An introduction to latent class analysis : methods and applications / / Nobuoki Eshima

• Record Nr.: UNISA996472039103316
• Autore: Eshima Nobuoki
• Pubbl/distr/stampa: Singapore : , : Springer, , [2022]; ©2022
• ISBN: 9789811909726; 9789811909719
• Descrizione fisica: 1 online resource (196 pages)
• Collana: Behaviormetrics: Quantitative Approaches to Human Behavior ; ; v.14
• Disciplina: 150.1943
• Soggetti: Human behavior - Research; Human behavior - Philosophy; Variables aleatòries; Conducta (Psicologia); Llibres electrònics
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Intro -- Preface -- References -- Acknowledgements -- Contents -- 1 Overview of Basic Latent Structure Models -- 1.1 Introduction -- 1.2 Latent Class Model -- 1.3 Latent Trait Model -- 1.4 Latent Profile Model -- 1.5 Factor Analysis Model -- 1.6 Latent Structure Models in a Generalized Linear Model Framework -- 1.7 The EM Algorithm and Latent Structure Models -- 1.8 Discussion -- References -- 2 Latent Class Cluster Analysis -- 2.1 Introduction -- 2.2 The ML Estimation of Parameters in the Latent Class Model -- 2.3 Examples -- 2.4 Measuring Goodness-of-Fit of Latent Class Models -- 2.5 Comparison of Latent Classes -- 2.6 Latent Profile Analysis -- 2.7 Discussion -- References -- 3 Latent Class Analysis with Ordered Latent Classes -- 3.1 Introduction -- 3.2 Latent Distance Analysis -- 3.3 Assessment of the Latent Guttman Scaling -- 3.4 Analysis of the Association Between Two Latent Traits with Latent Guttman Scaling -- 3.5 Latent Ordered-Class Analysis -- 3.6 The Latent Trait Model (Item Response Model) -- 3.7 Discussion -- References -- 4 Latent Class Analysis with Latent Binary Variables: An Application ; for Analyzing Learning Structures -- 4.1 Introduction -- 4.2 Latent Class Model for Scaling Skill Acquisition Patterns -- 4.3 ML Estimation Procedure for Model (4.3) with (4.4) -- 4.4 Numerical Examples (Exploratory Analysis) -- 4.5 Dynamic Interpretation of Learning (Skill Acquisition) Structures -- 4.6 Estimation of Mixed Proportions of Learning Processes -- 4.7 Solution of the Separating Equations -- 4.8 Path Analysis in Learning Structures -- 4.9 Numerical Illustration (Confirmatory Analysis) -- 4.10 A Method for Ordering Skill Acquisition Patterns -- 4.11 Discussion -- References -- 5 The Latent Markov Chain Model -- 5.1 Introduction -- 5.2 The Latent Markov Chain Model -- 5.3 The ML Estimation of the Latent Markov Chain Model.; 5.4 A Property of the ML Estimation Procedure via the EM Algorithm -- 5.5 Numerical Example I -- 5.6 Numerical Example II -- 5.7 A Latent Markov Chain Model with Missing Manifest Observations -- 5.8 A General Version of the Latent Markov Chain Model with Missing Manifest Observations -- 5.9 The Latent Markov Process Model -- 5.10 Discussion -- References -- 6 The Mixed Latent Markov Chain Model -- 6.1 Introduction -- 6.2 Dynamic Latent Class Models -- 6.3 The ML Estimation of the Parameters of Dynamic Latent Class Models -- 6.4 A Numerical Illustration -- 6.5 Discussion -- References -- 7 Path Analysis in Latent Class Models -- 7.1 Introduction -- 7.2 A Multiple-Indicator, Multiple-Cause Model -- 7.3 An Entropy-Based Path Analysis of Categorical Variables -- 7.4 Path Analysis in Multiple-Indicator, Multiple-Cause Models -- 7.4.1 The Multiple-Indicator, Multiple-Cause Model in Fig. 7.2a -- 7.4.2 The Multiple-Indicator, Multiple-Cause Model in Fig. 7.2b -- 7.5 Numerical Illustration I -- 7.5.1 Model I (Fig. 7.2a) -- 7.5.2 Model II (Fig. 7.2b) -- 7.6 Path Analysis of the Latent Markov Chain Model -- 7.7 Numerical Illustration II -- 7.8 Discussion -- References.