Modeling behavioral recovery following lesion induction in the rat dentate gyrus

Unilateral entorhinal lesions have enjoyed immense popularity as a model of recovery from damage. In part, the popularity has been supported the laminar organization of the hippocampal formation, which allows for the dissection of the contribution of individual afferent pathways to the recovery process. The commissural/associational pathway is of particular interest, since electrophysiological and gross anatomical data, although limited, have correlated sprouting in this pathway with behavioral recovery. Unfortunately, information relating recovery to synaptic structure is lacking. Addressing this issue, two analyses were conducted. Initially, a quantitative review of the literature reporting behavioral recovery following this type of lesion was conducted using meta-analytic techniques. Using this detailed information across decades of research, multiple linear regression analysis was conducted to address whether the morphological correlates of recovery could predict behavioral recovery. This resulted in an equation relating morphology and recovery that stood up well to several diagnostic tests. Moreover, this model suggests that synapse structure (in particular, synapse size and curvature, as well as terminal compartmentalization and the density of multi-synaptic terminals) holds a greater potential to predict behavioral recovery than increases in synapse number, which is typically seen as the optimal anatomical measure of recovery. This initial attempt to identify, quantify, and validate a model of lesion recovery is an important initial step in understanding how synaptic morphology may help mediate recovery of function.     

Power and sample size calculations for multivariate linear models with random explanatory variables

This article considers the problem of power and sample size calculations for normal outcomes within the framework of multivariate linear models. The emphasis is placed on the practical situation that not only the values of response variables for each subject are just available after the observations are made, but also the levels of explanatory variables cannot be predetermined before data collection. Using analytic justification, it is shown that the proposed methods extend the existing approaches to accommodate the extra variability and arbitrary configurations of the explanatory variables. The major modification involves the noncentrality parameters associated with the F approximations to the transformations of Wilks likelihood ratio, Pillai trace and Hotelling-Lawley trace statistics. A treatment of multivariate analysis of covariance models is employed to demonstrate the distinct features of the proposed extension. Monte Carlo simulation studies are conducted to assess the accuracy using a child’s intellectual development model. The results update and expand upon current work in the literature.The author wishes to thank the associate editor and the referees for comments which improve the paper considerably. This research was partially supported by a grant from the Natural Science Council of Taiwan.     

Testing indirect effect with a complete or incomplete dichotomous mediator

Past methodological research on mediation analysis mainly focused on situations where all variables were complete and continuous. When issues of categorical data occur combined with missing data, more methodological considerations are involved. Specifically, appropriate decisions need to be made on estimation methods of the indirect effects and on confidence intervals for testing the indirect effects with accommodations of missing data. We compare strategies that address these issues based on a model with a dichotomous mediator, aiming to provide guidelines for researchers facing such challenges in practice.     

Properties of the maximum likelihood solution in factor analysis regression

Algebraic properties of the normal theory maximum likelihood solution in factor analysis regression are investigated. Two commonly employed measures of the within sample predictive accuracy of the factor analysis regression function are considered: the variance of the regression residuals and the squared correlation coefficient between the criterion variable and the regression function. It is shown that this within sample residual variance and within sample squared correlation may be obtained directly from the factor loading and unique variance estimates, without use of the original observations or the sample covariance matrix.     

Considerations in psychometric modeling of response time

Semiparametric models express a set of distributions of event times in terms of (a) a single parameter which varies across distributions and (b) a single function which does not vary across distributions and which has an unspecified form. These models appear to be attractive alternatives to parametric models of response times in psychometrics. However, our use of such models may require incorporating additional functions which do not vary across distributions and may require expressing the models in terms of the joint distribution of response class and response time.     

Issues associated with repeated neuropsychological assessments

Distinguishing practice effects from other factors in repeated neuropsychological assessments are discussed in the context of research studies and clinical/forensic assessments. Potential methodological procedures for reducing the impact of practice effects in research settings are outlined. In contrast, the potential clinical utility and interpretation of practice effects in clinical assessments and forensic evaluations are highlighted.     

Multicategorical spline model for item response theory

Additional information contained in incorrect responses calls for a multicategorical rather than a binary analysis of multiple choice data. A nonparametric divided-by-total model for joint maximum likelihood estimation of probability-of-choice functions (for particular responses) and of latent ability is proposed. The model approximates probability functions by rational splines. Some illustrative examples of real test data analysis and the results of a Monte Carlo study are presented.The research in this paper was supported by the National Sciences and Engineering Research Council of Canada Grants OGP0105521 and APA 320 awarded to the first and the second author, respectively. The authors are indebted to R. Melzack and A. Baker for making available the data analyzed in this paper. We would also like to thank J. McKenna and B. Cont for their assistance in editing this paper.     

A Monte Carlo investigation of conjoint analysis index-of-fit: Goodness of fit,significance and power

Researchers in the field of conjoint analysis know the index-of-fit values worsen as the judgmental error of evaluation increases. This simulation study provides guidelines on the goodness of fit based on distribution of index-of-fit for different conjoint analysis designs. The study design included the following factors: number of profiles, number of attributes, algorithm used and judgmental model used. Critical values are provided for deciding the statistical significance of conjoint analysis results. Using these cumulative distributions, the power of the test used to reject the null hypothesis of random ranking is calculated. The test is found to be quite powerful except for the case of very small residual degrees of freedom.The authors thank the editor, the three reviewers and Ellen Foxman for helpful comments on the paper. Sanjay Mishra was a doctoral student at Washington State University at the time this research was completed. He is currently in the Department of Marketing at the University of Kansas.     

关于我国学生汉语阅读困难的研究

旨在探讨在我国学生的汉语学习中是否存在阅读困难问题。研究分别采用了目前国外较常使用的两种阅读困难操作定义和新近提出的截点法,对我国学生汉语学习中的阅读困难问题进行了考查。结果揭示出阅读困难在汉语学习者中同样存在,检出率分别为４．５５％和７．９６％．中国学生的阅读困难既表现在词汇上也表现在理解上;研究还发现同为阅读困难者男生和女生呈现出不同的特点。