Structural equations and causation: six counterexamples

Hall [(2007), Philosophical Studies, 132, 109–136] offers a critique of structural equations accounts of actual causation, and then offers a new theory of his own. In this paper, I respond to Hall’s critique, and present some counterexamples to his new theory. These counterexamples are then diagnosed.

Working memory capacity — facets of a cognitive ability construct

Working memory capacity is differentiated theoretically along two dimensions: contents and functions. The resulting 3×3 matrix was operationalized by 23 tasks sampled from the literature. Data for these tasks from 128 participants were analyzed by exploratory and confirmatory factor analysis. Regarding the content facet, spatial working memory was clearly distinct from the other two content categories. A distinction between verbal and numerical working memory was not warranted. On the functional dimension the postulated categories of simultaneous storage and transformation and of coordination could not be separated. The third category was clearly separate from the first two functions. This factor could be interpreted to reflect a mixture of variance due to mental speed and to supervisory functions of the central executive.     

Structural equations and causation

Structural equations have become increasingly popular in recent years as tools for understanding causation. But standard structural equations approaches to causation face deep problems. The most philosophically interesting of these consists in their failure to incorporate a distinction between default states of an object or system, and deviations therefrom. Exploring this problem, and how to fix it, helps to illuminate the central role this distinction plays in our causal thinking.

人际责任归因与助人意愿的关系

人际责任归因研究是归因研究与责任推断相结合的一个新领域,它将归因理论应用于对他人失败行为的责任分析之中,为行为责任的推断提供了一个崭新的视角。本研究结合助人行为探讨人际责任归因与助人意愿之间的内在联系,204名教育硕士生和大学生被试参与了此项研究,并通过EQS建立了有关归因的控制性、情感反应、责任推断以及助人行为之间关系的结构方程模型。研究结果支持Weiner的人际责任归因理论假设,并且表明在归因与助人意愿关系模型中责任推断与情感反应是双向的而非单向的关系。     

Variables in Psychology: A Critique of Quantitative Psychology

Mind is hidden from direct observation; it can be studied only by observing behavior. Variables encode information about behaviors. There is no one-to-one correspondence between behaviors and mental events underlying the behaviors, however. In order to understand mind it would be necessary to understand exactly what information is represented in variables. This aim cannot be reached after variables are already encoded. Therefore, statistical data analysis can be very misleading in studies aimed at understanding mind that underlies behavior. In this article different kinds of information that can be represented in variables are described. It is shown how informational ambiguity of variables leads to problems of theoretically meaningful interpretation of the results of statistical data analysis procedures in terms of hidden mental processes. Reasons are provided why presence of dependence between variables does not imply causal relationship between events represented by variables and absence of dependence between variables cannot rule out the causal dependence of events represented by variables. It is concluded that variable-psychology has a very limited range of application for the development of a theory of mind—psychology.

Paul Meehl and the evolution of statistical methods in psychology

In his landmark 1978 paper, Paul Meehl delineated, with remarkable clarity, some fundamental challenges facing soft psychology as it attempts to test theory with data. In the quarter century that followed, Meehl’s views stimulated much debate and progress, while continually evolving to keep pace with that progress. This paper pays homage to Meehl’s prescience, and traces the impact of his ideas on the recent shift of emphasis away from hypothesis testing and toward confidence interval estimates of effect size.     

Personality and gang embeddedness

Gang membership can be transient or persistent, and most persons who participate in such groups at one point in their lives grow out of the lifestyle. The personality dynamics driving gang membership are poorly understood, although the five-factor model of personality has proved an effective way to understanding many types of antisocial behaviour. Using dichotomous self-nomination 152 remand and sentenced participants incarcerated within a general prison in London, UK indicated their gang embeddedness as youths, prior to custody, within prison, and as an intent following release. This enabled behavioural intention to be incorporated into the model as well as historical factors. Constructs derived from a variety of personality measures and constructs were used to predict overall reported gang embeddedness. These predictors reduced to two underlying dimensions: personal resilience and antisocial personality. Path analysis showed the antisocial personality dimension predicted previous convictions and degree of gang embeddedness, whereas resilience did not. The direct and indirect effects of the composite antisocial personality dimension explained 50% of the overall observed variance in gang embeddedness. We suggest that gang membership may reflect normal assortative processes within the members of such groups.     

A closer look at moral dilemmas: Latent dimensions of morality and the difference between trolley and footbridge dilemmas

Although a distinction between moral-personal and moral-impersonal dilemmas (Greene, Sommerville, Nystrom, Darley, & Cohen, 2001 Greene, J. D., Sommerville, R. B., Nystrom, L. E., Darley, J. M. and Cohen, J. D. 2001. An fMRI investigation of emotional engagement in moral judgement. Science, 293: 2105–2108. doi:10.1126/science.1062872.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]) has been widely accepted as an explanation for a difference between the trolley and footbridge dilemmas (Thomson, 1985 Thomson, J. J. 1985. “The trolley problem”. In Ethics: Problems and principles, Edited by: Fischer, J. M. and Ravizza, M. Fort Worth, TX: Harcourt Brace Jovanovich.  [Google Scholar]), its psychometric properties remain a mystery. In this study 219 participants completed 62 moral dilemma tasks used in Greene et al. (2001 Greene, J. D., Sommerville, R. B., Nystrom, L. E., Darley, J. M. and Cohen, J. D. 2001. An fMRI investigation of emotional engagement in moral judgement. Science, 293: 2105–2108. doi:10.1126/science.1062872.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]), and the correlation structure among the dilemmas was analysed through factor analysis and structural equation modelling. Findings suggest that, first, moral-personal dilemmas are composed of one factor, indicating that the assumption in Greene et al. (2001 Greene, J. D., Sommerville, R. B., Nystrom, L. E., Darley, J. M. and Cohen, J. D. 2001. An fMRI investigation of emotional engagement in moral judgement. Science, 293: 2105–2108. doi:10.1126/science.1062872.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]) was supported. Second, moral-impersonal dilemmas are explained by two factors that reflect procedural and consequential aspects of decision making. Third, the trolley and footbridge dilemmas fall under the same factor category; therefore the difference between the two dilemmas cannot be attributed to emotional involvement. Additionally, the results of the structural equation modelling suggest that they differ in the engagement of rational processing.     

Multivariate modelling and personality organization: A comparative study of the Defense Mechanism Test and linguistic expressions

The main aim of the study is to establish an empirical connection between perceptual defences as measured by the Defense Mechanism Test (DMT)–a projective percept-genetic method–and manifest linguistic expressions based on word pattern analyses. The subjects were 25 psychiatric patients with the diagnoses neurotic personality organization (NPO), borderline personality organization (BPO) and psychotic personality organization (PPO) in accordance with Kernberg's theory. A set of 130 DMT variables and 40 linguistic variables were analyzed by means of partial least squares (PLS) discriminant analysis separately and then pooled together. The overall hypothesis was that it would be possible to define the personality organization of the patients in terms of an amalgam of perceptual defences and word patterns, and that these two kinds of data would confirm each other. The result of the combined PLS analysis revealed a very good separation between the diagnostic groups as measured by the pooled variable sets. Among other things, it was shown that NPO patients are principally characterized by linguistic variables, whereas BPO and PPO patients are better defined by perceptual defences as measured by the DMT method.     

What Are We Doing When We Translate from Quantitative Models?

Although quantitative analysis (in which behavior principles are defined in terms of equations) has become common in basic behavior analysis, translational efforts often examine everyday events through the lens of narrative versions of laboratory-derived principles. This approach to translation, although useful, is incomplete because equations may convey concepts that are difficult to capture in words. To support this point, we provide a nontechnical introduction to selected aspects of quantitative analysis; consider some issues that translational investigators (and, potentially, practitioners) confront when attempting to translate from quantitative models; and discuss examples of relevant translational studies. We conclude that, where behavior-science translation is concerned, the quantitative features of quantitative models cannot be ignored without sacrificing conceptual precision, scientific and practical insights, and the capacity of the basic and applied wings of behavior analysis to communicate effectively.     

模型构建方法与结构方程建模──与张建平同志商讨

本文就心理所张建平同志的“一种新的统计方法和研究思路一结构方程建模述评”一文讨论了结构方程建模一般概念与ＬＩＳＲＥＬ模型的差异。并简要介绍了另一种结构方程建模方法一偏最小平方模型（ＰａｒｔｉａｌＬｅａｓｔＳｑｕａｒｅＭｏｄｅｌ,简称ＰＬＳ）。     

Heterogeneous factor analysis models: A bayesian approach

Multilevel factor analysis models are widely used in the social sciences to account for heterogeneity in mean structures. In this paper we extend previous work on multilevel models to account for general forms of heterogeneity in confirmatory factor analysis models. We specify various models of mean and covariance heterogeneity in confirmatory factor analysis and develop Markov Chain Monte Carlo (MCMC) procedures to perform Bayesian inference, model checking, and model comparison.We test our methodology using synthetic data and data from a consumption emotion study. The results from synthetic data show that our Bayesian model perform well in recovering the true parameters and selecting the appropriate model. More importantly, the results clearly illustrate the consequences of ignoring heterogeneity. Specifically, we find that ignoring heterogeneity can lead to sign reversals of the factor covariances, inflation of factor variances and underappreciation of uncertainty in parameter estimates. The results from the emotion study show that subjects vary both in means and covariances. Thus traditional psychometric methods cannot fully capture the heterogeneity in our data.     

Multivariate analysis with linearizable regressions

We study the class of multivariate distributions in which all bivariate regressions can be linearized by separate transformation of each of the variables. This class seems more realistic than the multivariate normal or the elliptical distributions, and at the same time its study allows us to combine the results from multivariate analysis with optimal scaling and classical multivariate analysis. In particular a two-stage procedure which first scales the variables optimally, and then fits a simultaneous equations model, is studied in detail and is shown to have some desirable properties.