Sex differences in communication with close friends: testing Tannen's claims

In 1990 Tannen reported that men have fewer close friends than women, men discuss less intimate topics with their friends than women, and men and women respond differently to close friends' problems. 168 students completed a three-page survey designed to examine Tannen's claims. Consistent with Tannen's observations, men discussed less intimate topics (politics and school or work) with their five closest friends and reportedly were less likely to comfort or listen to their friends' problems than women were. Inconsistent with Tannen's statements, these women and men did not differ in the number of close friends they reported having or in the likelihood of offering advice when presented with a friend's problem. Present self-reports corroborate other researchers who questioned Tannen's findings and concluded that sex differences in interpersonal communication are small.     

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.     

Kripke Semantics, Undecidability and Standard Completeness for Esteva and Godo's Logic MTL∀

The present paper deals with the predicate version MTL of the logic MTL by Esteva and Godo. We introduce a Kripke semantics for it, along the lines of Ono's Kripke semantics for the predicate version of FL_ew (cf. [O85]), and we prove a completeness theorem. Then we prove that every predicate logic between MTL and classical predicate logic is undecidable. Finally, we prove that MTL is complete with respect to the standard semantics, i.e., with respect to Kripke frames on the real interval [0,1], or equivalently, with respect to MTL-algebras whose lattice reduct is [0,1] with the usual order.     

Magnitude comparisons distort mental representations of magnitude

Many cognitive processes rely on representations of magnitude, yet these representations are often malleable (H. Helson, 1964; J. Huttenlocher, L. V. Hedges, & J. L. Vevea, 2000; A. Parducci, 1965). It is likely that factors that affect these representations in turn affect the psychological processes that rely on them. The authors conducted 4 experiments to investigate whether language-expressible magnitude comparisons distort mental representations of compared magnitudes. Participants compared magnitudes and estimated those magnitudes in a variety of tasks. Experiments 1 through 3 demonstrated systematic comparison-induced distortions. Experiment 4 demonstrated that comparison-induced distortions might account for the asymmetric dominance effect discussed in the decision-making literature. Potential effects of comparison-induced distortions on other psychological processes (e.g., density effects, order effects, body-size estimation, pain estimation, and consumer decision making) are discussed.     

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.