Ant Colony Optimization and Local Weighted Structural Equation Modeling. A Tutorial on Novel Item and Person Sampling Procedures for Personality Research

Measurement in personality development faces many psychometric problems. First, theory‐based measurement models do not fit the empirical data in terms of traditional confirmatory factor analysis. Second, measurement invariance across age, which is necessary for a meaningful interpretation of age‐associated personality differences, is rarely accomplished. Finally, continuous moderator variables, such as age, are often artificially categorized. This categorization leads to bias when interpreting differences in personality across age. In this tutorial, we introduce methods to remedy these problems. We illustrate how Ant Colony Optimization can be used to sample indicators that meet prespecified demands such as model fit. Further, we use Local Structural Equation Modeling to resample and weight subjects to study differences in the measurement model across age as a continuous moderator variable. We also provide a detailed illustration for both tools with the Neuroticism scale of the openly available International Personality Item Pool – NEO inventory using data from the UK sample (N = 15 827). Combined, both tools can remedy persistent problems in research on personality and its development. In addition to a step‐by‐step illustration, we provide commented syntax for both tools. © 2019 European Association of Personality Psychology     

A Bivariate Generalized Linear Item Response Theory Modeling Framework to the Analysis of Responses and Response Times

A generalized linear modeling framework to the analysis of responses and response times is outlined. In this framework, referred to as bivariate generalized linear item response theory (B-GLIRT), separate generalized linear measurement models are specified for the responses and the response times that are subsequently linked by cross-relations. The cross-relations can take various forms. Here, we focus on cross-relations with a linear or interaction term for ability tests, and cross-relations with a curvilinear term for personality tests. In addition, we discuss how popular existing models from the psychometric literature are special cases in the B-GLIRT framework depending on restrictions in the cross-relation. This allows us to compare existing models conceptually and empirically. We discuss various extensions of the traditional models motivated by practical problems. We also illustrate the applicability of our approach using various real data examples, including data on personality and cognitive ability.     

Factor analytic approaches to personality item-level data

Factor analysis models have played a central role in formulating conceptual models in personality and personality assessment, as well as in empirical examinations of personality measurement instruments. Yet, the use of item-level data presents special problems for factor analysis, applications. In this article, we review recent developments in factor analysis that are appropriate for the type of item-level data often collected in personality. Included in this review are discussions of how these developments have been addressed in the context of two different (but formally related) statistical models item response theory (IRT: Hambleton, Swaminathan, & Rogers, 1991) and structural, equation modeling (Bollen 1989) for item-level data. We also discuss the relevance of item scaling in the context of these models. Using the restandardization data for the Minnesota Multiphasic Personality Inventory-2 Scale (cf. Butcher, Dahlstrom, Graham, Tellegen, & Kaemmer, 1989), we show brief examples of the utility of these approaches to address basic questions about responses to personality scale items regarding: (a) scale, dimensionality and general item properties, (b) the "appropriateness" of the observed responses, and (c) differential item functioning across subsamples. implications for analyses of personality item-level data in the IRT and factor analytic traditions are discussed.     

The Smell Identification Test as a measure of olfactory identification ability in schizophrenia and healthy populations: a Rasch psychometric study

This study examines University of Pennsylvania Smell Identification Test (UPSIT; R. L. Doty, 1995) performance in 133 controls and 54 chronic, medicated outpatients with schizophrenia (SZ) using item-response theory modeling. Results show that UPSIT items contribute to 1 factor, cover a range of 8 standard errors of measurement, and articulate 3 ability levels. Although it is not difficult enough to discriminate among persons of above-average ability, the test has diagnostic utility in detecting moderate impairment. Independent of item difficulty, 13 items differentiate patients from controls. When 45 patients and 45 controls were matched on gender and age, patient accuracy remained significantly reduced. The findings support the test's utility and demonstrate how traditional data analysis is insensitive to complexities in test performance.     

Model-Based Approaches for Teaching and Practicing Personality Assessment

Psychological assessment is a complex professional skill. Competence in assessment requires an extensive knowledge of personality, neuropsychology, social behavior, and psychopathology, a background in psychometrics, familiarity with a range of multimethod tools, cognitive flexibility, skepticism, and interpersonal sensitivity. This complexity makes assessment a challenge to teach and learn, particularly as the investment of resources and time in assessment has waned in psychological training programs over the last few decades. In this article, we describe 3 conceptual models that can assist teaching and learning psychological assessments. The transtheoretical model of personality provides a personality systems-based framework for understanding how multimethod assessment data relate to major personality systems and can be combined to describe and explain complex human behavior. The quantitative psychopathology—personality trait model is an empirical model based on the hierarchical organization of individual differences. Application of this model can help students understand diagnostic comorbidity and symptom heterogeneity, focus on more meaningful high-order domains, and identify the most effective assessment tools for addressing a given question. The interpersonal situation model is rooted in interpersonal theory and can help students connect test data to here-and-now interactions with patients. We conclude by demonstrating the utility of these models using a case example.     

An integrative review of process dissociation and related models in social cognition

This chapter reviews the use of formal dual process models in social psychology, with a focus on the process dissociation model and related multinomial models. The utility of the models is illustrated using studies of social and affective influences on memory, judgement and decision making, and social attitudes and stereotypes. We then compare and contrast the process dissociation model with other approaches, including implicit and explicit tests, signal detection theory, and multinomial models. Finally we show how several recently proposed multinomial models can be integrated into a single family of models, of which process dissociation is a specific instance. We describe how these process models can be used as both theoretical and measurement tools to answer questions about the role of automatic and controlled processes in social behaviour.     

Structural equation models and personality research

This article is a nontechnical introduction to the use of structural equation models in personality research Although such models can be fruitfully used to address a variety of important theoretical issues, the substantive focus in this article is on the use of such models for elucidating the construct validity of personality measures We include numerous more specific topics under our treatment of construct validity First of all, we show how structural equation models can be applied to the issues of convergent and discriminant validity Do our variables measure the constructs we want them to measure and not other constructs that we would prefer not to measure? Second, we show the utility of structural equation models for predictive validity Do our variables reliably predict other constructs with which they are theoretically linked? Finally, we examine the stability of personality constructs through structural equation models Through-out, our emphasis is on the particular advantages that structural equation models bring to these analytic tasks Ultimately, such models must be used in the service of theory, and when used appropriately, they can help us to refine both our measures and our theories of individual differences     

Sociogenomic Personality Psychology

ABSTRACT In this article, we address a number of issues surrounding biological models of personality traits. Most traditional and many contemporary biological models of personality traits assume that biological systems underlying personality traits are causal and immutable. In contrast, sociogenomic biology, which we introduce to readers in this article, directly contradicts the widely held assumption that something that is biological, heritable, or temperamental, is unchangeable. We provide examples of how seemingly unchanging biological systems, such as DNA, are both dependent on environments for elicitation and can be modified by environmental changes. Finally, we synthesize sociogenomic biology with personality psychology in a model of personality traits that integrates this more modern perspective on biology, physiology, and environment that we term sociogenomic personality psychology. We end the article with a discussion of the future directions of sociogenomic personality psychology.     

Relapse as a nonlinear dynamic system: application to patients with alcohol use disorders

Understanding the relapse process is one of the most important issues in addictive behaviors research. To date, most studies have taken a linear approach toward predicting relapse based on risk factors. Nonlinear dynamical systems theory can be used to describe processes that are not adequately modeled using a linear approach. In particular, the authors propose that catastrophe theory, a subset of nonlinear dynamical systems theory, can be used to describe the relapse process in addictive behaviors. Two small prospective studies using 6-month follow-ups of patients with alcohol use disorders (inpatient, n = 51; outpatient, n = 43) illustrate how cusp catastrophe theory may be used to predict relapse. Results from these preliminary studies indicate that a cusp catastrophe model has more predictive utility than traditional linear models.     

Analyzing psychopathology items: a case for nonparametric item response theory modeling

The authors discuss the applicability of nonparametric item response theory (IRT) models to the construction and psychometric analysis of personality and psychopathology scales, and they contrast these models with parametric IRT models. They describe the fit of nonparametric IRT to the Depression content scale of the Minnesota Multiphasic Personality Inventory--2 (J. N. Butcher, W. G. Dahlstrom, J. R. Graham, A. Tellegen, & B. Kaemmer, 1989). They also show how nonparametric IRT models can easily be applied and how misleading results from parametric IRT models can be avoided. They recommend the use of nonparametric IRT modeling prior to using parametric logistic models when investigating personality data.     

Bifactor models and rotations: exploring the extent to which multidimensional data yield univocal scale scores

The application of psychological measures often results in item response data that arguably are consistent with both unidimensional (a single common factor) and multidimensional latent structures (typically caused by parcels of items that tap similar content domains). As such, structural ambiguity leads to seemingly endless "confirmatory" factor analytic studies in which the research question is whether scale scores can be interpreted as reflecting variation on a single trait. An alternative to the more commonly observed unidimensional, correlated traits, or second-order representations of a measure's latent structure is a bifactor model. Bifactor structures, however, are not well understood in the personality assessment community and thus rarely are applied. To address this, herein we (a) describe issues that arise in conceptualizing and modeling multidimensionality, (b) describe exploratory (including Schmid-Leiman [Schmid & Leiman, 1957] and target bifactor rotations) and confirmatory bifactor modeling, (c) differentiate between bifactor and second-order models, and (d) suggest contexts where bifactor analysis is particularly valuable (e.g., for evaluating the plausibility of subscales, determining the extent to which scores reflect a single variable even when the data are multidimensional, and evaluating the feasibility of applying a unidimensional item response theory (IRT) measurement model). We emphasize that the determination of dimensionality is a related but distinct question from either determining the extent to which scores reflect a single individual difference variable or determining the effect of multidimensionality on IRT item parameter estimates. Indeed, we suggest that in many contexts, multidimensional data can yield interpretable scale scores and be appropriately fitted to unidimensional IRT models.     

Estimating Complex Measurement and Growth Models Using the R Package PLmixed

Measurement models, such as factor analysis and item response theory models, are commonly implemented within educational, psychological, and behavioral science research to mitigate the negative effects of measurement error. These models can be formulated as an extension of generalized linear mixed models within a unifying framework that encompasses various kinds of multilevel models and longitudinal models, such as partially nonlinear latent growth models. We introduce the R package PLmixed, which implements profile maximum likelihood estimation to estimate complex measurement and growth models that can be formulated within the general modeling framework using the existing R package lme4 and function optim. We demonstrate the use of PLmixed through two examples before concluding with a brief overview of other possible models.     

Applying Meta-theory to Achieve Generalisability and Precision in Personality Science

In contrast to traditional theories of personality, the CAPS perspective is a meta-theory that is deliberately "content-free". The generalisability of the theory depends on how widely its basic, common principles and methods can be applied to identify the constructs (e.g. situation-specific encodings, beliefs, values, etc.) relevant to a given domain of behavior and situations. In this commentary we will discuss how the target article illustrates the use of the CAPS meta-theory for developing domain-specific content-full models for smoking cessation, psychopathology, and organisational behavior.     

Examining assumptions about item responding in personality assessment: should ideal point methods be considered for scale development and scoring?

The present study investigated whether the assumptions of an ideal point response process, similar in spirit to Thurstone's work in the context of attitude measurement, can provide viable alternatives to the traditionally used dominance assumptions for personality item calibration and scoring. Item response theory methods were used to compare the fit of 2 ideal point and 2 dominance models with data from the 5th edition of the Sixteen Personality Factor Questionnaire (S. Conn & M. L. Rieke, 1994). The authors' results indicate that ideal point models can provide as good or better fit to personality items than do dominance models because they can fit monotonically increasing item response functions but do not require this property. Several implications of these findings for personality measurement and personnel selection are described.     

Using Multilevel Logistic Regression to Evaluate Person-Fit in IRT Models

I describe how multilevel logistic regression can be used to assess the consistency of an individual's response pattern with an item response theory measurement model. Specifically, by treating item responses as being nested within individuals, multilevel logistic regression is used to estimate a person-response curve that models how an individual's item endorsement rate decreases as a function of item difficulty. The slope of an individual's person-response curve is used as an indicator of the degree of response consistency or person-fit. I argue that the proposed multilevel modeling approach to person-fit assessment has several potential advantages over traditional techniques. The most important advantage being that the multilevel modeling approach allows explanatory variables to be entered into the model so that the causes of response inconsistency or differential test functioning can be investigated.     

Evolution and Personality: What Should a Comprehensive Theory Address and How?

Personality is arguably the most integrative area of psychology; yet, it is an area about which evolutionary psychologists have had comparatively little to say. This is unfortunate because evolutionary theory holds great potential as a framework that can link together the disparate aspects that make up a person. We suggest that progress in evolutionary personality psychology will be helped by clarification of precisely what an evolutionary theory of personality would need to address. To this end, we first describe and assess some extant contributions by theorists attempting to understand personality from an evolutionary perspective. Next, we endorse a working definition of what personality entails and outline three types of personality differences – character traits, goals/motives, and selves/identities – that any comprehensive evolutionary theory of personality should address. Finally, we suggest an approach forward, one where evolved species‐typical motives orient people toward adaptive ends and result in the differentiation of individuals’ unique selves.     

Thinking Ahead: Assuming Linear Versus Nonlinear Personality-Criterion Relationships in Personnel Selection

Recent studies suggest that the form of some personality–performance relationships may be curvilinear, meaning that traditional top-down selection is inefficient in capitalizing on underlying personality–performance relationships. This study examines how mean performance is affected by how well the selection method is aligned with the nature of personality–criterion relationships. A simulation manipulated the linearity or nonlinear inflection point of predictor–criterion relationships, and several selection approaches were implemented that varied in level of congruence with these relationships. Results indicate that incongruence can produce notable decrements in mean performance under some conditions. Some evidence also suggests that decrements can be greater when linearity is assumed but relationships are nonlinear (vs. when nonlinearity is assumed but relationships are linear), selection ratios are smaller, and a single predictor is used.     

“… And How About Now?”: Effects of Item Redundancy on Contextualized Self‐Reports of Personality

A common method of studying cross-situational variation in personality involves asking people to describe their behavior in several different contexts. Although this approach is intuitively appealing, it introduces a great deal of redundancy into the measurement process and may affect the interpretation of contextualized self-reports. Specifically, when participants are asked the same questions repeatedly in a single questionnaire, they may be compelled to focus on how their behavior is different. We tested this hypothesis by experimentally manipulating the number of contexts that were included in a measure of role-related personality. In 2 experiments (Study 1 N=377, Study 2 N=524), we found that multiple-role questionnaires produced greater variation in trait levels across roles, larger differences between general and role-specific ratings, and weaker correlations between general and role-specific ratings than single-role questionnaires. These findings illustrate how the measurement process can have an effect on the variability of responses to contextualized self-reports.