‘The moral irrelevance of moral coercion’

Philosophical Studies - An agent A morally coerces another agent, B, when A manipulates non-epistemological facts in order that B’s moral commitments enjoin B to do what A wants B to do, and...     

‘Believe in me,and I will too’: a study of how teachers’ expectations instilled confidence in Grade 10 students

Social Psychology of Education - Teacher expectation research has continued to establish an association between what teachers expect of their students and what students accomplish academically....     

Remembering changes: repetition effects in face recollection.

Two experiments examined effects of repetition and change on states of awareness in face recognition. Participants studied repeatedly presented photographs of faces, with the second presentation following either immediately after the first presentation (massed repetition) or following six intervening items (spaced repetition). To manipulate perceptual change, each repeated face was either identical or a mirror image of the first presentation. Subsequently, when recognising a face, participants indicated whether they consciously recollected its prior occurrence ("remembering") or recognised it on the basis of familiarity ("knowing"). Changes in appearance between repeated faces enhanced remember, but not know, responses, and these effects were accentuated for spaced, rather than massed, repetition. These findings suggest that distinctiveness of encoding supports the phenomenological experience of conscious remembering.     

On normal theory based inference for multilevel models with distributional violations

Data in social and behavioral sciences are often hierarchically organized though seldom normal, yet normal theory based inference procedures are routinely used for analyzing multilevel models. Based on this observation, simple adjustments to normal theory based results are proposed to minimize the consequences of violating normality assumptions. For characterizing the distribution of parameter estimates, sandwich-type covariance matrices are derived. Standard errors based on these covariance matrices remain consistent under distributional violations. Implications of various covariance estimators are also discussed. For evaluating the quality of a multilevel model, a rescaled statistic is given for both the hierarchical linear model and the hierarchical structural equation model. The rescaled statistic, improving the likelihood ratio statistic by estimating one extra parameter, approaches the same mean as its reference distribution. A simulation study with a 2-level factor model implies that the rescaled statistic is preferable.This research was supported by grants DA01070 and DA00017 from the National Institute on Drug Abuse and a University of North Texas faculty research grant. We would like to thank the Associate Editor and two reviewers for suggestions that helped to improve the paper.     

Concise formulas for the standard errors of component loading estimates

Concise formulas for the asymptotic standard errors of component loading estimates were derived. The formulas cover the cases of principal component analysis for unstandardized and standardized variables with orthogonal and oblique rotations. The formulas can be used under any distributions for observed variables as long as the asymptotic covariance matrix for sample covariances/correlations is available. The estimated standard errors in numerical examples were shown to be equivalent to those by the methods using information matrices.The author is indebted to anonymous reviewers for the corrections and suggestions on this study, which have led to improvements of earlier versions of this article.     

A unified approach to exploratory factor analysis with missing data,nonnormal data,and in the presence of outliers

Factor analysis is regularly used for analyzing survey data. Missing data, data with outliers and consequently nonnormal data are very common for data obtained through questionnaires. Based on covariance matrix estimates for such nonstandard samples, a unified approach for factor analysis is developed. By generalizing the approach of maximum likelihood under constraints, statistical properties of the estimates for factor loadings and error variances are obtained. A rescaled Bartlett-corrected statistic is proposed for evaluating the number of factors. Equivariance and invariance of parameter estimates and their standard errors for canonical, varimax, and normalized varimax rotations are discussed. Numerical results illustrate the sensitivity of classical methods and advantages of the proposed procedures.This project was supported by a University of North Texas Faculty Research Grant, Grant #R49/CCR610528 for Disease Control and Prevention from the National Center for Injury Prevention and Control, and Grant DA01070 from the National Institute on Drug Abuse. The results do not necessarily represent the official view of the funding agencies. The authors are grateful to three reviewers for suggestions that improved the presentation of this paper.     

Constraints have different concurrent effects and aftereffects on variability

Three experiments showed that constraints imposed early in learning have different effects on variability when they are in effect and after they are removed. Task constraints, which determine how something can be done, limited the number of possible responses in a computer game. Variability constraints, which specify how differently something must be done, required that each response differ from some number of prior responses. Less restrictive constraints (Experiments 1 and 2) produced higher variability during the constraints. More restrictive constraints (Experiments 2 and 3) led to higher variability after the constraints were relaxed. The authors discuss how these differences reflect strategies acquired during the constraints (default rules) and modified in closely related ways (exception rules) afterward.     

Category induction from distributional cues in an artificial language

The ability to identify the grammatical category of a word (e.g., noun, verb, adjective) is a fundamental aspect of competence in a natural language. Children show evidence of categorization by as early as 18 months, and in some cases younger. However, the mechanisms that underlie this ability are not well understood. The lexical co-occurrence patterns of words in sentences could provide information about word categories--for example, words that follow the in English often belong to the same category. As a step in understanding the role distributional mechanisms might play in language learning, the present study investigated the ability of adults to categorize words on the basis of distributional information. Forty participants listened for approximately 6 min to sentences in an artificial language and were told that they would later be tested on their memory for what they had heard. Participants were next tested on an additional set of sentences and asked to report which sentences they recognized from the first 6 min. The results suggested that learners performed a distributional analysis on the initial set of sentences and recognized sentences on the basis of their memory of sequences of categories of words. Thus, mechanisms that would be useful in natural language learning were shown to be active in adults in an artificial language learning task.