Identification of structural equation models with latent variables: A review of contributions by Bekker,Merckens, and Wansbeek

Identification, Equivalent Models, and Computer Algebra. Paul A. Bekker, Arjen Merckens, and Tom J. Wansbeek, New York: Academic Press, 1994, 211 pages, $59.95.     

Using structural equation modeling techniques to evaluate HIV risk models

Structural equation modeling (SEM) techniques were used to compare 5 methods of assessing HIV/AIDS sexual risk in a large prediction model. These were: (a) multiple measures; (b) a single latent factor; (c) modifying the computation of the dependent variables used in Methods 1 and 2 to weight sexual encounters by specific partner risk; (d) use of risk composites, obtained by multiplying number of sexual partners by number of occasions of unprotected sex; and (e) use of risk indexes that assign a number based on responses to general questions about risk behaviors. Data from 452 at‐risk women from a New England community were analyzed in 5 versions of an HIV/AIDS sexual risk prediction model. Models were compared in terms of SEM empirical fit indexes (x² [df], average absolute standardized residuals, and Comparative Fit Index); significant paths, explained variance, theoretical fit, and simplicity. Results indicate that: (a) multiple measures and latent factor models are preferable to all others by each of the standards of comparison, (b) in the composite dependent variable models, including information about the partners' number of partners provided little additional explained variance beyond knowing the number of occasions of unprotected sex, and (c) dependent measures that did not remain close to Centers for Disease Control criteria may not be adequately predicting HIV/AIDS sexual risk. Several recommendations are presented for selecting an appropriate conceptualization of HIV/AIDS sexual risk.     

Using structural equation modeling to fit models incorporating principal components

The aim of this article is to consider models incorporating principal components from the perspective of structural equation modeling. These models include the principal component analysis of patterned matrices, multiple analysis of variance based on principal components, and multigroup principal component analysis. We demonstrate that these models can be fit readily using the programs LISREL 8 and Mx. The models and certain extensions are discussed, and several illustrations are given.     

Latent growth curves within developmental structural equation models

This report uses structural equation modeling to combine traditional ideas from repeated-measures ANOVA with some traditional ideas from longitudinal factor analysis. A longitudinal model that includes correlations, variances, and means is described as a latent growth curve model (LGM). When merged with repeated-measures data, this technique permits the estimation of parameters representing both individual and group dynamics. The statistical basis of this model allows hypothesis testing of various developmental ideas, including models of alternative dynamic functions and models of the sources of individual differences in these functions. Aspects of these latent growth models are illustrated with a set of longitudinal WISC data from young children and by using the LISREL V computer program.     

A multilevel latent growth curve approach to predicting student proficiency

Value-added models and growth-based accountability aim to evaluate school??s performance based on student growth in learning. The current focus is on linking the results from value-added models to the ones from growth-based accountability systems including Adequate Yearly Progress decisions mandated by No Child Left Behind. We present a new statistical approach that extends the current value-added modeling possibilities and focuses on using latent longitudinal growth curves to estimate the probabilities of students reaching proficiency. The aim is to utilize time-series measures of student achievement scores to estimate latent growth curves and use them as predictors of a dichotomous outcome, such as proficiency or passing a high-stakes exam, within a single multilevel longitudinal model. We illustrated this method through analyzing a three-year data set of longitudinal achievement scores and California High School Exit Exam scores from a large urban school district. This latent variable growth logistic model is useful for (1) early identification of students at risk of failing or of those who are most in need; (2) a validation or/and adequacy of student growth over years with relation to distal outcome criteria; (3) evaluation of a longitudinal intervention study.     

An investigation of student teachers' study strategies with respect to certain variables

This study aimed to determine the study strategies of student teachers attending vocational education faculties and whether these habits are affected by certain variables (department, year of study, positive perception of university and lecturer, time spent studying). The study sample consisted of a total of 746 student teachers from 5 departments, 380 of whom were freshman students and 366 of whom were senior students. The results revealed that student teachers' study strategies were at a moderate level. Additionally, it was observed that study strategies scores were higher for freshman students than for senior students, for students with a positive perception of university than for those with a negative perception, and for students who studied more than 5 hr weekly than for those who studied 3 hr or less.     

Using propositions from clinical interviews as variables to compare student knowledge

During the past several years interest in using clinical interviews to assess students conceptual knowledge has increased. However, using clinical interviews when research questions require quantitative comparisons has been difficult. The central difficulty is to construct variables that can be used to quantify and statistically compare the results of interviews while maintaining exactly what student conceptions are associated with each variable. The purpose of this article is to present a technique for constructing variables that overcomes this difficulty. The technique involves the representation of each proposition a student uses in terms of a standard set of underlying predicates, concepts, and relationships among concepts. Each proposition represented in these standard terms can be used as a variable to quantitatively compare the knowledge of students as expressed in clinical interviews.     

Simultaneous study of individual and group patterns of latent longitudinal change using structural equation modeling

This article utilizes structural equation modeling for purposes of simultaneous study of individual and group latent change patterns on several longitudinally assessed variables. The approach is based on a special case of the comprehensive latent curve analysis by Meredith and Tisak (1990). Substantively interesting aspects of individual and group growth curves, as well as the interrelations among their patterns, are parameterized at the latent ability level. The method is illustrated on data from a two‐group study by Baltes, Dittmann‐Kohli, and Kliegl (1986).     

Local influence in structural equation models

Local‐influence diagnostics based on maximum likelihood (ML), generalized least squares (GLS), and unweighted least squares (ULS) fit functions are developed for structural equation models (SEMs). The influence of observations, components of observations, and variables is considered. The diagnostics are illustrated with an example data set, and comparisons are made with equivalent global measures of influence. The local influence of the data set on ML and GLS estimates is very similar, but it is much different from that of ULS. The local and global influence of observations is also very different. Although it is not possible to define a uniformly best measure of influence, the local‐influence diagnostics developed here are more versatile than global‐influence diagnostics in assessing an analysis with SEMs.     

利用学案促进学生自主学习和发展

课程改革是基础教育改革的重点，在新课程理念的指导下，特别强调教与学方法的转变，积极开展自主学习、自主发展的研究。如何将先进的教学理念转化为实际教学行为，调动学生的学习主动性？在教学实践中，我们通过认真研究教材大纲、教师教法、学生学法，针对教师、学生的实际情况和课时紧、容量大等问题，改变目前学生课堂上被动听讲、大量记笔记、学习效率低、学习积极性差和课业负担重的现状。因此，我们通过科学合理地设计学案并进行教学取得了明显的效果。[第一段]     

运用情感教学促进学生学习

在当今素质教育的新形势下,必须要有优质高效的英语教学,才能使英语教育焕发生命力.如何教育培养出素质高、专业精的人才,教育方法尤为重要.而作为"以人为本"的教育,情感教育是必不可少的,它是研究与激发学生内动力的重要方法.文章着重阐述了如何贯彻落实英语教育中的情感教育.     

Drug use and personality in adolescence and young adulthood: structural models with nonnormal variables

An introduction to structural modeling with nonnormal continuous variables is provided using the equations language of the micro-mainframe program EQS in the context of a longitudinal study of adolescent development that followed about 700 adolescents across an 8-year span into young adulthood. 2 models relating drug use and personality are developed to assess the influence of drug use on personality, and personality on drug use. A high level of self-acceptance is shown to have a small but significant inoculating effect against subsequent cannabis use in both early and late adolescence, while a small positive effect of cannabis use on subsequent self-acceptance occurs only during early adolescence. Substantial stability of self-derogation, cannabis use, and law abidance are demonstrated, with self-derogation during adolescence leading to increased law abidance during young adulthood.     

Effects of employing ridge regression in structural equation models

LISREL 8 invokes a ridge option when maximum likelihood or generalized least squares are used to estimate a structural equation model with a nonpositive definite covariance or correlation matrix. The implications of the ridge option for model fit statistics, parameter estimates, and standard errors are explored through the use of 2 examples. The results indicate that maximum likelihood estimates are quite stable with the ridge option, though fit statistics and standard errors vary considerably and therefore cannot be trusted. As a result of these findings, the application of the ridge method to structural equation models is not recommended.     

Modelling student migration to Karnataka for Higher Education using a partial least square structural equation model

With increasing student migration for Higher Education to Karnataka, it is important to understand the reasons which influence the migrants. Studies have indicated various push and pull factors for migration. This paper identifies career opportunities, value for education, brand equity, living conditions and others as pull factors of student migration using exploratory factor analysis. Further, the paper develops a structural equation model using a partial least square technique based on the factors explored. The research is based on primary data collected from students who migrated to Karnataka for Higher Education. The partial least square structural equations model shows that job opportunities and academic progression are the main factors influencing the decision to migrate to Karnataka.     

Nested structural equation models: Noncentrality and power of restriction test

This article is concerned with the difference in noncentrality parameters of nested structural equation models and their utility in evaluating statistical power associated with the pertinent restriction test. Based on the seminal work by Browne and Du Toit (1992), Steiger (1989, 1990), Steiger and Lind (1980), and Steiger, Shapiro, and Browne (1985), asymptotic confidence intervals for that difference are discussed. The intervals represent a useful adjunct to widely employed goodness‐of‐fit indexes and test statistics when assessing plausibility of constraints in nested models. The approach also permits estimating power of the test of validity of the nesting restrictions (cf. MacCallum, Browne, & Sugawara, 1996). It is illustrated on data from a 2‐group cognitive training study.     

Analyzing measurement models of latent variables through multilevel confirmatory factor analysis and hierarchical linear modeling approaches

Confirmatory factor analysis (CFA) is often used in the social sciences to estimate a measurement model in which multiple measurement items are hypothesized to assess a particular latent construct. This article presents the utility of multilevel CFA (MCFA; Muthén, 1991, 1994) and hierarchical linear modeling (HLM; Raudenbush, Rowan, & Kang, 1991) methods in testing measurement models in which the underlying attribute may vary as a function of various levels of observation. An illustrative example using a real dataset is provided in which an unconditional model specification and parameter estimates from the MCFA and HLM are shown. The article demonstrates the comparability of the two methods in estimating measurement parameters of interest (i.e., true variance at levels the measures are used and measurement errors).     

Using student learning and development outcomes to evaluate a first-year undergraduate group video project

Students in an interdisciplinary undergraduate introductory course were required to complete a group video project focused on nutrition and healthy eating. A mixed-methods approach to data collection involved observing and rating video footage of group work sessions and individual and focus group interviews. These data were analyzed and used to evaluate the effectiveness of the assignment in light of two student learning outcomes and two student development outcomes at the University of Minnesota. Positive results support the continued inclusion of the project within the course, and recommend the assignment to other programs as a viable means of promoting both content learning and affective behavioral objectives.