Notes on selective influence, probabilistic causality, and probabilistic dimensionality

The paper provides conceptual clarifications for the issues related to the dependence of jointly distributed systems of random entities on external factors. This includes the theory of selective influence as proposed in Dzhafarov [(2003a). Selective influence through conditional independence. Psychometrika, 68, 7-26] and generalized versions of the notions of probabilistic causality [Suppes, P., & Zanotti, M. (1981). When are probabilistic explanations possible? Synthese, 48, 191-199] and dimensionality in the latent variable models [Levine, M. V. (2003). Dimension in latent variable models. Journal of Mathematical Psychology, 47, 450-466]. One of the basic observations is that any system of random entities whose joint distribution depends on a factor set can be represented by functions of two arguments: a single factor-independent source of randomness and the factor set itself. In the case of random variables (i.e., real-valued random entities endowed with Borel sigma-algebras) the single source of randomness can be chosen to be any random variable with a continuous distribution (e.g., uniformly distributed between 0 and 1).     

Maximum Likelihood Methods in Treating Outliers and Symmetrically Heavy-Tailed Distributions for Nonlinear Structural Equation Models with Missing Data

By means of more than a dozen user friendly packages, structural equation models (SEMs) are widely used in behavioral, education, social, and psychological research. As the underlying theory and methods in these packages are vulnerable to outliers and distributions with longer-than-normal tails, a fundamental problem in the field is the development of robust methods to reduce the influence of outliers and the distributional deviation in the analysis. In this paper we develop a maximum likelihood (ML) approach that is robust to outliers and symmetrically heavy-tailed distributions for analyzing nonlinear SEMs with ignorable missing data. The analytic strategy is to incorporate a general class of distributions into the latent variables and the error measurements in the measurement and structural equations. A Monte Carlo EM (MCEM) algorithm is constructed to obtain the ML estimates, and a path sampling procedure is implemented to compute the observed-data log-likelihood and then the Bayesian information criterion for model comparison. The proposed methodologies are illustrated with simulation studies and an example. The research described herein was fully supported by a grant (CUHK 4243/03H) from the Rearch Grants Council of the Hong Kong Special Administration Region. The authors are thankful to the Editor, the Associate Editor, and anonymous reviewers for valuable comments which improve the paper significantly, and are grateful to ICPSR and the relevant funding agency for allowing the use of their data. Requests for reprints should be sent to S. Y. Lee, Department of Statistics, The Chinese University of Hong Kong, Shatin, N. T., Hong Kong.     

A Bayesian analysis of finite mixtures in the LISREL model

In this paper, we propose a Bayesian framework for estimating finite mixtures of the LISREL model. The basic idea in our analysis is to augment the observed data of the manifest variables with the latent variables and the allocation variables. The Gibbs sampler is implemented to obtain the Bayesian solution. Other associated statistical inferences, such as the direct estimation of the latent variables, establishment of a goodness-of-fit assessment for a posited model, Bayesian classification, residual and outlier analyses, are discussed. The methodology is illustrated with a simulation study and a real example.This research was supported by a Hong Kong UGC Earmarked grant CUHK 4026/97H. The authors are indebted to the Editor, the Associate Editor, and three anonymous reviewers for constructive comments in improving the paper, and also to ICPSR and the relevant funding agency for allowing the use of the data. The assistance of Michael K.H. Leung and Esther L.S. Tam is gratefully acknowledged.     

Prior Divergence: Do Researchers and Participants Share the Same Prior Probability Distributions?

Do participants bring their own priors to an experiment? If so, do they share the same priors as the researchers who design the experiment? In this article, we examine the extent to which self‐generated priors conform to experimenters’ expectations by explicitly asking participants to indicate their own priors in estimating the probability of a variety of events. We find in Study 1 that despite being instructed to follow a uniform distribution, participants appear to have used their own priors, which deviated from the given instructions. Using subjects’ own priors allows us to account better for their responses rather than merely to test the accuracy of their estimates. Implications for the study of judgment and decision making are discussed.     

Computational Evidence That Frequency Trajectory Theory Does Not Oppose But Emerges From Age‐of‐Acquisition Theory

According to the age‐of‐acquisition hypothesis, words acquired early in life are processed faster and more accurately than words acquired later. Connectionist models have begun to explore the influence of the age/order of acquisition of items (and also their frequency of encounter). This study attempts to reconcile two different methodological and theoretical approaches (proposed by Lambon Ralph & Ehsan, 2006 and Zevin & Seidenberg, 2002 ) to age‐limited learning effects. The current simulations extend the findings reported by Zevin and Seidenberg (2002) that have shown that frequency trajectories (FTs) have limited and specific effects on word‐reading tasks. Using the methodological framework proposed by Lambon Ralph and Ehsan (2006) , which makes it possible to compare word‐reading and picture‐naming tasks in connectionist networks, we were able to show that FT has a considerable influence on age‐limited learning effects in a picture naming task. The findings show that when the input–output mappings are arbitrary (simulating picture naming tasks), the links formed by the network become entrenched as a result of early experience and that subsequent variations in frequency of exposure of the items have only a minor impact. In contrast, when the mappings between input‐output are quasi‐systematic or systematic (simulating word‐reading tasks), the training of new items was generalized and resulted in the suppression of age‐limited learning effects. At a theoretical level, we suggest that FT, which simultaneously takes account of time and the level of exposure across time, represents a more precise and modulated measure compared with the order of introduction of the items and may lead to innovative hypotheses in the field of age‐limited learning effects.     

Within-individual variability in self-concepts and personality states: Applying density distribution and situation-behavior approaches across cultures

Within-individual variability in self-concepts and everyday personality states and affects was investigated in two experience sampling studies using density distribution and situation-behavior approaches. In all seven cultures sampled, within-individual variability was substantial and self-concept and personality state variability exhibited moderate convergence. Variability in personality and affect states was moderately predicted by perceived need satisfaction in the situations. The density distribution and situation-behavior approaches were moderately convergent in identifying the most variable individuals, but the pattern of cultural differences differed in the two methods. Contrary to cultural psychology perspectives, cultural differences in within-individual variability did not consistently correspond to cultural differences in individualism–collectivism, dialecticism, or tightness.     

Ethical considerations and proposed guidelines for the use of radio frequency identification: Especially concerning its use for promoting public safety and national security

Radio Frequency Identification (RFID) is quickly growing in its applications. A variety of uses for the technology are beginning to be developed, including chips which can be used in identification cards, in individual items, and for human applications, allowing a chip to be embedded under the skin. Such chips could provide numerous benefits ranging from day-to-day convenience to the increased ability of the federal government to adequately ensure the safety of its citizens. However, there are also valid concerns about the potential of this technology to infringe on privacy, creating fears of a surveillance society. These are concerns that must be addressed quickly, with sensitivity to individual interests and societal welfare, allowing humanity to reap the benefits of convenience and safety without paying an unacceptable price in the loss of privacy. An earlier version of this paper was presented at the 2005 conference, Ethics and Social Responsibility in Engineering and Technology, Linking Workplace Ethics and Education, co-hosted by Gonzaga University and Loyola Marymount University, Los Angeles, CA, USA, 9–10 June 2005.     

On the inhomogeneity of a test compounded of two Rasch homogeneous subscales

Van den Wollenberg stated a theorem specifying the conditions for a test, which is composed of two Rasch homogeneous subscales, as also behaving Rasch homogeneously. In our note, simple numerical counterexamples are given for which this theorem fails.While preparing the final version of this note, Ilse Rop passed away. The first author therefore takes sole responsibility for this final version.     

MMPI中国常模中量表8及量表9K校正系数的探讨

本文针对MMPI中国常模中各临床量表一般不作K分校正的建议作了实证研究。其分析了912名被试的K、Sc及Ma量表数据资料,其中有345名精神分裂症患者,111名躁狂症患者以及456名正常被试。利用半和方区分比率法和频数分析法进行分析的结果表明,为了使MMPI在辅助诊断心理失常者时达到较好的效果,①量表8原始分数需加1K校正分数;②量表9原始分数以0.2K分数进行校正较不作校正为好。由此本研究建议,在应用MMPI时应采用加K分的中国常模。     

Testing for Granger Causality in the Frequency Domain: A Phase Resampling Method

This article introduces phase resampling, an existing but rarely used surrogate data method for making statistical inferences of Granger causality in frequency domain time series analysis. Granger causality testing is essential for establishing causal relations among variables in multivariate dynamic processes. However, testing for Granger causality in the frequency domain is challenging due to the nonlinear relation between frequency domain measures (e.g., partial directed coherence, generalized partial directed coherence) and time domain data. Through a simulation study, we demonstrate that phase resampling is a general and robust method for making statistical inferences even with short time series. With Gaussian data, phase resampling yields satisfactory type I and type II error rates in all but one condition we examine: when a small effect size is combined with an insufficient number of data points. Violations of normality lead to slightly higher error rates but are mostly within acceptable ranges. We illustrate the utility of phase resampling with two empirical examples involving multivariate electroencephalography (EEG) and skin conductance data.