Bayesian hierarchical response time modelling—A tutorial

Response time modelling is developing rapidly in the field of psychometrics, and its use is growing in psychology. In most applications, component models for response times are modelled jointly with component models for responses, thereby stabilizing estimation of item response theory model parameters and enabling research on a variety of novel substantive research questions. Bayesian estimation techniques facilitate estimation of response time models. Implementations of these models in standard statistical software, however, are still sparse. In this accessible tutorial, we discuss one of the most common response time models—the lognormal response time model—embedded in the hierarchical framework by van der Linden (2007). We provide detailed guidance on how to specify and estimate this model in a Bayesian hierarchical context. One of the strengths of the presented model is its flexibility, which makes it possible to adapt and extend the model according to researchers' needs and hypotheses on response behaviour. We illustrate this based on three recent model extensions: (a) application to non-cognitive data incorporating the distance-difficulty hypothesis, (b) modelling conditional dependencies between response times and responses, and (c) identifying differences in response behaviour via mixture modelling. This tutorial aims to provide a better understanding of the use and utility of response time models, showcases how these models can easily be adapted and extended, and contributes to a growing need for these models to answer novel substantive research questions in both non-cognitive and cognitive contexts.     

Simple matrix methods for analyzing diffusion models of choice probability, choice response time, and simple response time

Diffusion processes (e.g., Wiener process, Ornstein-Uhlenbeck process) are powerful approaches to model human information processes in a variety of psychological tasks. Lack of mathematical tractability, however, has prevented broad applications of these models to empirical data. This tutorial explains step by step, using a matrix approach, how to construct these models, how to implement them on a computer, and how to calculate the predictions made by these models. In particular, we present models for binaries choices for unidimensional and multiattribute choice alternatives; for simple reaction time tasks; and for three alternatives choice problems.     

Introduction to the Special Section on Mixture Modeling in Personality Assessment

Latent variable models offer a conceptual and statistical framework for evaluating the underlying structure of psychological constructs, including personality and psychopathology. Complex structures that combine or compare categorical and dimensional latent variables can be accommodated using mixture modeling approaches, which provide a powerful framework for testing nuanced theories about psychological structure. This special series includes introductory primers on cross-sectional and longitudinal mixture modeling, in addition to empirical examples applying these techniques to real-world data collected in clinical settings. This group of articles is designed to introduce personality assessment scientists and practitioners to a general latent variable framework that we hope will stimulate new research and application of mixture models to the assessment of personality and its pathology.     

Are Nonfatal Suicide Attempts Instrumental in Achieving Personal and Interpersonal Goals?

This study focused on the well-being of the survivors of suicide attempts and the well-being of their interpersonal relationships after the attempt. The data came from a sample of 392 college students from 10 Muslim majority countries who reported having attempted suicide in the last 4 years. Suicide was conceptualized as a goal-directed behavior embedded in a sociocultural context and motivated by personal or interpersonal goals. We tested a process that linked culturally shaped self-construal to the postsuicidal personal and interpersonal well-being. We posited that this process would operate through the attitudes towards suicide, motives for suicide, the strength of the intention to die. Our model indicated that the acceptability of suicide was positively associated with escape motives, and this association was even stronger for the individuals with interdependent self-construals. Escape motives were negatively associated with postsuicidal personal and interpersonal well-being, but communication motives were not associated with these outcomes. We also found evidence that having an interdependent self-construal might be beneficial for postsuicidal personal and interpersonal well-being. Our results further suggested that the postsuicidal personal and interpersonal well-being of highly interdependent individuals may depend on the interpretation of their act of suicide by their close others.     

A tutorial on how to do a Mokken scale analysis on your test and questionnaire data

Over the past decade, Mokken scale analysis (MSA) has rapidly grown in popularity among researchers from many different research areas. This tutorial provides researchers with a set of techniques and a procedure for their application, such that the construction of scales that have superior measurement properties is further optimized, taking full advantage of the properties of MSA. First, we define the conceptual context of MSA, discuss the two item response theory (IRT) models that constitute the basis of MSA, and discuss how these models differ from other IRT models. Second, we discuss dos and don'ts for MSA; the don'ts include misunderstandings we have frequently encountered with researchers in our three decades of experience with real‐data MSA. Third, we discuss a methodology for MSA on real data that consist of a sample of persons who have provided scores on a set of items that, depending on the composition of the item set, constitute the basis for one or more scales, and we use the methodology to analyse an example real‐data set.     

A tutorial on methods for the modeling and analysis of social network data

This article provides a tutorial review of some fundamental ideas and important methods for the modeling of empirical social network data. It describes basic concepts from graph theory and central elements from social network theory. It presents models for the network degree distribution and for network roles and positions, as well as algebraic approaches, before reviewing recent work on statistical methods to analyze social networks, including boot-strap procedures for testing the prevalence of network structures, basic edge- and dyad-independent statistical models, and more recent statistical network models that assume dependence, exponential random graph models and dynamic stochastic actor oriented models. Network social influence models are reviewed. The article concludes with a summary of new developments relating to models for time-ordered transactions.     

Predicting Suicide Intent: The Roles of Experiencing or Committing Violent Acts

According to the interpersonal theory of suicide (Joiner, 2005), repeated exposure to painful or provocative experiences is associated with lethal or nearly lethal suicide attempts. However, suicide research often focuses on suicide ideation or attempts, rather than intent. Using data from the Collaborative Psychiatric Epidemiological Surveys, we examined traumatic experiences, with a focus on repeated exposure to traumas, in individuals who described their suicide attempts as a strong intent to die versus a cry for help. Only repeated acts of committing violence were associated with high suicide intent, suggesting that individuals who engage in violence are at heightened risk for suicide.     

Le protocole individuel en psychologie : tutoriel à l’usage des psychologues praticiens

Although single-case design seems to be an appropriate method for practicing psychologists as it fits perfectly with the evidence-based practice framework, their use remains quasi-anecdotal in psychology. The aim of the present article is to present the advantages of single-case protocols and to demonstrate the ease of their implementation. It provides practitioners with a tutorial of a free open-source statistical program (SCDA) to analyze single-case data. The main results from a single-case protocol are presented and illustrated through an example. Strategies for integrating these protocols in the clinical practice are discussed. This tutorial could encourage the use of the single-case design in research and practice settings.     

Suicide and blacks: a conceptual framework.

Suicide among black Americans has risen sharply in this decade. The suicide pattern differs from that of white Americans in that black suicide is mainly a youth phenomenon and the rates between black females and black males are relatively equal. The lack of research into these phenomena is highlighted along with an exploration of current explanations. A specific conceptual framework is proposed that includes extragroup pressure, intragroup pressure, and value orientation. The framework is extended into the area of mental health planning, as well as behavioral science practice.     

Conceptual complexity and the bias/variance tradeoff

In this paper we propose that the conventional dichotomy between exemplar-based and prototype-based models of concept learning is helpfully viewed as an instance of what is known in the statistical learning literature as the bias/variance tradeoff. The bias/variance tradeoff can be thought of as a sliding scale that modulates how closely any learning procedure adheres to its training data. At one end of the scale (high variance), models can entertain very complex hypotheses, allowing them to fit a wide variety of data very closely—but as a result can generalize poorly, a phenomenon called overfitting. At the other end of the scale (high bias), models make relatively simple and inflexible assumptions, and as a result may fit the data poorly, called underfitting. Exemplar and prototype models of category formation are at opposite ends of this scale: prototype models are highly biased, in that they assume a simple, standard conceptual form (the prototype), while exemplar models have very little bias but high variance, allowing them to fit virtually any combination of training data. We investigated human learners’ position on this spectrum by confronting them with category structures at variable levels of intrinsic complexity, ranging from simple prototype-like categories to much more complex multimodal ones. The results show that human learners adopt an intermediate point on the bias/variance continuum, inconsistent with either of the poles occupied by most conventional approaches. We present a simple model that adjusts (regularizes) the complexity of its hypotheses in order to suit the training data, which fits the experimental data better than representative exemplar and prototype models.     

Modeling Temporal Interaction Dynamics in Organizational Settings

Most workplace phenomena take place in dynamic social settings and emerge over time, and scholars have repeatedly called for more research into the temporal dynamics of organizational behavior. One reason for this persistent research gap could be that organizational scholars are not aware of the methodological advances that are available today for modeling temporal interactions and detecting behavioral patterns that emerge over time. To facilitate such awareness, this Methods Corner contribution provides a hands-on tutorial for capturing and quantifying temporal behavioral patterns and for leveraging rich interaction data in organizational settings. We provide an overview of different approaches and methodologies for examining temporal interaction patterns, along with detailed information about the type of data that needs to be gathered in order to apply each method as well as the analytical steps (and available software options) involved in each method. Specifically, we discuss and illustrate lag sequential analysis, pattern analysis, statistical discourse analysis, and visualization methods for identifying temporal patterns in interaction data. We also provide key takeaways for integrating these methods more firmly in the field of organizational research and for moving interaction analytical research forward.     

Race, Culture, and Ethnicity: Implications for a Community Intervention

We present an overview of research on African American families and emphasize in this discussion limitations of approaches used in the past to study African American families. We also discuss and identify challenges for designing and implementing community, family-based programs targeting competence-promoting behaviors in African American families. In addition, we attempt to accomplish the following objectives: (1) identify themes in studies of African American families, including bias in prior research; (2) discuss ways in which prior approaches and conceptual models guiding studies of African Americans have created challenges for the development and implementation of community-based intervention and research programs; and (3) present the Parents Matter! Program as an example of a community-based prevention effort that attempted to overcome many of the challenges created by prior research efforts.     

Understanding Gender and Intimate Partner Abuse

This issue represents our efforts to apply a feminist or gender lens to the research on violence in intimate relationships and to forward our understanding of gender and interpersonal violence. The paper introduces the articles in this special issue on “Understanding Gender and Intimate Partner Violence” within a framework of theoretical and methodological issues in feminist research. The current articles are viewed as contributing to our understanding of gender and interpersonal violence: by investigating patterns of interpersonal violence; by examining interpersonal violence with the context of lifespan and culture; by positing or testing theoretical models for gender and interpersonal violence; and by arguing for methodological or conceptual advances in the field.     

Qualitative and quantitative distinctions in personality disorder

The categorical-dimensional debate has catalyzed a wealth of empirical advances in the study of personality pathology. However, this debate is merely one articulation of a broader conceptual question regarding whether to define and describe psychopathology as a quantitatively extreme expression of normal functioning or as qualitatively distinct in its process. In this article I argue that dynamic models of personality (e.g., object relations, cognitive-affective processing system) offer the conceptual scaffolding to reconcile these seemingly incompatible approaches to characterizing the relationship between normal and pathological personality. I propose that advances in personality assessment that sample behavior and experiences intensively provide the empirical techniques, whereas interpersonal theory offers an integrative theoretical framework, for accomplishing this goal.     

Suicide: A Multidimensional Malaise

No one really knows why human beings commit suicide. The goal of this paper is to provide a psychological point of view on the topic, among the many other perspectives that are needed. It addresses the question by providing a theory of suicide, arguing that it is theory that allows us to sort out the booming buzzing mess of experience (Wm. James). Suicide is a multidimensional malaise. Metaphorically speaking, it is an intrapsychic drama on an interpersonal stage. As sound theory must be empirically observable, the theory is next applied to research of suicide notes, studying such factors as age, sex, and method of suicide, cross-culture and cross-time. Next, because all theory must have clinical applicability, a clinical case study of Goethe's Werther is provided. Overall, it is concluded that we need to continue to develop models to understand the suicidal mind.     

A tutorial on Bayesian nonparametric models

A key problem in statistical modeling is model selection, that is, how to choose a model at an appropriate level of complexity. This problem appears in many settings, most prominently in choosing the number of clusters in mixture models or the number of factors in factor analysis. In this tutorial, we describe Bayesian nonparametric methods, a class of methods that side-steps this issue by allowing the data to determine the complexity of the model. This tutorial is a high-level introduction to Bayesian nonparametric methods and contains several examples of their application.