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  1. Book ; Online ; Thesis: Repeated Measures ANOVA with Latent Variables: A New Approach Based on Structural Equation Modeling

    Langenberg, Benedikt [Verfasser]

    2022  

    Author's details Benedikt Langenberg
    Keywords Psychologie ; Psychology
    Subject code sg150
    Language English
    Publisher Universitätsbibliothek Bielefeld
    Publishing place Bielefeld
    Document type Book ; Online ; Thesis
    Database Digital theses on the web

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  2. Article ; Online: Subgroup discovery in structural equation models.

    Kiefer, Christoph / Lemmerich, Florian / Langenberg, Benedikt / Mayer, Axel

    Psychological methods

    2022  

    Abstract: Structural equation modeling is one of the most popular statistical frameworks in the social and behavioral sciences. Often, detection of groups with distinct sets of parameters in structural equation models (SEM) are of key importance for applied ... ...

    Abstract Structural equation modeling is one of the most popular statistical frameworks in the social and behavioral sciences. Often, detection of groups with distinct sets of parameters in structural equation models (SEM) are of key importance for applied researchers, for example, when investigating differential item functioning for a mental ability test or examining children with exceptional educational trajectories. In the present article, we present a new approach combining subgroup discovery-a well-established toolkit of supervised learning algorithms and techniques from the field of computer science-with structural equation models termed SubgroupSEM. We provide an overview and comparison of three approaches to modeling and detecting heterogeneous groups in structural equation models, namely, finite mixture models, SEM trees, and SubgroupSEM. We provide a step-by-step guide to applying subgroup discovery techniques for structural equation models, followed by a detailed and illustrated presentation of pruning strategies and four subgroup discovery algorithms. Finally, the SubgroupSEM approach will be illustrated on two real data examples, examining measurement invariance of a mental ability test and investigating interesting subgroups for the mediated relationship between predictors of educational outcomes and the trajectories of math competencies in 5th grade children. The illustrative examples are accompanied by examples of the R package subgroupsem, which is a viable implementation of our approach for applied researchers. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
    Language English
    Publishing date 2022-10-06
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2103345-6
    ISSN 1939-1463 ; 1082-989X
    ISSN (online) 1939-1463
    ISSN 1082-989X
    DOI 10.1037/met0000524
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Estimating and Testing Causal Mediation Effects in Single-Case Experimental Designs Using State-Space Modeling.

    Langenberg, Benedikt / Wurpts, Ingrid C / Geuke, Gemma G M / Onghena, Patrick

    Evaluation & the health professions

    2022  Volume 45, Issue 1, Page(s) 8–21

    Abstract: In this article, we present single-case causal mediation analysis as the application of causal mediation analysis to data collected within a single-case experiment. This method combines the focus on the individual with the focus on mechanisms of change, ... ...

    Abstract In this article, we present single-case causal mediation analysis as the application of causal mediation analysis to data collected within a single-case experiment. This method combines the focus on the individual with the focus on mechanisms of change, rendering it a promising approach for both mediation and single-case researchers. For this purpose, we propose a new method based on time-discrete state-space modeling to estimate the direct and indirect treatment effects. We demonstrate how to estimate the model for a single-case experiment on stress and craving in a routine alcohol consumer before and after an imposed period of abstinence. Furthermore, we present a simulation study that examines the estimation and testing of the standardized indirect effect. All parameters used to generate the data were recovered with acceptable precision. We use maximum likelihood and permutation procedures to calculate
    MeSH term(s) Causality ; Computer Simulation ; Humans ; Models, Statistical ; Research Design
    Language English
    Publishing date 2022-03-04
    Publishing country United States
    Document type Journal Article
    ZDB-ID 603792-6
    ISSN 1552-3918 ; 0163-2787
    ISSN (online) 1552-3918
    ISSN 0163-2787
    DOI 10.1177/01632787211067533
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.B 2417: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

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  4. Article ; Online: A tutorial on using the paired t test for power calculations in repeated measures ANOVA with interactions.

    Langenberg, Benedikt / Janczyk, Markus / Koob, Valentin / Kliegl, Reinhold / Mayer, Axel

    Behavior research methods

    2022  Volume 55, Issue 5, Page(s) 2467–2484

    Abstract: The a priori calculation of statistical power has become common practice in behavioral and social sciences to calculate the necessary sample size for detecting an expected effect size with a certain probability (i.e., power). In multi-factorial repeated ... ...

    Abstract The a priori calculation of statistical power has become common practice in behavioral and social sciences to calculate the necessary sample size for detecting an expected effect size with a certain probability (i.e., power). In multi-factorial repeated measures ANOVA, these calculations can sometimes be cumbersome, especially for higher-order interactions. For designs that only involve factors with two levels each, the paired t test can be used for power calculations, but some pitfalls need to be avoided. In this tutorial, we provide practical advice on how to express main and interaction effects in repeated measures ANOVA as single difference variables. In particular, we demonstrate how to calculate the effect size Cohen's d of this difference variable either based on means, variances, and covariances of conditions or by transforming [Formula: see text] or [Formula: see text] from the ANOVA framework into d. With the effect size correctly specified, we then show how to use the t test for sample size considerations by means of an empirical example. The relevant R code is provided in an online repository for all example calculations covered in this article.
    MeSH term(s) Humans ; Research Design ; Sample Size ; Probability ; Analysis of Variance
    Language English
    Publishing date 2022-08-24
    Publishing country United States
    Document type Journal Article
    ZDB-ID 231560-9
    ISSN 1554-3528 ; 0743-3808 ; 1554-351X
    ISSN (online) 1554-3528
    ISSN 0743-3808 ; 1554-351X
    DOI 10.3758/s13428-022-01902-8
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Bonn / Germany

    Z 76/512: Show issues

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  5. Article ; Online: Repeated Measures ANOVA with Latent Variables to Analyze Interindividual Differences in Contrasts.

    Langenberg, Benedikt / Helm, Jonathan L / Mayer, Axel

    Multivariate behavioral research

    2020  Volume 57, Issue 1, Page(s) 2–19

    Abstract: Repeated measures analysis of variance (RM-ANOVA) is a broadly used statistical method to analyze data from experimental designs. RM-ANOVA aims at investigating effects of experimental conditions (i.e., factors) and predictors that affect the outcome of ... ...

    Abstract Repeated measures analysis of variance (RM-ANOVA) is a broadly used statistical method to analyze data from experimental designs. RM-ANOVA aims at investigating effects of experimental conditions (i.e., factors) and predictors that affect the outcome of interest. It mainly considers contrasts that test standard main and interaction effects, even though more complex contrasts can in principle be used. Analyses, however, only focus on drawing conclusions about average effects and do not take into consideration interindividual differences in these effects. We propose an alternative approach to RM-ANOVA for analyzing repeated measures data, termed latent repeated measures analysis of variance (L-RM-ANOVA). The new approach is based on structural equation modeling and extends the latent growth components approach. L-RM-ANOVA enables the researcher to not only consider mean differences between different experimental conditions (i.e., average effects), but also to investigate interindividual differences in effects. Such interindividual differences are considered with regard to standard main and interactions effects and also with regard to customized contrasts that allow for testing specific hypotheses of interest. Furthermore, L-RM-ANOVA can include a measurement model for latent variables and can be used for the analysis of complex multi-factorial repeated measures designs. We conclude the presentation by demonstrating L-RM-ANOVA using a minimal repeated measures example.
    MeSH term(s) Analysis of Variance ; Research Design
    Language English
    Publishing date 2020-08-17
    Publishing country United States
    Document type Journal Article
    ISSN 1532-7906
    ISSN (online) 1532-7906
    DOI 10.1080/00273171.2020.1803038
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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