Most studies of political participation have either focused on specific political behaviours or combined several behaviours into additive scales of institutional versus non‐institutional participation. Through a multi‐group latent class analysis of participation in 15 different political actions, conducted among citizens from four Western European countries, we identified five empirically grounded participant types that differ in their political engagement, socio‐demographic characteristics and political attitudes: ‘voter specialists’, ‘expressive voters’, ‘online participants’, ‘all‐round activists’ and ‘inactives’. While the same participant types were identified in all four countries, the proportion of citizens assigned to each type varies across countries. Our results challenge the claim that some citizens specialize in protest politics at the expense of electoral politics. Furthermore, our typological approach challenges previous findings on the individual characteristics associated with political (in)action.

Despite the centrality of national identity in the exclusionary discourse of the European radical right, scholars have not investigated how popular definitions of nationhood are connected to dispositions toward Muslims. Moreover, survey‐based studies tend to conflate anti‐Muslim attitudes with general anti‐immigrant sentiments. This article contributes to research on nationalism and out‐group attitudes by demonstrating that varieties of national self‐understanding are predictive of anti‐Muslim attitudes, above and beyond dispositions toward immigrants. Using latent class analysis and regression models of survey data from 41 European countries, it demonstrates that conceptions of nationhood are heterogeneous within countries and that their relationship with anti‐Muslim attitudes is contextually variable. Consistent with expectations, in most countries, anti‐Muslim attitudes are positively associated with ascriptive – and negatively associated with elective (including civic) – conceptions of nationhood. Northwestern Europe, however, is an exception to this pattern: in this region, civic nationalism is linked to greater antipathy toward Muslims. It is suggested that in this region, elective criteria of belonging have become fused with exclusionary notions of national culture that portray Muslims as incompatible with European liberal values, effectively legitimating anti‐Muslim sentiments in mainstream political culture. This may heighten the appeal of anti‐Muslim sentiments not only on the radical right, but also among mainstream segments of the Northwestern European public, with important implications for social exclusion and political behaviour.

This study investigates a discourse about billionaire philanthropy established in letters submitted by 187 of 209 signatories of the Giving Pledge. The philanthropy of the wealthy is gaining increasing public attention and is subject to growing criticism, which demands additional study of how the wealthy collectively explain their generosity. The mixed-method analysis finds a strong emphasis on education and health causes and identifies two distinct and coherent rationales for being generous. The majority of letters express a social–normative rationale, consisting of two prevailing explanations: an expressed gratitude and desire to “give back” (1) and references to family upbringing as a socializing force (2). A minority of letters articulate a personal–consequentialist rationale, highlighting three separate explanations: a large inheritance may harm offspring (1), giving as personal gratification (2), and an acknowledgment of excess wealth with no better use (3). An expressed desire to have impact and make a difference appears in both rationales. The overall dominance of a social–normative rationale projects a discourse emphasizing benevolence as well as a narrative in which billionaires are an exceptionally productive and grateful subset of society. While previous studies have primarily focused on identifying individual psychological motives, this study shows how the Giving Pledge letters reflect a philanthropic discourse among the wealthy going back to Andrew Carnegie’s Gospel of Wealth.

A combination of computer-aided qualitative data analysis (CAQDAS) and latent class analysis (LCA) can substantially augment the qualitative analysis of textual data sources used in third-sector studies. This article explains how to employ both techniques iteratively to capture often implicit ideas and meaning-making by third-sector leaders, donors, and other stakeholders. CAQDAS facilitates the coding, organization, and quantification of qualitative data, effectively creating parallel qualitative and quantitative data structures. LCA facilities the discovery of latent concepts, document classification, and the identification of exemplary qualitative evidence to aid interpretation. For third-sector research, CAQDAS and LCA are particularly promising because diverse stakeholders usually do not share homogenous views about core issues such as organizational effectiveness, collaboration, impact measurement, or philanthropic approaches, for example. The procedure explained here provides a rigorous method for discovering and understanding diversity in perspectives and is especially useful in medium-n research settings common to third-sector scholarship.

The relevance and impact of political scientists’ professional activities outside of universities has become the focus of public attention, partly due to growing expectations that research should help address society’s grand challenges. One type of such activity is policy advising. However, little attention has been devoted to understanding the extent and type of policy advising activities political scientists engage in. This paper addresses this gap by adopting a classification that distinguishes four ideal types of policy advisors representing differing degrees of engagement. We test this classification by calculating a multi-level latent class model to estimate key factors explaining the prevalence of each type based on an original dataset obtained from a survey of political scientists across 39 European countries. Our results challenge the wisdom that political scientists are sitting in an “ivory tower”: the vast majority (80%) of political scientists in Europe are active policy advisers, with most of them providing not only expert guidance but also normative assessments.

Process data, in particular, log data collected from a computerized test, documents the sequence of actions performed by an examinee in pursuit of solving a problem, affording an opportunity to understand test-taking behavioral patterns that account for demographic group differences in key outcomes of interest, for instance, final score on a cognitive item. Addressing this aim, this article proposes a latent class mediation analysis procedure. Using continuous process features extracted from action sequence data as indicators, latent classes underlying the test-taking behavior are identified in a latent class mediation model, where an examinee’s nominal latent class membership enters as the mediator between the observed grouping and outcome variables. A headlong search algorithm for selecting the subset of process features that maximizes the total indirect effect of the latent class mediator is implemented. The proposed procedure is validated with a series of simulations. An application to a large-scale assessment highlights how the proposed method can be used to explain performance gaps between students with learning disability and their typically developing peers on the National Assessment of Educational Progress (NAEP) math assessment.

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by inattention and/or hyperactivity-impulsivity, accompanied by deficits in executive function (EF). However, how the two core symptoms of ADHD are affected by EF deficits remains unclear. 649 children with ADHD were recruited. Data were collected from ADHD rating scales, the Behavior Rating Inventory of EF (BRIEF), and other demographic questionnaires. Regression and path analyses were conducted to explore how deficits in cool and hot EF influence different ADHD core symptoms. Latent class analysis and logistic regression were employed to further examine whether classification of ADHD subtypes is associated with specific EF deficits. EF deficits significantly predicted the severity of ADHD core symptoms, with cool EF being a greater predictor of inattention and hot EF having a more significant effect on hyperactivity/impulsivity. Moreover, person-centered analyses revealed higher EF deficits in subtypes of ADHD with more severe symptoms, and both cool and hot EF deficits could predict the classification of ADHD subtypes. Our findings identify distinct roles for cool and hot EF deficits in the two core symptoms of ADHD, which provide scientific support for the development of ADHD diagnostic tools and personalized intervention from the perspective of specific EF deficits.

There is a growing focus on understanding the complexity of dietary patterns and how they relate to health and other factors. Approaches that have not traditionally been applied to characterise dietary patterns, such as latent class analysis and machine learning algorithms, may offer opportunities to characterise dietary patterns in greater depth than previously considered. However, there has not been a formal examination of how this wide range of approaches has been applied to characterise dietary patterns. This scoping review synthesised literature from 2005 to 2022 applying methods not traditionally used to characterise dietary patterns, referred to as novel methods. MEDLINE, CINAHL and Scopus were searched using keywords including latent class analysis, machine learning and least absolute shrinkage and selection operator. Of 5274 records identified, 24 met the inclusion criteria. Twelve of twenty-four articles were published since 2020. Studies were conducted across seventeen countries. Nine studies used approaches with applications in machine learning, such as classification models, neural networks and probabilistic graphical models, to identify dietary patterns. The remaining studies applied methods such as latent class analysis, mutual information and treelet transform. Fourteen studies assessed associations between dietary patterns characterised using novel methods and health outcomes, including cancer, cardiovascular disease and asthma. There was wide variation in the methods applied to characterise dietary patterns and in how these methods were described. The extension of reporting guidelines and quality appraisal tools relevant to nutrition research to consider specific features of novel methods may facilitate consistent reporting and enable synthesis to inform policies and programs.

We present an hierarchical Bayes approach to modeling parameter heterogeneity in generalized linear models. The model assumes that there are relevant subpopulations and that within each subpopulation the individual-level regression coefficients have a multivariate normal distribution. However, class membership is not known a priori, so the heterogeneity in the regression coefficients becomes a finite mixture of normal distributions. This approach combines the flexibility of semiparametric, latent class models that assume common parameters for each sub-population and the parsimony of random effects models that assume normal distributions for the regression parameters. The number of subpopulations is selected to maximize the posterior probability of the model being true. Simulations are presented which document the performance of the methodology for synthetic data with known heterogeneity and number of sub-populations. An application is presented concerning preferences for various aspects of personal computers.

This commentary addresses the modeling and final analytical path taken, as well as the terminology used, in the paper “Hierarchical diagnostic classification models: a family of models for estimating and testing attribute hierarchies” by Templin and Bradshaw (Psychometrika, doi:10.1007/s11336-013-9362-0, 2013). It raises several issues concerning use of cognitive diagnostic models that either assume attribute hierarchies or assume a certain form of attribute interactions. The issues raised are illustrated with examples, and references are provided for further examination.

Diagnostic classification models are confirmatory in the sense that the relationship between the latent attributes and responses to items is specified or parameterized. Such models are readily interpretable with each component of the model usually having a practical meaning. However, parameterized diagnostic classification models are sometimes too simple to capture all the data patterns, resulting in significant model lack of fit. In this paper, we attempt to obtain a compromise between interpretability and goodness of fit by regularizing a latent class model. Our approach starts with minimal assumptions on the data structure, followed by suitable regularization to reduce complexity, so that readily interpretable, yet flexible model is obtained. An expectation–maximization-type algorithm is developed for efficient computation. It is shown that the proposed approach enjoys good theoretical properties. Results from simulation studies and a real application are presented.

Several articles in the past fifteen years have suggested various models for analyzing dichotomous test or questionnaire items which were constructed to reflect an assumed underlying structure. This paper shows that many models are special cases of latent class analysis. A currently available computer program for latent class analysis allows parameter estimates and goodness-of-fit tests not only for the models suggested by previous authors, but also for many models which they could not test with the more specialized computer programs they developed. Several examples are given of the variety of models which may be generated and tested. In addition, a general framework for conceptualizing all such models is given. This framework should be useful for generating models and for comparing various models.

A probabilistic choice model is developed for paired comparisons data about psychophysical stimuli. The model is based on Thurstone's Law of Comparative Judgment Case V and assumes that each stimulus is measured on a small number of physical variables. The utility of a stimulus is related to its values on the physical variables either by means of an additive univariate spline model or by means of multivariate spline model. In the additive univariate spline model, a separate univariate spline transformation is estimated for each physical dimension and the utility of a stimulus is assumed to be an additive combination of these transformed values. In the multivariate spline model, the utility of a stimulus is assumed to be a general multivariate spline function in the physical variables. The use of B splines for estimating the transformation functions is discussed and it is shown how B splines can be generalized to the multivariate case by using as basis functions tensor products of the univariate basis functions. A maximum likelihood estimation procedure for the Thurstone Case V model with spline transformation is described and applied for illustrative purposes to various artificial and real data sets. Finally, the model is extended using a latent class approach to the case where there are unreplicated paired comparisons data from a relatively large number of subjects drawn from a heterogeneous population. An EM algorithm for estimating the parameters in this extended model is outlined and illustrated on some real data.

The standard tobit or censored regression model is typically utilized for regression analysis when the dependent variable is censored. This model is generalized by developing a conditional mixture, maximum likelihood method for latent class censored regression. The proposed method simultaneously estimates separate regression functions and subject membership in K latent classes or groups given a censored dependent variable for a cross-section of subjects. Maximum likelihood estimates are obtained using an EM algorithm. The proposed method is illustrated via a consumer psychology application.

A normally distributed person-fit index is proposed for detecting aberrant response patterns in latent class models and mixture distribution IRT models for dichotomous and polytomous data.

This article extends previous work on the null distribution of person-fit indices for the dichotomous Rasch model to a number of models for categorical data. A comparison of two different approaches to handle the skewness of the person-fit index distribution is included.