Due to the coronavirus disease 2019 (COVID-19) pandemic, many employees have been strongly encouraged or mandated to work from home. The present study sought to understand the attitudes and experiences of the general public toward remote work by analyzing Twitter data from March 30 to July 5 of 2020. We web scraped over 1 million tweets using keywords such as “telework,” “work from home,” “remote work,” and so forth, and analyzed the content using natural language processing (NLP) techniques. Sentiment analysis results show generally positive attitudes expressed by remote work-related tweets, with minor dips during the weekend. Topic modeling results uncovered themes among tweets including home office, cybersecurity, mental health, work–life balance, teamwork, and leadership, with minor changes in topics revealed over the 14-week period. Findings point to topics of particular concern regarding working from home and can help guide hypothesis generation for future research.

Consistent use of information has been identified as a critical issue that can undermine expert predictions. Using three personnel assessment datasets, we conduct Monte Carlo simulations to compare the accuracy of expert judgements for predicting the job performance of managers against four different weighting schemes: consistent random weights, completely random weights, unit weights, and optimal weights. Expert accuracy fell within the completely random weight distribution in two samples and at the low end of the consistent random weight distribution in one sample. In other words, consistent random weights reliably outperformed expert judgment for hiring decisions across three datasets with a total sample size of 847. We see this as a call to develop decision making systems that help control consistency or to manage consistency by aggregating multiple expert judgments. judgment and decision making, personnel selection

Planned missingness (PM) can be implemented for survey studies to reduce study length and respondent fatigue. Based on a large sample of Big Five personality data, the present study simulates how factors including PM design (three-form and random percentage [RP]), amount of missingness, and sample size affect the ability of full-information maximum likelihood (FIML) estimation to treat missing data. Results show that although the effectiveness of FIML for treating missing data decreases as sample size decreases and amount of missing data increases, estimates only deviate substantially from truth in extreme conditions. Furthermore, the specific PM design, whether it be a three-form or RP design, makes little difference although the RP design should be easier to implement for computer-based surveys. The examination of specific boundary conditions for the application of PM as paired with FIML techniques has important implications for both the research methods literature and practitioners regularly conducting survey research

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Understanding which students enter and leave psychology majors in college is critical to understanding the pipeline into the field. In this study, we compared psychology majors with nonpsychology majors on the basis of demographic, degree planning, academic preparedness, and academic performance variables using a unique longitudinal sample of nearly a million college students at 249 colleges and universities. Guided by prior research, we examined which students would persist in psychology, enter psychology from another major, or leave psychology for another major between three points in time: intended major before entering college, second-year college major, and fourth-year college major. Critically, most students who majored in psychology did not initially express interest in the field, but entering and persisting in the field was strongly associated with high school exposure and performance in psychology. Students with poorer performance in college often transfer into psychology from majors that may be perceived as difficult (e.g., science, technology, engineering, and math), whereas higher-performing students appear to leave psychology for these same majors, which may also be perceived as more lucrative. These results are concerning for the field of psychology if individuals with high potential who are originally interested in the field eventually leave.