词素的结构对称效应:结构对称汉语成语认知特点的进一步研究

运用词素启动作业,进一步考察了结构对称性汉语成语的识别过程.结果发现(1)无论是用成语的前二个字还是后二个字启动,在成语识别过程中都存在明显的词素结构对称效应,即结构对称成语中的词素启动效应明显大于非对称成语中的词素启动效应.(2)成语的结构对称性、熟悉度和启动词素位置之间存在显著的三阶相互作用.简单效应的分析表明无论成语前二个字还是后二个字启动,词素的结构对称效应均只在低熟悉度成语的识别过程中显著;在前二个字启动时,非对称成语的识别还存在熟悉度效应.     

A multitrait-multimethod model with minimal assumptions

A new model of confirmatory factor analysis (CFA) for multitrait-multimethod (MTMM) data sets is presented. It is shown that this model can be defined by only three assumptions in the framework of classical psychometric test theory (CTT). All other properties of the model, particularly the uncorrelated-ness of the trait with the method factors are logical consequences of the definition of the model. In the model proposed there are as many trait factors as different traits considered, but the number of method factors is one fewer than the number of methods included in an MTMM study. The covariance structure implied by this model is derived, and it is shown that this model is identified even under conditions under which other CFA-MTMM models are not. The model is illustrated by two empirical applications. Furthermore, its advantages and limitations are discussed with respect to previously developed CFA models for MTMM data.     

Robust mean and covariance structure analysis through iteratively reweighted least squares

Robust schemes in regression are adapted to mean and covariance structure analysis, providing an iteratively reweighted least squares approach to robust structural equation modeling. Each case is properly weighted according to its distance, based on first and second order moments, from the structural model. A simple weighting function is adopted because of its flexibility with changing dimensions. The weight matrix is obtained from an adaptive way of using residuals. Test statistic and standard error estimators are given, based on iteratively reweighted least squares. The method reduces to a standard distribution-free methodology if all cases are equally weighted. Examples demonstrate the value of the robust procedure.The authors acknowledge the constructive comments of three referees and the Editor that lead to an improved version of the paper. This work was supported by National Institute on Drug Abuse Grants DA01070 and DA00017 and by the University of North Texas Faculty Research Grant Program.     

Theology, Science, and Postmodernism: Responding to Stanley Grenz

This article responds to Stanley J. Grenz's Templeton Lecture, “Why Do Theologians Need to Be Scientists?” published in the June 2000 issue of Zygon (Grenz 2000). In the first part I outline my reasons for finding the kind of theological reflections in which Grenz engages worthy of attention by noting my disagreement with the view that a sufficient response to theological issues can be formulated on the basis of an examination of our biological nature. I assert, in that connection, the autonomy of reason as a way of investigating and understanding the world. In the second part I respond directly to Grenz by explaining my disagreement with the postmodern critique of science upon which he relies and his adherence to Christian eschatology as an answer to the conundrums into which, he posits, we are drawn as a result of that critique. I note that I agree with Grenz, however, that the activity of valuing is necessarily a forward‐looking Godlike endeavor that is not derivable from science. In the third part I suggest that we must be open to the investigation of the possible existence of an objective realm of value and that, in any case, rejection of the postmodern critique of science in many cases pro‐vides a sound basis for the disciplined resolution of factual questions that frequently lie at the base of disagreements about values.     

J. Wentzel van Huyssteen: Refiguring Rationality in the Postmodern Age

In his three books J. Wentzel van Huyssteen develops a complex and helpful notion of rationality, avoiding the extremes of foundationalism and postmodern relativism and deconstruction. Drawing from several postmodern philosophers of science and evolutionary epistemologists who seek to devise a usable notion of rationality, he weaves together a view that allows for a genuine duet betweenscience and theology. In the process he challenges much contemporary nonfoundationalist theology as well as the philosophical naïveté of some cosmologists and sociobiologists.     

Analysing multisource feedback with multilevel structural equation models: Pitfalls and recommendations from a simulation study

When multisource feedback instruments, for example, 360-degree feedback tools, are validated, multilevel structural equation models are the method of choice to quantify the amount of reliability as well as convergent and discriminant validity. A non-standard multilevel structural equation model that incorporates self-ratings (level-2 variables) and others’ ratings from different additional perspectives (level-1 variables), for example, peers and subordinates, has recently been presented. In a Monte Carlo simulation study, we determine the minimal required sample sizes for this model. Model parameters are accurately estimated even with the smallest simulated sample size of 100 self-ratings and two ratings of peers and of subordinates. The precise estimation of standard errors necessitates sample sizes of 400 self-ratings or at least four ratings of peers and subordinates. However, if sample sizes are smaller, mainly standard errors concerning common method factors are biased. Interestingly, there are trade-off effects between the sample sizes of self-ratings and others’ ratings in their effect on estimation bias. The degree of convergent and discriminant validity has no effect on the accuracy of model estimates. The χ² test statistic does not follow the expected distribution. Therefore, we suggest using a corrected level-specific standardized root mean square residual to analyse model fit and conclude with further recommendations for applied organizational research.     

Out-of-bag Prediction Error: A Cross Validation Index for Generalized Structured Component Analysis

Cross validation is a useful way of comparing predictive generalizability of theoretically plausible a priori models in structural equation modeling (SEM). A number of overall or local cross validation indices have been proposed for existing factor-based and component-based approaches to SEM, including covariance structure analysis and partial least squares path modeling. However, there is no such cross validation index available for generalized structured component analysis (GSCA) which is another component-based approach. We thus propose a cross validation index for GSCA, called Out-of-bag Prediction Error (OPE), which estimates the expected prediction error of a model over replications of so-called in-bag and out-of-bag samples constructed through the implementation of the bootstrap method. The calculation of this index is well-suited to the estimation procedure of GSCA, which uses the bootstrap method to obtain the standard errors or confidence intervals of parameter estimates. We empirically evaluate the performance of the proposed index through the analyses of both simulated and real data.     

A Limited Information Estimator for Dynamic Factor Models

Structural equation modeling (SEM) is an increasingly popular method for examining multivariate time series data. As in cross-sectional data analysis, structural misspecification of time series models is inevitable, and further complicated by the fact that errors occur in both the time series and measurement components of the model. In this article, we introduce a new limited information estimator and local fit diagnostic for dynamic factor models within the SEM framework. We demonstrate the implementation of this estimator and examine its performance under both correct and incorrect model specifications via a small simulation study. The estimates from this estimator are compared to those from the most common system-wide estimators and are found to be more robust to the structural misspecifications considered.     

Striking coincidences: How realists should reason about them

Many metaethicists assume that our normative judgments are both by and large true, and the product of causal forces. In other words, many metaethicists assume that the set of normative judgments that causal forces have led us to make largely coincides with the set of true normative judgments. How should we explain this coincidence? This is what Sharon Street (2006) calls the practical/theoretical puzzle. Some metaethicists can easily solve this puzzle, but not all of them can, Street argues; she takes the puzzle to constitute a specific challenge for normative realism. In this article I elucidate Street’s puzzle and outline possible solutions to it, framed in terms of a general strategy for reasoning about coincidences. I argue that the success of Street’s challenge crucially depends on how we set the ‘reference class’ of normative judgments that we could have endorsed, assuming realism. I conclude that while the practical/theoretical puzzle falls short of posing a general challenge for normative realism, it can be successful as a selective challenge for specific realist views.