New Instruments for Popular Understandings of Democracy

This chapter reviews pertinent research on varying understandings of democracy to assess the empirical challenges in studying this elusive concept and to propose some new survey instruments (i.e., the PUD instruments) with theoretical justification. In particular, it emphasizes the embedded tensions and critical trade-offs as people view and assess democracy and brings such tensions and trade-offs to the center of instrument selection. The chapter further examines the validity and reliability of the PUD instruments using both survey experiments and different psychometric models to establish a solid methodological foundation for subsequent empirical analysis.

Varying Understandings of Democracy in the Contemporary World

This chapter presents systematic descriptive evidence on the status of popular conceptions of democracy in today’s world, using GBS II data from seventy-one societies. To make the descriptive analysis more informative, we have included comparable information from the United States and relied on different psychometric models to uncover people’s latent characteristics that shape their responses to the PUD instruments. We have consistently found that the PUD instruments are sufficiently sensitive to the socioeconomic and political environment, thus revealing significant and substantial variation in popular conceptions of democracies across regions, between societies, and among individuals. To ensure that the variation documented in the PUD instruments is not something transient or idiosyncratic, we further explore the longitudinal dynamics of this critical attitude using the ABS two-wave rolling-cross-sectional surveys from thirteen East Asian societies.

An Autoencoder-Based Response Time Model and Its Application in Anomaly Detection

Most psychometric models of response times are primarily theory-driven, meaning they are based on various sets of assumptions about how the data should behave. Although useful in certain contexts, such models are often inadequate for the complexities of realistic testing situations and display a poor fit on empirical data. Therefore, as a functional alternative, the present study proposes a data-driven approach, an autoencoder-based response time model, to modeling response times of correctly answered responses. Also, this study introduces the application of the proposed model in anomaly detection (including aberrant examinee and item detection). The result shows this model has an acceptable performance in both response time modeling and anomaly detection.

Recent Integrations of Latent Variable Network Modeling With Psychometric Models

The combination of network modeling and psychometric models has opened up exciting directions of research. However, there has been confusion surrounding differences among network models, graphic models, latent variable models and their applications in psychology. In this paper, I attempt to remedy this gap by briefly introducing latent variable network models and their recent integrations with psychometric models to psychometricians and applied psychologists. Following this introduction, I summarize developments under network psychometrics and show how graphical models under this framework can be distinguished from other network models. Every model is introduced using unified notations, and all methods are accompanied by available R packages inducive to further independent learning.

Application of a Longitudinal IRTree Model: Response Style Changes Over Time

Traditional psychometric models focus on studying observed categorical item responses, but these models often oversimplify the respondent cognitive response process, assuming responses are driven by a single substantive trait. A further weakness is that analysis of ordinal responses has been primarily limited to a single substantive trait at one time point. This study applies a significant expansion of this modeling framework to account for complex response processes across multiple waves of data collection using the item response tree (IRTree) framework. This study applies a novel model, the longitudinal IRTree, for response processes in longitudinal studies, and investigates whether the response style changes are proportional to changes in the substantive trait of interest. To do so, we present an empirical example using a six-item sexual knowledge scale from the National Longitudinal Study of Adolescent to Adult Health across two waves of data collection. Results show an increase in sexual knowledge from the first wave to the second wave and a decrease in midpoint and extreme response styles. Model validation revealed failure to account for response style can bias estimation of substantive trait growth. The longitudinal IRTree model captures midpoint and extreme response style, as well as the trait of interest, at both waves.

Why not both? Rethinking categorical and continuous approaches to bilingualism

Aims and Objectives: Bilingualism is a complex construct, and it can be difficult to define and model. This paper proposes that the field of bilingualism can draw from other fields of psychology, by integrating advanced psychometric models that incorporate both categorical and continuous properties. These models can unify the widespread use of bilingual and monolingual groups that exist in the literature with recent proposals that bilingualism should be viewed as a continuous variable. Approach: In the paper, we highlight two models of potential interest: the factor mixture model and the grade-of-membership model. These models simultaneously allow for the formation of different categories of speakers and for continuous variation to exist within these categories. We discuss how these models could be implemented in bilingualism research, including how to develop these models. When using either of the two models, researchers can conduct their analyses on either the categorical or continuous information, or a combination of the two, depending on which is most appropriate to address their research question. Conclusions: The field of bilingualism research could benefit from incorporating more complex models into definitions of bilingualism. To help various subfields of bilingualism research converge on appropriate models, we encourage researchers to pre-register their model selection and planned analyses, as well as to share their data and analysis scripts. Originality: The paper uniquely proposes the incorporation of advanced statistical psychometric methods for defining and modeling bilingualism. Significance: Conceptualizing bilingualism within the context of these more flexible models will allow a wide variety of research questions to be addressed. Ultimately, this will help to advance theory and lead to a fuller and deeper understanding of bilingualism.

An Attention-Based Diffusion Model for Psychometric Analyses

The emergence of computer-based assessments has made response times, in addition to response accuracies, available as a source of information about test takers’ latent abilities. The development of substantively meaningful accounts of the cognitive process underlying item responses is critical to establishing the validity of psychometric tests. However, existing substantive theories such as the diffusion model have been slow to gain traction due to their unwieldy functional form and regular violations of model assumptions in psychometric contexts. In the present work, we develop an attention-based diffusion model based on process assumptions that are appropriate for psychometric applications. This model is straightforward to analyse using Gibbs sampling and can be readily extended. We demonstrate our model’s good computational and statistical properties in a comparison with two well-established psychometric models.

Disentangling the Psycholinguistic Loci of Anomia with Cognitive Psychometric Models

This article reviews advanced statistical techniques for measuring impairments in object naming, particularly in the context of stroke-induced aphasia. Traditional testing strategies can be challenged by the multifaceted nature of impairments that arise due to the complex relationships between localized brain damage and disruption to the cognitive processes required for successful object naming. Cognitive psychometric models can combine response-type analysis with item-response theory to yield accurate estimates of multiple abilities using data collected from a single task. The models also provide insights about how the test items can be challenging in different ways. Although more work is needed to fully optimize their clinical utility in practice, these formal concepts can guide thoughtful selection of stimuli used in treatment or assessment, as well as providing a framework to interpret response-type data.

A Data-Driven Typology of Emotion Regulation Profiles

Typologies serve to organize knowledge and advance theory for many scientific disciplines, including more recently in the social and behavioral sciences. To date, however, no typology exists to categorize an individual’s use of emotion regulation strategies. This is surprising given that emotion regulation skills are used daily and that deficits in this area are robustly linked with mental health symptoms. Here we attempted to identify and validate a working typology of emotion regulation across six samples (collectively comprised of 1492 participants from multiple populations) by using a combination of computational techniques, psychometric models, and growth curve modeling. We uncovered evidence for three types of regulators: a type (Lo) that infrequently uses emotion regulation strategies, a type (Hi) that uses them frequently but indiscriminately, and a third type (Mix) that selectively uses some (cognitive reappraisal and situation selection), but not other (expressive suppression), emotion regulation strategies frequently. Results showed that membership in the Hi and Mix types is associated with better mental health, with the Mix type being the most adaptive of the three. These differences were stable over time and across different samples. These results carry important implications for both our basic understanding of emotion regulation behavior and for informing future interventions aimed at improving mental health.