Dependent Data in Social Sciences Research

Edited by Mark Stemmler (), Wolfgang Wiedermann () and Francis L. Huang ()

in Springer Books from Springer

Date: 2024
Edition: 2nd ed. 2024
ISBN: 978-3-031-56318-8
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Chapters in this book:

Ch Chapter 1 Continuous Time Modeling in the Social Sciences: History and Philosophical Background

Johan H. L. Oud

Ch Chapter 10 Exploration of Dependence Structures in Longitudinal Categorical Data with Ordinal Responses

Li Wang, Shu-Min Liao and Daeyoung Kim

Ch Chapter 11 Bayesian Network for Discovering the Potential Causal Structure in Observational Data

Cody S. Ding

Ch Chapter 12 Missing Data in the Analysis of Multilevel and Dependent Data

Simon Grund, Oliver Lüdtke and Alexander Robitzsch

Ch Chapter 13 Bootstrap Methods for Robust Multilevel Analysis

Yichi Zhang, Winnie Wing-Yee Tse and Mark H. C. Lai

Ch Chapter 14 Investigating the Use of Robust Standard Errors to Account for Two-Way Clustering in Cross-Classified Data Structures

Bixi Zhang and Francis L. Huang

Ch Chapter 15 Self-Normalized, Score-Based Tests of Parameter Heterogeneity in Mixed Models

Ting Wang and Edgar C. Merkle

Ch Chapter 16 Statistical Power in Cross-Sectional Multilevel Experiments in Education

Spyros Konstantopoulos, Wei Li and Bixi Zhang

Ch Chapter 17 Exploring Temporal Pattern of Intergenerational Educational Mobility in Germany: An Application of Configural Frequency Analysis Using Weighted Prediction

Jörg-Henrik Heine, Florian G. Hartmann and Christian Tarnai

Ch Chapter 18 Configural Frequency Analysis Under Multinormality

Alexander von Eye and Wolfgang Wiedermann

Ch Chapter 19 Configural Frequency Analysis Under Multinormality in Incomplete Tables

Alexander von Eye and Wolfgang Wiedermann

Ch Chapter 2 Time in Latent Growth Curve Models

Matt L. Miller and Paolo Ghisletta

Ch Chapter 20 Higher-Order Configural Frequency Analysis of Groups of Variables: Dependencies in Test Data

Alexander von Eye and Wolfgang Wiedermann

Ch Chapter 21 Visualization of Dependence in Multidimensional Contingency Tables with an Ordinal Dependent Variable via Copula Regression

Shu-Min Liao, Li Wang and Daeyoung Kim

Ch Chapter 22 Mental Health Symptom Profiles Over Time: A Three-Step Latent Transition Cognitive Diagnosis Modeling Analysis with Covariates

Qianru Liang, Jimmy de la Torre, Mary E. Larimer and Eun-Young Mun

Ch Chapter 23 Multiple Imputation of Longitudinal Data: A Comparison of Robust Imputation Methods Regarding Sample Size Requirements, with an Application to Corporal Punishment Data

Kristian Kleinke, Markus Fritsch, Mark Stemmler and Friedrich Lösel

Ch Chapter 24 Multiple Imputation of Incomplete Panel Data Based on a Piecewise Growth Curve Model: An Evaluation and Application to Juvenile Delinquency Data

Kristian Kleinke and Jost Reinecke

Ch Chapter 25 Impact of Inconsistent Imputation Models in Mediation Analysis with Clustered Data

Bo Ye, Recai Yucel and Donna L. Coffman

Ch Chapter 26 Ecological Momentary Assessment (EMA) Designs with Planned Missingness

Diane Losardo, Sy-Miin Chow, A. T. Panter, Melissa Burkley and Edward Burkley

Ch Chapter 27 Variants of Estimating an IRT-Based Actor-Partner Interdependence Model (APIM) with R

Rainer W. Alexandrowicz, Linda Maurer, Anna Schultz and Marcus Mund

Ch Chapter 28 Assessing Individual Change: A Comparison of Reliable Change Indices Based on Classical Test Theory and Various Item Response Theory Models

Ferdinand Keller and Rainer W. Alexandrowicz

Ch Chapter 29 Assessing Unobserved Within-Group Individual Differences

Hoben Thomas

Ch Chapter 3 Score-Guided Recursive Partitioning of Continuous-Time Structural Equation Models

Manuel Arnold, Pablo F. Cáncer, Eduardo Estrada and Manuel C. Voelkle

Ch Chapter 4 Studying the Interaction Between Harsh Parenting and the Child’s Social Behavior Problems over Time Using Continuous Time Modeling

Mark Stemmler and Friedrich Lösel

Ch Chapter 5 A Variational Approach to Continuous Time Dynamic Models

Hannes Meinlschmidt, Meike Sons and Mark Stemmler

Ch Chapter 6 Finite Mixture Models for an Underlying Beta Distribution with an Application to COVID-19 Data

Cédric Noel and Jang Schiltz

Ch Chapter 7 What the Fuzz!? Leveraging Ambiguity in Dynamic Network Models

Jonathan J. Park, Sy-Miin Chow and Peter C. M. Molenaar

Ch Chapter 8 Causal Discovery with Hidden Variables Based on Non-Gaussianity and Nonlinearity

Takashi Nicholas Maeda, Yan Zeng and Shohei Shimizu

Ch Chapter 9 Direction of Dependence in Non-linear Models via Linearization

Wolfgang Wiedermann and Bixi Zhang

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DOI: 10.1007/978-3-031-56318-8

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