Check your outliers! An introduction to identifying statistical outliers in R with easystats

Beyond the challenge of keeping up to date with current best practices regarding the diagnosis and treatment of outliers, an additional difficulty arises concerning the mathematical implementation of the recommended methods. Here, we provide an overview of current recommendations and best practices and demonstrate how they can easily and conveniently be implemented in the R statistical computing software, using the {performance} package of the easystats ecosystem. We cover univariate, multivariate, and model-based statistical outlier detection methods, their recommended threshold, standard output, and plotting methods. We conclude by reviewing the different theoretical types of outliers, whether to exclude or winsorize them, and the importance of transparency. A preprint of this paper is available at: 10.31234/osf.io/bu6nt.

Distinguishing four types of Person × Situation interactions: An integrative framework and empirical examination

People differ in their reaction to situations, resulting in Person × Situation interactions. These interactions have been emphasized by many theoretical accounts of personality. Nevertheless, empirical progress on Person × Situation interactions has been slow. This is in part attributable to an insufficient distinction of person and situation variables and of different types of interaction effects. We propose a framework distinguishing four nested types of interaction effects varying in specificity: (a) P × S: broad Person × Situation interaction variance, (b) P × Sspec: between-person differences in situation variable-outcome associations, (c) Pspec × S: between-situation differences in person variable-outcome associations, and (d) Pspec × Sspec: specific Person Variable × Situation Variable interactions. We conducted two large online studies (N = 622 and N = 818) with standardized situation stimuli (N = 62 pictures and N = 62 first-person perspective videos) and assessed Big Five traits, DIAMONDS situation characteristics, and Big Five states. Using preregistered multilevel models, we systematically quantified the different interaction types. We found (a) large Person × Situation interaction variance in personality states, (b) sizable individual differences in situation characteristic-state contingencies, (c) consistent but smaller between-situation differences in trait-state associations, and (d) some significant but (very) small Personality Trait × Situation Characteristic interactions. Our findings highlight Person × Situation interactions as potentially important predictors of psychological states, although the explanation of these interactions through specific person variables remains difficult. Individual differences in the reaction to situations should be incorporated into our models of personality. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Automatic Scatterplot Design Optimization for Clustering Identification

Scatterplots are among the most widely used visualization techniques. Compelling scatterplot visualizations improve understanding of data by leveraging visual perception to boost awareness when performing specific visual analytic tasks. Design choices in scatterplots, such as graphical encodings or data aspects, can directly impact decision-making quality for low-level tasks like clustering. Hence, constructing frameworks that consider both the perceptions of the visual encodings and the task being performed enables optimizing visualizations to maximize efficacy. In this article, we propose an automatic tool to optimize the design factors of scatterplots to reveal the most salient cluster structure. Our approach leverages the merge tree data structure to identify the clusters and optimize the choice of subsampling algorithm, sampling rate, marker size, and marker opacity used to generate a scatterplot image. We validate our approach with user and case studies that show it efficiently provides high-quality scatterplot designs from a large parameter space.

The Effects of Urban Living Conditions on Subjective Well-Being: The Case of German Foreign Service Employees

In an increasingly urbanized world, understanding the determinants of urban well-being will continue to grow in importance. Although the effects of different indicators of living conditions on well-being have been widely studied individually, little is known about their relative impact when examined jointly. In this study, we use a unique multi-source dataset that allows us to investigate the effect and relative importance of a variety of subjectively and objectively assessed aspects of urban living conditions on the subjective well-being (SWB) of German Foreign Service expatriates. The study captures living conditions in metropolises around the world at different stages of development, and assesses living conditions in a culturally comparably homogeneous set of participants, thus being potentially less confounded with cultural differences. Using linear regression and dominance analysis, we find that 'quality of and access to nature' (i.e., green space), 'quality of housing', and 'quality of public goods' (i.e., water, air, and sewage systems) have the strongest associations with SWB. Subjectively rated characteristics show stronger associations with SWB than externally assessed characteristics. Additionally, we examine whether the size of a city or the level of development of a country has an effect on SWB. Both living in a megacity (≥ 10 million inhabitants) and a lower development status have negative effects on SWB. However, these effects disappear when the various indicators of living conditions are controlled for. Our findings can inform organisations sending employees abroad as well as urban planners seeking to improve their policies and decision-making.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11482-023-10169-w.

Not All Effects Are Indispensable: Psychological Science Requires Verifiable Lines of Reasoning for Whether an Effect Matters

To help move researchers away from heuristically dismissing "small" effects as unimportant, recent articles have revisited arguments to defend why seemingly small effect sizes in psychological science matter. One argument is based on the idea that an observed effect size may increase in impact when generalized to a new context because of processes of accumulation over time or application to large populations. However, the field is now in danger of heuristically accepting all effects as potentially important. We aim to encourage researchers to think thoroughly about the various mechanisms that may both amplify and counteract the importance of an observed effect size. Researchers should draw on the multiple amplifying and counteracting mechanisms that are likely to simultaneously apply to the effect when that effect is being generalized to a new and likely more dynamic context. In this way, researchers should aim to transparently provide verifiable lines of reasoning to justify their claims about an effect's importance or unimportance. This transparency can help move psychological science toward a more rigorous assessment of when psychological findings matter for the contexts that researchers want to generalize to.

Toward a better understanding of the causal effects of role demands on work-family conflict: A genetic modeling approach

Over the past several decades, there has been considerable interest in the theoretical causes of work-family conflict (WFC). Most studies have focused on situational determinants, often ignoring the role of personal factors such as disposition and heritable elements. We increase understanding of person versus situation influences on WFC through estimation of the relationship between role demands and WFC after controlling for genetic confounding, measured personality traits, family confounds, and other stable dispositions. Based on twin data from the National Survey of Midlife Development in the United States (MIDUS), we examine the role of genetic factors in explaining variation in WFC (both work interference with family [WIF] and family interference with work [FIW]). Results support WFC has an additive genetic component, accounting for 31% [95% CI 18%, 45%] and 16% [95% CI 2%, 30%] of the variance in WIF and FIW, respectively. In addition, we test two competing hypotheses with regard to the relationship between role demands and WFC. Results support the phenotypic causal relationship for WIF, consistent with the notion the relationship between work demands and WIF reflect situational processes. However, results support the genetic confounding hypothesis for FIW, indicating observed relationships between family demands and FIW are primarily due to genetic factors. Our results provide new insights into the nature of WFC relationships and underscore that ignoring the influence of heritability can bias estimates of role demand effects in WFC research. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty

This study explores how researchers' analytical choices affect the reliability of scientific findings. Most discussions of reliability problems in science focus on systematic biases. We broaden the lens to emphasize the idiosyncrasy of conscious and unconscious decisions that researchers make during data analysis. We coordinated 161 researchers in 73 research teams and observed their research decisions as they used the same data to independently test the same prominent social science hypothesis: that greater immigration reduces support for social policies among the public. In this typical case of social science research, research teams reported both widely diverging numerical findings and substantive conclusions despite identical start conditions. Researchers' expertise, prior beliefs, and expectations barely predict the wide variation in research outcomes. More than 95% of the total variance in numerical results remains unexplained even after qualitative coding of all identifiable decisions in each team's workflow. This reveals a universe of uncertainty that remains hidden when considering a single study in isolation. The idiosyncratic nature of how researchers' results and conclusions varied is a previously underappreciated explanation for why many scientific hypotheses remain contested. These results call for greater epistemic humility and clarity in reporting scientific findings.

Melodic Intonation Therapy for aphasia: A multi-level meta-analysis of randomized controlled trials and individual participant data

Melodic Intonation Therapy (MIT) is a prominent rehabilitation program for individuals with post-stroke aphasia. Our meta-analysis investigated the efficacy of MIT while considering quality of outcomes, experimental design, influence of spontaneous recovery, MIT protocol variant, and level of generalization. Extensive literature search identified 606 studies in major databases and trial registers; of those, 22 studies-overall 129 participants-met all eligibility criteria. Multi-level mixed- and random-effects models served to separately meta-analyze randomized controlled trial (RCT) and non-RCT data. RCT evidence on validated outcomes revealed a small-to-moderate standardized effect in noncommunicative language expression for MIT-with substantial uncertainty. Unvalidated outcomes attenuated MIT's effect size compared to validated tests. MIT's effect size was 5.7 times larger for non-RCT data compared to RCT data (g̅_{case report} = 2.01 vs. g̅_RCT = 0.35 for validated Non-Communicative Language Expression measures). Effect size for non-RCT data decreased with number of months post-stroke, suggesting confound through spontaneous recovery. Deviation from the original MIT protocol did not systematically alter benefit from treatment. Progress on validated tests arose mainly from gains in repetition tasks rather than other domains of verbal expression, such as everyday communication ability. Our results confirm the promising role of MIT in improving trained and untrained performance on unvalidated outcomes, alongside validated repetition tasks, and highlight possible limitations in promoting everyday communication ability.

Data quality of reported child immunization coverage in 194 countries between 2000 and 2019

Analyzing immunization coverage data is crucial to guide decision-making in national immunization programs and monitor global initiatives such as the Immunization Agenda 2030. We aimed to assess the quality of reported child immunization coverage data for 194 countries over 20 years. We analyzed child immunization coverage as reported to the World Health Organization (WHO) and the United Nations Children's Fund (UNICEF) between 2000-2019 by all WHO Member States for Bacillus Calmette-Guérin (BCG) vaccine birth dose, first and third doses of diphtheria-tetanus-pertussis-containing vaccine (DTP1, DTP3), and first dose of measles-containing vaccine (MCV1). We assessed completeness, consistency, integrity, and congruence and assigned data quality flags in case anomalies were detected. Generalized linear mixed-effects models were used to estimate the probability of flags worldwide and for different country groups over time. The probability of data quality flags was 18.2% globally (95% confidence interval [CI] 14.8-22.3). The lowest probability was seen in South-East Asia (6.3%, 3.3-11.8, p = 0.002), the highest in the Americas (29.7%, 22.7-37.9, p < 0.001). The probability of data quality flags declined by 5.1% per year globally (3.2-7.0, p < 0.001). The steepest decline was seen in Africa (-9.6%, -13.0 to -5.8, p < 0.001), followed by Europe (-5.4%, -9.2 to -1.6, p = 0.0055), and the Americas (-4.9%, -9.2 to -0.6, p = 0.026). Most country groups showed a statistically significant decline, and none had a statistically significant increase. Over the past two decades, the quality of global immunization coverage data appears to have improved. However, progress has not been universal. The results highlight the need for joint efforts so that all countries collect, report, and use high-quality data for action in immunization.

Alternative model of personality disorders traits predict residential addictions treatment completion

BACKGROUND

Among people receiving residential treatment for a substance use disorder (SUD), premature treatment termination predicts poor post-treatment outcomes. We examined the utility of the alternative model for personality disorders (AMPD) for predicting premature residential SUD treatment termination, including interactions with age and gender.

METHODS

Participants (N = 374) were receiving residential treatment for SUD and enrolled in a clinical trial with two conditions: Skills for Improving Distress Intolerance (SIDI) and Supportive Counseling (NCT01741415). Participants were assessed at intake on AMPD traits using the Personality Inventory for DSM-5 (PID-5) and tracked longitudinally. After establishing gender and age measurement invariance, we used competing risk models to predict treatment completion versus premature termination using interactions of PID-5 scores with age and gender.

FINDINGS

Disinhibition and Negative Emotionality domains and facets predicted premature treatment termination, particularly among younger, male participants. There were positive effects of SIDI on treatment completion for participants with high levels of domain and facet Negative Emotionality. A small proportion (≈ 12 %) of the PID-5 items showed differential item functioning by age or gender; however, the aggregate impact on test-level total scores was negligible.

CONCLUSIONS

Participants (particularly young men) displaying poor self-control and emotional regulation are at risk for premature termination. These findings, together with minimal aggregate differential item functioning at the scale level, suggest that the PID-5 is a practically useful, construct-valid, non-proprietary measure, aspects of which can be used for screening in residential SUD treatment. Furthermore, among those with high negative emotionality, SIDI may be effective in preventing premature treatment termination.

Social comparisons and social anxiety in daily life: An experience-sampling approach

Judgments about the self compared to internalized standards are central to theoretical frameworks of social anxiety. Yet, empirical research on social comparisons-how people view themselves relative to others-and social anxiety is sparse. This research program examines the nature of everyday social comparisons in the context of social anxiety across 2 experience-sampling studies containing 8,396 unique entries from 273 adults. Hypotheses and analyses were preregistered with the Open Science Foundation (OSF) prior to data analysis. Study 1 was a 3-week daily diary study with undergraduates, and Study 2 was a 2-week ecological momentary assessment (EMA) study with a clinical sample of adults diagnosed with social anxiety disorder (SAD) and a psychologically healthy comparison group. In both studies, social anxiety was associated with less favorable, more unstable social comparisons. In both studies, favorable social comparisons were associated with higher positive affect and lower negative affect and social anxiety. In both studies, social comparisons and momentary affect/social anxiety were more strongly linked in people with elevated trait social anxiety/SAD compared to less socially anxious participants. Participants in Study 2-even those with SAD-made more favorable social comparisons when they were with other people than when alone. Taken together, results suggest that social anxiety is associated with unfavorable, unstable self-views that are linked to compromised well-being. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Vaccine acceptability, uptake and completion amongst men who have sex with men: A systematic review, meta-analysis and theoretical framework

BACKGROUND

Due to an increased risk of sexually transmitted infections (STIs), gay, bisexual and other men who have sex with men (MSM) have been recommended to receive vaccinations against human papillomavirus, meningitis C and hepatitis A/B. This review aimed to compare the rates of vaccine acceptability, uptake and completion, and to identify determinants of vaccine outcomes specific to MSM to inform a theoretical framework.

METHODS

In January 2020 four databases were explored to identify vaccination behaviours and associated factors among MSM. A narrative systematic review and meta-analysis were performed. Data were synthesised for theoretical modelling.

RESULTS

Seventy-eight studies, mostly from the USA, were included. The average vaccine acceptability was 63% (median = 72%, range: 30%-97%), vaccine uptake 45% (median = 42%, range: 5%-100%) and vaccine completion 47% (median = 45%, range: 12%-89%). Six categories of factors associated with vaccination acceptability, uptake and completion were conceptualised: Individual (e.g., demographic and psychosocial); Interpersonal (e.g., peer education); Healthcare provider (e.g., vaccine recommendation); Organisational and practice setting (e.g., routine collection of patient sexual orientation information that is integrated into a clinical decision support system); Community environment (e.g., targeted health promotion campaigns); and National, state and local policy environment (e.g., public health guidelines targeting MSM).

CONCLUSION

Despite overall high levels of acceptability, uptake and completion rates were below targets predicted by cost-effectiveness modelling across all recommended vaccines. These parameters may need to be adjusted for more precise estimations of cost-effectiveness. Addressing the multiple levels of determinants, as outlined in our theoretical framework, will help guide interventions to increase vaccine completion among MSM.

Using Mobile Sensors to Study Personality Dynamics

Abstract. Research interest in personality dynamics over time is rapidly growing. Passive personality assessment via mobile sensors offers an intriguing new approach for measuring a wide variety of personality dynamics. In this paper, we address the possibility of integrating sensor-based assessments to enhance personality dynamics research. We consider a variety of research designs that can incorporate sensor-based measures and address pitfalls and limitations in terms of psychometrics and practical implementation. We also consider analytic challenges related to data quality and model evaluation that researchers must address when applying machine learning methods to translate sensor data into composite personality assessments.

Meta-analytic criterion profile analysis

Intraindividual patterns or configurations are intuitive explanations for phenomena, and popular in both lay and research contexts. Criterion profile analysis (CPA; Davison & Davenport, 2002) is a well-established, regression-based pattern matching procedure that identifies a pattern of predictors that optimally relate to a criterion of interest and quantifies the strength of that association. Existing CPA methods require individual-level data, limiting opportunities for reanalysis of published work, including research synthesis via meta-analysis and associated corrections for psychometric artifacts. In this article, we develop methods for meta-analytic criterion profile analysis (MACPA), including new methods for estimating cross-validity and fungibility of criterion patterns. We also review key methodological considerations for applying MACPA, including homogeneity of studies in meta-analyses, corrections for statistical artifacts, and second-order sampling error. Finally, we present example applications of MACPA to published meta-analyses from organizational, educational, personality, and clinical psychological literatures. R code implementing these methods is provided in the configural package, available at https://cran.r-project.org/package=configural and at https://doi.org/10.17605/osf.io/aqmpc. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Appropriate Use of Bifactor Analysis in Psychopathology Research: Appreciating Benefits and Limitations

Co-occurrence of psychiatric disorders is well documented. Recent quantitative efforts have moved toward an understanding of this phenomenon, with the general psychopathology or p-factor model emerging as the most prominent characterization. Over the past decade, bifactor model analysis has become increasingly popular as a statistical approach to describe common/shared and unique elements in psychopathology. However, recent work has highlighted potential problems with common approaches to evaluating and interpreting bifactor models. Here, we argue that bifactor models, when properly applied and interpreted, can be useful for answering some important questions in psychology and psychiatry research. We review problems with evaluating bifactor models based on global model fit statistics. We then describe more valid approaches to evaluating bifactor models and highlight 3 types of research questions for which bifactor models are well suited to answer. We also discuss the utility and limits of bifactor applications in genetic and neurobiological research. We close by comparing advantages and disadvantages of bifactor models with other analytic approaches and note that no statistical model is a panacea to rectify limitations of the research design used to gather data.

Familial factors and the risk of borderline personality pathology: genetic and environmental transmission

BACKGROUND

Parental characteristics and practices predict borderline personality disorder (BPD) symptoms in children. However, it is difficult to disentangle whether these effects are genetically or environmentally mediated. The present study examines the contributions of genetic and environmental influences by comparing the effects of familial risk factors (i.e. parental psychopathology and borderline traits, maladaptive parenting, marital discord) on child BPD traits in genetically related (biological) and non-related (adoptive) families.

METHODS

Data are from 409 adoptive and 208 biological families who participated in the Siblings Interaction and Behavior Study (SIBS) and 580 twin families the Minnesota Twin Family Study (MTFS). Parent characteristics and practices included parental psychopathology (measured via structured clinical interviews), parental BPD traits, parenting behaviors, and marital discord. A series of multi-level regression models were estimated to examine the relationship of familial risk factors to child BPD traits and to test whether children's adoptive status moderated the association.

RESULTS

Symptom counts of parents' conduct disorder, adult antisocial behavior, nicotine, alcohol, and illicit drug dependence, and paternal BPD traits substantially predicted child BPD traits only in biological offspring, implying genetic transmission. Maternal BPD traits and both maternal and paternal conflict, lack of regard, and lack of involvement predicted offspring BPD traits regardless of the adoptive status, implying environmental transmission.

CONCLUSIONS

Parental externalizing psychopathology and father's BPD traits contribute genetic risk for offspring BPD traits, but mothers' BPD traits and parents' poor parenting constitute environmental risks for the development of these offspring traits.

Meta-analytic five-factor model personality intercorrelations: Eeny, meeny, miney, moe, how, which, why, and where to go

Meta-analysis is frequently combined with multiple regression or path analysis to examine how the Big Five/Five-Factor Model (FFM) personality traits relate to work outcomes. A common approach in such studies is to construct a synthetic correlation matrix by combining new meta-analyses of FFM-criterion correlations with previously published meta-analytic FFM intercorrelations. Many meta-analytic FFM intercorrelation matrices exist in the literature, with 3 matrices being frequently used in industrial-organizational (I-O) psychology and related fields (i.e., Mount, Barrick, Scullen, & Rounds, 2005; Ones, 1993; van der Linden, te Nijenhuis, & Bakker, 2010). However, it is unknown how the choice of FFM matrix influences study conclusions, why we observe such differences in the matrices, and which matrix researchers and practitioners should use for their specific studies. We conducted 3 studies to answer these questions. In Study 1, we demonstrate that researchers' choice of FFM matrix can substantively alter conclusions from meta-analytic regressions or path analyses. In Study 2, we present a new meta-analysis of FFM intercorrelations using measures explicitly constructed around the FFM and based on employee samples. In Study 3, we systematically explore the sources of differences in FFM intercorrelations using second-order meta-analyses of 44 meta-analytic FFM matrices. We find that personality rating source (self vs. other) and inventory-specific substantive and methodological features are the primary moderators of meta-analytic FFM intercorrelations. Based on the findings from these studies, we provide a framework to guide future researchers in choosing a meta-analytic FFM matrix that is most appropriate for their specific studies, research questions, and contexts. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Obtaining Unbiased Results in Meta-Analysis: The Importance of Correcting for Statistical Artifacts

Most published meta-analyses address only artifactual variance due to sampling error and ignore the role of other statistical and psychometric artifacts, such as measurement error variance (due to factors including unreliability of measurements, group misclassification, and variable treatment strength) and selection effects (including range restriction or enhancement and collider biases). These artifacts can have severe biasing effects on the results of individual studies and meta-analyses. Failing to account for these artifacts can lead to inaccurate conclusions about the mean effect size and between-studies effect-size heterogeneity, and can influence the results of meta-regression, publication-bias, and sensitivity analyses. In this article, we provide a brief introduction to the biasing effects of measurement error variance and selection effects and their relevance to a variety of research designs. We describe how to estimate the effects of these artifacts in different research designs and correct for their impacts in primary studies and meta-analyses. We consider meta-analyses of correlations, observational group differences, and experimental effects. We provide R code to implement the corrections described.

Not Restricted to Selection Research: Accounting for Indirect Range Restriction in Organizational Research

Range restriction is a common problem in organizational research and is an important statistical artifact to correct for in meta-analysis. Historically, researchers have had to rely on range-restriction corrections that only make use of range-restriction information for one variable, but it is not uncommon for researchers to have such information for both variables in a correlation (e.g., when studying the correlation between two predictor variables). Existing meta-analytic methods incorporating bivariate range-restriction corrections overlook their unique implications for estimating the sampling variance of corrected correlations and for accurately assigning weights to studies in individual-correction meta-analyses. We introduce new methods for computing individual-correction and artifact-distribution meta-analyses using the bivariate indirect range restriction (BVIRR; “Case V”) correction and describe improved methods for applying BVIRR corrections that substantially reduce bias in parameter estimation. We illustrate the effectiveness of these methods in a large-scale simulation and in meta-analyses of expatriate data. We provide R code to implement the methods described in this article; more comprehensive and robust functions for applying these methods are available in the psychmeta package for R.

psychmeta: An R Package for Psychometric Meta-Analysis

Over the past four decades, psychometric meta-analysis (PMA) has emerged a key way that psychological disciplines build cumulative scientific knowledge. Despite the importance and popularity of PMA, software implementing the method has tended to be closed-source, inflexible, limited in terms of the psychometric corrections available, cumbersome to use for complex analyses, and/or costly. To overcome these limitations, we created the psychmeta R package: a free, open-source, comprehensive program for PMA.

Protean and boundaryless career orientations: A critical review and meta-analysis

The protean/boundaryless career concepts refer to people becoming more self-directed and flexible in managing their careers in response to societal shifts in work arrangements. A sizable literature has emerged on protean/boundaryless career orientations/preferences (PBCO). Questions remain, however, about the structure of PBCO and whether they predict important criteria. The PBCO literature is largely disconnected from broader individual-level career research, making it unclear how PBCO intersect with career models based on other characteristics. We address these questions by systematically reviewing/meta-analyzing PBCO research. On the basis of 135 demographically/occupationally diverse samples from 35 countries (45,288 individuals), we find no support for traditional distinctions between protean and boundaryless orientations-protean self-directed, protean values-driven, and boundaryless psychological mobility all load onto a single general factor, labeled proactive career orientation, and are only weakly related to boundaryless physical mobility preferences. We also find that PBCO predict career self-management behaviors and career satisfaction but are less related to non-career-focused attitudes, objective success, or physical mobility behavior. PBCO are strongly related to proactivity-related and self-efficacy personality traits. We use these findings to propose an integrative model for how PBCO and other dispositions mutually influence career behavior. We discuss when PBCO may have advantages over broad traits for understanding careers, implications for counseling practice, and directions for future research. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Creative Interests and Personality

Abstract. The present study used intraindividual criterion profile analysis to investigate the relationship between creative artistic and investigative interests and the Big Five personality traits. In 19 samples, we found that artistic and investigative interests showed distinct intraindividual personality profile patterns. Investigative interests were associated with elevated openness to intellect, conscientiousness, and emotional stability and low extraversion and agreeableness, relative to individuals’ other traits. Artistic interests were associated with personal strengths for openness to experiences and personal weaknesses for conscientiousness, assertiveness, and emotional stability. Across creative interests, profile pattern, not absolute trait level, drove the relationship between personality traits and interests. These findings replicated across numerous personality inventories and levels of interest specificity (RIASEC, basic interests, occupation-specific interests). We discuss the implications of these results for the complementary use of personality and interest scales in vocational counseling and personnel selection.

Age and Employee Green Behaviors: A Meta-Analysis

Recent economic and societal developments have led to an increasing emphasis on organizational environmental performance. At the same time, demographic trends are resulting in increasingly aging labor forces in many industrialized nations. Commonly held stereotypes suggest that older workers are less likely to be environmentally responsible than younger workers. To evaluate the degree to which such age differences are present, we meta-analyzed 132 independent correlations and 336 d-values based on 4676 professional workers from 22 samples in 11 countries. Contrary to popular stereotypes, age showed small positive relationships with pro-environmental behaviors, suggesting that older adults engaged in these workplace behaviors slightly more frequently. Relationships with age appeared to be linear for overall, Conserving, Avoiding Harm, and Taking Initiative pro-environmental behaviors, but non-linear trends were observed for Transforming and Influencing Others behaviors.

Intraindividual Personality Profiles Associated With Realistic Interests

Investigations of the link between the Big Five personality traits and vocational interests have typically found no consistent relationships between personality traits and Realistic interests. The present article uses intraindividual criterion profile analysis in two studies to identify patterns in the relationships between personality traits and Realistic interests not found by previous investigations. In the first study, personality scores from two inventories were used to predict Realistic overall, basic, and occupational interests for 574 adults. Realistic–Producing interests were driven by high intraindividual Openness and low Extraversion. Realistic-Adventuring interests were marked by low intraindividual Agreeableness and high Extraversion. The Intellect aspect of Openness, not the Experiencing aspect, drove the Openness–Producing relationship. In the second study, reanalysis of 19 additional samples from the literature confirmed the important role of Openness to Intellect, but not Experiencing, in driving Producing interests. Gender moderated profile pattern shapes. Generally, personality profile pattern, not absolute levels of traits, drove the validity of personality traits in explaining Realistic vocational interests.

The Modeling and Assessment of Work Performance

Individual work role performance drives the entire economy. It is organizational psychology and organizational behavior’s (OP/OB’s) most crucial dependent variable. In this review, alternative specifications for the definition and latent structure of individual performance are reviewed and summarized. Setting aside differences in terminology, the alternatives are remarkably similar. The Campbell (2012) model is offered as a synthesized description of the content of the latent structure. Issues pertaining to performance dynamics are then reviewed, along with the role played by individual adaptability to changing performance requirements. Using the synthesized model of the latent content structure and dynamics of performance as a backdrop, issues pertaining to the assessment of performance are summarized. The alternative goals of performance assessment, general measurement issues, and the construct validity of specific methods (e.g., ratings, simulations) are reviewed and described. Cross-cultural issues and future research needs are noted.