A Generalized Speed–Accuracy Response Model for Dichotomous Items

We propose a generalization of the speed–accuracy response model (SARM) introduced by Maris and van der Maas (Psychometrika 77:615–633, 2012). In these models, the scores that result from a scoring rule that incorporates both the speed and accuracy of item responses are modeled. Our generalization is similar to that of the one-parameter logistic (or Rasch) model to the two-parameter logistic (or Birnbaum) model in item response theory. An expectation–maximization (EM) algorithm for estimating model parameters and standard errors was developed. Furthermore, methods to assess model fit are provided in the form of generalized residuals for item score functions and saddlepoint approximations to the density of the sum score. The presented methods were evaluated in a small simulation study, the results of which indicated good parameter recovery and reasonable type I error rates for the residuals. Finally, the methods were applied to two real data sets. It was found that the two-parameter SARM showed improved fit compared to the one-parameter SARM in both data sets.     

Is Justice the Same for Everyone? Examining Fairness Items Using Multiple-group Analysis

Purpose  The purpose of this study was to examine whether fairness assessed in a widely used multisource instrument written by practitioners possessed a similar factor structure as fairness measured in academic literature, and whether different groups based on their relationship to the ratee (e.g., peers, subordinates, supervisors) perceived a similar structure to the construct. Design/methodology/approach  Multisource data were collected for 141 managers from a variety of organizations across the United States, who participated in a leadership development program offered by Personnel Decisions International (PDI), a global management consulting firm. Findings  The study results show that fairness measured with a widely used multisource instrument indeed assesses the same construct as that examined in the academic literature. Peers, subordinates, and supervisors perceived fairness similarly, whereas self-reports of fairness differed, suggesting that they function as a systematic source of variance in the measurement of fairness. Implications  The findings in this study demonstrate that leaders can be confident that their constituents are all conceptualizing fairness the same way, such that differences are meaningful and not due to differences in the understanding of the items in the instrument. Originality/value  The proliferation of the use of multisource feedback instruments in leadership development programs has facilitated the assessment of the fairness of managers. This study shows that perceptions of fairness of the manager as reported by his or her constituencies (e.g., peers, supervisor, subordinates) change very little as a function of who is doing the perceiving, which heretofore has not been shown.

Finite Mixture Multilevel Multidimensional Ordinal IRT Models for Large Scale Cross-Cultural Research

We present a class of finite mixture multilevel multidimensional ordinal IRT models for large scale cross-cultural research. Our model is proposed for confirmatory research settings. Our prior for item parameters is a mixture distribution to accommodate situations where different groups of countries have different measurement operations, while countries within these groups are still allowed to be heterogeneous. A simulation study is conducted that shows that all parameters can be recovered. We also apply the model to real data on the two components of affective subjective well-being: positive affect and negative affect. The psychometric behavior of these two scales is studied in 28 countries across four continents.     

Using Threshold Autoregressive Models to Study Dyadic Interactions

Considering a dyad as a dynamic system whose current state depends on its past state has allowed researchers to investigate whether and how partners influence each other. Some researchers have also focused on how differences between dyads in their interaction patterns are related to other differences between them. A promising approach in this area is the model that was proposed by Gottman and Murray, which is based on nonlinear coupled difference equations. In this paper, it is shown that their model is a special case of the threshold autoregressive (TAR) model. As a consequence, we can make use of existing knowledge about TAR models with respect to parameter estimation, model alternatives and model selection. We propose a new estimation procedure and perform a simulation study to compare it to the estimation procedure developed by Gottman and Murray. In addition, we include an empirical example based on interaction data of three dyads.     

Polytomous Latent Scales for the Investigation of the Ordering of Items

We propose three latent scales within the framework of nonparametric item response theory for polytomously scored items. Latent scales are models that imply an invariant item ordering, meaning that the order of the items is the same for each measurement value on the latent scale. This ordering property may be important in, for example, intelligence testing and person-fit analysis. We derive observable properties of the three latent scales that can each be used to investigate in real data whether the particular model adequately describes the data. We also propose a methodology for analyzing test data in an effort to find support for a latent scale, and we use two real-data examples to illustrate the practical use of this methodology.     

On Association Coefficients for 2×2 Tables and Properties That Do Not Depend on the Marginal Distributions

We discuss properties that association coefficients may have in general, e.g., zero value under statistical independence, and we examine coefficients for 2×2 tables with respect to these properties. Furthermore, we study a family of coefficients that are linear transformations of the observed proportion of agreement given the marginal probabilities. This family includes the phi coefficient and Cohen’s kappa. The main result is that the linear transformations that set the value under independence at zero and the maximum value at unity, transform all coefficients in this family into the same underlying coefficient. This coefficient happens to be Loevinger’s H.     

A Robust Bayesian Approach for Structural Equation Models with Missing Data

In this paper, normal/independent distributions, including but not limited to the multivariate t distribution, the multivariate contaminated distribution, and the multivariate slash distribution, are used to develop a robust Bayesian approach for analyzing structural equation models with complete or missing data. In the context of a nonlinear structural equation model with fixed covariates, robust Bayesian methods are developed for estimation and model comparison. Results from simulation studies are reported to reveal the characteristics of estimation. The methods are illustrated by using a real data set obtained from diabetes patients.     

A Note on Comparing the Estimates of Models for Cluster-Correlated or Longitudinal Data with Binary or Ordinal Outcomes

When using linear models for cluster-correlated or longitudinal data, a common modeling practice is to begin by fitting a relatively simple model and then to increase the model complexity in steps. New predictors might be added to the model, or a more complex covariance structure might be specified for the observations. When fitting models for binary or ordered-categorical outcomes, however, comparisons between such models are impeded by the implicit rescaling of the model estimates that takes place with the inclusion of new predictors and/or random effects. This paper presents an approach for putting the estimates on a common scale to facilitate relative comparisons between models fit to binary or ordinal outcomes. The approach is developed for both population-average and unit-specific models.     

Discrete Choice Models for Ordinal Response Variables: A Generalization of the Stereotype Model

In this paper I present a class of discrete choice models for ordinal response variables based on a generalization of the stereotype model. The stereotype model can be derived and generalized as a random utility model for ordered alternatives. Random utility models can be specified to account for heteroscedastic and correlated utilities. In the case of the generalized stereotype model this includes category-specific random effects due to individual differences in response style. But unlike standard random utility models the generalized stereotype model is better suited for ordinal response variables and can be interpreted as a kind of unidimensional unfolding model. This paper discusses the specification, interpretation, identification, and estimation of generalized stereotype models. Two applications are provided for illustration. This paper benefited significantly from the comments and suggestions of the editor, associate editor, and three anonymous reviewers. It is dedicated to my late colleague, peer, and friend Bradley D. Crouch.     

Confirmatory Factor Analysis of Arnold et al.’s Parenting Scale Across Race, Age, and Sex

We used confirmatory factor analysis (CFA) to investigate the factor structure of several models of Arnold et al.’s Parenting Scale [Arnold et al. (1993). Psychological Assessment, 5, 137–144] across children from various age groups and races. Participants were parents of children (ages 2–16 years) presenting to four community-based pediatric practices for routine care. Parents completed questionnaires pertaining to various aspects of parenting and child behavior problems. Results indicated that a two-factor revision proposed by Reitman et al. [(2001). Journal of Clinical Child Psychology, 30, 514–524] represented the data well and better than the original three-factor structure and other two-factor models. Results from multigroup CFA analyses indicated that this factor structure did not vary across child sex, child age, and parental race. Results of validity analyses indicated that scores on both factors were related to reports of children’s behavior and parental affect and cognitions. This study was the first to use multigroup CFA procedures to demonstrate that relationships between individual items and factors of the Parenting Scale are similar for parents of children across various age groups.     

Cognitive-Behavioral Processes Based on Using the ABC Analysis by Trainees’ for Their Personal Development

As cognitive-behavior therapy (CBT) has proven to be an evidence-based intervention for many mental health problems, the requirement for training programs has increased. Although there is promising data on the skills outcomes of such programs, trainees’ affective/behavioral changes mechanisms and in their faulty thinking patterns during the personal development of such training are unknown. The aim of this study is to investigate which are the most common irrational/dysfunctional beliefs of trainees during a cognitive-behavioral intervention training and their maladaptive consequences, as well as the methods of restructuring that they prefer to change these beliefs into rational/functional ones and achieve more adaptive consequences. 94 participants in a cognitive-behavioral interventions training program filled out 340 ABC, forms related to negative events at work and in personal life, as part of the personal development component in the training program. The obtained qualitative data was coded by three trained ratters in accordance to the current cognitive model of CBT. Contingencies analysis showed that demandingness, awfulizing and global evaluation (GE) are most frequently associated with anxiety, while low frustration tolerance is associated with anger. Comfort, affiliation, achievement themes were most frequently associated with anxiety, while fairness was most frequently associated with anger. Pragmatic cognitive restructuring was the most frequently used by trainees. We found evidence that confirmed many of the theoretical predictions form the cognitive model of CBT in respect to the associations between irrational/dysfunctional cognitions and dysfunctional emotions as well as some particularities for this specific population.     

Estimating Structural Equation Models Using James–Stein Type Shrinkage Estimators

Psychometrika - We propose a two-step procedure to estimate structural equation models (SEMs). In a first step, the latent variable is replaced by its conditional expectation given the observed...     

Psychological Sense of University Membership: An Adaptation Study of the PSSM Scale for Turkish University Students

The Psychological Sense of School Membership Scale (PSSM) is a widely used instrument to assess the sense of belonging to a school among adolescents. Despite its widespread use in middle and high school students, to date no particular adaptation study has been conducted for its use among university students. For this reason, the present study conducted an adaptation of the PSSM scale for these students. Five hundred and nine students at a Turkish university voluntarily participated in the study, and the PSSM Scale's factor structure was examined by exploratory and confirmatory factor analyses, identifying three factors representing the students' sense of university membership with acceptable internal consistencies: acceptance by faculty members (.70), belonging (.75), and acceptance by students (.76). The internal consistency of the 18-item scale was calculated as .84. As hypothesized, the convergent and discriminant validity of the scale was also tested. The self-report sense of belonging and degree of satisfaction with the university were positively correlated with the three dimensions of the scale. Also, the scores regarding the students' intention to drop out of university along with loneliness were negatively correlated with all the dimension of the PSSM scale.     

Variable Neighborhood Search Heuristics for Selecting a Subset of Variables in Principal Component Analysis

The selection of a subset of variables from a pool of candidates is an important problem in several areas of multivariate statistics. Within the context of principal component analysis (PCA), a number of authors have argued that subset selection is crucial for identifying those variables that are required for correct interpretation of the components. In this paper, we adapt the variable neighborhood search (VNS) paradigm to develop two heuristics for variable selection in PCA. The performances of these heuristics were compared to those obtained by a branch-and-bound algorithm, as well as forward stepwise, backward stepwise, and tabu search heuristics. In the first experiment, which considered candidate pools of 18 to 30 variables, the VNS heuristics matched the optimal subset obtained by the branch-and-bound algorithm more frequently than their competitors. In the second experiment, which considered candidate pools of 54 to 90 variables, the VNS heuristics provided better solutions than their competitors for a large percentage of the test problems. An application to a real-world data set is provided to demonstrate the importance of variable selection in the context of PCA.     

The Marlowe–Crowne Social Desirability Scale outperforms the BIDR Impression Management Scale for identifying fakers

Self-report personality tests are used widely, but it is not uncommon for an individual’s scale score to be invalid due to Socially Desirable Responding (SDR): answering to be viewed favourably. Various indices exist to detect SDR (e.g., faking). The Marlowe–Crowne Social Desirability Scale (MCSDS) formerly was the most popular. The current gold standard is the Balanced Inventory of Desirable Responding (BIDR), considered more sensitive because its development incorporated newer theoretical and empirical understanding of SDR and more sophisticated multivariate techniques. We compare the efficacy of these measures with surprising results: the MCSDS consistently outperforms the BIDR in identifying fakers. This finding indicates that the MCSDS should be retained because it captures elements of faking more effectively than the modern scale.     

Time Delay Embedding Increases Estimation Precision of Models of Intraindividual Variability

This paper investigates the precision of parameters estimated from local samples of time dependent functions. We find that time delay embedding, i.e., structuring data prior to analysis by constructing a data matrix of overlapping samples, increases the precision of parameter estimates and in turn statistical power compared to standard independent rows of panel data. We show that the reason for this effect is that the sign of estimation bias depends on the position of a misplaced data point if there is no a priori knowledge about initial conditions of the time dependent function. Hence, we reason that the advantage of time delayed embedding is likely to hold true for a wide variety of functions. We support these conclusions both by mathematical analysis and two simulations.     

A Field-Tested Task Analysis for Creating Single-Subject Graphs Using Microsoft<Superscript>®</Superscript> Office Excel

Creating single-subject (SS) graphs is challenging for many researchers and practitioners because it is a complex task with many steps. Although several authors have introduced guidelines for creating SS graphs, many users continue to experience frustration. The purpose of this article is to minimize these frustrations by providing a field-tested task analysis for creating SS graphs using Microsoft^® Office Excel. Results from the field test are presented and the task analysis, which includes steps for creating a variety of SS graphs, is provided. The article includes various illustrations, a list of prerequisite skills, tips, and troubleshooting items.     

Development of a Response Inconsistency Scale for the Personality Inventory for DSM–5

The advent of a dimensional model of personality disorder included in DSM–5 has necessitated the development of a new measurement scheme, specifically a self-report questionnaire termed the Personality Inventory for DSM–5 (PID–5; Krueger, Derringer, Markon, Watson, &; Skodol, 2012 Krueger, R. F., Derringer, J., Markon, K. E., Watson, D., &; Skodol, A. E. (2012). Initial construction of a maladaptive personality trait model and inventory for DSM–5. Psychological Medicine, 42, 1879–1890. doi:10.1017/S0033291711002674[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]). However, there are many threats to the validity of a self-report measure, including response inconsistency. This study outlines the development of an inconsistency scale for the PID–5. Across both college student and clinical samples, the inconsistency scale was able to reliably differentiate real from random responding. Random responses led to increased scores on the PID–5 facets, indicating the importance of detecting inconsistent responding prior to test interpretation. Thus, this inconsistency scale could be of use to researchers and clinicians in detecting inconsistent responses to this new personality disorder measure.     

Assessing the Level of Structural Integration Using Operationalized Psychodynamic Diagnosis (OPD): Implications for DSM-5

A key ingredient in the current proposal of the DSM-5 Work Group on Personality and Personality Disorders is the assessment of overall severity of impairment in personality functioning: the Levels of Personality Functioning Scale (LPFS). The aim of this article is to contribute a conceptual and empirical discussion of the LPFS from the perspective of the Operationalized Psychodynamic Diagnosis (OPD) system (OPD Task Force, 2008 ). First, we introduce the OPD Levels of Structural Integration Axis (OPD-LSIA), a measure of individual differences in severity of personality dysfunction that is rooted in psychodynamic theory. We show that the OPD-LSIA is reliable, valid, and highly associated with observer ratings of personality disorders. In the second part, we present results from an OPD expert consensus study exploring potential limitations of the current LPFS item set from the perspective of the OPD-LSIA. We conclude with highlighting implications for future revisions of the DSM-5 proposal.     

The Professional Context as a Predictor for Response Distortion in the Adaption-Innovation Inventory—An Investigation Using Mixture Distribution Item Response Theory Models

The Adaption-Innovation Inventory (AII), originally developed by Kirton (1976), is a widely used self-report instrument for measuring problem-solving styles at work. The present study investigates how scores on the AII are affected by different response styles. Data are collected from a combined sample (N = 738) of students, employees, and entrepreneurs. Using mixture distribution item response theory, we identify individuals with normal and distorted response styles. Additionally, we investigate antecedents and consequences of distorted responses. For antecedents, results indicate influences of cognitive schemata resulting from an individual's professional work context. For consequences, distorted responses show different relationships with occupational outcomes in comparison to normal responses.