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We conducted a randomized factorial experiment to determine how displaying school information to parents in different ways affects what schools they choose for their children in a hypothetical school district. In a sample of 3,500 low-income parents of school-aged children, a small design manipulation, such as changing the default order in which schools were presented, induced meaningful changes in the types of schools selected. Other design choices such as using icons to represent data, instead of graphs or just numbers, or presenting concise summaries instead of detailed displays, also led parents to choose schools with higher academic performance. We also examined effects on parents' understanding of the information and their self-reported satisfaction and ease of use. In some cases, there were trade-offs. For example, representing data using only numbers maximized understanding, but adding graphs maximized satisfaction at the expense of understanding.

Background: Bayesian statistics have become popular in the social sciences, in part because they are thought to present more useful information than traditional frequentist statistics. Unfortunately, little is known about whether or how interpretations of frequentist and Bayesian results differ. Objectives: We test whether presenting Bayesian or frequentist results based on the same underlying data influences the decisions people made. Research design: Participants were randomly assigned to read Bayesian and frequentist interpretations of hypothetical evaluations of new education technologies of various degrees of uncertainty, ranging from posterior probabilities of 99.8% to 52.9%, which have equivalent frequentist p values of.001 and.65, respectively. Subjects: Across three studies, 933 U.S. adults were recruited from Amazon Mechanical Turk. Measures: The primary outcome was the proportion of participants who recommended adopting the new technology. We also measured respondents' certainty in their choice and (in Study 3) how easy it was to understand the results. Results: When presented with Bayesian results, participants were more likely to recommend switching to the new technology. This finding held across all degrees of uncertainty, but especially when the frequentist results reported a p value >.05. Those who recommended change based on Bayesian results were more certain about their choice. All respondents reported that the Bayesian display was easier to understand. Conclusions: Presenting the same data in either frequentist or Bayesian terms can influence the decisions that people make. This finding highlights the importance of understanding the impact of the statistical results on how audiences interpret evaluation results. [ABSTRACT FROM AUTHOR]

Despite their prevalence in the marketplace, little empirical attention has been paid to how employee uniforms affect consumer reactions to service experiences. We propose that employee uniforms facilitate the shared categorization of employees and their organization in the mind of the customer, which affects many of the inferences that customers draw following service encounters. Study 1 shows that uniforms lead to greater attribution of responsibility to the company for employee behavior, especially following poor service. Studies 2 and 3 show that uniforms also lead to more assimilation of judgments across employees, increasing the impact of one employee's behavior on judgments of other employees of the same organization. Study 3 shows that employee uniforms lead to more extreme judgments of the company following service encounters. It also shows that bad (good) service from a uniformed employee makes competing companies look better (worse), indicating that uniforms can elicit contrast effects across companies. In sum, the mere presence of a uniform on an unsatisfactory service or retail employee can damage judgments of the organization and its employees and improve judgments of rival organizations compared to identical service from a nonuniformed employee. Managers seem unaware of these negative consequences. These same principles are likely to apply to a wide variety of uniformed services, including police, military, firefighters, and health‐care providers. [ABSTRACT FROM AUTHOR]

Clinical psychological research studies often require individuals with specific characteristics. The Internet can be used to recruit broadly, enabling the recruitment of rare groups such as people with specific psychological disorders. However, Internet-based research relies on participant self-report to determine eligibility, and thus, data quality depends on participant honesty. For those rare groups, even low levels of participant dishonesty can lead to a substantial proportion of fraudulent survey responses, and all studies will include careless respondents who do not pay attention to questions, do not understand them, or provide intentionally wrong responses. Poor-quality responses should be thought of as categorically different from high-quality responses. Including these responses will lead to the overestimation of the prevalence of rare groups and incorrect estimates of scale reliability, means, and correlations between constructs. We demonstrate that for these reasons, including poor-quality responses-which are usually positively skewed-will lead to several data-quality problems including spurious associations between measures. We provide recommendations about how to ensure that fraudulent participants are detected and excluded from self-report research studies. (PsycINFO Database Record (c) 2019 APA, all rights reserved). [ABSTRACT FROM AUTHOR]

Intentional harms are perceived as more painful and more deserving of compensation than unintentional harms. In conjunction with research demonstrating that people are poor judges of intent, this observation may explain why people are more willing to help victims whose suffering appears to be caused by others. This account further explains the authors' finding that people high in cognitive empathy are especially sensitive to other-caused harm, and aligns well with existing attributional accounts of why perceived victim responsibility reduces helping behavior. Finally, this account suggests a number of novel predictions about the determinants of donor behavior. [ABSTRACT FROM AUTHOR]

To investigate the influence of media figures on self-perception, online gamers reported how central their main videogame character (avatar) is to their own identity and answered questions about their avatar's body size either before or after questions about their own body size. When the avatar was not central to the gamer's identity, the avatar's body size influenced gamer's own body judgments only when the avatar was brought to mind by preceding questions. When the avatar was central to the gamer's identity, it influenced gamers' own body judgments independent of question order. In both cases, accessible avatars elicited assimilation effects on self-judgment. We conclude that media figures exert a chronic influence on self-judgment when they are central to the self. [ABSTRACT FROM AUTHOR]

The article discusses the paper on estimating the reproducibility of psychological science and evaluation of three statistical error contained in the paper. Topics discussed include collaborative effort created by the Open Science Collaboration (OSC) to find reproducibility of psychological science, OSC's replication studies and statistical error connected with these studies and discussion on the issue of biasness.

Several abstracts are presented on comment on the article related to estimating the reproducibility of psychological science and abstract on response on the comment.

Background and Objectives: The Transforming Clinical Practices Initiative (TCPI) was a 4-year, large-scale, collaborative, peer-based learning initiative for physician practices of all specialties with broad goals to improve the quality of patient care, spend health care dollars more wisely, and assist practices in being ready to succeed under value-based payment (VBP). We investigated whether the COVID-19 pandemic had erased or diminished practice transformation progress made during the TCPI program period, through a follow-up survey of participating practices fielded in October 2021., Methods: In October 2021 to April 2022, we surveyed a probability sample of 2207 primary care and specialty practices that participated in the TCPI, receiving 610 responses. We asked about practice characteristics, financial and ownership stability, clinical performance, and quality improvement efforts, both now and prior to COVID-19. The COVID-19 content was part of a larger survey. Responses were weighted to account for sample selection, unknown eligibility status, and nonresponse. We generated weighted univariate descriptive statistics representative of practices with clinicians enrolled in TCPI. These estimated percentages have a 95% confidence interval of about ±5%. Multivariate analysis of unweighted data examined associations between practice characteristics and other variables of interest., Results: For all but one of 13 practice transformation activities engaged in prior to the COVID-19 public health emergency, a majority of practices (at least 52%) reported that the progress on these activities were either not hurt or were helped by their COVID-19 experience. Compared to January 2020, only about 7% of practices reported that their quality of care or clinical performance was worse due to COVID-19, and 32% reported that their quality of care was better. More rural than urban practices reported that half or more of their transformation activities were hurt by COVID-19 (29% and 14%, respectively). Physician-owned practices were more likely to report quality is better today than prior to COVID-19 relative to practices with other ownership types such as hospitals or health systems (43% vs 24%)., Conclusion: Most practices have been able to recover from the deep stress of the COVID-19 pandemic and continue their efforts to improve patient care and performance to succeed under VBP. These results suggest a high perceived return on investment in value-readiness support along with emergency financial support in times of crisis, as well as room to continue preparation for any future pandemic and the national movement toward increased VBP, especially in rural settings., Competing Interests: None of the authors have any conflicts of interest to disclose. Funded by the US Department of Health & Human Services, Centers for Medicare & Medicaid Services, under contracts HHSM-500-2014-00034I and HHSM[1]500-2014-000331., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

The field of personality disorder research has grown since the publication of the Diagnostic and Statistical Manual of Mental Disorders, Third Edition , in 1980; with a notable evolution in the way that personality disorders are defined and operationalized. In evaluating this research, it is necessary to consider the range of sampling practices used. The goal of this study was to describe current sampling methods in personality disorder research and provide recommendations to guide sample design in future personality disorder research. To do this, we coded sampling practices described in recent empirical articles published in four journals that showcase research on personality disorders. We summarized aspects of sampling design including the combination of study question and sample characteristics (e.g., sample size, sample source, and use of screening), study design, and demographic representation of samples. Findings reveal a need for studies to better consider whether their samples are fit for purpose and to make explicit their target population and sampling frame, as well as the specific procedures (i.e., recruitment) used to carry out sampling. We also discuss issues that arise when attempting to capture low-base rate pathology, which is often associated with high comorbidity. We emphasize a process-oriented approach to developing a sampling strategy for personality disorders research. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Data collection in psychology increasingly relies on "open populations" of participants recruited online, which presents both opportunities and challenges for replication. Reduced costs and the possibility to access the same populations allows for more informative replications. However, researchers should ensure the directness of their replications by dealing with the threats of participant nonnaiveté and selection effects.