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.

Contesting Sphere Boundaries Online: Private/Technical/Public Discourses in Polycystic Ovarian Syndrome Discussion Groups

The internet is fast becoming a means for people to obtain information, creating a unique forum for the intersection of the public, technical, and private spheres. To ground my research theoretically, I used Jürgen Habermas’s sphere theory. Habermas (1987) explains that the technical sphere colonizes the private sphere, which decreases democratic potential. In particular, the internet is a place for altering technical colonization of the private and public spheres. My research focuses on women’s health because it is a particularly useful case study for examining sphere tensions. Historically, the biomedical health establishment has been a powerful agent of colonization, resulting in detrimental effects for women and their health. The purpose of this study is to examine how the internet encourages expert and female patient deliberation, which empowers women to challenge the experts and, thus, make conversations between the private/technical spheres more democratic. I used PCOS (Polycystic Ovarian Syndrome) as a case to observe the changing sphere boundaries by studying the discourse that took place on multiple patient and doctor websites over a four-year period. Through my research, I found that the PCOS women challenge the biomedical model by appropriating medical language. By understanding the medical talk, the women are able to feel confident when discussing their health conditions with the doctor and with each other. The PCOS women also become lay-experts who have personal and medical experience with PCOS, reducing private sphere colonization. This case study exemplifies how female empowerment can influence expert culture, challenging our conventional understanding of democracy.