The Role of Organization in the Learning of a Sensori-Motor Task

Two experiments investigated the ability of five- and six-month-old Guatemalan infants to discriminate human faces visually and to associate faces and voices differing in age and sex. The 32 infants in Experiment I discriminated between photographs of a man, a woman, and a seven-year-old boy. They did not, however, discriminate between photographs of two men, two women, or of two seven-year-old boys. The 24 infants in Experiment II were presented three different pairs of photographs of faces. One pair consisted of a man and a woman, another of a woman and a child, and the third of a man and a child. Voices appropriate to each pair of photographs accompanied the presentation of the visual stimulus. Looking at the woman and child seemed to be influenced by the presentation of a woman's voice and a boy's voice. Appropriate voices did not alter looking behavior with regard to the woman and man or the man and child.     

Single and Multiple Ability Estimation in the SEM Framework: A Noninformative Bayesian Estimation Approach

Latent variable models with many categorical items and multiple latent constructs result in many dimensions of numerical integration, and the traditional frequentist estimation approach, such as maximum likelihood (ML), tends to fail due to model complexity. In such cases, Bayesian estimation with diffuse priors can be used as a viable alternative to ML estimation. This study compares the performance of Bayesian estimation with ML estimation in estimating single or multiple ability factors across 2 types of measurement models in the structural equation modeling framework: a multidimensional item response theory (MIRT) model and a multiple-indicator multiple-cause (MIMIC) model. A Monte Carlo simulation study demonstrates that Bayesian estimation with diffuse priors, under various conditions, produces results quite comparable with ML estimation in the single- and multilevel MIRT and MIMIC models. Additionally, an empirical example utilizing the Multistate Bar Examination is provided to compare the practical utility of the MIRT and MIMIC models. Structural relationships among the ability factors, covariates, and a binary outcome variable are investigated through the single- and multilevel measurement models. The article concludes with a summary of the relative advantages of Bayesian estimation over ML estimation in MIRT and MIMIC models and suggests strategies for implementing these methods.     

Harmonic Regression and Scale Stability

Monitoring a very frequently administered educational test with a relatively short history of stable operation imposes a number of challenges. Test scores usually vary by season, and the frequency of administration of such educational tests is also seasonal. Although it is important to react to unreasonable changes in the distributions of test scores in a timely fashion, it is not a simple matter to ascertain what sort of distribution is really unusual. Many commonly used approaches for seasonal adjustment are designed for time series with evenly spaced observations that span many years and, therefore, are inappropriate for data from such educational tests. Harmonic regression, a seasonal-adjustment method, can be useful in monitoring scale stability when the number of years available is limited and when the observations are unevenly spaced. Additional forms of adjustments can be included to account for variability in test scores due to different sources of population variations. To illustrate, real data are considered from an international language assessment.     

A Cost of Pretending

Two experiments were conducted to assess the effect of pretending on the pretenders' self-esteem. People use social pretenses to avoid criticism and receive praise to maintain and augment this self-esteem. Nevertheless, there is a hidden opportunity cost of pretending. Participants were led (or not) to pretend that they possessed knowledge they did not have (i.e., made-up words), and did or did not receive praise for being knowledgeable. Pretending blunts the effects of praise. Praise raised control participants' self-esteem more than it did that of pretenders.     

Psychoanalytic training experience and postgraduate professional development – Part II

This exploratory study looks at the training and postgraduate experience of the 2008–2014 graduates of the Chicago Institute for Psychoanalysis. It follows our former study of all living graduates through the year 2007 (Schneider et al., 2014). The survey developed and used in the first study, with a few additional questions added to increase our understanding of the training experience, was sent to 38 graduates with a return rate of 58%. As with the first survey, graduates were invited to assess, among other training experiences, their training analysis, classroom work, and supervision, and to tell of their post‐graduation involvement in teaching, supervising, study groups and other professional endeavors. They were also asked to rate their satisfaction with themselves as psychoanalysts and with their analytic career. The questions added to the previous survey related to the graduates’ theoretical orientation, the influence on their training experiences of the change in gender distribution, and of the diversity of professions now represented in the analytic training program. They were also encouraged to provide spontaneous narrative data. The data from our second survey showed important differences when compared with our first. In the first survey male respondents were in the majority; in the second, women held the majority. Of the professions represented in the training program, psychiatry was the majority in the first survey, psychology and social work held the majority in the second. Most respondents claimed an object‐relation theoretical orientation. Analytic immersion continues to decrease, with most respondents having two patients at the time of graduation and one at the time of the survey.     

The use of feedback in education: a complex instructional strategy

The use of feedback to improve students' academic performance involves complex instructional strategies. There are a number of instructional problems within these strategies that may be overlooked. These include the influence of emotional responses, interactions between teacher and student, and levels of cognitive processing involved. Feedback should be viewed as an interactive, empirically driven, problem-solving process.     

Bias in Estimation of Misclassification Rates

When a simple random sample of size n is employed to establish a classification rule for prediction of a polytomous variable by an independent variable, the best achievable rate of misclassification is higher than the corresponding best achievable rate if the conditional probability distribution is known for the predicted variable given the independent variable. In typical cases, this increased misclassification rate due to sampling is remarkably small relative to other increases in expected measures of prediction accuracy due to samplings that are typically encountered in statistical analysis. This issue is particularly striking if a polytomous variable predicts a polytomous variable, for the excess misclassification rate due to estimation approaches 0 at an exponential rate as n increases. Even with a continuous real predictor and with simple nonparametric methods, it is typically not difficult to achieve an excess misclassification rate on the order of n ⁻¹. Although reduced excess error is normally desirable, it may reasonably be argued that, in the case of classification, the reduction in bias is related to a more fundamental lack of sensitivity of misclassification error to the quality of the prediction. This lack of sensitivity is not an issue if criteria based on probability prediction such as logarithmic penalty or least squares are employed, but the latter measures typically involve more substantial issues of bias. With polytomous predictors, excess expected errors due to sampling are typically of order n ⁻¹. For a continuous real predictor, the increase in expected error is typically of order n ^−2/3     

Providing Relationship Interventions to Same-Sex Couples: Clinical Considerations,Program Adaptations,and Continuing Education

Despite remarkable similarities to different-sex couples in terms of core relationship processes and outcomes, same-sex couples differ from different-sex couples in important ways, including relational strengths (e.g., more egalitarian) and challenges associated with their sexual minority identity (e.g., discrimination). Given that most cognitive-behavioral relationship interventions have been designed for and tested on different-sex couples, clinicians wishing to serve same-sex couples will need to make appropriate adaptations to these interventions in order to remove heterosexist bias and sensitively meet the unique needs of same-sex couples. Further, clinicians should strive to be culturally competent in serving this population by developing knowledge of same-sex couple dynamics and issues, and by building a sense of comfort working with these families, which may involve addressing personal biases. The current paper seeks to provide an introduction to same-sex couple relational processes, and offers clinical recommendations and intervention adaptations to better serve this population. Some examples will refer to the development of the Strengthening Same-Sex Relationships programs, culturally sensitive relationship education programs specifically designed for and successfully piloted with male and female same-sex couples.