Researchers have recently started to collect data on online platforms such as Amazon’s Mechanical Turk. The advantage of these platforms such as easy access to more representative samples though comes at the cost of a problem that has recently been discovered: statistical bots. These bots are used in large amounts to automatically complete surveys by persons with a financial gain. They contaminate any data obtained from online platforms. In this article, we will provide a Bayesian latent class model that can be routinely applied to identify statistical bots in online questionnaires and tests. The model is very flexible and is based on plausible assumptions that are met in most empirical settings. It provides a confirmatory framework in that the latent classes are predetermined in their meanings (bots vs. non-bots). In a simulation study, we show very beneficial estimation characteristics of (a) identifying bots, and (b) providing unbiased estimates for the remaining participants at the same time. We will illustrate the model and its capabilities with data from an empirical political ideation survey with known bots. 

Zachary Joseph Roman, Holger Brandt, & Jason Michael Miller (accepted for publication). Automated Bot Detection using Bayesian Latent Class Models in Online Surveys. Frontiers in Psychology 
 

This article asks for fruitful points of connection between the theory of social worlds and arenas and ethnomethodology. Using the well-known ethnomethodological concept of doing gender as an example, I ask about deficits within the concept and how these could be overcome with the help of a pragmatist understanding of the situation, as laid out in Situational Analysis. 

Offenberger, Ursula (im Erscheinen). Un/doing gender in social worlds and arenas: Perspectives for aligning ethnomethodology with situational analysis. In Leslie Gauditz, Anna-Lisa Klages, Stefanie Kruse, Eva Marr, Ana Mazur, Tamara Schwertel, & Olaf Tietje (Hrsg.), Die Situationsanalyse als Forschungsprogramm. Wiesbaden: VS. 

The working alliance (WA) has been widely identified as the key concept for psychotherapy and allied health care services. The WA, measured at different phases of diverse kinds of therapies, has been shown to robustly predict posttreatment outcomes. But the way the clients’ conceptualization of the alliance evolves overtime, and the relation between this kind of conceptual change and subsequent symptom improvements, has not been investigated. 
In this article, we investigated the psychometric properties of the WA Inventory with regard to its evolution – and fusion – of the underlying dimensions of the initial three-dimensional WA construct (task, goal, bond) to a single dimension over the course of treatment. Using Dynamic Latent Class Structural Equation Models (DLC-SEM) we investigated data from two randomized clinical trials of cognitive-behavioral therapy for generalized anxiety disorder to evaluate the structural changes in patients’ self-reports of the quality of the alliance and subsequent treatment outcomes. Results indicated a dimensional fusion for 63% and 66% of the clients. This study shows a potential to empirically explore prior theoretical propositions of the evolutions (or stability) of the alliance overtime as it enfolds over time.

Christoph Flückiger, Adam O. Horvath, & Holger Brandt (2022). The Evolution of Patients’ Concept of the Alliance and Its Relation to Outcome: A Dynamic Latent-Class Structural Equation Modeling Approach. Journal of Counseling Psychology, 69(1), 51-62.
 

An important step in scale development and assessment is to evaluate differential item functioning (DIF) across segments of the population. Recent approaches use lasso regularization to simultaneously detect DIF in all items and avoid incorrect anchor item assumptions that incur inflated error rates for classical DIF evaluation methods. Although promising, lasso methods cause underestimated standard errors and incorrect p-values. In this article, alternative Bayesian regularization methods such as Spike-and-Slab and Laplace priors are proposed to investigate DIF in multi-group Item Response Theory and Moderated Nonlinear Factor Analysis models. The performance of these approaches is evaluated using a simulation studies with. 

Siyuan Marco Chen, Daniel J. Bauer, William M. Belzak & Holger Brandt (2022) Advantages of Spike and Slab Priors for Detecting Differential Item Functioning Relative to Other Bayesian Regularizing Priors and Frequentist Lasso, Structural Equation Modeling: A Multidisciplinary Journal, 29:1, 122-139, DOI: 10.1080/10705511.2021.1948335
 

Spatial analytic approaches (or social network auto-regressive models) are classic models in econometric literature, but relatively new in social and behavioral sciences. These models have two major benefits. First, dependent data, either socially or spatially, must be accounted for to acquire unbiased results. Second, analysis of the dependence provides rich additional information such as spillover effects. In this article, we provide a cohesive nonlinear spatial structural equation modeling framework which can simultaneously estimate latent interaction/polynomial effects and account for spatial effects with both exogenous and endogenous latent variables, the Bayesian Spatial Auto-Regressive Structural Equation Model (BARDSEM). 

Zachary Joseph Roman & Holger Brandt (2021). A Latent Auto-regressive Approach for Bayesian Structural Equation Modeling of Spatially or Socially Dependent Data. Multivariate Behavioral Research
 

The article explains how religious symbolization of gender is related to a common belief in the naturalness of gender orders. It asks how contemporary changes of modernization theories impact on the relationship between religion and gender. 

Offenberger, Ursula (2021a). Christentum und Geschlecht. Oder: Wie divers sind Eva und Adam? In Fahimah Ulfat & Ali Ghandour (Hrsg.), Sexualität, Gender und Religion in gegenwärtigen Diskursen. Wiesbaden: VS. 

This article reports on the process of creating a social science webcomic about the Hull House settlement in Chicago. It offers reflections on the appropriateness of scientific communication for the respective subject matter. 

Offenberger, Ursula (2021b). Verwandlung von Lehrstoff in einen Comic. Ein Experiment mit den Siedlerinnen von Hull House, Chicago. In Birgit Blättel-Mink (Hrsg.), Gesellschaft unter Spannung. Verhandlungen des 40. Kongresses der Deutschen Gesellschaft für Soziologie. 

Stefano Noventa and Daniel W. Heck, published in the Journal of Mathematical Psychology a proof of representation of basic probabilisitc models from Knowedge Structure Theory (specifically, the Basic Local Independence Model and the Simple Learning Model) within the general framework of Multinomial Processing Tree models. By highlighting such link and its implications for modeling violations of local stochastic independence in Item Response Theory, the authors hope to facilitate an exchange of theoretical results, statistical methods, and software across these different domains of mathematical psychology and psychometrics.

Heck*, D.W., Noventa*, S. (2020). Representing Probabilistic Models of
Knowledge Space Theory by Multinomial Processing Tree Models. Journal
of Mathematical Psychology, 96. DOI: 10.1016/j.jmp.2020.102329.

_{*shared co-first authorship}

This article should help readers in three ways: to assess the significance of qualitative methods and social science theories for interdisciplinary health research, to see the importance of a focus of health services research on the role of patient organisations and movements, and to understand that health research that is less strongly oriented towards instrumental use and direct application orientation can also provide useful extensions of perspectives for the design of health care in the future. 

Offenberger, Ursula (2020). Perspektiven und Potenziale qualitativer Gesundheitsforschung. Ein Plädoyer für interdisziplinäre Brückenschläge. Das Gesundheitswesen. 

Augustin Kelava and Holger Brandt published a new nonlinear dynamic latent class structural equation model (NDLC-SEM) framework in the Structural Equation Modeling Journal. The NDLC-SEM is capable of intra-individual psychological processes (e.g., changes in affective states as trajectories in mathematics studies), which for example could predict a drop-out. These processes are decomposed into parts which include individual-specific components (e.g., vulnerabilities, stable risk factors such as personality factors or cognitive abilities) and time-specific components. The NDLC-SEM acts as a very comprehensive framework that allows to integrate information of different data-levels and flexible relationships between variables (e.g., specific interactions).

Kelava, A. & Brandt, H. (2019). A nonlinear dynamic latent class structural equation model. Structural Equation Modeling: A Multidisciplinary Journal, 26(4), 509-528.  doi: 10.1080/10705511.2018.1555692