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Stochastic matrices --- Probabilities --- Multivariate analysis --- Latent variables
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The author presents an accessible guide to latent class scaling models for binary response variables. Covered in the book are: a survey on academic cheating; children's mastery of spatial tasks; medical diagnosis of lung disease.
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Mathematical statistics --- Latent variables --- Latent structure analysis --- Factor analysis --- Variables latentes --- Analyse de structure latente --- Analyse factorielle
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This volume gathers refereed papers presented at the 1994 UCLA conference on "La tent Variable Modeling and Application to Causality. " The meeting was organized by the UCLA Interdivisional Program in Statistics with the purpose of bringing together a group of people who have done recent advanced work in this field. The papers in this volume are representative of a wide variety of disciplines in which the use of latent variable models is rapidly growing. The volume is divided into two broad sections. The first section covers Path Models and Causal Reasoning and the papers are innovations from contributors in disciplines not traditionally associated with behavioural sciences, (e. g. computer science with Judea Pearl and public health with James Robins). Also in this section are contri butions by Rod McDonald and Michael Sobel who have a more traditional approach to causal inference, generating from problems in behavioural sciences. The second section encompasses new approaches to questions of model selection with emphasis on factor analysis and time varying systems. Amemiya uses nonlinear factor analysis which has a higher order of complexity associated with the identifiability condi tions. Muthen studies longitudinal hierarchichal models with latent variables and treats the time vector as a variable rather than a level of hierarchy. Deleeuw extends exploratory factor analysis models by including time as a variable and allowing for discrete and ordi nal latent variables. Arminger looks at autoregressive structures and Bock treats factor analysis models for categorical data.
Latent variables --- Latent structure analysis --- Causation --- Congresses. --- Analyse de structure latente --- Causalité --- Congrès --- Probabilities. --- Applied mathematics. --- Engineering mathematics. --- Mathematical models. --- Probability Theory and Stochastic Processes. --- Applications of Mathematics. --- Mathematical Modeling and Industrial Mathematics. --- Models, Mathematical --- Simulation methods --- Engineering --- Engineering analysis --- Mathematical analysis --- Probability --- Statistical inference --- Combinations --- Mathematics --- Chance --- Least squares --- Mathematical statistics --- Risk --- Latent variables. --- Constructs, Hypothetical --- Hypothetical constructs --- Variables, Latent --- Multivariate analysis --- Variables (Mathematics) --- Latent variables - Congresses. --- Latent structure analysis - Congresses. --- Causation - Congresses.
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Mathematical statistics --- Latent variables --- Latent structure analysis --- Factor analysis --- Variables latentes --- Analyse de structure latente --- Analyse factorielle --- 519.237 --- Multivariate statistical methods --- Factor analysis. --- Latent structure analysis. --- Latent variables. --- 519.237 Multivariate statistical methods --- Constructs, Hypothetical --- Hypothetical constructs --- Variables, Latent --- Multivariate analysis --- Variables (Mathematics) --- Correlation (Statistics) --- Analysis, Factor --- Factorial analysis --- Structural equation modeling --- Wiskundige statistiek
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Mathematical statistics --- Latent variables --- Structural equation modeling --- Latent structure analysis. --- Latent variables. --- Structural equation modeling. --- SEM (Structural equation modeling) --- Multivariate analysis --- Factor analysis --- Regression analysis --- Path analysis (Statistics) --- Constructs, Hypothetical --- Hypothetical constructs --- Variables, Latent --- Latent structure analysis --- Variables (Mathematics) --- Correlation (Statistics)
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This title shows how uniting ordinary loglinear analysis and latent class analysis into a general loglinear model with latent variables can result in a modified LISREL approach. This modified LISREL model will enable researchers to analyze categorical data in the same way that they have been able to use LISREL to analyze continuous data.
Mathematical statistics --- Log-linear models. --- Latent variables. --- #SBIB:303H523 --- #SBIB:303H10 --- #PBIB:2003.3 --- Log-linear models --- Latent variables --- Constructs, Hypothetical --- Hypothetical constructs --- Variables, Latent --- Latent structure analysis --- Multivariate analysis --- Variables (Mathematics) --- Models, Log-linear --- Regression analysis --- Methoden sociale wetenschappen: associatie, correlatie --- Methoden en technieken: algemene handboeken en reeksen --- Mathematical Statistics --- Mathematics --- Physical Sciences & Mathematics
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Quantitative methods in social research --- Mathematical statistics --- Latent variables. --- 303.7 --- #SBIB:303H520 --- Analysetechnieken. Statistische analyse --(sociaal onderzoek) --- Methoden sociale wetenschappen: techniek van de analyse, algemeen --- Latent structure analysis. --- 303.7 Analysetechnieken. Statistische analyse --(sociaal onderzoek) --- Latent structure analysis --- Latent variables --- Constructs, Hypothetical --- Hypothetical constructs --- Variables, Latent --- Multivariate analysis --- Variables (Mathematics) --- Correlation (Statistics)
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