The analysis of structural equation models by means of derivative free nonlinear least squares

It is shown that the PAR Derivative-Free Nonlinear Regression program in BMDP can be used to fit structural equation models, producing generalized least squares estimates, standard errors, and goodness-of-fit test statistics. Covariance structure models more general than LISREL can be analyzed. The approach is particularly useful for dealing with new non-standard models and experimenting with alternate methods of estimation. The research of the second author was supported by the NSF grant MCS 83-01587. We wish to thank our referees for some very valuable suggestions.     

A SIMPLIFIED TIME-SERIES ANALYSIS FOR EVALUATING TREATMENT INTERVENTIONS

Time-series analysis procedures for analyzing behavioral data are receiving increasing support. However, several authorities strongly recommend using at least 50–100 points per experimental phase. A complex mathematical model must then be empirically developed using computer programs to extract serial dependency from the data before the effects of treatment interventions can be evaluated. The present discussion provides a simple method of evaluating intervention effects that can be used with as few as 8 points per experimental phase. The calculations are easy enough to do by hand.     

ANOVA and ANCOVA of pre- and post-test,ordinal data

With random assignment to treatments and standard assumptions, either a one-way ANOVA of post-test scores or a two-way, repeated measures ANOVA of pre- and post-test scores provides a legitimate test of the equal treatment effect null hypothesis for latent variable . In an ANCOVA for pre- and post-test variablesX andY which are ordinal measures of and , respectively, random assignment and standard assumptions ensure the legitimacy of inferences about the equality of treatment effects on latent variable . Sample estimates of adjustedY treatment means are ordinal estimators of adjusted post-test means on latent variable .     

Estimating correlations with missing data,a bayesian approach

The posterior distribution of the bivariate correlation is analytically derived given a data set wherex is completely observed buty is missing at random for a portion of the sample. Interval estimates of the correlation are then constructed from the posterior distribution in terms of highest density regions (HDRs). Various choices for the form of the prior distribution are explored. For each of these priors, the resulting Bayesian HDRs are compared with each other and with intervals derived from maximum likelihood theory.     

Technology and the management of trust in insurance medicine

This article deals with the question how technologycontributed to the performing of objective assessmentsof health risks and to the public trust in theinsurance institution. Many authors have pointed tothe relevance of medical or statistical technologywith regard to the constitution of objectivity,because these technologies should be capable ofdiminishing the influence of social interactions – the``human element' – on the process of producingknowledge about health risks. However, in this articleit is shown that the constitution of objective riskassessments and public trust cannot be seen as theproduct of one particular type of technology, but thatit is the product of a socio-technical network, inwhich several heterogeneous elements becomeinterrelated and interdependant. The historicalreconstruction of this network also sheds a new lighton the role of `the human element' in the constitutionof objectivity and trust. It shows that elements inthe network which regulate the social interactionbetween the subjects involved are of no lessimportance to generate trust than technologies whichtend to abstract from this interaction. In otherwords, objective and subjective elements areintertwined much more than is often recognized, andpublic trust is to a fairly large degree depends onconventions in social interaction.     

警惕心理学研究中的统计误用

正确地使用统计方法对心理学研究至关重要。但心理学研究中存在着诸多统计误用现象。该着重分析了心理学研究中常见的一些统计误用现象：小或有偏样本的使用、在概率的理解上存在偏差、显性检验问题、夸大的统计图、相关分析的误用等等,并在此基础上提出了提高研究自身的统计素养、强调理论在心理学研究中的重要性、运用多样化的研究方法等应对之策。     

A statistical model for discriminating between subliminal and near-liminal performance

The concept of a psychophysical threshold is foundational in perceptual psychology. In practice, thresholds are operationalized as stimulus values that lead to a fairly high level of performance such as .75 or .707 in two-choice tasks. These operationalizations are not useful for assessing subliminality—the state in which a stimulus is so weak that performance is at chance. We present a hierarchical Bayesian model of performance that incorporates a threshold that divides subliminal from near-liminal performance. The model provides a convenient means to measure at-chance thresholds and therefore is useful for testing theories of subliminal priming. The hierarchical nature of the model is critical for efficient analysis as strength is pooled across people and stimulus values. A comparison to Rasch psychometric models is provided.     

The error statistical philosopher as normative naturalist

We argue for a naturalistic account for appraising scientific methods that carries non-trivial normative force. We develop our approach by comparison with Laudan’s (American Philosophical Quarterly 24:19–31, 1987, Philosophy of Science 57:20–33, 1990) “normative naturalism” based on correlating means (various scientific methods) with ends (e.g., reliability). We argue that such a meta-methodology based on means–ends correlations is unreliable and cannot achieve its normative goals. We suggest another approach for meta-methodology based on a conglomeration of tools and strategies (from statistical modeling, experimental design, and related fields) that affords forward looking procedures for learning from error and for controlling error. The resulting “error statistical” appraisal is empirical—methods are appraised by examining their capacities to control error. At the same time, this account is normative, in that the strategies that pass muster are claims about how actually to proceed in given contexts to reach reliable inferences from limited data.     

Parameters, Predictions, and Evidence in Computational Modeling: A Statistical View Informed by ACT-R

Model validation in computational cognitive psychology often relies on methods drawn from the testing of theories in experimental physics. However, applications of these methods to computational models in typical cognitive experiments can hide multiple, plausible sources of variation arising from human participants and from stochastic cognitive theories, encouraging a "model fixed, data variable" paradigm that makes it difficult to interpret model predictions and to account for individual differences. This article proposes a likelihood-based, "data fixed, model variable" paradigm in which models are treated as stochastic processes in experiments with participant-to-participant variation that can be applied to a broad range of mechanistic cognitive architectures. This article discusses the implementation and implications of this view in model validation, with a concrete focus on a simple class of ACT-R models of cognition. This article is not intended as a recipe for broad application of these preliminary, proof-of-concept methods, but as a framework for communication between statisticians searching for interesting problems in the cognitive modeling sphere, and cognitive modelers interested in generalizing from deterministic to stochastic model validation, in the face of random variation in human experimental data.     

统计认知理论及应用研究

长期以来大家认为人类认知尽管可以看成是非确定的推理计算过程,但它的知识表达、模型结构、及计算方法和概率统计理论在本质上是不同的,因此认知科学和概率统计方法存在巨大的鸿沟,过去两者基本上独立发展。近年来随着Bayesian概率统计模型研究的一系列突破性工作和认知过程本质的不断被发现和挖掘,两者的相关性和互补性逐渐突显出来。许多研究者认为认知是近似遵循概率统计推理原则的,一些研究工作显示两者的结合有可能对人工智能发展产生深远的影响。本文对当前统计认知理论及应用研究的现状进行系统的梳理,并结合自身的研究对它今后的发展提出自己的看法。