The Fantasy Questionnaire: A Measure to Assess Creative and Imaginative Fantasy

This article reports the construction and validation of a comprehensive self-report measure of fantasy. Unlike previous measures of fantasy, which focus on psychopathology, we conceive fantasy as a trait with positive connotation. Principal component analysis (N = 318) and confirmatory factor analyses (N = 345) were conducted using 2 sociodemographically diverse samples. The results provided support for a 2-factor conceptualization of the construct, with the dimensions imaginative fantasy and creative fantasy. Imaginative fantasy refers to vivid imagination and absorption in these images and daydreams. Creative fantasy refers to the activity of using fantasy to create new ideas. The trait measure showed good internal consistency, test–retest reliability, discriminant and convergent construct validity, as well as incremental validity. Moreover, in 3 behavioral studies, we put fantasy scores in relationship with behavioral data to provide further proof of validity. A comprehensive measure of fantasy can contribute to our understanding of individual differences in inner experiences, creative processes, and problem solving.     

Beyond the aggregate score: Using multilevel modeling to examine trajectories of laboratory-measured aggression

Aggression is often measured in the laboratory as an iterative “tit-for-tat” sequence, in which two aggressors repeatedly inflict retaliatory harm upon each other. Aggression researchers typically quantify aggression by aggregating across participants’ aggressive behavior on such iterative encounters. However, this “aggregate approach” cannot capture trajectories of aggression across the iterative encounters and needlessly eliminates rich information in the form of within-participant variability. As an alternative approach, I used multilevel modeling (MLM) to examine the slope of aggression across the 25-trial Taylor Aggression Paradigm as a function of trait physical aggression and experimental provocation. Across two preregistered studies (combined N = 392), participants exhibited a modest decline in aggression. This decline reflected a reciprocal strategy, in which participants responded to an initially-provocative opponent with greater aggression that then decreased over time to match their opponent's declining levels of aggression. Against predictions, trait physical aggression and experimental provocation did not affect participants’ overall trajectories of aggression. Yet, exploratory analyses suggested that the participants’ tendency to reciprocate their opponent's aggression with more aggression was greater at higher levels of trait physical aggression and attenuated among participants who had already been experimentally provoked by their opponent. These findings (a) illustrate several advantages of an MLM approach as compared with an aggregate approach to iterative laboratory aggression paradigms; (b) demonstrate that the magnifying effects of trait aggression and experimental provocation on laboratory aggression are stable over brief time-frames; and (c) suggest that modeling the opponent's behavior on such tasks reveals important information.     

Multilevel Factor Analysis and Structural Equation Modeling of Daily Diary Coping Data: Modeling Trait and State Variation

This study used multilevel modeling of daily diary data to model within-person (state) and between-person (trait) components of coping variables. This application included the introduction of multilevel factor analysis (MFA) and a comparison of the predictive ability of these trait/state factors. Daily diary data were collected on a large (n = 366) multiethnic sample over the course of 5 days. Intraclass correlation coefficient for the derived factors suggested approximately equal amounts of variability in coping usage at the state and trait levels. MFAs showed that Problem-Focused Coping and Social Support emerged as stable factors at both the within-person and between-person levels. Other factors (Minimization, Emotional Rumination, Avoidance, Distraction) were specific to the within-person or between-person levels but not both. Multilevel structural equation modeling (MSEM) showed that the prediction of daily positive and negative affect differed as a function of outcome and level of coping factor. The Discussion section focuses primarily on a conceptual and methodological understanding of modeling state and trait coping using daily diary data with MFA and MSEM to examine covariation among coping variables and predicting outcomes of interest.     

Multilevel factor analysis and structural equation modeling of daily diary coping data: Modeling trait and state variation

The current study used multilevel modeling of daily diary data to model within-person (state) and between-person (trait) components of coping variables. This application included the introduction of multilevel factor analysis (MFA) and a comparison of the predictive ability of these trait/state factors. Daily diary data was collected on a large (n = 366) multiethnic sample over the course of five days. Intraclass correlation coefficient for the derived factors suggested approximately equal amounts of variability in coping usage at the state and trait levels. MFAs showed that Problem-Focused Coping and Social Support emerged as stable factors at both the within-person and between-person levels. Other factors (Minimization, Emotional Rumination, Avoidance, Distraction) were specific to the within-person or between-person levels, but not both. Multilevel structural equation modeling (MSEM) showed that the prediction of daily positive and negative affect differed as a function of outcome and level of coping factor. The Discussion section focuses primarily on a conceptual and methodological understanding of modeling state and trait coping using daily diary data with MFA and MSEM to examine covariation among coping variables and predicting outcomes of interest.     

A Computationally More Efficient and More Accurate Stepwise Approach for Correcting for Sampling Error and Measurement Error

Over the last decade or two, multilevel structural equation modeling (ML-SEM) has become a prominent modeling approach in the social sciences because it allows researchers to correct for sampling and measurement errors and thus to estimate the effects of Level 2 (L2) constructs without bias. Because the latent variable modeling software Mplus uses maximum likelihood (ML) by default, many researchers in the social sciences have applied ML to obtain estimates of L2 regression coefficients. However, one drawback of ML is that covariance matrices of the predictor variables at L2 tend to be degenerate, and thus, estimates of L2 regression coefficients tend to be rather inaccurate when sample sizes are small. In this article, I show how an approach for stabilizing covariance matrices at L2 can be used to obtain more accurate estimates of L2 regression coefficients. A simulation study is conducted to compare the proposed approach with ML, and I illustrate its application with an example from organizational research.     

Trait emotional intelligence as a predictor of socioemotional outcomes in early adolescence

Trait emotional intelligence (EI) refers to a constellation of emotional self-perceptions located at the lower levels of personality hierarchies. This study investigated the predictive and incremental validity of this construct in a sample of 1140 pupils aged 11-13 years. Trait EI showed strong concurrent and predictive validity in relation to three measures of socioemotional competence: self-reported psychopathology as assessed by the Strengths and Difficulties Questionnaire, a sociometric measure of peer relations, and a peer-assessed measure of social behavior. Socioemotional measures were taken twice over a period of seven months. Structural equation modeling revealed trait EI and IQ effects on socioemotional competence (SEC), the former being stronger than the latter. Hierarchical regression analyses suggested an association also of trait with level of change in SEC (controlling for both IQ and SEC at time 1). The findings corroborate an important role for trait emotional intelligence in peer relations and socioemotional competence.     

问题解决测验中过程数据的特征抽取与能力评估

基于计算机的问题解决测验可以实时记录被试探索环境和解决问题时的详细行动痕迹,并保存为过程数据。首先介绍了过程数据的分析流程,然后从问题解决测验入手,分别对过程数据的特征抽取和能力估计建模两方面的研究进行了梳理和评价。未来研究应注意：提高分析结果的可解释性;特征提取时纳入更多信息;实现更复杂问题情景下的能力评估;注重方法的实用性;以及融合与借鉴不同领域的分析方法。     

A Validation Study of the Need for Affect Questionnaire–Short Form in Legal Contexts

We evaluated the psychometric properties of scores on the Need for Affect–Short Form (NAQ–S) in 3 samples: undergraduate students (Sample I), jury-eligible community members (Sample II), and forensic clinicians (Sample III). Concerning factor structure, the NAQ–S 2-factor structure displayed good fit to the data in Sample I, with mostly acceptable levels of internal consistency for both approach and avoidance scores. Construct validity patterns were observed such that approach scores were most strongly correlated with female gender and trait agreeableness scores, whereas avoidance scores were most strongly correlated to trait agreeableness scores. Criterion validity associations emerged in that approach scores displayed main effects on mock juror judgments in hate crimes, and forensic clinician judgments of violence risk estimation. Finally, avoidance scores displayed moderating effects on recommended sentencing length by hate crime victim type. Implications are discussed for emotion in legal decision making and future research.     

La fonction évaluative de la personnologie, vers la mise en évidence de deux dimensions de la valeur : la désirabilité sociale et l'utilité sociale

It is now widely recognized that Evaluation is a very powerful and fundamental dimension underlying trait structures, but what is Evaluation? In this paper we review various models (and empirical data) related to the signification of the Evaluative factor in personality traits. This review will lead us to defend the hypothesis that personality traits communicate two dimensions of value: (1) social desirability (SD) which refers to affective valence or motivation and (2) social utility (SU) which corresponds to the fundamental principle of evaluation in a society. We'll present empirical data supporting this conception.     

A Psychometric Analysis of the Trait Emotional Intelligence Questionnaire–Short Form (TEIQue–SF) Using Item Response Theory

Trait emotional intelligence refers to a constellation of emotional self-perceptions located at the lower levels of personality hierarchies. In 2 studies, we sought to examine the psychometric properties of the Trait Emotional Intelligence Questionnaire–Short Form (TEIQue–SF; Petrides, 2009) using item response theory (IRT). Study 1 (N= 1,119, 455 men) showed that most items had good discrimination and threshold parameters and high item information values. At the global level, the TEIQue–SF showed very good precision across most of the latent trait range. Study 2 (N= 866, 432 men) used similar IRT techniques in a new sample based on the latest version of the TEIQue–SF (version 1.50). Results replicated Study 1, with the instrument showing good psychometric properties at the item and global level. Overall, the 2 studies suggest the TEIQue-SF can be recommended when a rapid assessment of trait emotional intelligence is required.     

Personality Moderating Variables: A Warning about Statistical Artifact and a Comparison of Analytic Techniques

ABSTRACT Personality moderating variables act to qualify the relationship between a personality trait measure and a relevant behavioral criterion. Two data analytic techniques that can be used to test for significant moderating effects are the "median split" (MS) approach and the "moderated multiple regression" (MMR) approach. The goals of the present research were ( a ) to apply the MS approach to computer-simulated data in which the moderator and trait extremity are confounded, to determine the extent of artifact, and ( b ) to compare the performance (Type I and Type II error rates) of the two approaches when applied to confounded and nonconfounded data. It was found that when the MS approach was applied to confounded data in which no real moderating effect existed, this approach produced an alarming rate of apparent, but spurious, moderating effects. When the MMR approach was applied to the same data, the rate of spurious effects was reduced to that expected by chance. When both approaches were applied to simulated data which contained genuine moderating effects, the MMR approach consistently resulted in more correct detections of these effects than the MS approach. We conclude that researchers should always employ the MMR rather than the MS approach when testing for personality moderator variable effects.     

Support for a two-factor model of impulsivity and hazardous substance use in British and Australian young adults

Multiple lines of evidence suggest impulsivity comprises two distinct components relevant to substance misuse. Reward drive reflects sensitivity to rewarding stimuli and subsequent approach motivation. Rash impulsiveness reflects the ability to inhibit such approach behavior in light of negative consequences. However, several studies suggest the latter trait to be a more robust predictor. This begs the question as to whether a less parsimonious two-factor model is necessary. This study employed structural equation modeling to compare the fit of one- and two-factor impulsivity models to alcohol and drug use data provided by British (n = 183) and Australian (n = 271) young adults. Results consistently supported the two-factor model and its cross-cultural consistency, with rash impulsiveness being the more robust predictor.     

State and Trait Anger Predicting Creative Process Engagement—The Role of Emotion Regulation

Drawing on the specific emotion approach, and based on the emotional regulation theory and cognitive and activation perspectives on emotions, this study examined the differentiated impact of state and trait anger on creative process engagement (CPE) and the moderating influences of emotion reappraisal and suppression. Data were obtained from daily surveys (N = 422) of 98 employees from three consultancy companies. Hierarchical linear modeling analysis revealed that trait anger has a stronger impact on CPE than state anger does. Furthermore, the relationship between state anger and CPE is stronger when emotion reappraisal is lower, rather than higher, and the relationship between trait anger and CPE is also stronger when emotion suppression is lower, rather than higher.     

Optional stopping: No problem for Bayesians

Optional stopping refers to the practice of peeking at data and then, based on the results, deciding whether or not to continue an experiment. In the context of ordinary significance-testing analysis, optional stopping is discouraged, because it necessarily leads to increased type I error rates over nominal values. This article addresses whether optional stopping is problematic for Bayesian inference with Bayes factors. Statisticians who developed Bayesian methods thought not, but this wisdom has been challenged by recent simulation results of Yu, Sprenger, Thomas, and Dougherty (2013) and Sanborn and Hills (2013). In this article, I show through simulation that the interpretation of Bayesian quantities does not depend on the stopping rule. Researchers using Bayesian methods may employ optional stopping in their own research and may provide Bayesian analysis of secondary data regardless of the employed stopping rule. I emphasize here the proper interpretation of Bayesian quantities as measures of subjective belief on theoretical positions, the difference between frequentist and Bayesian interpretations, and the difficulty of using frequentist intuition to conceptualize the Bayesian approach.     

How to build a brain: from function to implementation

To have a fully integrated understanding of neurobiological systems, we must address two fundamental questions: 1. What do brains do (what is their function)? and 2. How do brains do whatever it is that they do (how is that function implemented)? I begin by arguing that these questions are necessarily inter-related. Thus, addressing one without consideration of an answer to the other, as is often done, is a mistake. I then describe what I take to be the best available approach to addressing both questions. Specifically, to address 2, I adopt the Neural Engineering Framework (NEF) of Eliasmith &; Anderson [Neural engineering: Computation representation and dynamics in neurobiological systems. Cambridge, MA: MIT Press, 2003] which identifies implementational principles for neural models. To address 1, I suggest that adopting statistical modeling methods for perception and action will be functionally sufficient for capturing biological behavior. I show how these two answers will be mutually constraining, since the process of model selection for the statistical method in this approach can be informed by known anatomical and physiological properties of the brain, captured by the NEF. Similarly, the application of the NEF must be informed by functional hypotheses, captured by the statistical modeling approach.     

Item Response Modeling of Paired Comparison and Ranking Data

The comparative format used in ranking and paired comparisons tasks can significantly reduce the impact of uniform response biases typically associated with rating scales. Thurstone's (1927, 1931) model provides a powerful framework for modeling comparative data such as paired comparisons and rankings. Although Thurstonian models are generally presented as scaling models, that is, stimuli-centered models, they can also be used as person-centered models. In this article, we discuss how Thurstone's model for comparative data can be formulated as item response theory models so that respondents' scores on underlying dimensions can be estimated. Item parameters and latent trait scores can be readily estimated using a widely used statistical modeling program. Simulation studies show that item characteristic curves can be accurately estimated with as few as 200 observations and that latent trait scores can be recovered to a high precision. Empirical examples are given to illustrate how the model may be applied in practice and to recommend guidelines for designing ranking and paired comparisons tasks in the future.     

Detecting Curvilinear Relationships: A Comparison of Scoring Approaches Based on Different Item Response Models

There is a growing use of noncognitive assessments around the world, and recent research has posited an ideal point response process underlying such measures. A critical issue is whether the typical use of dominance approaches (e.g., average scores, factor analysis, and the Samejima's graded response model) in scoring such measures is adequate. This study examined the performance of an ideal point scoring approach (e.g., the generalized graded unfolding model) as compared to the typical dominance scoring approaches in detecting curvilinear relationships between scored trait and external variable. Simulation results showed that when data followed the ideal point model, the ideal point approach generally exhibited more power and provided more accurate estimates of curvilinear effects than the dominance approaches. No substantial difference was found between ideal point and dominance scoring approaches in terms of Type I error rate and bias across different sample sizes and scale lengths, although skewness in the distribution of trait and external variable can potentially reduce statistical power. For dominance data, the ideal point scoring approach exhibited convergence problems in most conditions and failed to perform as well as the dominance scoring approaches. Practical implications for scoring responses to Likert-type surveys to examine curvilinear effects are discussed.     

A multi-method investigation of the personality correlates of digital aggression

Digital aggression (DA) refers to the use of computer-mediated technologies to harm others. A handful of previous studies have provided mixed results regarding the personality correlates of DA. We clarified these findings by analyzing the associations between three measures of DA (behavioral, Twitter, and self-report) and the Big Five traits using data from 1167 undergraduate participants. Big Five personality trait measures predicted all three DA measures, but results varied across particular assessments of DA. These results point to possible moderators and potentially important differences within the broader construct of DA.     

Connecting DSM-5 Personality Traits and Pathological Beliefs: Toward a Unifying Model

Dissatisfaction with the DSM-IV model of personality disorders has led to the development of alternative conceptualizations, including pathological trait models and models linked to particular theoretical approaches, such as Beck and Freeman’s (1990) cognitive framework. An important issue involves the potential to interweave such models into a single, parsimonious system that combines their distinct advantages. In this study, pathological trait and dysfunctional belief data from 616 individuals in a non-clinical sample were evaluated for commensurability using structural equation modeling. These models can be integrated via five higher-order factors, and that specific dimensions of dysfunctional beliefs can be differentiated based on features of the DSM-5 trait model. Overall, these results suggest that traits provide scaffolding for individual differences in pathological personality, within which dysfunctional beliefs offer specific vectors for clinical intervention in a cognitive framework. Implications of the empirical commensurability of trait and cognitive models are discussed.     

Culture and personality: toward an integrated cultural trait psychology

Two theoretical perspectives currently dominate research on culture and personality, the cross-cultural trait psychology approach, in which the trait concept is central, and the cultural psychology approach, in which the trait concept is questioned. Here I review theory and research from both perspectives and propose that the tenets of cultural psychology, at least in their more moderate forms, can be synthesized with the trait psychology approach, resulting in an integrated cultural trait psychology perspective.