Online credit recovery will likely expand in the coming years as school districts try to address increased course failure rates brought on by the coronavirus pandemic. Some researchers and policymakers, however, raise concerns over how much students learn in online courses, and there is limited evidence about the effectiveness of online credit recovery. This article presents findings from a multisite randomized study, conducted prior to the pandemic, to expand the field's understanding of online credit recovery's effectiveness. Within 24 high schools from a large urban district, the study randomly assigned 1,683 students who failed Algebra 1 or ninth grade English to a summer credit recovery class that either used an online curriculum with in-class teacher support or the school's business-as-usual teacher-directed class. The results suggest that online credit recovery had relatively insignificant effects on student course experiences and content knowledge, but significantly lower credit recovery rates for English. There was limited heterogeneity in effects across students and schools. Non-response on the study-administered student survey and test limit our confidence in the student experience and content knowledge results, but the findings are robust to different approaches to handling the missing data (multiple imputation or listwise deletion). We discuss how the findings add to the evidence base about online credit recovery and the implications for future research.

This article examined the discriminant and convergent validity of commonly used self-report measures of self-criticism, self-esteem, and shame. A confirmatory factor analysis (CFA) using multiple self-report measures of each construct showed low levels of discriminant validity between self-reported self-esteem, shame, and self-criticism and instead demonstrated correspondingly high levels of shared variance. However, bifactor analyses on the items across each measure suggested that self-report measures of self-esteem, shame, and self-criticism may contain distinct characteristics that are underrepresented in current measures of each construct. Based on the factor loadings in item-level bifactor analyses, a new measure, the Negative Self-Evaluation Scale (NSES), was constructed to improve the assessment of the unique characteristics of shame, self-esteem, and self-criticism. Implications for current and future practices concerning the measurement of each construct are discussed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The sustaining environments thesis hypothesizes that PreK effects are more likely to persist into later grades if children experience high-quality learning environments in the years subsequent to PreK. This study tests this hypothesis using data from a statewide PreK randomized experiment in Tennessee that found positive effects at the end of PreK that did not persist past kindergarten. These data were combined with teacher observation and school-level value-added scores from Tennessee's formal evaluation system to determine whether positive effects of PreK persisted for the subgroup of students exposed to higher-quality learning environments between kindergarten and 3rd grade. Neither exposure to highly effective teachers nor attending a high-quality school was sufficient by itself to explain differences in achievement between PreK participants and non-participants in 3rd-grade. However, this study found evidence that having both was associated with a sustained advantage for PreK participants in both math and ELA. Notably, however, very few children were exposed to high-quality learning environments after PreK, suggesting that maximizing PreK investments may require attending to the quality of learning environments during PreK and beyond.

Purpose The purpose of this study is to test how individuals’ emotion reactions (fear vs anger) to expressed anger influence their intended conflict management styles. It investigates two interventions for managing their reactions: hot vs cold processing and enhancing conflict self-efficacy. Design/methodology/approach Hypotheses were tested in two experiments using an online simulation. After receiving an angry or a neutral message from a coworker, participants either completed a cognitive processing task (E1) or a conflict self-efficacy task (E2), and then self-reported their emotions, behavioral activation/inhibition and intended conflict management styles. Findings Fear is associated with enhanced behavioral inhibition, which results in greater intentions to avoid and oblige and lower intentions to dominate. Anger is associated with enhanced behavioral activation, which results in greater intentions to integrate and dominate, as well as lower intentions to avoid and oblige. Cold (vs hot) processing does not reduce fear or reciprocal anger but increasing individuals’ conflict self-efficacy does. Research limitations/implications The studies measured intended reactions rather than behavior. The hot/cold manipulation effect was small, potentially limiting its ability to diminish emotional responses. Practical implications These results suggest that increasing employees’ conflict self-efficacy can be an effective intervention for helping them manage the natural fear and reciprocal anger responses when confronted by others expressing anger. Originality/value Enhancing self-efficacy beliefs is more effective than cold processing (stepping back) for managing others’ anger expressions. By reducing fear, enhanced self-efficacy diminishes unproductive responses (avoiding, obliging) to a conflict.

There is demand among policy-makers for the use of state education longitudinal data systems, yet laws and policies regulating data disclosure limit access to such data, and security concerns and risks remain high. Well-developed synthetic datasets that statistically mimic the relations among the variables in the data from which they were derived, but which contain no records that represent actual persons, present a viable solution to these laws, policies, concerns, and risks. We present a case study in the development of a synthetic data system and highlight potential applications of synthetic data. We begin with an overview of synthetic data, what it is, how it has been utilized thus far, and the potential benefits and concerns in its application to education data systems. We then describe our federally-funded project, proposing the steps required to synthesize a statewide longitudinal data system covering high school, postsecondary, and workforce data. Last, for use as a template for other agencies considering synthetic data, we review the challenges we have confronted in the development of our synthetic data system for research and policy evaluation purposes.

Mediation analysis has become one of the most popular statistical methods in the social sciences. However, many currently available effect size measures for mediation have limitations that restrict their use to specific mediation models. In this article, we develop a measure of effect size that addresses these limitations. We show how modification of a currently existing effect size measure results in a novel effect size measure with many desirable properties. We also derive an expression for the bias of the sample estimator for the proposed effect size measure and propose an adjusted version of the estimator. We present a Monte Carlo simulation study conducted to examine the finite sampling properties of the adjusted and unadjusted estimators, which shows that the adjusted estimator is effective at recovering the true value it estimates. Finally, we demonstrate the use of the effect size measure with an empirical example. We provide freely available software so that researchers can immediately implement the methods we discuss. Our developments here extend the existing literature on effect sizes and mediation by developing a potentially useful method of communicating the magnitude of mediation. (PsycINFO Database Record

"A General Measure of Effect Size for Indirect Effects in Mediation Analysis." Multivariate Behavioral Research, 52(1), pp. 109–110

Behavioral integrity (BI)—a perception that a person acts in ways that are consistent with their words—has been shown to have an impact on many areas of work life. However, there have been few studies of BI in Eastern cultural contexts. Differences in communication style and the nature of hierarchical relationships suggest that spoken commitments are interpreted differently in the East and the West. We performed three scenario-based experiments that look at response to word–deed inconsistency in different cultures. The experiments show that Indians, Koreans, and Taiwanese do not as readily revise BI downward following a broken promise as do Americans (Study 1), that the U.S.–Indian difference is especially pronounced when the speaker is a boss rather than a subordinate (Study 2), and that people exposed to both cultures adjust perceptions of BI based on the cultural context of where the speaking occurs (Study 3).

This paper examines the ways in which people react emotionally to expressions of anger by others during workplace conflicts, and the influence of those emotional reactions on behavioral activation/inhibition and conflict styles. We find that emotional reactions of fear are associated with behavioral inhibition as well as conflict styles such as avoiding and obliging. We find that reactions of hostility are associated with behavioral activation as well as conflict styles such as dominating. We also look at two approaches to dampen emotional responses to other’s anger-enhancing conflict self-efficacy, and shifting to cold rather than hot cognitive processing. We find that self-efficacy eliminates emotional responses to other’s anger, while hot processing has no moderating effects. We assess the implications of these findings for managing difficult people in the workplace.

Mediation analysis has become one of the most widely used tools for investigating the mechanisms through which variables influence each other. When conducting mediation analysis with fully nested data (e.g., individuals working in teams) or partially nested data (e.g., individuals working alone in one study arm but working in teams in another arm) special considerations arise. In this article we (a) review traditional approaches for analyzing mediation in nested data, (b) describe multilevel structural equation modeling (MSEM) as a versatile technique for assessing mediation in fully nested data, and (c) explain how MSEM can be adapted for assessing mediation in partially nested data (MSEM-PN) and introduce two new MSEM-PN specifications. MSEM-PN affords options for testing equality of level-specific mediation effects in the nested arm with mediation effects in the nonnested arm. We demonstrate the application of MSEM and MSEM-PN in simulated examples from the group processes literature involving fully and partially nested data. Finally, we conclude by providing software syntax and guidelines for implementation.