An Alternative Approach for Nonlinear Latent Variable Models

In the last decades there has been an increasing interest in nonlinear latent variable models. Since the seminal paper of Kenny and Judd, several methods have been proposed for dealing with these kinds of models. This article introduces an alternative approach. The methodology involves fitting some third-order moments in addition to the means and covariances. This article discusses how the model equations can be formulated and how several standard tests, like the model fit and Lagrange multiplier tests, can be performed. The new method compares favorably with the maximum likelihood method in several studies and can provide evidence of interaction that earlier approaches might ignore.     

Bias Reduction in Quasi-Experiments With Little Selection Theory but Many Covariates

Abstract: In observational studies, selection bias will be completely removed only if the selection mechanism is ignorable, namely, all confounders of treatment selection and potential outcomes are reliably measured. Ideally, well-grounded substantive theories about the selection process and outcome-generating model are used to generate the sample of covariates. However, covariate selection is more heuristic in actual practice. Using two empirical data sets in a simulation study, we investigate four research questions about bias reduction when the selection mechanism is not known but many covariates are measured: (1) How important is the conceptual heterogeneity of the covariate domains in the data set? (2) How important is the number of covariates assessing each domain? (3) What are the joint effects of this conceptual heterogeneity and of the number of covariates per domain? (4) What happens to bias reduction when the set of covariates is deliberately impoverished by removing the covariates most responsible for selection bias, thus ensuring a slightly smaller but still heterogeneous set of covariates? The results indicate: (1) increasingly more bias is reduced as the number of covariate domains and the number of covariates per domain increase, though the rate of bias reduction is diminishing in each case; (2) sampling covariates from multiple heterogeneous covariate domains is more important than choosing many measures from fewer domains; (3) the most heterogeneous set of covariate domains removes almost all of the selection bias when at least five covariates are assessed in each domain; and (4) omitting the most crucial covariates generally replicates the pattern of results due to the number of domains and the number of covariates per domain, but the amount of bias reduction is less than when all variables are included and will surely not satisfy all consumers of causal research.     

具有部分依从现象的临床试验中因果效应的上下界估计

在随机处理——对照的临床试验中,除出现完全依从和完全不依从的现象外,还会出现部分依从的现象,即患者只服用部分药品。在仅出现完全依从和不依从情况时,Balke and Pearl利用线性规划的方法获得了ACE估计量的上下界,对于部分依从的情况,是将这些数据全部并入完全依从的数据,这样处理的合理性没有论述。同时,利用他们所提供的方法,有时会出现下界为负数,显然,这样的下界没什么实际意义。本文根据Angrist,Imbebns＆Rubin讨论工具变量时所提出一些假设条件,导出了在部分依从情况下,计算ACE估计量的上下界的方法,并证明了其下界一定是非负的。     

Meta‐Analytic Methodology and Inferences About the Efficacy of Formative Assessment

In a recent article published in EM:IP, Kingston and Nash report on the results of a meta‐analysis on the efficacy of formative assessment. They conclude that the average effect of formative assessment on student achievement is about .20 SD units. This would seem to dispel the myth that effects between .40 and .70 can be attributed to formative assessment. They also find that there is considerable variability in effect sizes across studies, and that only the content area in which the treatment is situated explains a significant proportion of study variability. However, there are issues in the meta‐analytic methodology employed by the authors that make their findings somewhat equivocal. This commentary focuses on four methodological concerns about the Kingston and Nash meta‐analysis: (1) the approach taken to select studies for inclusion, (2) the application of study inclusion criteria, (3) the extent to which the effect sizes being combined are biased, and (4) the relationship between effect size magnitude and characteristics of outcome measures. After examining these issues in the context of the Kingston and Nash review, it appears that considerable uncertainty remains about the effect that formative assessment practices have on student achievement.     

A Note on Cluster Effects in Latent Class Analysis

This article examines the effects of clustering in latent class analysis. A comprehensive simulation study is conducted, which begins by specifying a true multilevel latent class model with varying within- and between-cluster sample sizes, varying latent class proportions, and varying intraclass correlations. These models are then estimated under the assumption of a single-level latent class model. The outcomes of interest are measures of bias in the Bayesian Information Criterion (BIC) and the entropy R ² statistic relative to accounting for the multilevel structure of the data. The results indicate that the size of the intraclass correlation as well as between- and within-cluster sizes are the most prominent factors in determining the amount of bias in these outcome measures, with increasing intraclass correlations combined with small between-cluster sizes resulting in increased bias. Bias is particularly noticeable in the BIC. In addition, there is evidence that class separation interacts with the size of the intraclass correlations and cluster sizes in producing bias in these measures.     

A Comparison of Three-Step Approaches for Auxiliary Variables in Latent Class and Latent Profile Analysis

In this article, 3-step methods to include predictors and distal outcomes in commonly used mixture models are evaluated. Two Monte Carlo simulation studies were conducted to compare the pseudo class (PC), Vermunt’s (2010), and the Lanza, Tan, and Bray (LTB) 3-step approaches with respect to bias of parameter estimates in latent class analysis (LCA) and latent profile analysis (LPA) models with auxiliary variables. For coefficients of predictors of class membership, results indicated that Vermunt’s method yielded more accurate estimates for LCA and LPA compared to the PC method. With distal outcomes of latent classes and latent profiles, the LTB method produced the lowest relative bias of coefficient estimates and Type I error rates close to nominal levels.     

The Principle of the Nomograph in Education

ABSTRACT

Educational researchers frequently study the impact of treatments or interventions on educational outcomes. However, when observational or quasiexperimental data are used for such investigations, selection bias can adversely impact researchers’ abilities to make causal inferences about treatment effects. One way to deal with selection bias is to use propensity score methods. The authors introduce educational researchers to the general principles underlying propensity score methods, describe 2 practical applications of these methods, and discuss their limitations.     

我国农村居民教育支出倾向变化及其解释

农村居民的平均教育支出倾向和边际教育支出倾向呈倒"U"形是一个值得关注的问题。与城市相比,农村平均教育支出倾向正逐步趋同,边际教育支出倾向下降更快;与东部和中部相比,西部平均教育支出倾向波动最大;与其他阶层相比,最低收入阶层平均教育支出倾向最高,但近几年下降幅度也最大。进一步研究表明,教育支出消费与投资的双重属性不平衡及影响消费和投资的要素运动,是农村居民教育支出倾向变化的主要原因,弱势地区和弱势群体面临着更大的不确定性,更弱的风险承受能力,在面对就业率下降等外部冲击时,其支出倾向下降的幅度更大,因而必须给予特别的补偿性政策。