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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).

Existing research has consistently supported a relationship between creative achievement and specific personality traits (e.g. openness to experience). However, such work has largely focused on univariate associations, potentially obscuring complex interactions among multiple personality factors, rendering an incomplete picture of the creative personality. We applied a psychometric network approach to characterize the multidimensional personality structure of highly creative individuals in the arts ("artists") and sciences ("scientists"), using data from three samples (N = 4,015): college students, a representative adult sample, and the Big-C project of eminent creative professionals. Replicating past work, we found that artists showed reliably higher levels of openness to experience compared to scientists and a control group of less creative people. Psychometric network analysis revealed that artists were characterized by higher connectivity (i.e. co-occurrence) with other personality traits for openness, indicating that openness may be more heterogeneous in how it co-occurs with other personality traits in highly creative people. Across all three samples, we found that the scientists' personality network structure was more cohesive than the personality network of artists and the control group, indicating greater homogeneity in the personality characteristics of scientists. Our findings uncover a constellation of traits that give rise to creative achievement in the arts and sciences.

Existing research has consistently supported a relationship between creative achievement and specific personality traits (e.g. openness to experience). However, such work has largely focused on univariate associations, potentially obscuring complex interactions among multiple personality factors, rendering an incomplete picture of the creative personality. We applied a psychometric network approach to characterize the multidimensional personality structure of highly creative individuals in the arts ("artists") and sciences ("scientists"), using data from three samples (N = 4,015): college students, a representative adult sample, and the Big-C project of eminent creative professionals. Replicating past work, we found that artists showed reliably higher levels of openness to experience compared to scientists and a control group of less creative people. Psychometric network analysis revealed that artists were characterized by higher connectivity (i.e. co-occurrence) with other personality traits for openness, indicating that openness may be more heterogeneous in how it co-occurs with other personality traits in highly creative people. Across all three samples, we found that the scientists' personality network structure was more cohesive than the personality network of artists and the control group, indicating greater homogeneity in the personality characteristics of scientists. Our findings uncover a constellation of traits that give rise to creative achievement in the arts and sciences. [ABSTRACT FROM AUTHOR]

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 [ABSTRACT FROM AUTHOR]

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 psychological 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 following 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 measured at the level of general intelligence ( r = 0.11) and in terms of mental rotation ability ( r = .07). Recommendations are given for revision of the SBSOD scale based on item-level analyses. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]