Why Jesus?

Jesus plays potentially a uniquely important role in relation to the world's greatest needs. To respond to the global crisis, we need historical consciousness. The source of historical consciousness is the prophetic tradition. Through Jesus the prophetic tradition became widely available and important. However, this consciousness has been responsible for great evil as well as great good, often bound up with we/they thinking and the use of violence. Jesus’ teaching of love of the enemy in principle overcomes this tendency and points to way forward.     

Star Performers in Twenty‐First Century Organizations

We argue that changes in the nature of work in 21st‐century organizations have led to the emergence of star performers—a few individuals who contribute a disproportionate amount of output. We describe how stars negate the long‐held belief that the distribution of individual performance is normal and, instead, suggest an underlying power law distribution. In addition, we offer 9 propositions to guide future empirical research on star performers and an underlying power law distribution of individual performance. We describe how the presence of stars is likely to affect all individual‐, team‐, and firm‐level management theories addressing individual performance directly or indirectly, but focus on specific implications for those addressing human capital, turnover, compensation, downsizing, leadership, teamwork, corporate entrepreneurship, and microfoundations of strategy. In addition, we discuss methodological considerations necessary to carry out our proposed research agenda. Finally, we discuss how a consideration of star performers has important implications for management practice.     

Accurate and biased perceptions of responsive support predict well-being

The current research examined effects of accurate and biased perceptions of romantic partners’ responsive support provision on perceivers’ well-being. Perceivers discussed a personal problem with their romantic partners (“targets”). Perceivers’ perceptions of targets’ responsive support following the discussion were related to external indicators of targets’ behavior, but these perceptions also were predicted by perceivers’ sentiments toward targets, suggesting that processes underlying perceivers’ perceptions were a blend of both accuracy and bias. In addition, both accurately perceived and biased perceptions of targets’ responsiveness predicted perceivers’ personal well-being (i.e., affect, coping, self-efficacy) and interpersonal well-being (i.e., more positive sentiments toward targets) immediately after the support interaction, 2 weeks later, and 6 months later. Results suggest that accurate and biased cognition during interpersonal interactions can have important consequences for perceivers’ personal and interpersonal well-being through effects on perceived partner responsiveness.     

The State of Research on Situational Judgment Tests: A Content Analysis and Directions for Future Research

Situational judgment tests (SJTs) have become popular selection methods, with 59 empirical studies having been published since 1990. In contrast to prior narrative reviews or meta-analyses, this study develops (a) a comprehensive structure of SJT features, or “attributes,” (b) uses this structure to quantitatively and qualitatively summarize existing research in a content analysis, and then (c) uses the content analysis to generate directions for future research. The review is organized around a single question: What are the current practices in SJT research? Using this question as a foundation, we focus the content analysis on three significant theoretical and practical themes: (a) SJT development, scoring methods, and uses; (2) types of reliability estimates reported for SJTs, and (3) attributes that enhance or reduce internal consistency reliability.     

Exploring Differential Subgroup Functioning on SAT Writing Items: What Happens When English Is Not a Test Taker's Best Language?

The purpose of this study is to explore patterns in model-data fit related to subgroups of test takers from a large-scale writing assessment. Using data from the SAT, a calibration group was randomly selected to represent test takers who reported that English was their best language from the total population of test takers (N = 322,011). A reference scale for the items was constructed based on EBL responses. Response behaviors of test takers who reported that English was not their best language (ENBL) were examined in relationship to this reference scale. This study illustrates the use of differential subgroup analyses to identify patterns related to person misfit within subgroups, as well as subsets of items, that may affect the validity of writing scores for ENBL test takers. The methodology described here offers an approach that can be used to explore, understand, and improve the validity of scores obtained from ENBL test takers in large-scale writing assessments.     

Testing for independence between pairs of autocorrelated binomial data sequences

A problem arises in analyzing the existence of interdependence between the behavioral sequences of two individuals: tests involving a statistic such as chi-square assume independent observations within each behavioral sequence, a condition which may not exist in actual practice. Using Monte Carlo simulations of binomial data sequences, we found that the use of a chi-square test frequently results in unacceptable Type I error rates when the data sequences are autocorrelated. We compared these results to those from two other methods designed specifically for testing for intersequence independence in the presence of intrasequence autocorrelation. The first method directly tests the intersequence correlation using an approximation of the variance of the intersequence correlation estimated from the sample autocorrelations. The second method uses tables of critical values of the intersequence correlation computed by Nakamuraet al. (J. Am. Stat. Assoc., 1976,71, 214–222). Although these methods were originally designed for normally distributed data, we found that both methods produced much better results than the uncorrected chi-square test when applied to binomial autocorrelated sequences. The superior method appears to be the variance approximation method, which resulted in Type I error rates that were generally less than or equal to 5% when the level of significance was set at .05.     

The Influence of Rater Effects in Training Sets on the Psychometric Quality of Automated Scoring for Writing Assessments

Automated essay scoring engines (AESEs) are becoming increasingly popular as an efficient method for performance assessments in writing, including many language assessments that are used worldwide. Before they can be used operationally, AESEs must be “trained” using machine-learning techniques that incorporate human ratings. However, the quality of the human ratings used to train the AESEs is rarely examined. As a result, the impact of various rater effects (e.g., severity and centrality) on the quality of AESE-assigned scores is not known. In this study, we use data from a large-scale rater-mediated writing assessment to examine the impact of rater effects on the quality of AESE-assigned scores. Overall, the results suggest that if rater effects are present in the ratings used to train an AESE, the AESE scores may replicate these effects. Implications are discussed in terms of research and practice related to automated scoring.