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.

Stability of Order in Solvent-Annealed Block Copolymer Thin Films

ABSTRACT: One way to produce high order in a block copolymer thin film is by solution casting a thin film and slowly evaporating the solvent in a sealed vessel. Such a solvent-annealing process is a versatile method to produce a highly ordered thin film of a block copolymer. However, the ordered structure of the film degrades over time when stored under ambient conditions. Remarkably, this aging process occurs in mesoscale thin films of polystyrene-polyisoprene triblock copolymer where the monolayer of vitrified 15 nm diameter polystyrene cylinders sink in a 20 nm thick film at 22 °C. The transformation is studied by atomic force microscopy (AFM). We describe the phenomena, characterize the aging process, and propose a semiquantitative model to explain the observations. The residual solvent effects are important but not the primary driving force for the aging process. The study may lead to effective avenue to improve order and make the morphology robust and possibly the solvent-annealing process more effective.