchers using SEM techniques, evaluation of the fit of a hypothesized model to sample data is crucial to the analysis. A key feature of SEM is the test of the null hypothesis of ∑ = ∑ = (θ), also known as the test of exact fit, where ∑ is the population covariance matrix, ∑(θ) is the covariance matrix implied by a specific model, and θ is a vector of free parameters defined by the model. The model test statistic T enables an asymptotic test of the null hypothesis of H 0 : ∑ = ∑ (θ). A significant T, often reported as the model chi-square, would suggest misspecification of the model. However, such a test of exact fit of the proposed model is generally unrealistic, as hardly any model using real data is without error (e.g., Browne and Cudeck 1993). A trivial misspecification, particularly with large sample sizes, can lead to rejection of the model even when it may otherwise adequately reproduce the population covariance matrix.

In this article, the authors examine the most common type of improper solutions: zero or negative error variances. They address the causes of, consequences of, and strategies to handle these issues. Several hypotheses are evaluated using Monte Carlo simulation models, including two structural equation models with several misspecifications of each model. Results suggested several unique findings. First, increasing numbers of omitted paths in the measurement model were associated with decreasing numbers of improper solutions. Second, bias in the parameter estimates was higher in samples with improper solutions than in samples including only proper solutions. Third, investigations of the consequences of using constrained estimates in the presence of improper solutions indicated that inequality constraints helped some samples achieve convergence. Finally, the use of confidence intervals as well as four other proposed tests yielded similar results when testing whether the error variance was greater than or equal to zero.

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Most research on access to health care focuses on individual-level determinants such as income and insurance coverage. The role of community-level factors in helping or hindering individuals in obtaining needed care, however, has not received much attention. We address this gap in the literature by examining how neighborhood socioeconomic disadvantage is associated with access to health care. We find that living in disadvantaged neighborhoods reduces the likelihood of having a usual source of care and of obtaining recommended preventive services, while it increases the likelihood of having unmet medical need. These associations are not explained by the supply of health care providers. Furthermore, though controlling for individual-level characteristics reduces the association between neighborhood disadvantage and access to health care, a significant association remains. This suggests that when individuals who are disadvantaged are concentrated into specific areas, disadvantage becomes an "emergent characteristic " of those areas that predicts the ability of residents to obtain health care.