Psychological research has increasingly recognized the importance of integrating temporal dynamics into its theories, and innovations in longitudinal designs and analyses have allowed such theories to be formalized and tested. However, psychological researchers may be relatively unequipped to analyze such data, given its many characteristics and the general complexities involved in longitudinal modeling. The current paper introduces time series analysis to psychological research, an analytic domain that has been essential for understanding and predicting the behavior of variables across many diverse fields. First, the characteristics of time series data are discussed. Second, different time series modeling techniques are surveyed that can address various topics of interest to psychological researchers, including describing the pattern of change in a variable, modeling seasonal effects, assessing the immediate and long-term impact of a salient event, and forecasting future values. To illustrate these methods, an illustrative example based on online job search behavior is used throughout the paper, and a software tutorial in R for these analyses is provided in the Supplementary Materials.

In 6 studies, we systematically explored for the 1st time the ameliorative effects of multicultural experience on intergroup bias and investigated the role of epistemic unfreezing as the motivational mechanism underlying these effects. We found that multicultural exposure led to a reduction in stereotype endorsement (Studies 1, 4, and 6), symbolic racism (Study 5), and discriminatory hiring decisions (Study 2). We further demonstrated that experimental exposure to multicultural experience caused a reduction in need for cognitive closure (NFCC; Studies 3 and 6) and that the ameliorative effects of multiculturalism experience on intergroup bias were fully mediated by lower levels of NFCC (Studies 4, 5, and 6). The beneficial effects of multiculturalism were found regardless of the targeted stereotype group (African Americans, Ethiopians, homosexuals, and native Israelis), regardless of whether multicultural experience was measured or manipulated, and regardless of the population sampled (Caucasian Americans or native Israelis), demonstrating the robustness of this phenomenon. Overall, these results demonstrate that multicultural experience plays a critical role in increasing social tolerance through its relationship to motivated cognitive processes.

SummaryIn order to identify the employees who are most likely to be engaged in their work, we conducted a meta‐analysis of 114 independent samples (N = 44,224) to provide estimates of the relationship between eight personality traits and employee engagement. Results indicated that these personality traits explained 48.10% of the variance in engagement. Supporting energy management theories, relative weights analysis revealed that positive affectivity was by far the strongest predictor of engagement (31.10% of the explained variance; ρ = .62), followed by proactive personality (19.60%; ρ = .49), conscientiousness (14.10%; ρ = .39), and extraversion (12.10%; ρ = .40), whereas neuroticism, negative affectivity, agreeableness, and openness to experience were the least important. We highlight the importance of positive affectivity for engagement and support personality‐based selection as a viable means for organizations to build a highly engaged workforce. Implications for using personality assessment to select engaged employees are discussed.

With more and more social network data being released, protecting the sensitive information within social networks from leakage has become an important concern of publishers. Adversaries with some background structural knowledge about a target individual can easily re-identify him from the network, even if the identifiers have been replaced by randomized integers(i.e., the network is naively-anonymized). Since there exists numerous topological information that can be used to attack a victim's privacy, to resist such structural re-identification becomes a great challenge. Previous works only investigated a minority of such structural attacks, without considering protecting against re-identification under any potential structural knowledge about a target. To achieve this objective, in this paper we propose -symmetry model, which modifies a naively-anonymized network so that for any vertex in the network, there exist at least − 1 structurally equivalent counterparts. We also propose sampling methods to extract approximate versions of the original network from the anonymized network so that statistical properties of the original network could be evaluated. Extensive experiments show that we can successfully recover a variety of such properties of the original network through aggregations on quite a small number of sample graphs.

Communications to stimulate weight loss include exercise-promotion messages that often produce unsatisfactory results due to compensatory behavioral and metabolic mechanisms triggered by physical activity. This research investigated potential automatic facilitation of eating immediately after exercise messages in the absence of actual exercise. Two controlled experiments demonstrated greater than control food intake following exposure to print messages typical of exercise campaigns as well as subliminal presentation of action words associated with exercise (e.g., “active”). These inadvertent effects may explain the limited efficacy of exercise-promotion programs for weight loss, particularly when systematic dietary guidelines are absent.

We propose the use of Twitter analysis as an alternative source of data to document weekly trends in emotion and stress, and attempt to use the method to estimate the work recovery effect of weekends. On the basis of 2,102,176,189 Tweets, we apply Pennebakers linguistic inquiry word count (LIWC) approach to measure daily Tweet content across 18 months, aggregated to the US national level of analysis. We derived a word count dictionary to assess work stress and applied p-technique factor analysis to the daily word count data from 19 substantively different content areas covered by the LIWC dictionaries. Dynamic factor analysis revealed two latent factors in day-level variation of Tweet content. These two factors are: (a) a negative emotion/stress/somatic factor, and (b) a positive emotion/food/friends/home/family/leisure factor, onto which elements of work, money, achievement, and health issues have strong negative loadings. The weekly trend analysis revealed a clear "Friday dip" for work stress and negative emotion expressed on Twitter. In contrast, positive emotion Tweets showed a "mid-week dip" for Tuesday-Wednesday-Thursday and "weekend peak" for Friday through Sunday, whereas work/money/achievement/health problem Tweets showed a small "weekend dip" on Fridays through Sundays. Results partially support the Effort-Recovery theory. Implications and limitations of the method are discussed.

No abstract

No abstract