Failure of Retrospective Inference in Rats' Taste Aversion

Three experiments explored the possibility of retrospective inference in the rat. Experiment 1 revealed that poisoning of an element of a taste compound after single compound poisoning enhanced aversion to the other element, and that presentation of an element without poison after the compound poisoning reduced aversion to the other element. These results were opposite to those predicted by retrospective inference. Experiment 2 eliminated some confounding variables and examined the effect of element poisoning after compound poisoning. The result again was opposite to what the retrospective view predicts. The results, however, accorded with the idea that within-compound learning was established during compound presentation, and subsequent poisoning or nonpoisoning of one element affected response to the other element via the within-compound learning. In Experiment 3, the possibility of within-compound learning was reduced by using sequential presentation of tastes, but there was no indication of retrospective inference even under this condition.     

差数显著性t检验与元分析的对比研究

利用计算机构造被试总体、模拟实验研究程序进行抽样研究,探讨显著性ｔ检验方法与元分析方法在检验实验结果数据方面的差异。在模拟实验过程中,ｔ验受到显著性水平、样本容量和总体效果大小的影响,因此最终影响了统计推断的可靠性,建议：在进行显著性检验过程中,应对统计检验能力进行估计;元分析方法以样本为元素对总体进行推断,因此具有很高的准确性和可靠性,它将很有可能成为今后心理学研究的重要统计工具。     

The knowledge content of statistical data

An information-theoretic framework is used to analyze the knowledge content in multivariate cross classified data. Several related measures based directly on the information concept are proposed: the knowledge content (S) of a cross classification, its terseness (Zeta), and the separability (Gamma _X ) of one variable, given all others. Exemplary applications are presented which illustrate the solutions obtained where classical analysis is unsatisfactory, such as optimal grouping, the analysis of very skew tables, or the interpretation of well-known paradoxes. Further, the separability suggests a solution for the classic problem of inductive inference which is independent of sample size.     

Effects of lines of progress and semilogarithmic charts on ratings of charted data

The extent to which interrater agreement and ratings of significance on both changes in level and trend are affected by lines of progress and semilogarithmic charts was investigated. Thirteen graduate students rated four sets of charts, each set containing 19 phase changes. Set I data were plotted on equal interval charts. In Set II a line of progress was drawn through each phase on each chart. In Set III data points were replotted on semilogarithmic charts. In Set IV a line of progress was drawn through each phase of each Set III chart. A significant main effect on interrater agreement was found for lines of progress as well as a significant 2-way interaction between lines of progress and change type. Three main effects (chart type, lines of progress, and type of change) and a significant 3-way interaction were found for ratings of significance. Implications of these data for visual analysis of charted data are discussed.     

Statistical inference for false positive and false negative error rates in mastery testing

This paper describes an asymptotic inferential procedure for the estimates of the false positive and false negative error rates. Formulas and tables are described for the computations of the standard errors. A simulation study indicates that the asymptotic standard errors may be used even with samples of 25 cases as long as the Kuder-Richardson Formula 21 reliability is reasonably large. Otherwise, a large sample would be required.This work was performed pursuant to Grant No NIE-G-78-0087 with the National Institute of Education, Department of Health, Education and Welfare, Huynh Huynh, Principal Investigator. Points of view or opinions stated do not necessarily reflect NIE position or policy and no official endorsement should be inferred. The editorial assistance of Joseph C. Saunders is gratefully acknowledged.     

CRITICAL REALISM REDUX: A RESPONSE TO JOSH REEVES

This article combines an appreciation of several themes in Josh Reeves's Against Methodology in Science and Religion: Recent Debates on Rationality and Theology while arguing in favor of critical realism. The author holds that critical realism manages to combine the objective truth reached through inference and especially cognitive acts of judgment as well as the various, contingent historical contexts that also define where science is practiced. Reeves advocates a historical perspective, but this article claims that in order for critical realism to be credible, a philosophical perspective must be maintained.     

THEORIZING RELIGION AND QUESTIONING THE FUTURE OF ISLAM AND SCIENCE

Will there be any joint future for science and Islam? Although such questions have recently received considerable attention, more basic questions are often ignored. This article aims at addressing some of those more basic questions through exploring the assumptions that underlie different possible understandings of the question about the future of Islam and science. By investigating the relation between conceptualizations of religion and the question about the future of Islam and science, it will be argued that different understandings of the concept of religion (i.e., whether it denotes real objects, whether it is universal, and whether it is belief centered) lead to extremely different readings of the question. Besides, it will be argued that different answers to the question about the future of Islam and science can be understood in terms of the inference to best theological explanation; thus, the criteria that one assumes for the best theological explanation result in different criteria for evaluation of the answers.     

The Power to Detect and Predict Individual Differences in Intra-Individual Variability Using the Mixed-Effects Location-Scale Model

Our goal is to provide empirical scientists with practical tools and advice with which to test hypotheses related to individual differences in intra-individual variability using the mixed-effects location-scale model. To that end, we evaluate Type I error rates and power to detect and predict individual differences in intra-individual variability using this model and provide empirically-based guidelines for building scale models that include random and/or systematically-varying fixed effects. We also provide two power simulation programs that allow researchers to conduct a priori empirical power analyses. Our results aligned with statistical power theory, in that, greater power was observed for designs with more individuals, more repeated occasions, greater proportions of variance available to be explained, and larger effect sizes. In addition, our results indicated that Type I error rates were acceptable in situations when individual differences in intra-individual variability were not initially detectable as well as when the scale-model individual-level predictor explained all initially detectable individual differences in intra-individual variability. We conclude our paper by providing study design and model building advice for those interested in using the mixed-effects location-scale model in practice.     

方差分析的统计检验力和效果大小的常用方法比较

本文对用方差分析统计检验力和效果大小进行估计的几种不同方法作了简要的介绍和比较。     

Valuing intervention and observation

Understanding causal relations is fundamental to effective action but causal data can be confounded. We examined the value that participants placed on data derived from a hypothetical intervention or observation. Our materials involved a possible cause (“bottled water”), a possible confound (“food”), and a context (“a restaurant”). We supposed that participants seek to draw as specific a causal inference as possible from presented data and value information sources more highly that allow them to do so. On this basis, we predicted that in circumstances where an intervention removed the confounding causal factor but observation did not, participants would prefer data derived from an intervention when the possible cause was present (the bottled water was drunk) but show the reverse preference when the possible cause was absent (the bottled water was not drunk). Experiment 1 confirmed this prediction. Using a between-subjects design, Experiment 2 tested for a difference in confidence in causal judgements given identical data, including data on the confound, as a function of method of data collection (intervention or observation). There was no significant difference in confidence ratings between the two methods but confidence ratings were sensitive to the probability of an effect (illness) given the cause. Using a within-subjects design, Experiment 3 revealed systematic individual differences in preference for the two methods. Participants were divided between those who considered intervention more confounded and those who considered observation more confounded. Our experiments point to the subtleties of participants' evaluation of data from studies of human beings.