Constrained maximum likelihood estimation of two-level covariance structure model via EM type algorithms

In this paper, the constrained maximum likelihood estimation of a two-level covariance structure model with unbalanced designs is considered. The two-level model is reformulated as a single-level model by treating the group level latent random vectors as hypothetical missing-data. Then, the popular EM algorithm is extended to obtain the constrained maximum likelihood estimates. For general nonlinear constraints, the multiplier method is used at theM-step to find the constrained minimum of the conditional expectation. An accelerated EM gradient procedure is derived to handle linear constraints. The empirical performance of the proposed EM type algorithms is illustrated by some artifical and real examples.This research was supported by a Hong Kong UCG Earmarked Grant, CUHK 4026/97H. We are greatly indebted to D.E. Morisky and J.A. Stein for the use of their AIDS data in our example. We also thank the Editor, two anonymous reviewers, W.Y. Poon and H.T. Zhu for constructive suggestions and comments in improving the paper. The assistance of Michael K.H. Leung and Esther L.S. Tam is gratefully acknowledged.     

Philosophy,Logic, Science,History

Analytic philosophy is sometimes said to have particularly close connections to logic and to science, and no particularly interesting or close relation to its own history. It is argued here that although the connections to logic and science have been important in the development of analytic philosophy, these connections do not come close to characterizing the nature of analytic philosophy, either as a body of doctrines or as a philosophical method. We will do better to understand analytic philosophy—and its relationship to continental philosophy—if we see it as a historically constructed collection of texts, which define its key problems and concerns. It is true, however, that analytic philosophy has paid little attention to the history of the subject. This is both its strength—since it allows for a distinctive kind of creativity—and its weakness—since ignoring history can encourage a philosophical variety of “normal science.”     

Age and gender differences in adolescents' homework experiences

Extant data collected through the Experience Sampling Method were analyzed to describe adolescents' subjective experiences of homework. Analyses explored age and gender differences in the time adolescents spend doing homework, and the situational variations (location and companions) in adolescents' reported concentration, effort, interest, positive affect and stress while doing homework. Regarding age differences, middle school students reported more positive experiences when homework was done with companions and in locations other than home, whereas high school students reported more positive experiences when homework was done alone and at home. Regarding gender differences, girls, regardless of age, reported greater stress than boys when doing homework alone, and lower stress when doing homework with friends. High school girls reported lower interest than middle school boys when doing homework alone. Findings provide an understanding of age and gender differences in adolescents' perceptions of homework, which might help educators and parents structure engaging homework environments.     

Asymptotic Standard Errors of Generalized Partial Credit Model True Score Equating Using Characteristic Curve Methods

In this study, the delta method was applied to estimate the standard errors of the true score equating when using the characteristic curve methods with the generalized partial credit model in test equating under the context of the common-item nonequivalent groups equating design. Simulation studies were further conducted to compare the performance of the delta method with that of the bootstrap method and the multiple imputation method. The results indicated that the standard errors produced by the delta method were very close to the criterion empirical standard errors as well as those yielded by the bootstrap method and the multiple imputation method under all the manipulated conditions.     

认知负荷与编码方式影响小学数学学业不良生的前瞻记忆及其成分

通过两个实验探讨认知负荷对小学数学学业不良生前瞻记忆及其成分的影响, 以及是否可以通过执行意向编码提高其前瞻记忆及其成分的成绩。实验1结果显示数学学业不良生前瞻记忆、前瞻成分及回溯成分正确率显著低于数学学优生, 高认知负荷条件前瞻记忆、前瞻成分及回溯成分的正确率显著低于低认知负荷条件; 实验2结果显示执行意向编码条件下, 数学学业不良生前瞻记忆成绩显著好于标准编码条件, 前瞻成分和回溯成分的正确率高于标准编码条件。结果表明数学学业不良生前瞻记忆及其成分表现差于数学学优生; 无论认知负荷高低, 执行意向编码均可通过提升前瞻成分和回溯成分改善数学学业不良生前瞻记忆表现。     

Client as expert: A Delphi poll of clients’ subjective experience of therapeutic alliance formation variables

This study extends prior research that identified client-derived variables believed to contribute to therapeutic alliance development. Forty-two clients participated in a three-round-modified Delphi poll in which the subjective importance of 74 client-derived, common alliance formation variables was rated using a six-point scale. Participants consensually identified 23 variables as highly important, five as moderately important and three as low in importance to the formation of a strong alliance. Variables related to validation and asking about parts of the client’s life other than the presenting concern were considered as most important by at least one-third of the sample. Limitations of the study are presented and implications for theory, measurement, practice and future research are briefly discussed.     

基于结构方程模型的多重中介效应分析

多重中介模型是指存在多个中介变量的模型。多重中介模型可以分析特定中介效应、总的中介效应和对比中介效应。指出了目前多重中介模型分析普遍存在的问题,包括分析不完整、使用Sobel检验带来的局限。建议通过增加辅助变量的方法进行完整的多重中介效应分析,使用偏差校正的Bootstrap方法进行中介检验。总结出一个多重中介SEM分析流程,并有示例和相应的MPLUS程序。随后展望了辅助变量和中介效应检验方法的发展方向。     

Proactive control of proactive interference using the method of loci

Proactive interferencebuilds up with exposure to multiple lists of similar items with a resulting reduction in recall. This study examined the effectiveness of using a proactive strategy of the method of loci to reduce proactive interference in a list recall paradigm of categorically similar words. While all participants reported using some form of strategy to recall list words, this study demonstrated that young adults were able to proactively use the method of loci after 25 min of instruction to reduce proactive interference as compared with other personal spontaneous strategies. The implications of this study are that top-down proactive strategies such as the method of loci can significantly reduce proactive interference, and that the use of image and sequence or location are especially useful in this regard.     

Testing autocorrelation and partial autocorrelation: Asymptotic methods versus resampling techniques

Autocorrelation and partial autocorrelation, which provide a mathematical tool to understand repeating patterns in time series data, are often used to facilitate the identification of model orders of time series models (e.g., moving average and autoregressive models). Asymptotic methods for testing autocorrelation and partial autocorrelation such as the 1/T approximation method and the Bartlett's formula method may fail in finite samples and are vulnerable to non-normality. Resampling techniques such as the moving block bootstrap and the surrogate data method are competitive alternatives. In this study, we use a Monte Carlo simulation study and a real data example to compare asymptotic methods with the aforementioned resampling techniques. For each resampling technique, we consider both the percentile method and the bias-corrected and accelerated method for interval construction. Simulation results show that the surrogate data method with percentile intervals yields better performance than the other methods. An R package pautocorr is used to carry out tests evaluated in this study.