Data selection and responsible conduct: Was millikan a fraud?

This paper addresses a problem in reporting scientific research. The problem is how to distinguish between justifiable and unjustifiable data selection. Robert Millikan is notorious for an infamous remark that he used all his data when in fact he had used a selection. On this basis he has been accused of fraud. There is a tension here — historians and his defenders see his selection as understandable and legitimate, while current statements about the Responsible Conduct of Research imply his selection was illegitimate. This paper discusses two main issues that arise in assessing his conduct, whether he was intentionally misleading and whether he actually did mislead the scientific community about some facts of nature. It is argued that he was not intentionally misleading, and that it is unlikely that he misled the scientific community.     

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.     

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.     

Mechanisms of source confusion and discounting in short-term priming: 1. Effects of prime duration and prime recognition

Huber, Shriffrin, Lyle, and Ruys (2001) measured short-term repetition priming effects in perceptual identification with two-alternative forced-choice testing. There was a preference to choose repeated words following passive viewing of primes and a preference against choosing repeated words following active responding to primes. In this present study, we explored conditions of prime processing that produce this pattern of results. Experiment 1 revealed that increased prime duration under passive viewing instructions produces the active priming pattern. Experiment 2 assessed memory for primes: With poor recognition of primes, there was a strong preference for repeated words; however, with good recognition of primes, this preference was eliminated. These results are modeled by a computational theory of optimal decision making, responding optimally with unknown sources of evidence (ROUSE). In ROUSE, a preference for repeated words results from source confusion between primes and choice words. A reversal in the direction of preference arises from the discounting of words known to have also appeared as primes.     

How does background information improve memory for text content?

In two experiments, we investigated whether reading background information benefits memory for text content by influencing the amount of content encoded or the organization of the encoded content. In Experiment 1, half of the participants read background information about the issues to be discussed in the text material, whereas half did not. All the participants were then tested for free recall and cued recall of text content. Free recall was greater for individuals who read issue information than for those who did not. The groups did not differ on cued recall, suggesting that background information did not facilitate the encoding of more text content. Measures of representational organization indicated that increased recall in the issue information group resulted from better organization of content in memory. Experiment 2 extended these findings, using background information about text sources, demonstrated that the efficacy of background information depends on the semantic relationship between that information and text content.     

Recognition memory for source and occurrence: The importance of recollection

Previous recognition memory studies indicate that when both recollection and familiarity are expected to contribute to recognition performance (e.g., discriminating studied items from nonstudied items) the dual-process and the unequal-variance signal detection models provide comparable accounts of performance. When familiarity is not expected to be useful (e.g., when items from two equally familiar sources are discriminated between), the dual-process model provides a significantly better account of performance. In the present study, source recognition was tested under conditions in which familiarity could have been used to perform a list-discrimination task; participants were required to discriminate between strong studied items, weak studied items, and new items. The dual-process model provided a better account of performance than did the unequal-variance model. Moreover, the results indicated that the unequal-variance assumption in a single-process signal detection model was not a valid substitution for recollection and that recollection was used to make recognition judgments even when assessments of familiarity were useful.     

Strong cues are not necessarily weak: Thomson and Tulving (1970) and the encoding specificity principle revisited

Performance on tests in which there is control over reporting (e.g., cued recall with the option to withhold responses) can be characterized by four parameters: free- and forced-report retrieval (correct responses retrieved from memory when the option to withhold responses is exercised and when it is not, respectively), monitoring (discrimination between correct and incorrect potential responses), and report bias (willingness to report responses). Typically, researchers do not examine all these components in cued-test performance; blanks are sometimes counted the same as errors, meaning that the (free-report) performance index is contaminated with report bias and monitoring ability. In this research, a two-stage testing procedure is described that allows measures of free- and forced-report retrieval, monitoring, and bias to be derived from the original encoding specificity experiments (Thomson & Tulving, 1970). The results show that their cue-reinstatement manipulation affected free-report retrieval, but once report bias and monitoring effects were removed by forcing output, retrieval was unaffected.     

The effect of category learning on sensitivity to within-category correlations

A salient property of many categories is that they are not just sets of independent features but consist of clusters of correlated features. Although there is much evidence that people are sensitive to between-categories correlations, the evidence about within-category correlations is mixed. Two experiments tested whether the disparities might be due to different learning and test tasks. Subjects learned about categories either by classifying items or by inferring missing features of items. Their knowledge of the correlations was measured with classification, prediction, typicality, and production tests. The inference learners, but not the classification learners, showed sensitivity to the correlations, although different tests were differentially sensitive. These results reconcile some earlier disparities and provide a more complete understanding of people's sensitivities to within-category correlations.