Why Tenth Graders Fail to Finish High School: A Dropout Typology Latent Class Analysis

A large percentage of the students who drop out of K–12 schools in the United States do so at the end of high school, at some point after grade 10. Yet little is known about the differences between types of students who drop out near the end of high school. The purpose of this study is to examine a typology of high school dropouts from a large nationally representative dataset (ELS:2002) using latent class analysis (LCA). We found three significantly different types of dropouts; Quiet, Jaded, and Involved. Based on this typology of three subgroups, we discuss implications for future dropout intervention research, policy, and practice.     

Conducting Confirmatory Latent Class Analysis Using Mplus

Latent class analysis (LCA) is an increasingly popular tool that researchers can use to identify latent groups in the population underlying a sample of responses to categorical observed variables. LCA is most commonly used in an exploratory fashion whereby no parameters are specified a priori. Although this exploratory approach is reasonable when very little prior research has been conducted in the area under study, it can be very limiting when much is already known about the variables and population. Confirmatory latent class analysis (CLCA) provides researchers with a tool for modeling and testing specific hypotheses about response patterns in the observed variables. CLCA is based on placing specific constraints on the parameters to reflect these hypotheses. The popular and easy-to-use latent variable modeling software package Mplus can be used to conduct a variety of CLCA types using these parameter constraints. This article focuses on the basic principles underlying the use of CLCA, and the Mplus programming code necessary for carrying it out.     

Using Latent Class Analysis to Set Academic Performance Standards

This study investigated using latent class analysis to set performance standards for assessments comprised of multiple-choice and performance assessment items. Employing this procedure, it is possible to use a sample of student responses to accomplish four goals: (a) determine how well a specified latent structure fits student performance data; (b) determine which latent structure best represents the relationships in the data; (c) obtain estimates of item parameters for each latent class; and (d) identify to which class within that latent structure each response pattern most likely belongs. Comparisons with the Angoff and profile rating methods revealed that the approaches agreed with each other quite well, indicating that both empirical and test-based judgmental approaches may be used for setting performance standards for student achievement.     

A Conceptual Analysis of State Support for Higher Education: Appropriations Versus Need-Based Financial Aid

In this paper, we use economic concepts to examine the choice that states make between giving appropriations to public colleges or need-based financial aid to students. We begin by reviewing the economic justification for state support for higher education. Next, we introduce a simple economic model for comparing and contrasting appropriations and need-based aid for supporting higher education. We then provide a graphical depiction of the model and simulate the effects of each policy on access to higher education. We show that it is in the best interest of states to provide need-based aid and not appropriations. Finally, we conclude with a discussion of the factors that complicate the reallocation of state funding away from appropriations and towards need-based aid.     

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.     

PROC LCA: A SAS Procedure for Latent Class Analysis

Latent class analysis (LCA) is a statistical method used to identify a set of discrete, mutually exclusive latent classes of individuals based on their responses to a set of observed categorical variables. In multiple-group LCA, both the measurement part and structural part of the model can vary across groups, and measurement invariance across groups can be empirically tested. LCA with covariates extends the model to include predictors of class membership. In this article, we introduce PROC LCA, a new SAS procedure for conducting LCA, multiple-group LCA, and LCA with covariates. The procedure is demonstrated using data on alcohol use behavior in a national sample of high school seniors.     

日本私立高校的政府资助体系——21世纪以来的变迁与动因分析

日本现代私立高等教育已有100余年的历史,且在各方共同努力之下形成了相对较为健全的政府资助与扶持体系。从1970年政府资助制度化开始至今,政府资助受时代需求等各方因素的影响,发生了微妙的变化。考察政府资助的变化及其动因有助于加深对日本私立高等教育的认识和理解,从而反思我国民办教育的发展。     

高校学生资助影响因素的多水平分析

我国大学生资助体系的建立是对高等教育日益下降的可负担性的回应,也是为了进一步开放弱势群体的高等教育机会.以北京市54所高校16058名在校生调查数据为基础,使用多水平分析对高校学生资助的影响因素进行实证分析,研究结果发现目前重点高校学生获得资助的水平显著地高于其他学生,而且就读于重点高校显著地提高了个人获得资助的可能性.此外,女性、贫困家庭子女、父亲教育程度较高学生和学生党员获得资助的水平也较高.大学高年级学生、高考成绩较高学生和理工科学生获得的资助也显著地高于其他学生.研究结论为调整我国高校学生资助政策提供了实证依据.     

Using Class Climate Scales to Evaluate Gifted Inservice Programs

This study represents a novel approach to evaluating the effectiveness of an inservice program designed to help teachers enrich classroom instruction to make it more appropriate for gifted students. Results indicate that the number of inservice courses taken by teachers over a two and one‐half year period was associated with increased emphasis in their classes on higher‐level thinking skills and illustrates the use of class climate scales as a tool for program evaluation.     

Responsibilities of Special Educators in Rural Schools: A Latent Class Analysis

Despite the importance of special educators in the current era of educational reform, there is no overarching portrait of special educators' roles, especially in rural schools where the provision of services is especially challenging. Using latent class analysis and data from a national sample of rural school districts, we identified four distinct types of special educators and related their prevalence to key policy variables. Our findings provide a way of understanding the use of special educators in rural schools and current needs in special educator training. They also suggest means of studying special educators' roles more generally.     

MIMIC Latent Class Analysis Model for Alcoholic Diagnosis

The multiple indicators multiple causes (MIMIC) latent class analysis (LCA) model is an excellent classification method when researchers cannot find a "gold standard" to classify participants. The MIMIC-LCA model includes features of a typical LCA model and also introduces a new relation between the latent class and covariates. In other words, a logistic regression type of analysis between participants' categorical latent status and their background information is added. Detailed statistical setups of the MIMIC-LCA model and algorithmic procedures are derived. The model features, parameter estimations, and model selections for MIMIC-LCA models are also presented. Specifically, the MIMIC-LCA model is estimated by a generalized expectation-maximization algorithm under the maximum likelihood frameworks. A substantive application of the MIMIC-LCA model in diagnosing alcoholics and, in particular, examining potential risk factors for alcoholism is demonstrated.     

Financial Aid and Attainment Among Students in a State with Changing Demographics

Using event history analysis, this study investigated to what extent differentiated forms of aid affected the educational attainment of various student populations with particular interest on the Latinos/as within this emerging settlement state: Indiana. Findings suggest that the effects of aid are moderated by race and ethnicity. State grants, primarily need-based, played a significant if not modest role in encouraging persistence among Latino students, more so than peers from other racial/ethnic groups. No significant direct relationship was found between federal grants and loans for Latino students with respect to graduation.     

From Comprehensive to Singular: A Latent Class Analysis of College Teaching Practices

While decades of research on college teaching has investigated several forms of classroom practices, much of this research approaches teaching as falling into mutually exclusive paradigms (e.g., active learning vs. lecturing). This paper enters inside the college classroom using external raters to understand patterns of pedagogical practices embedded in heterogeneous groups of courses. The study used quantitative observation and draws on data from a multi-institutional study of 587 courses across nine institutions to understand the patterns of teaching practices within courses. Latent class analyses demonstrated that there were five patterns of seven course practices that cluster around active learning, lecturing, and cognitively responsive practices: Comprehensive, Traditional Lecture, Active Lecture, Integrative Discussion, and Active Only.     

高校贫困大学生资助工作效果评析

本文在调查的基础上,分析了国家、学校、社会和学生个人在高校帮困工作中取得的成效,并探讨了帮困过程中显现的问题和相应对策.     

A Nonlinear Dynamic Latent Class Structural Equation Model

In this article, we propose a nonlinear dynamic latent class structural equation modeling (NDLC-SEM). It can be used to examine intra-individual processes of observed or latent variables. These processes are decomposed into parts which include individual- and time-specific components. Unobserved heterogeneity of the intra-individual processes are modeled via a latent Markov process that can be predicted by individual- and time-specific variables as random effects. We discuss examples of sub-models which are special cases of the more general NDLC-SEM framework. Furthermore, we provide empirical examples and illustrate how to estimate this model in a Bayesian framework. Finally, we discuss essential properties of the proposed framework, give recommendations for applications, and highlight some general problems in the estimation of parameters in comprehensive frameworks for intensive longitudinal data.     

A Survey of State Teacher-Competency Examination Programs

As compared to the 1987 "wall chart" issued by U.S. Secretary of Education William J. Bennett and to other recent reports on teacher testing, this article by Lehmann and Phillips provides considerably more detail on the nature of teacher testing. On the basis of a survey of all the states, the authors provide an analysis of what areas are covered in teacher tests, exactly which tests are used, and how the tests are used in both current and planned state teacher testing programs.     

Using State School Accountability Data to Evaluate Federal Programs: A Long Uphill Road

Evaluations of federal programs designed to improve student achievement generally depend on data gathered by the states for school accountability purposes, rather than data specifically designed for program evaluation. In addition, these data are available at the school level but not at the student level. This article first discusses issues related to the quality of school-level data collected as part of state accountability systems, including the reliability and validity of school-level test scores as a measure of the value added by schools to student learning. It then outlines various ways in which school-level data can be usefully analyzed and illustrates the challenges inherent in doing so, including the challenges of aggregating data across states to find an overall program effect. The final section discusses the implications of the arguments presented here for measuring changes in school performance and linking these effects to a specific program. Ultimately, our ability to measure changes in outcomes and link them back to the intervention depends on three factors: (a) identifying a set of activities attributable to the program, (b) measuring the quality of implementation of these activities, and (c) obtaining a valid and reliable measure of the desired outcome. The article makes it clear that none of these is easy to come by.