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We investigated the relationships between creative achievement, cognitive ability, temperament, and vocational interests using a large and diverse internet-based sample. Ten creative domains (visual arts, music, creative writing, dance, drama, architecture, humor, scientific discovery, inventions, culinary arts) were positively associated with higher cognitive ability, intellect and extraversion, and lower agreeableness. With regard to cognitive ability, there was no evidence for the threshold effect on achievements. With regard to age, younger individuals endorsed a greater number of low-level achievements and older individuals more high-level achievements across the 10 creative domains. Other characteristics of individual differences (e.g., vocational interests) were more domain-specific for predicting creative achievement. We also introduce a revised method for the assessment and scoring of creative achievements, and discuss suggestions for future research.

Whether women and men are psychologically very similar or quite different is a contentious issue in psychological science. This article clarifies this issue by demonstrating that larger and smaller sex/gender differences can reflect differing ways of organizing the same data. For single psychological constructs, larger differences emerge from averaging multiple indicators that differ by sex/gender to produce scales of a construct’s overall typicality for women versus men. For example, averaging self-ratings on personality traits more typical of women or men yields much larger sex/gender differences on measures of the femininity and masculinity of personality. Sex/gender differences on such broad-gauge, thematic variables are large relative to differences on their component indicators. This increased effect magnitude for aggregated scales reflects gains in both their reliability and validity as indicators of sex/gender. In addition, in psychological domains such as vocational interests that are composed of many variables, at least some of which differ by sex/gender, the multivariate distance between women and men is typically larger than the differences on the component variables. These analyses encourage recognition of the interdependence of sex/gender similarity and difference in psychological data.

Measurement is fundamental to all research in psychology and should be accorded greater scrutiny than typically occurs. Among other claims, McNeish and Wolf (Thinking twice about sum scores. Behavior Research Methods, 52, 2287-2305) argued that use of sum scores (a) implies that a highly constrained latent variable model underlies items comprising a scale, and (b) may misrepresent or bias relations with other criteria. The central claim by McNeish and Wolf that use of sum scores requires the assumption that a parallel test model underlies item responses is incorrect and without psychometric merit. Instead, if a set of items is unidimensional, estimators of reliability are available even if the factor model underlying the set of items does not have a highly constrained form. Thus, dimensionality of a set of items is the key issue, and whether strict constraints on parameter estimates do or do not hold dictate the appropriate way to estimate reliability. McNeish and Wolf also claimed that more precise forms of scoring, such as estimating factor scores, would be preferable to sum scores. We provide analytic bases for reliability estimation and then provide several demonstrations of reliability estimation and the relative advantages of sum scores and factor scores. We contend that several claims by McNeish and Wolf are questionable and that, as a result, multiple recommendations they made and conclusions they drew are incorrect. The upshot is that, once the dimensional structure of a set of items is verified, sum scores often have a solid psychometric basis and therefore are frequently quite adequate for psychological research.

Objective The circular structure of values has been verified mostly at a between-person level and on measures of general value preferences. In this manuscript, we argue that it is a simplification that neglected significant aspects of the value structures and distinguish four different types of structures: (a) between-person structure of value traits, (b) within-person structure of value traits, (c) between-person structure of value states, and (d) within-person structure of value states. We argue that the within-person structure of value states addresses the circular structure of values most accurately. Method To compare all four structures, we collected three partially dependent samples (N1 = 449, N2 = 293, N3 = 218) of adults (age 17–57, M = 24). At three time points, separated by 5–7 weeks, respondents completed a questionnaire measure (Portrait Values Questionnaire-Revised [PVQ-RR]) of value preferences (value traits) and reported the importance of values in their everyday actions (value states) for 1 week in an experience sampling study. Results The four types of value structures were stable over time. All four were also consistent with Schwartz's value model to some degree, but at the same time, there were some deviations. Conclusions It is important to distinguish four types of value structures and be aware of their different interpretations that we outlined in this paper.

The Hierarchical Taxonomy of Psychopathology (HiTOP) is a quantitative diagnostic system that addresses limitations in categorical rubrics. Despite growing interest in using HiTOP as a research framework, there is no consensus regarding the optimal way to estimate criterion validity effects of hierarchical dimensions. Using simulations, we quantified and compared the performance of latent variable (i.e., structural equation modeling; SEM) and factor score approaches of representing hierarchical dimensions in criterion validity analyses. In some conditions, relative to SEM, Bayesian plausible values and empirical Bayes modal factor scores introduced notable bias. SEM showed fewer Type I errors than the factor score approaches, particularly in large sample sizes. These results underscore the importance of focusing on effect size in addition to statistical significance when using factor score estimates in large samples. We designed an R function (https://osf.io/u3j5d/) that substantive investigators can use to execute any of the approaches studied here in other datasets.

Abstract. The predictive accuracy of personality-criterion regression models may be improved with statistical learning (SL) techniques. This study introduced a novel SL technique, BISCUIT (Best Items Scale that is Cross-validated, Unit-weighted, Informative, and Transparent). The predictive accuracy and parsimony of BISCUIT were compared with three established SL techniques (the lasso, elastic net, and random forest) and regression using two sets of scales, for five criteria, across five levels of data missingness. BISCUIT’s predictive accuracy was competitive with other SL techniques at higher levels of data missingness. BISCUIT most frequently produced the most parsimonious SL model. In terms of predictive accuracy, the elastic net and lasso dominated other techniques in the complete data condition and in conditions with up to 50% data missingness. Regression using 27 narrow traits was an intermediate choice for predictive accuracy. For most criteria and levels of data missingness, regression using the Big Five had the worst predictive accuracy. Overall, loss in predictive accuracy due to data missingness was modest, even at 90% data missingness. Findings suggest that personality researchers should consider incorporating planned data missingness and SL techniques into their designs and analyses.

The measurement of individual differences in cognitive ability has a long and important history in psychology, but it has been impeded by the proprietary nature of most assessment measures. With the development of validated open-source measures of ability (collected in the International Cognitive Ability Resource, or ICAR, available at ICAR-project.com ), it is now possible for many researchers to assess ability in large surveys or small, lab-based studies without the expenses associated with proprietary measures. We review the history of ability measurement and discuss how the growing set of items included in ICAR allows ability assessments to be more generally available to all researchers.

BackgroundMen sexually interested in children of a specific combination of maturity and sex tend to show some lesser interest in other categories of persons. Patterns of men's sexual interest across erotic targets' categories of maturity and sex have both clinical and basic scientific implications.MethodWe examined the structure of men's sexual interest in adult, pubescent, and prepubescent males and females using multidimensional scaling (MDS) across four datasets, using three large samples and three indicators of sexual interest: phallometric response to erotic stimuli, sexual offense history, and self-reported sexual attraction. The samples were highly enriched for men sexually interested in children and men accused of sexual offenses.ResultsResults supported a two-dimensional MDS solution, with one dimension representing erotic targets' biological sex and the other dimension representing their sexual maturity. The dimension of sexual maturity placed adults and prepubescent children on opposite ends, and pubescent children intermediate. Differences between men's sexual interest in adults and prepubescent children of the same sex were similar in magnitude to the differences between their sexual interest in adult men and women. Sexual interest in adult men was no more associated with sexual interest in boys than sexual interest in adult women was associated with sexual interest in girls.ConclusionsErotic targets' sexual maturity and biological sex play important roles in men's preferences, which are predictive of sexual offending. The magnitude of men's preferences for prepubescent children v. adults of their preferred sex is large.

Reliability is a fundamental problem for measurement in all of science. Although defined in multiple ways, and estimated in even more ways, the basic concepts seem straightforward and need to be understood by practitioners as well as methodologists. Reliability theory is not just for the psychometrician estimating latent variables, it is for everyone who wants to make inferences from measures of individuals or of groups. For the case of a single test administration, we consider multiple measures of reliability, ranging from the worst (β) to average (α, λ3) to best (λ4) split half reliabilities, and consider why model-based estimates (ωh, ωt) should be reported. We also address the utility of test-retest and alternate form reliabilities. The advantages of immediate versus delayed retests to decompose observed score variance into specific, state, and trait scores are discussed. But reliability is not just for test scores, it is also important when evaluating the use of ratings. Estimates that may be applied to continuous data include a set of intraclass correlations while discrete categorical data needs to take advantage of the family of κ statistics. Examples of these various reliability estimates are given using state and trait measures of anxiety given with different delays and under different conditions. An online supplemental materials is provided with more detail and elaboration. The online supplemental materials is also used to demonstrate applications of open source software to examples of real data, and comparisons are made between the many types of reliability. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Abstract. In pursuit of a more systematic and comprehensive framework for personality assessment, we introduce procedures for assessing personality traits at the lowest level: nuances. We argue that constructing a personality taxonomy from the bottom up addresses some of the limitations of extant top-down assessment frameworks (e.g., the Big Five), including the opportunity to resolve confusion about the breadth and scope of traits at different levels of the organization, evaluate unique and reliable trait variance at the item level, and clarify jingle/jangle issues in personality assessment. With a focus on applications in survey methodology and transparent documentation, our procedures contain six steps: (1) identification of a highly inclusive pool of candidate items, (2) programmatic evaluation and documentation of item characteristics, (3) test-retest analyses of items with adequate qualitative and quantitative properties, (4) analysis of cross-ratings from multiple raters for items with adequate retest reliability, (5) aggregation of ratings across diverse samples to evaluate generalizability across populations, (6) evaluations of predictive utility in various contexts. We hope these recommendations are the first step in a collaborative effort to identify a comprehensive pool of personality nuances at the lowest level, enabling subsequent construction of a robust hierarchy – from the bottom up.

Computational models of personality offer a means of bridging the gap between “bottom-up” neuroscience and contemporary individual difference researchers’ efforts to build “top-down” self-report questionnaires. Dynamic models like Atkinson and Birch's Dynamics of Action model (1970), Read and colleagues’ Virtual Personalities model ( Read & Miller, 2002 ; Read et al., 2010 ), and Revelle and Condon's Cues-Tendencies-Actions (CTA) model (2015) generate the observable between-persons' differences in behavior and effect on which self-report measures are based, from the unobservable within-persons differences on which modern personality theories are premised. Here, we focus on the “CTARST” model, which unites CTA and a reinforcement sensitivity theory (RST) simulation. CTARST has been used to simulate, in detail, data from three real-life studies that linked within- and between-person variability: Smillie, Cooper, Wilt, and Revelle (2012) , Wilt, Bleidorn, and Revelle (2017) , and Wilt, Funkhouser, and Revelle (2011) . Results demonstrated that the CTARST model is able to usefully approximate actual data regarding psychological dynamics.

The study of personality dynamics has a long history of being said to be important, but a much shorter history of actually being examined. We give an overview of the past 100 years of research on dynamic processes and suggest how recent methodological and analytic techniques can be applied to the important problem of studying individual differences in the coherent patterning over time of affect, behavior, and cognition.

We argue that it is useful to distinguish between three key goals of personality science—description, prediction and explanation—and that attaining them often requires different priorities and methodological approaches. We put forward specific recommendations such as publishing findings with minimum a priori aggregation and exploring the limits of predictive models without being constrained by parsimony and intuitiveness but instead maximizing out-of-sample predictive accuracy. We argue that naturally occurring variance in many decontextualized and multidetermined constructs that interest personality scientists may not have individual causes, at least as this term is generally understood and in ways that are human-interpretable, never mind intervenable. If so, useful explanations are narratives that summarize many pieces of descriptive findings rather than models that target individual cause–effect associations. By meticulously studying specific and contextualized behaviours, thoughts, feelings and goals, however, individual causes of variance may ultimately be identifiable, although such causal explanations will likely be far more complex, phenomenon-specific and person-specific than anticipated thus far. Progress in all three areas—description, prediction and explanation—requires higher dimensional models than the currently dominant ‘Big Few’ and supplementing subjective trait-ratings with alternative sources of information such as informant-reports and behavioural measurements. Developing a new generation of psychometric tools thus provides many immediate research opportunities. © 2020 European Association of Personality Psychology

We argue that it is useful to distinguish between three key goals of personality science – description, prediction and explanation – and that attaining them often requires different priorities and methodological approaches. We put forward specific recommendations such as publishing findings with minimum a priori aggregation and exploring the limits of predictive models without being constrained by parsimony and intuitiveness but instead maximising out-of-sample predictive accuracy. We argue that naturally-occurring variance in many decontextualized and multi-determined constructs that interest personality scientists may not have individual causes, at least as this term is generally understood and in ways that are human-interpretable, never mind intervenable. If so, useful explanations are narratives that summarize many pieces of descriptive findings rather than models that target individual cause-effect associations. By meticulously studying specific and contextualized behaviours, thoughts, feelings and goals, however, individual causes of variance may ultimately be identifiable, although such causal explanations will likely be far more complex, phenomenon-specific and person-specific than anticipated thus far. Progress in all three areas – description, prediction, and explanation – requires higher-dimensional models than the currently-dominant “Big Few” and supplementing subjective trait-ratings with alternative sources of information such as informant-reports and behavioural measurements. Developing a new generation of psychometric tools thus provides many immediate research opportunities.

Personality consists of stable patterns of cognitions, emotions, and behaviors, yet personality psychologists rarely study behaviors. Even when examined, behaviors typically are considered to be validation criteria for traditional personality items. In the current study (N = 332,489), we conceptualize (self-reported, yearlong) behavioral frequencies as measures of personality. We investigate whether behavioral frequencies have incremental validity over traditional personality items in correlating personality with six outcome criteria. We use BISCUIT, a statistical learning technique, to find the optimal number of items for each criterion’s model, across three pools of items: traditional personality items (k = 696), behavioral frequencies (k = 425), and a combined pool. Compared to models using only traditional personality items, models using the combined pool are more strongly correlated to four criteria. We find mixed evidence of congruence between the type of criterion and the type of personality items that are most strongly correlated with it (e.g., behavioral criteria are most strongly correlated to behavioral frequencies). Findings suggest that behavioral frequencies are measures of personality that offer a unique effect in describing personality-criterion relationships beyond traditional personality items. We provide an updated, public-domain item pool of behavioral frequencies: the BARE (Behavioral Acts, Revised and Expanded) Inventory.

A web-based computer simulation of a psychology experiment is used in a research methods course to give students an appreciation of the complexities of research, including the challenges of integrating the available literature into a set of testable hypotheses. By using a simulation, the tedium of data collection is minimized and the power of statistical analysis is emphasized. Main effects, interactions, and confounding variables allow for a discussion of power and statistical inference. Students analyze their data using the open-source statistical system R and are able to prepare publication-ready graphical displays.

Modern data collection techniques allow for intensive measurement within subjects. Analyzing this type of data requires analyzing data at the within subject as well as between subject level. Although sometimes conclusions will be the same at both levels, it is frequently the case that examining within subject data will show much more complex patterns of results than when they are simply aggregated. This tutorial is a simple introduction to the kind of data analytic strategies that are possible using the open source statistical language, R .

The predictive accuracy of personality-criterion regression models may be improved with statistical learning (SL) techniques. This study introduced a novel SL technique, BISCUIT (Best Items Scale that is Cross-validated, Unit-weighted, Informative and Transparent). The predictive accuracy and parsimony of BISCUIT was compared with three established SL techniques (the lasso, elastic net, and random forest) and regression using two sets of scales, for five criteria, across five levels of data missingness. BISCUIT’s predictive accuracy was competitive with other SL techniques at higher levels of data missingness. BISCUIT most frequently produced the most parsimonious SL model. The elastic net and lasso dominated other techniques in terms of predictive accuracy with complete data and in conditions with up to 50% data missingness. In terms of predictive accuracy, regression using 27 narrow traits was an intermediate choice. For most criteria and levels of data missingness, regression using the Big Five had the worst predictive accuracy. Overall, loss in predictive accuracy due to data missingness was modest, even at 90% data missingness. Findings suggest that personality researchers should consider incorporating planned data missingness and SL techniques into their designs and analyses.

Researchers have shown an interest in the aggregated Big Five personality of U.S. states, but typically they have relied on scores from a single sample (Rentfrow, Gosling, & Potter, 2008). We examine the replicability of U.S. state personality scores from two studies (Rentfrow et al., 2008; Rentfrow, Gosling, Jokela, & Stillwell, 2013) across a total of seven samples, two of them new. Same-trait correlations across samples are, on average, positive for all five traits, indicating score agreement. Additionally, three traits (Conscientiousness, Neuroticism, and Openness) show strongly consistent patterns of correlations with sociodemographic variables across samples. We find rank order stability in state personality scores for a 16-year period (1999-2015).

We discuss methods of data collection and analysis that emphasize the power of individual personality items for predicting real world criteria (e.g., smoking, exercise, self-rated health). These methods are borrowed by analogy from radio astronomy and human genomics. Synthetic Aperture Personality Assessment (SAPA) applies a matrix sampling procedure that synthesizes very large covariance matrices through the application of massively missing at random data collection. These large covariance matrices can be applied, in turn, in Persome Wide Association Studies (PWAS) to form personality prediction scores for particular criteria. We use two open source data sets (N=4,000 and 126,884 with 135 and 696 items respectively) for demonstrations of both of these procedures. We compare these procedures to the more traditional use of “Big 5” or a larger set of narrower factors (the “little 27”). We argue that there is more information at the item level than is used when aggregating items to form factorially derived scales.

Although the measurement of intelligence is important, researchers sometimes avoid using them in their studies due to their history, cost, or burden on the researcher. To encourage the use of cognitive ability items in research, we discuss the development and validation of the International Cognitive Ability Resource (ICAR), a growing set of items from 19 different subdomains. We consider how these items might benefit open science in contrast to more established proprietary measures. A short summary of how these items have been used in outside studies is provided in addition to ways we would love to see the use of public-domain cognitive ability items grow.

Hans Eysenck was the leading personality and individual differences theorist of the 20th century. His goal was to combine the best theories and practices of experimental psychology with the best measurement techniques of individual differences. From his earliest analyses of the dimensions of individual differences, through multiple iterations at theory building to his lasting achievements in building a paradigm for personality research he left a legacy of broad and rigorous research. He strove to integrate behaviour genetics, psychophysiology, cognitive psychology, aesthetics, and psychometrics into a unified theory of personality and individual differences. Although best known for his biological theory of extraversion, his impact upon the field was much more than that and cannot be summarized in a brief article. I review his major theoretical contributions and relate them to modern personality theory and show how his many contributions continue to shape current personality research.

Nesselroade and Molenaar (2015:in press) suggest that it is the relationships between latent variables within subjects that are invariant across subjects and thus the appropriate unit of analysis. We disagree and take the view that between factor correlations may differ systematically across subjects. And further, individual differences in these correlations may be an important source of information about each unique individual. Following from this premise, analyses of consistencies and differences between subjects of the within subject pattern of inter-factor correlations is a step towards an integrative science of behavior. We give several examples demonstrating how this approach has the potential to yield novel insights into personality functioning.

Background Negative symptoms occur early in the clinical high risk (CHR) state and indicate increased risk of conversion to psychotic disorder and poor functional outcome. However, while the negative symptom domain has shown to be parsimoniously explained by a 2-factor construct in schizophrenia, there has yet to be an established factor structure of negative symptoms in CHR. Methods 214 individuals meeting the Structured Interview for Psychosis-Risk Syndromes (SIPS) criteria for CHR were recruited through 3 active research programs in the United States. Exploratory Factor Analysis was conducted on the 6 negative symptom items of the SIPS, and factors were evaluated with respect to functional outcome and depression. Results Factor analysis indicated a 2-factor hierarchical model with 2 negative symptom dimensions reflecting volition (Occupational Functioning and Avolition) and emotion (Expression of Emotion, Experience of Emotion and Social Anhedonia). Linear Regression showed that the emotion factor was associated with poor social function, and the volition factor was associated with poor role function and depression. Conclusions Similar to factor solutions identified in adults diagnosed with psychotic disorders, results indicated that the SIPS negative symptom subscale is not a unidimensional construct. Rather, the SIPS negative subscale has 2 distinct factors that have different associations with clinical outcome and should be interpreted independently. Results have significant relevance for informing the valid assessment and conceptual interpretation of early clinical phenomenology in the psychosis prodrome.

Prior research shows that personality traits predict time spent with different people and frequency of engagement in different activities. Further, personality traits, company, and activity are related to the experience of affect. However, little research has examined personality, context, and affect together in the same study. In the current study, 78 people described their Big Five traits and took part in a 1-week experience sampling study using mobile phones as a means for data collection. Participants indicated their current company, activity, and momentary affect along the dimensions of energetic arousal (EA), tense arousal (TA), and hedonic tone (HT). Poisson regressions revealed that traits predicted higher frequencies of trait-consistent contexts: for example, extraversion was related to more frequently being with various types of company. Results predicting contexts from multilevel logistic regressions were sparser. Multilevel models revealed that traits and contexts had main effects on affect, yet there were relatively few interactions of traits X contexts predicting affect. We discuss more specific implications of these findings.

article i nfo The ability to locate and orient ourselves with respect to environmental space is known as sense of direction ("SOD"). While there is considerable evidence for the predictive utility of self-report measures of this psycholog- ical construct, relatively little research has investigated the psychometric properties of the self-report scale by which it is most commonly measured - the Santa Barbara Sense of Direction scale (SBSOD, Hegarty et al., 2002) - or the broader personality correlates. The present study evaluated the factor structure of the SBSOD fol- lowing administration to 12,155 individuals and situated it among prominent sources of individual differences, specifically the Big Five personality traits and intelligence. Findings suggest that the SBSOD scale has relatively high general factor saturation, and that a considerable portion of the variance in SBSOD scores is explained by other personality traits, including Conscientiousness (r = 0.33), Intellect (r = 0.27), Emotional Stability (r = 0.26), and Extraversion (r = 0.23). Cognitive ability was less highly correlated with SBSOD scores when mea- sured at the level of general intelligence (r = 0.11) and in terms of mental rotation ability (r = .07). Recommen- dations are given for revision of the SBSOD scale based on item-level analyses.

Personality psychology is concerned with affect (A), behaviour (B), cognition (C) and desire (D), and personality traits have been defined conceptually as abstractions used to either explain or summarise coherent ABC (and sometimes D) patterns over time and space. However, this conceptual definition of traits has not been reflected in their operationalisation, possibly resulting in theoretical and practical limitations to current trait inventories. Thus, the goal of this project was to determine the affective, behavioural, cognitive and desire (ABCD) components of Big–Five personality traits. The first study assessed the ABCD content of items measuring Big–Five traits in order to determine the ABCD composition of traits and identify items measuring relatively high amounts of only one ABCD content. The second study examined the correlational structure of scales constructed from items assessing ABCD content via a large, web–based study. An assessment of Big–Five traits that delineates ABCD components of each trait is presented, and the discussion focuses on how this assessment builds upon current approaches of assessing personality. Copyright © 2015 European Association of Personality Psychology

Many studies have examined attention mechanisms involved in specific behavioral tasks (e.g., search, tracking, distractor inhibition). However, relatively little is known about the relationships among those attention mechanisms. Is there a fundamental attention faculty that makes a person superior or inferior at most types of attention tasks, or do relatively independent processes mediate different attention skills? We focused on individual differences in voluntary visual-attention abilities using a battery of 11 representative tasks. An application of parallel analysis, hierarchical-cluster analysis, and multidimensional scaling to the intertask correlation matrix revealed 4 functional clusters, representing spatiotemporal attention, global attention, transient attention, and sustained attention, organized along 2 dimensions, one contrasting spatiotemporal and global attention and the other contrasting transient and sustained attention. Comparison with the neuroscience literature suggests that the spatiotemporal-global dimension corresponds to the dorsal frontoparietal circuit and the transient-sustained dimension corresponds to the ventral frontoparietal circuit, with distinct subregions mediating the separate clusters within each dimension. We also obtained highly specific patterns of gender difference and of deficits for college students with elevated attention-deficit/hyperactivity disorder traits. These group differences suggest that different mechanisms of voluntary visual attention can be selectively strengthened or weakened based on genetic, experiential, and/or pathological factors.

The personality trait extraversion is associated with higher positive affect, and individuals who behave in an extraverted way experience increased positive affect. Across 2 studies, we examine whether the positive affectivity of extraverts can be explained in terms of qualitative aspects of social experience resulting from extraverted (i.e., bold, assertive) behavior. In our first study (N = 225, 58% female), we found that social well-being, a broad measure of quality of social life (Keyes, 1998) was a significant mediator of the relation between trait extraversion and trait positive affect. This effect was specific to 1 aspect of social well-being-social contribution, one's sense of making an impact on one's social world. In our second study (N = 81, 75% female), we found that a momentary assessment of social well-being mediated the effect of experimentally manipulated extraverted behavior (in the context of 2 brief discussion tasks) on state positive affect. Furthermore, perceived contribution to the discussion tasks accounted for up to 70% of the effect of enacted extraversion on positive affect. This is the first identified mediator of the effect of enacted extraversion on positive affect. Implications and suggestions for extensions of this research are discussed.

Separating the signal in a test from the irrelevant noise is a challenge for all measurement. Low test reliability limits test validity, attenuates important relationships, and can lead to regression artifacts. Multiple approaches to the assessment and improvement of reliability are discussed. The advantages and disadvantages of several different approaches to reliability are considered. Practical advice on how to assess reliability using open source software is provided.

The present research examined whether perceived rate of progress toward a goal (velocity) mediated the relationships between personality states and affective states. Drawing from control theories of self-regulation, we hypothesized (i) that increased velocity would mediate the association between state extraversion and state positive affect, and (ii) that decreased velocity would mediate the association between state neuroticism and state negative affect. We tested these hypotheses in 2 experience sampling methodology studies that each spanned 2 weeks. Multilevel modeling analyses showed support for each of the bivariate links in our model, and multilevel path analyses supported our mediation hypotheses. We discuss implications for understanding the relations between personality states and affective states, control theories of self-regulation, and goal striving.

Personality is the unique patterning of affect, behavior, cognition, and desire across different situations, and therefore the study of situations is integral to the science of personality. This chapter reviews the role of personality factors in situational choice and situational experience and shows how the concepts of latency and persistence help facilitate a dynamic understanding of these topics. Situational choice is influenced by trait-consistency, expected and desired affect, and higher-order goals. Choices to engage in different situations over time have potential developmental implications for personality stability and change. Personality traits influence situational experience in terms of cognitive construal, affect, and state manifestations of personality. The distinction between nomothetic and idiographic approaches adds another level of nuance to the study of situational experience. The chapter concludes with a graphical demonstration of the relations between personality and affect across different situations.

Current health literacy measures have been criticized for solely measuring reading and numeracy skills when a broader set of skills is necessary for making informed health decisions, especially when information is often conveyed verbally and through multimedia video. The authors devised 9 health tasks and a corresponding 190-item assessment to more comprehensively measure health literacy skills. A sample of 826 participants between the ages of 55 and 74 years who were recruited from an academic general internal medicine practice and three federally qualified health centers in Chicago, Illinois, completed the assessment. Items were reduced using hierarchical factor analysis and item response theory resulting in the 45-item Comprehensive Health Activities Scale. All 45 items loaded on 1 general latent trait, and the resulting scale demonstrated high reliability and strong construct validity using measures of health literacy and global cognitive functioning. The predictive validity of the Comprehensive Health Activities Scale using self-reported general, physical, and mental health status was comparable to or better than widely used measures of health literacy, depending on the outcome. Despite comprehensively measuring health literacy skills, items in the Comprehensive Health Activities Scale supported 1 primary construct. With similar psychometric properties, current measures may be adequate, depending on the purpose of the assessment.

Depression, which is characterized by low positive affect (PA) and high negative affect (NA), is relatively common during late adolescence and young adulthood. During this period, interaction with an increasing social sphere gains importance, and interest in romantic companions assumes a central role. However, little is known about how PA and NA manifest in the daily lives of depressed youth, particularly in romantic settings. Current companionship, location, and affect were assessed 4 to 6 times per day over 2 weeks in 51 adolescents and young adults (mean age = 18 years) with clinically-significant depressive symptoms. Multilevel modeling was used to examine the associations of companionship and location with changes in the between-person mean effects as well as the within-person covariation effects of PA and NA. Generally, when alone versus with people, PA decreased, NA increased, and changes in the levels of PA and NA were relatively independent of each other. In the context of romantic companionship,...

For all of its versatility and sophistication, the extant toolkit of cognitive ability measures lacks a public-domain method for large-scale, remote data collection. While the lack of copyright protection for such a measure poses a theoretical threat to test validity, the effective magnitude of this threat is unknown and can be offset by the use of modern test-development techniques. To the extent that validity can be maintained, the benefits of a public-domain resource are considerable for researchers, including: cost savings; greater control over test content; and the potential for more nuanced understanding of the correlational structure between constructs. The International Cognitive Ability Resource was developed to evaluate the prospects for such a public-domain measure and the psychometric properties of the first four item types were evaluated based on administrations to both an offline university sample and a large online sample. Concurrent and discriminative validity analyses suggest that the public-domain status of these item types did not compromise their validity despite administration to 97,000 participants. Further development and validation of extant and additional item types are recommended.

Two experiments examined whether aspects of extraversion concerned with agency (leadership, dominance) and affiliation (friendliness, positive emotion) reflect susceptibility to positive affect (PA). In experiment 1, both aspects of extraversion predicted increased activated PA (vigor, excitement) following an appetitive mood induction, replicating our recent findings in this area (Smillie, Cooper, Wilt, & Revelle, 2012). Neither agentic nor affiliative extraversion predicted increased pleasant PA (contentment, satisfaction) following a pleasant mood induction. In experiment 2, induced pleasant PA was again unrelated to extraversion, as well as to two indicators of overall happiness. These observations suggest that extraverts are not generally more susceptible to PA, and that affective-reactivity cannot explain the strong relationship between extraversion and indicators of overall happiness.