Experimental analysis of academic performance in a classroom setting

We evaluated the effects of specific stimulus prompts on accuracy of academic performance with adolescents who displayed academic problems in a public special education classroom. We conducted analyses of instructional strategies within a multielement design with two students who had mild disabilities. Differential effects of the strategies on reading comprehension were not apparent on demand (independent-level) tasks but were apparent on high-demand (instructional-level) tasks. These results suggested that the application of experimental analysis methodologies to instructional variables may facilitate the identification of stimulus prompts associated with accurate academic performance.     

Multilevel covariance structure analysis of sibling substance use and intrafamily conflict

Conventional covariance structure analysis, such as factor analysis, is often applied to data that are obtained in a hierarchical fashion, such as siblings observed within families. A more appropriate specification is demonstrated which explicitly models the within-level and between-level covariance matrices of sibling substance use and intrafamily conflict. Participants were 267 target adolescents (mean age=13.11 years) and 318 siblings (mean age=15.03 years). The level of homogeneity within sibling clusters, and heterogeneity among families, was sufficient to conduct a multilevel covariance structure analysis (MCA). Parental and family-level variables consisting of marital status, socioeconomic status, marital discord, parent use, and modeling of substances were used to explain heterogeneity among families. Marital discord predicted intrafamily conflict, and parent marital status and modeling of substances predicted sibling substance use. Advantages and uses of hierarchical designs and conventional covariance structure software for multilevel data are discussed.     

东莨菪碱所致记忆障碍的脑内突触机制

研究问题是东莨菪碱所致记忆障碍的脑内突触机制,在东莨菪碱所致记忆障碍模型上定量分析屯小鼠海马ＣＡ３区ＧｒａｙＩ突触界面结构参数的变化。     

Fitting multivariage normal finite mixtures subject to structural equation modeling

This paper is about fitting multivariate normal mixture distributions subject to structural equation modeling. The general model comprises common factor and structural regression models. The introduction of covariance and mean structure models reduces the number of parameters to be estimated in fitting the mixture and enables one to investigate a variety of substantive hypotheses concerning the differences between the components in the mixture. Within the general model, individual parameters can be subjected to equality, nonlinear and simple bounds constraints. Confidence intervals are based on the inverse of the Hessian and on the likelihood profile. Several illustrations are given and results of a simulation study concerning the confidence intervals are reported.     

Education and Understanding Structural Causes for Group Inequalities

Group inequalities in the United States are most often attributed to the characteristics of the individuals who belong to these groups; thinking about structural causes of group inequalities is rare. This paper reviews cognitive, cultural, and systemic reasons for this bias. The efficacy of education as a way to increase structural thinking was investigated in two studies of college students' causal thinking about group inequalities. Both studies involved a course on intergroup relations that covered structural sources of racial or ethnic inequalities. Results supported hypotheses that the course would increase structural thinking about racial or ethnic inequality, and that structural thinking would generalize to inequalities not explicitly covered in the course. Both course content and active learning pedagogy were related to structural thinking about inequalities. Active learning was also related to applying structural thinking to targets of change.     

The role of structural consistency between categories and attributes in hierarchical category learning

This study investigated how consistency between categories and attributes determines attribute selection in hierarchical category learning. Participants learned six categories for which number and color were equally relevant attributes, followed by a transfer task, to test which attribute was used. Before that, half of them learned embedding higher-level categories for which numbers were likely to be used. Orthogonal to this factor, the hierarchical structure was made explicit for half of them by category labels. The results showed that participants used numbers in the prior learning, but that the use of numbers was inhibited in the subsequent six-category learning task. However, this inhibitory effect was reduced when the hierarchical structure was explicit. The pattern of results suggests that attribute selection is determined by structural consistency between categories and attributes, not by a prior use of an attribute.     

On structural models for analyzing change

The recommendation to base the analysis of multi-wave data upon explicit models for change is advocated. Several univariate and multivariate models are described, which emerge from an interaction between the classical test theory and the structural equation modeling approach. The resulting structural models for analyzing change reflect in some of their parameters substantively interesting aspects of intra- and interindividual change in follow-up studies. The models are viewed as an alternative to an ANOVA-based analysis of longitudinal data, and are illustrated on data from a cognitive intervention study of old adults (Bakes et al , 1986). The approach presents a useful means of analyzing change over time, and is applicable for purposes of (latent) growth curve analysis when analysis of variance assumptions are violated (e.g., Schaie & Hertzog, 1982; Morrison, 1976).     

Classical test theory based and factor analytic structural models for analyzing change: A note on differences and similarities

This note is concerned with differences and similarities between structural models for analyzing change, which are conceptualized within two different modelling traditions: the one based on the classical test theory, and that within the factor-analytic approach. It is shown that these two possibilities lead to models for studying change, which are indistinguishable when using for data analytic purposes structural modeling programs, such as LISREL, EQS, COSAN, LISCOMP, RAMONA, EzPATH, SAS PROC CALIS. The reason for this data-analytic equivalence of the two conceptually different types of models is the confounding of their differences in the corresponding implied covariance matrix structures.     

Development of Adolescent Problem Behavior

The developmental model of adolescent antisocial behavior advanced by Patterson and colleagues (e.g., Patterson, Reid, & Dishion, 1992) appears to generalize the development of a diverse set of problem behaviors. Structural equation modeling methods were applied to 18-month longitudinal data from 523 adolescents. The problem behavior construct included substance use, antisocial behavior, academic failure, and risky sexual behavior. Families with high levels of conflict were less likely to have high levels of parent–child involvement. Such family conditions resulted in less adequate parental monitoring of adolescent behavior, making associations with deviant peers more likely. Poor parental monitoring and associations with deviant peers were strong predictors of engagement in problem behavior. These constructs accounted for 46% of the variance in problem behavior. Although association with deviant peers was the most proximal social influence on problem behavior, parental monitoring and family factors (conflict and involvement) were key parenting practices that influenced this developmental process.     

言语生成中内隐学习现象的研究

言语生成中的结构启动和语误现象,通常分别用瞬时激活和语音的约束范围来解释它们产生的机制。20世纪60年代末发端的内隐学习的研究,为深入理解言语生成机制提供了崭新的视角。言语生成的各种现象中,语言经验和学习机制发挥着重要作用,结构启动和语误更倾向于是一种内隐的序列学习。