Models Clarified: Responding to Langdon Gilkey

We respond to concerns raised by Langdon Gilkey. The discussion addresses the nature of theological thinking today, the question of truth within the situation of pluralism, the identity and difference between theological models and scientific models, and the proposed methods for testing theological models.     

Reducing ethnic prejudice: an evaluation of seven recommended principles for incorporation in public campaigns

The present study investigated the effects of seven principles drawn from cognitive models of ethnic prejudice, general information processing models and persuasion models, on levels of ethnic prejudice. It was hypothesized that exposure to cue cards incorporating these principles would result in lower levels of prejudice than cue cards which did not incorporate the principles. A total of 400 caucasian participants were randomly assigned to experimental conditions (where participants were exposed to a cue card incorporating or omitting each of the seven principles), or the control condition (with no cue card exposure), and completed a questionnaire measuring ethnic prejudice. Results revealed that the absence of the principles in the cards led in several cases to unwanted negative effects (higher levels of prejudice than the control group). Reasons for these findings and implications for launching poster campaigns to reduce ethnic prejudice are discussed. Copyright © 2003 John Wiley & Sons, Ltd.     

On growth curves and mixture models

The multilevel model of change and the latent growth model are flexible means to describe all sorts of population heterogeneity with respect to growth and development, including the presence of sub‐populations. The growth mixture model is a natural extension of these models. It comes at hand when information about sub‐populations is missing and researchers nevertheless want to retrieve developmental trajectories from sub‐populations. We argue that researchers have to make rather strong assumptions about the sub‐populations or latent trajectory classes in order to retrieve existing population differences. A simulated example is discussed, showing that a sample of repeated measures drawn from two sub‐populations easily leads to the mistaken inference of three sub‐populations, when assumptions are not met. The merits of methodological advises on this issue are discussed. It is concluded that growth mixture models should be used with understanding, and offer no free way to growth patterns in unknown sub‐populations. Copyright © 2006 John Wiley & Sons, Ltd.     

The crisis of psychoanalysis and psychoanalytical training: The suffering of the candidate on the long road towards qualifi cation1

The author assesses the impact of the so‐called ‘crisis of psychoanalysis’ on the training of candidates, and on those who accompany them through the course. Different causes of the most relevant symptom of the crisis, i.e. the diffi culty of fi nding patients for a four‐sessions‐weekly analysis, are considered. According to the author, analysts themselves must bear some of the responsibility for it. She draws attention to a number of interrelated phenomena, such as: trainees' tension in their encounters with potential analysands, due to awareness of their own needs as trainees; the necessity to accept very disturbed patients whose selection might arouse criticism from the training committee; analyses in which trainees seem to become patients' hostages because of ever‐present fears of interruption; the diffi cult construction of a psychoanalytic identity in trainees who also are in full‐time psychiatric practice; trainees' profound uncertainty about the future both of psychoanalysis in general and their own careers in particular. In agreement with Kernberg, the author stresses the importance of considering the ‘crisis of psychoanalysis’ as a phenomenon whose development may be infl uenced by the analysts themselves.     

Multilevel Model Prediction

Multilevel models are proven tools in social research for modeling complex, hierarchical systems. In multilevel modeling, statistical inference is based largely on quantification of random variables. This paper distinguishes among three types of random variables in multilevel modeling—model disturbances, random coefficients, and future response outcomes—and provides a unified procedure for predicting them. These predictors are best linear unbiased and are commonly known via the acronym BLUP; they are optimal in the sense of minimizing mean square error and are Bayesian under a diffuse prior. For parameter estimation purposes, a multilevel model can be written as a linear mixed-effects model. In this way, parameters of the many equations can be estimated simultaneously and hence efficiently. For prediction purposes, we show that it is more convenient to retain the multiple equation feature of multilevel models. In this way, the efficient BLUPs are easy to compute and retain their intuitively appealing recursive form. We also derive explicit equations for standard errors of these different types of predictors. Prediction in multilevel modeling is important in a wide range of applications. To demonstrate the applicability of our results, this paper discusses prediction in the context of a study of school effectiveness. This research was supported by a grant from the Graduate School at the University of Wisconsin at Madision and the National Science Foundation, Grant number SES-0436274. We are grateful to Norman Webb at Wisconsin Center for Education Research for making available the data used in the reported application.     

新型农村合作医疗补偿模式现状分析

新型农村合作医疗工作试点推广以来,各地都在探索适合本地实际的运行机制和模式。尝试在归纳我国新型农村合作医疗补偿模式的基础上,分析其发生发展的动因度机制,以供研究和实践者参考。     

Properties of and Algorithms for Fitting Three-Way Component Models with Offset Terms

Prior to a three-way component analysis of a three-way data set, it is customary to preprocess the data by centering and/or rescaling them. Harshman and Lundy (1984) considered that three-way data actually consist of a three-way model part, which in fact pertains to ratio scale measurements, as well as additive “offset” terms that turn the ratio scale measurements into interval scale measurements. They mentioned that such offset terms might be estimated by incorporating additional components in the model, but discarded this idea in favor of an approach to remove such terms from the model by means of centering. Then estimates for the three-way component model parameters are obtained by analyzing the centered data. In the present paper, the possibility of actually estimating the offset terms is taken up again. First, it is mentioned in which cases such offset terms can be estimated uniquely. Next, procedures are offered for estimating model parameters and offset parameters simultaneously, as well as successively (i.e., providing offset term estimates after the three-way model parameters have been estimated in the traditional way on the basis of the centered data). These procedures are provided for both the CANDECOMP/PARAFAC model and the Tucker3 model extended with offset terms. The successive and the simultaneous approaches for estimating model and offset parameters have been compared on the basis of simulated data. It was found that both procedures perform well when the fitted model captures at least all offset terms actually underlying the data. The simultaneous procedures performed slightly better than the successive procedures. If fewer offset terms are fitted than there are underlying the model, the results are considerably poorer, but in these cases the successive procedures performed better than the simultaneous ones. All in all, it can be concluded that the traditional approach for estimating model parameters can hardly be improved upon, and that offset terms can sufficiently well be estimated by the proposed successive approach, which is a simple extension of the traditional approach. The author is obliged to Jos M.F. ten Berge and Marieke Timmerman for helpful comments on an earlier version of this paper. The author is obliged to Iven van Mechelen for making available the data set used in Section 6.     

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).     

Expectations about the short-term functional role played by defensive behavior in interpersonal interactions

Based on the theory of interpersonal defense (Westerman, 1998, Westerman, 2005 and Westerman and Steen, 2005), an interpersonal conceptualization of defense processes, we conducted a set of studies to investigate (a) implicit lay beliefs (i.e., implicit beliefs widely held by people in our culture) about the effects of defensive behavior on short-term interaction events, and (b) individual differences in implicit beliefs about the costs and benefits of defensive behavior and their relation to behaving defensively. In these studies, participants imagined themselves in several interpersonal situations presented in a paper-and-pencil protocol and responded to questions about what they expected would happen after they behaved defensively or nondefensively. As predicted, results indicated that, according to participants’ implicit lay beliefs, defensive behavior as compared to nondefensive behavior makes short-term clear-cut feared occurrences less likely, reduces the likelihood of clear-cut wished-for short-term outcomes, and affects how these outcomes will be experienced when they do take place. Also, findings indicated that there was an inverse association between self-reports of behaving defensively and the extent to which participants appreciated the short-term costs of defensive behavior. Alternative models for explaining this association are considered, including the idea that expectations minimizing the costs of defensive behavior develop because they support previously established, emotionally charged interpersonal behavior patterns.     

Variability in affective change among aging men: Longitudinal findings from the VA Normative Aging Study

We examined change in both positive and negative affect over ages 45 to 97 among 1534 men (mean age = 69 years). Positive affect demonstrated a linear decline with age, while negative affect declined until approximately 70 years, and thereafter showed a gradual increase. Results indicated significant individual differences in rates of change for both positive and negative affect. We then examined whether personality, health, and work status might account for individual differences in levels and rates of change in affect. Higher extraversion was associated with higher levels of positive affect, but not with rate of change. Higher neuroticism predicted higher levels of negative affect, as well as an attenuated decline in middle-adulthood, and a steeper increase in later adulthood. Better health was associated with higher levels of positive affect and lower levels of negative, but was also found to predict less decline in negative affect. Work predicted lower levels of positive affect and higher levels of negative affect, as well as greater declines in positive affect. These findings indicate that individuals differ in the manner in which they change in affect over time.