Exploratory Factor Analysis Trees: Evaluating Measurement Invariance Between Multiple Covariates

Measurement invariance (MI) describes the equivalence of a construct across groups. To be able to meaningfully compare latent factor means between groups, it is crucial to establish MI. Although methods exist that test for MI, these methods do not perform well when many groups have to be compared or when there are no hypotheses about them. We suggest a method called Exploratory Factor Analysis Trees (EFA trees) that are an extension to SEM trees. EFA trees combine EFA with a recursive partitioning algorithm that can uncover non-invariant subgroups in a data-driven manner. An EFA is estimated and then tested for parameter instability on multiple covariates (e.g., age, education, etc.) by a decision tree based method. Our goal is to provide a method with which MI can be addressed in the earliest stages of questionnaire development or prior to analyses between groups. We show how EFA trees can be implemented in the software R using lavaan and partykit. In a simulation, we demonstrate the ability of EFA trees to detect a lack of MI under various conditions. Our online material contains a template script that can be used to apply EFA trees on one’s own questionnaire data. Limitations and future research ideas are discussed

Evaluating Model Fit of Measurement Models in Confirmatory Factor Analysis

Confirmatory factor analyses (CFA) are often used in psychological research when developing measurement models for psychological constructs. Evaluating CFA model fit can be quite challenging, as tests for exact model fit may focus on negligible deviances, while fit indices cannot be interpreted absolutely without specifying thresholds or cutoffs. In this study, we review how model fit in CFA is evaluated in psychological research using fit indices and compare the reported values with established cutoff rules. For this, we collected data on all CFA models in Psychological Assessment from the years 2015 to 2020 (Formula presented.). In addition, we reevaluate model fit with newly developed methods that derive fit index cutoffs that are tailored to the respective measurement model and the data characteristics at hand. The results of our review indicate that the model fit in many studies has to be seen critically, especially with regard to the usually imposed independent clusters constraints. In addition, many studies do not fully report all results that are necessary to re-evaluate model fit. We discuss these findings against new developments in model fit evaluation and methods for specification search

Predictors and outcomes in primary depression care (POKAL) – a research training group develops an innovative approach to collaborative care

BACKGROUND: The interdisciplinary research training group (POKAL) aims to improve care for patients with depression and multimorbidity in primary care. POKAL includes nine projects within the framework of the Chronic Care Model (CCM). In addition, POKAL will train young (mental) health professionals in research competences within primary care settings. POKAL will address specific challenges in diagnosis (reliability of diagnosis, ignoring suicidal risks), in treatment (insufficient patient involvement, highly fragmented care and inappropriate long-time anti-depressive medication) and in implementation of innovations (insufficient guideline adherence, use of irrelevant patient outcomes, ignoring relevant context factors) in primary depression care. METHODS: In 2021 POKAL started with a first group of 16 trainees in general practice (GPs), pharmacy, psychology, public health, informatics, etc. The program is scheduled for at least 6 years, so a second group of trainees starting in 2024 will also have three years of research-time. Experienced principal investigators (PIs) supervise all trainees in their specific projects. All projects refer to the CCM and focus on the diagnostic, therapeutic, and implementation challenges. RESULTS: The first cohort of the POKAL research training group will develop and test new depression-specific diagnostics (hermeneutical strategies, predicting models, screening for suicidal ideation), treatment (primary-care based psycho-education, modulating factors in depression monitoring, strategies of de-prescribing) and implementation in primary care (guideline implementation, use of patient-assessed data, identification of relevant context factors). Based on those results the second cohort of trainees and their PIs will run two major trials to proof innovations in primary care-based a) diagnostics and b) treatment for depression. CONCLUSION: The research and training programme POKAL aims to provide appropriate approaches for depression diagnosis and treatment in primary care

Integration fluktuierender erneuerbarer Energien durch konvergente Nutzung von Strom- und Gasnetzen - Konvergenz Strom- und Gasnetze (KonStGas) - Abschlussbericht

Für die Energiewende in Deutschland ist zeitnah ein nennenswerter Ausbau der Stromnetze auf Transport- und Verteilnetzebene erforderlich. Mittel- bis langfristig werden für die Umstellung der Strom- und Energieversorgung auf erneuerbaren Energien (EE) zusätzlich große Speicherkapazitäten benötigt. Dabei sind kostengünstige und mit minimalen Energieverlusten verbundene Speicher- und Erzeugungstechnologien anzustreben. Lösungsansätze dafür werden bisher überwiegend auf der Stromseite diskutiert. Chancen, die sich aus der Kopplung von Strom- und Gasnetzen ergeben, werden kaum wahrgenommen. Das erhebliche Lösungspotential der vorhandenen Gasinfrastruktur und -Anwendungstechnologien mittels Power-to-Gas sowie die damit verbundenen Auswirkungen auf eine nachhaltige Gestaltung der Energiewende finden zu wenig Beachtung. Vor diesem Hintergrund hatte das Forschungsvorhaben "Integration fluktuierender erneuerbarer Energien durch konvergente Nutzung von Strom und Gasnetzen - Konvergenz Strom- und Gasnetze" zum Ziel, unter Berücksichtigung der Kopplung von Strom- und Gasnetzen, (1) die Potenziale zur Aufnahme, Speicherung und Verteilung von EE zu bestimmen, (2) die dynamischen Energieströme aus Angebot und Nachfrage in der gesamten Energieversorgungsstruktur zu modellieren, (3) die Kopplung volkswirtschaftlich zu analysieren und (4) Handlungsempfehlungen für den Ausbau der Netzinfrastrukturen und die Entwicklung eines zukünftigen Energiemarktes abzuleiten

Exploratory Factor Analysis Trees: Evaluating Measurement Invariance Between Multiple Covariates

Measurement invariance (MI) describes the equivalence of a construct across groups. To be able to meaningfully compare latent factor means between groups, it is crucial to establish MI. Although methods exist that test for MI, these methods do not perform well when many groups have to be compared or when there are no hypotheses about them. We suggest a method called Exploratory Factor Analysis Trees (EFA trees) that are an extension to SEM trees (Brandmaier et al., 2013). EFA trees combine EFA with a recursive partitioning algorithm that can uncover non-invariant subgroups in a data-driven manner. An EFA is estimated and then tested for parameter instability on multiple covariates (e.g., age, education, etc.) by a decision tree based method. Our goal is to provide a method with which MI can be addressed in the earliest stages of questionnaire development or prior to analyses between groups. We show how EFA trees can be implemented in the software R using lavaan (Rosseel, 2012) and partykit (Hothorn & Zeileis, 2015). In a simulation, we demonstrate the ability of EFA trees to detect a lack of MI under various conditions. Our online material contains a template script that can be used to apply EFA trees on one's own questionnaire data. Limitations and future research ideas are discussed

Exploratory Factor Analysis Trees: Evaluating Measurement Invariance Between Multiple Covariates

Measurement invariance (MI) describes the equivalence of a construct across groups. To be able to meaningfully compare latent factor means between groups, it is crucial to establish MI. Although methods exist that test for MI, these methods do not perform well when many groups have to be compared or when there are no hypotheses about them. We suggest a method called Exploratory Factor Analysis Trees (EFA trees) that are an extension to SEM trees (Brandmaier et al., 2013). EFA trees combine EFA with a recursive partitioning algorithm that can uncover non-invariant subgroups in a data-driven manner. An EFA is estimated and then tested for parameter instability on multiple covariates (e.g., age, education, etc.) by a decision tree based method. Our goal is to provide a method with which MI can be addressed in the earliest stages of questionnaire development or prior to analyses between groups. We show how EFA trees can be implemented in the software R using lavaan (Rosseel, 2012) and partykit (Hothorn & Zeileis, 2015). In a simulation, we demonstrate the ability of EFA trees to detect a lack of MI under various conditions. Our online material contains a template script that can be used to apply EFA trees on one's own questionnaire data. Limitations and future research ideas are discussed

Everything has its Price: Foundations of Cost-Sensitive Learning and its Application in Psychology

Psychology has seen an increase in machine learning (ML) methods. In many applications, observations are classified into one of two groups (binary classification). Off-the-shelf classification algorithms assume that the costs of a misclassification (false-positive or false-negative) are equal. Because this is often not reasonable (e.g., in clinical psychology), cost-sensitive learning (CSL) methods can take different cost ratios into account. We present the mathematical foundations and introduce a taxonomy of the most commonly used CSL methods, before demonstrating their application and usefulness on psychological data, i.e., the drug consumption dataset ($N = 1885$) from the UCI Machine Learning Repository. In our example, all demonstrated CSL methods noticeably reduce mean misclassification costs compared to regular ML algorithms. We discuss the necessity for researchers to perform small benchmarks of CSL methods for their own practical application. Thus, our open materials provide R code, demonstrating how CSL methods can be applied within the mlr3 framework (https://osf.io/cvks7/)

New Developments in Measurement Invariance Testing - An Overview and Comparison of EFA-based Approaches

When comparing relations and means of latent variables, it is important to establish measurement invariance (MI). Most methods to assess MI are based on confirmatory factor analysis (CFA). Recently, new methods have been developed based on exploratory factor analysis (EFA); most notably, as extensions of multi-group EFA, researchers introduced mixture multi-group EFA, multi-group exploratory factor alignment, EFA trees, and multi-group factor rotation to resolve rotational indeterminacy in EFA. The main advantage of EFA-based (compared to CFA-based) assessment of MI is that no potentially too restrictive measurement model has to be specified. This allows for a more thorough investigation because violations of MI due to cross-loadings can be considered, too. For each method, we address the model specification and recommendations for application, detailing their strengths and weaknesses. We demonstrate each method in combination with multi-group factor rotation in an empirical example. Differences to and possible combinations with CFA-based methods are discussed

A Causal Framework for the Comparability of Latent Variables

Measurement invariance (MI) describes the equivalence of measurement models of a construct across groups or time. When comparing latent means, MI is often stated as a prerequisite of meaningful group comparisons. The most common way to investigate MI is multi-group confirmatory factor analysis (MG-CFA). Although numerous guides exist, a recent review showed that MI is rarely investigated in practice. We argue that one reason might be that the results of MG-CFA are uninformative as to why MI does not hold between groups. Consequently, under this framework, it is difficult to regard the study of MI an interesting and constructive step in the modeling process. We show how directed acyclic graphs (DAGs) from the causal inference literature can guide researchers in reasoning about the causes of non-invariance. For this, we first show how DAGs for measurement models can be translated into the path diagrams used in the linear structural equation model (SEM) literature. We then demonstrate how insights gained from this causal perspective can be used to explicitly model encoded causal assumptions with moderated SEMs, allowing for a more enlightening investigation of MI. Ultimately, our goal is to provide a framework in which the investigation of MI is not deemed a “gateway test” that simply licenses further analyses. By enabling researchers to consider MI as an interesting part of the modeling process, we hope to increase the prevalence of investigations of MI altogether