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Latent Class and Latent Transition Analysis

latent class models, Latent Class and Latent Transition Analysis reflects improvements in statistical computing as the most up-to-date reference;

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An Introduction to Latent Class Analysis

dominated by certain factors. This book plays a significant role in introducing latent structure analysis to not only young researchers and students;

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Applied Latent Class Analysis

Applied Latent Class Analysis introduces several innovations in latent class analysis to a wider audience of researchers. Many of the;

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Advances in Latent Class Analysis

today. Closer to its time of inception, latent class analysis was viewed primarily as a categorical data analysis technique, often framed as a;

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Latent Class Analysis of Survey Error

related Web site houses the lEM software. Extensively class-tested to ensure an accessible presentation, Latent Class Analysis of Survey Error;

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Latent Markov Models for Longitudinal Data

Drawing on the authors' extensive research in the analysis of categorical longitudinal data, Latent Markov Models for Longitudinal Data;

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Latent Markov Models for Longitudinal Data

Drawing on the authors' extensive research in the analysis of categorical longitudinal data, Latent Markov Models for Longitudinal Data;

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Modern Methods for Business Research

in a particular subject area--is to provide an understanding of each method with a minimum of mathematical derivations. The chapters are;

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Latent Variable Models

Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis introduces latent variable models by utilizing;

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Latent Variable Models

Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis introduces latent variable models by utilizing;

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Modern Methods for Business Research

in a particular subject area--is to provide an understanding of each method with a minimum of mathematical derivations. The chapters are;

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Latent Variable Models and Factor Analysis

Latent Variable Models and Factor Analysis provides a comprehensive and unified approach to factor analysis and latent variable modeling;

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Structural Equation Modeling

statistical power analysis for SEM Structural Equation Modeling is an excellent book for researchers and graduate students of SEM who want to;

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New Developments in Categorical Data Analysis for the Social and Behavioral Sciences

their application to relevant and interesting data sets. This volume concentrates on latent class analysis and item response theory. These;

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Structural Equation Modeling (SEM)

Bayesian approach based on SEM; an examination of predictors and outcomes related to school climate using latent class analysis and the testing;

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Semialgebraic Statistics and Latent Tree Models

Semialgebraic Statistics and Latent Tree Models explains how to analyze statistical models with hidden (latent) variables. It takes a;

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Semialgebraic Statistics and Latent Tree Models

Semialgebraic Statistics and Latent Tree Models explains how to analyze statistical models with hidden (latent) variables. It takes a;

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Handbook of Latent Variable and Related Models

This Handbook covers latent variable models, which are a flexible class of models for modeling multivariate data to explore relationships;

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Generalized Latent Variable Modeling

, item response or factor models, latent class or finite mixture models, and structural equation models. Following a gentle introduction to;

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Latent Curve Models

the need for this timely publication. Latent Curve Models: A Structural Equation Perspective provides an effective technique to analyze latent;

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Data Analysis with Mplus

A practical introduction to using Mplus for the analysis of multivariate data, this volume provides step-by-step guidance, complete with;

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Latent Variable and Latent Structure Models

This edited volume features cutting-edge topics from the leading researchers in the areas of latent variable modeling. Content highlights;

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Latent Variable Analysis and Signal Separation

This book constitutes the proceedings of the 13th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2017;

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Latent Variable and Latent Structure Models

This edited volume features cutting-edge topics from the leading researchers in the areas of latent variable modeling. Content highlights;

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Individual Latent Error Detection (I-LED)

Offers an in-depth exploration of the phenomenon of spontaneous recall of past event, leading to error detection and recovery of latent error;

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Test Data Engineering

analysis (LCA), latent rank analysis (LRA), biclustering (co-clustering), and Bayesian network model (BNM). CTT and IRT are methods for analyzing;

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Introduction To Pseudo-differential Operators, An

This book gives a straightforward account of a class of pseudo-differential operators. It is ideal for courses in functional analysis;

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