Specifying the effects of religion on medical helpseeking: The case of infertility

Several recent studies have examined the connection between religion and medical service utilization. This relationship is complicated because religiosity may be associated with beliefs that either promote or hinder medical helpseeking. The current study uses structural equation modeling to examine the relationship between religion and fertility-related helpseeking using a probability sample of 2183 infertile women in the United States. We found that, although religiosity is not directly associated with helpseeking for infertility, it is indirectly associated through mediating variables that operate in opposing directions. More specifically, religiosity is associated with greater belief in the importance of motherhood, which in turn is associated with increased likelihood of helpseeking. Religiosity is also associated with greater ethical concerns about infertility treatment, which are associated with decreased likelihood of helpseeking. Additionally, the relationships are not linear throughout the helpseeking process. Thus, the influence of religiosity on infertility helpseeking is indirect and complex. These findings support the growing consensus that religiously-based behaviors and beliefs are associated with levels of health service utilization.

Evaluating Measurement Invariance with Censored Ordinal Data: A Monte Carlo Comparison of Alternative Model Estimators and Scales of Measurement

Evaluations of measurement invariance provide essential construct validity evidence. However, the quality of such evidence is partly dependent upon the validity of the resulting statistical conclusions. The presence of Type I or Type II errors can render measurement invariance conclusions meaningless. The purpose of this study was to determine the effects of categorization and censoring on the behavior of the chi-square/likelihood ratio test statistic and two alternative fit indices (CFI and RMSEA) under the context of evaluating measurement invariance. Monte Carlo simulation was used to examine Type I error and power rates for the (a) overall test statistic/fit indices, and (b) change in test statistic/fit indices. Data were generated according to a multiple-group single-factor CFA model across 40 conditions that varied by sample size, strength of item factor loadings, and categorization thresholds. Seven different combinations of model estimators (ML, Yuan-Bentler scaled ML, and WLSMV) and specified measurement scales (continuous, censored, and categorical) were used to analyze each of the simulation conditions. As hypothesized, non-normality increased Type I error rates for the continuous scale of measurement and did not affect error rates for the categorical scale of measurement. Maximum likelihood estimation combined with a categorical scale of measurement resulted in more correct statistical conclusions than the other analysis combinations. For the continuous and censored scales of measurement, the Yuan-Bentler scaled ML resulted in more correct conclusions than normal-theory ML. The censored measurement scale did not offer any advantages over the continuous measurement scale. Comparing across fit statistics and indices, the chi-square-based test statistics were preferred over the alternative fit indices, and ΔRMSEA was preferred over ΔCFI. Results from this study should be used to inform the modeling decisions of applied researchers. However, no single analysis combination can be recommended for all situations. Therefore, it is essential that researchers consider the context and purpose of their analyses.