An Evaluation of Latent Growth Models for Propensity Score Matched Groups

Because random assignment is not possible in observational studies, estimates of treatment effects might be biased due to selection on observable and unobservable variables. To strengthen causal inference in longitudinal observational studies of multiple treatments, we present 4 latent growth models for propensity score matched groups, and evaluate their performance with a Monte Carlo simulation study. We found that the 4 models performed similarly with respect to model fit, bias of parameter estimates, Type I error, and power to test the treatment effect. To demonstrate a multigroup latent growth model with dummy treatment indicators, we estimated the effect of students changing schools during elementary school years on their reading and mathematics achievement, using data from the Early Childhood Longitudinal Study Kindergarten Cohort.     

Modeling Unobserved Heterogeneity Using Latent Profile Analysis: A Monte Carlo Simulation

Latent profile analysis (LPA) has become a popular statistical method for modeling unobserved population heterogeneity in cross-sectionally sampled data, but very few empirical studies have examined the question of how well enumeration indexes accurately identify the correct number of latent profiles present. This Monte Carlo simulation study examined the ability of several classes of enumeration indexes to correctly identify the number of latent population profiles present under 3 different research design conditions: sample size, the number of observed variables used for LPA, and the separation distance among the latent profiles measured in Mahalanobis D units. Results showed that, for the homogeneous population (i.e., the population has k = 1 latent profile) conditions, many of the enumeration indexes used in LPA were able to correctly identify the single latent profile if variances and covariances were freely estimated. However, for a heterogeneous population (i.e., the population has k = 3 distinct latent profiles), the correct identification rate for the enumeration indexes in the k = 3 latent profile conditions was typically very low. These results are compared with the previous cross-sectional mixture modeling studies, and the limitations of this study, as well as future cross-sectional mixture modeling and enumeration index research possibilities, are discussed.     

Models and Strategies for Factor Mixture Analysis: An Example Concerning the Structure Underlying Psychological Disorders

The factor mixture model (FMM) uses a hybrid of both categorical and continuous latent variables. The FMM is a good model for the underlying structure of psychopathology because the use of both categorical and continuous latent variables allows the structure to be simultaneously categorical and dimensional. This is useful because both diagnostic class membership and the range of severity within and across diagnostic classes can be modeled concurrently. Although the conceptualization of the FMM has been explained in the literature, the use of the FMM is still not prevalent. One reason is that there is little research about how such models should be applied in practice and, once a well-fitting model is obtained, how it should be interpreted. In this article, the FMM is explored by studying a real data example on conduct disorder. By exploring this example, this article aims to explain the different formulations of the FMM, the various steps in building a FMM, and how to decide between an FMM and alternative models.     

Testing Measurement Invariance Across Groups in Longitudinal Data: Multigroup Second-Order Latent Growth Model

In latent growth modeling, measurement invariance across groups has received little attention. Considering that a group difference is commonly of interest in social science, a Monte Carlo study explored the performance of multigroup second-order latent growth modeling (MSLGM) in testing measurement invariance. True positive and false positive rates in detecting noninvariance across groups in addition to bias estimates of major MSLGM parameters were investigated. Simulation results support the suitability of MSLGM for measurement invariance testing when either forward or iterative likelihood ratio procedure is applied.     

Disentangling Shape from Level Effects in Person-Centered Analyses: An Illustration Based on University Teachers’ Multidimensional Profiles of Effectiveness

This study compares alternative ways of disentangling the effects of level (the tendency for a person to be high, medium, or low across all factors) and shape (the tendency for a person to have a distinct pattern of factors on which they are high, medium, or low) in profile analyses. This issue is particularly relevant to performance appraisals where it is often useful to identify specific strengths and weaknesses over and above a person global performance, but also to person-centered analyses more generally where the observation of qualitative (shape) differences between profiles is often used as justification for the added value of profiles. Substantively, this study illustrates these issues in the identification of profiles of teachers based on multidimensional students’ ratings of their effectiveness, using an archival data set of 31,951 class-average ratings based on the Students’ Evaluations of Educational Quality (SEEQ) instrument collected over a 13-year period. The results show the superiority of a factor mixture operationalization of teaching effectiveness in which a global effectiveness factor was used to control for unnecessary level effects in the profiles.     

Fitting Nonlinear Latent Growth Curve Models With Individually Varying Time Points

Individual growth trajectories of psychological phenomena are often theorized to be nonlinear. Additionally, individuals’ measurement schedules might be unique. In a structural equation framework, latent growth curve model (LGM) applications typically have either (a) modeled nonlinearity assuming some degree of balance in measurement schedules, or (b) accommodated truly individually varying time points, assuming linear growth. This article describes how to fit 4 popular nonlinear LGMs (polynomial, shape-factor, piecewise, and structured latent curve) with truly individually varying time points, via a definition variable approach. The extension is straightforward for certain nonlinear LGMs (e.g., polynomial and structured latent curve) but in the case of shape-factor LGMs requires a reexpression of the model, and in the case of piecewise LGMs requires introduction of a general framework for imparting piecewise structure, along with tools for its automation. All 4 nonlinear LGMs with individually varying time scores are demonstrated using an empirical example on infant weight, and software syntax is provided. The discussion highlights some advantages of modeling nonlinear growth within structural equation versus multilevel frameworks, when time scores individually vary.     

Mixed Mode Latent Class Analysis: An Examination of Fit Index Performance for Classification

This Monte Carlo study examines the performance of fit indices commonly used by applied researchers interested in finite mixture modeling for the purposes of classification. Conditions for the simulation study were selected to reflect conditions found in applied educational and psychological research. The factors included in the investigation were metric level of indicators, sample size, and class prevalence. All models contained a combination of categorical and continuous indicators. All categorical indicators were dichotomous, and continuous indicators were normally distributed. The fit indices examined were Akaike’s information criterion, Bayesian information criterion (BIC), sample size-adjusted Bayesian information criterion (SSBIC), integrated classification likelihood criterion with Bayesian-type approximation, and Lo–Mendell–Rubin likelihood ratio test. Overall, SSBIC tended to identify the correct solution with higher frequency than other indices. BIC tended to identify the correct solution with higher frequency than the other indices in models with more continuous than categorical indicators, or when rare classes were absent.     

弱肉强食:"和谈"背后的潜规则--《离婚》赏析

鲁迅的小说《离婚》围绕江南水乡平息纠纷的习俗“和谈”进行叙事,反映了1920年代中国的社会现实。“和谈”是乡土社会固有道德秩序“公正”的表征,其潜在规则却是“弱肉强食”,这种道德秩序的虚伪性和残酷性昭然若揭。     

我国幼儿园管理质量的现状、类别及其影响因素——基于潜在剖面分析的结果

幼儿园管理是影响幼儿园保教质量的重要因素。本研究采用分层抽样的方法,从我国5个省份选取了100所不同性质的城乡幼儿园,利用《走向优质——中国幼儿园教育质量评价标准》中的“管理引领”子标准对其管理质量进行研究。结果发现我国幼儿园管理质量整体处于中等偏下水平,其中“自评改进”项目得分最低。同时,潜在剖面分析的结果显示,我国幼儿园管理质量存在3种潜在类别,即“管理质量不适宜”型(10%)、“管理质量合格”型(72%)和“管理质量良好”型(18%)。进一步的线性回归和logistics回归分析结果表明,幼儿园的办园性质、所在地区和班级数量能够预测幼儿园的管理质量水平。与非城区幼儿园相比,城区幼儿园更不可能成为“管理质量不适宜”和“管理质量合格”型的幼儿园。与民办性质的幼儿园相比,公办性质的幼儿园成为“管理质量不适宜”型幼儿园的可能性更低。今后应加强对幼儿园管理质量的研究,持续改进幼儿园管理质量,重点关注和支持可能成为“管理质量不适宜”型的幼儿园。     

大学公共性的实现在于公共资源的优化配置

在我国经济社会和高等教育快速发展和转型之际,大学的公共性问题日益受到人们的高度关注。大学作为公共教育场所,应该以实现公共利益为目标,然而由于以财政投入为主的公共资源的短缺、公共权力滥用、追求规模忽视质量的发展模式、传统公私观中公共理念的缺乏等原因,高等教育机会平等和教育质量受到损害,大学公共性形成潜在危机。作为一个资源依赖型的组织,大学优化公共资源配置有利于其公共性的实现和提升。