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Bootstrapping Latent Class Models
2005This paper deals with improved measures of statistical accuracy for parameter estimates of latent class models. It introduces more precise confidence intervals for the parameters of this model, based on parametric and nonparametric bootstrap. Moreover, the label-switching problem is discussed and a solution to handle it introduced.
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2018
Latent class analysis (LCA) is a statistical method for identifying unobserved groups based on patterns of categorical data. LCA is related to cluster analysis (see Chapter 4, this volume) in that both methods are concerned with the classification of cases (e.g., people or objects) into groups that are not known or specified a priori.
Karen M. Samuelsen, C. Mitchell Dayton
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Latent class analysis (LCA) is a statistical method for identifying unobserved groups based on patterns of categorical data. LCA is related to cluster analysis (see Chapter 4, this volume) in that both methods are concerned with the classification of cases (e.g., people or objects) into groups that are not known or specified a priori.
Karen M. Samuelsen, C. Mitchell Dayton
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2016
Διπλωματική εργασία - Πάντειο Πανεπιστήμιο. Τμήμα Οικονομικής και Περιφερειακής Ανάπτυξης, ΠΜΣ, κατεύθυνση Εφηρμοσμένων Οικονομικών και Διοίκησης ...
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Διπλωματική εργασία - Πάντειο Πανεπιστήμιο. Τμήμα Οικονομικής και Περιφερειακής Ανάπτυξης, ΠΜΣ, κατεύθυνση Εφηρμοσμένων Οικονομικών και Διοίκησης ...
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Latent Class and Latent Profile Analysis
The goal of Latent Class Analysis (LCA) and Latent Profile Analysis (LPA) is to identify latent subpopulations of people, characterized by distinct configurations-or different profiles-of scores on a set of variables (Hofmans et al., 2020). Because of its ability to identify subpopulations of employees who score similarly on a set of variables, LCA/LPAopenaire +2 more sources
Latent Class Analysis: A Guide to Best Practice
Journal of Black Psychology, The, 2020Bridget E Weller
exaly
Latent Trait and Latent Class Models.
Contemporary Sociology, 1989Allen L. McCutcheon +2 more
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