About the Connection Between Knowledge Structures and Latent Class Models

Methodology ◽  
2005 ◽  
Vol 1 (3) ◽  
pp. 93-103 ◽  
Author(s):  
Martin Schrepp
Keyword(s):  
Latent Class ◽  
Latent Class Model ◽  
Knowledge Structure ◽  
Latent Class Models ◽  
Knowledge Structures ◽  
Structure Theory ◽  
Response Patterns ◽  
Class Model ◽  
Exploratory Data ◽  
Class Models

This paper tries to establish a connection between knowledge structures and latent class models. We will show that knowledge structures can be interpreted as a special type of constrained latent class model. Latent class models offer a well-founded theoretical framework to investigate the connection of a given latent class model to observed data. If we establish a connection between latent class models and knowledge structures, we can also use this framework in knowledge structure theory. We will show that the connection to latent class models offers us a possibility to construct a knowledge structure by exploratory data analysis from observed response patterns. Other possible applications are the empirical comparison of hypothetical knowledge structures and the statistical test of a given knowledge structure.

Bayesian nonparametric latent class model for longitudinal data

2020 ◽  
Vol 29 (11) ◽  
pp. 3381-3395
Author(s):  
Wonmo Koo ◽  
Heeyoung Kim
Keyword(s):  
Longitudinal Data ◽  
Latent Class ◽  
Latent Class Model ◽  
Latent Class Models ◽  
Model Parameters ◽  
Latent Classes ◽  
Bayesian Nonparametric ◽  
Class Model ◽  
Proposed Model ◽  
Class Models

Latent class models have been widely used in longitudinal studies to uncover unobserved heterogeneity in a population and find the characteristics of the latent classes simultaneously using the class allocation probabilities dependent on predictors. However, previous latent class models for longitudinal data suffer from uncertainty in the choice of the number of latent classes. In this study, we propose a Bayesian nonparametric latent class model for longitudinal data, which allows the number of latent classes to be inferred from the data. The proposed model is an infinite mixture model with predictor-dependent class allocation probabilities; an individual longitudinal trajectory is described by the class-specific linear mixed effects model. The model parameters are estimated using Markov chain Monte Carlo methods. The proposed model is validated using a simulated example and a real-data example for characterizing latent classes of estradiol trajectories over the menopausal transition using data from the Study of Women’s Health Across the Nation.

Reliable Inference in Highly Stratified Contingency Tables: Using Latent Class Models as Density Estimators

2011 ◽  
Vol 19 (2) ◽  
pp. 173-187 ◽  
Author(s):  
Drew A. Linzer
Keyword(s):  
Latent Class ◽  
Latent Class Model ◽  
Contingency Tables ◽  
Latent Class Models ◽  
Small Samples ◽  
Categorical Variables ◽  
Density Estimator ◽  
Class Model ◽  
Wide Range ◽  
Demographic Subgroups

Contingency tables are among the most basic and useful techniques available for analyzing categorical data, but they produce highly imprecise estimates in small samples or for population subgroups that arise following repeated stratification. I demonstrate that preprocessing an observed set of categorical variables using a latent class model can greatly improve the quality of table-based inferences. As a density estimator, the latent class model closely approximates the underlying joint distribution of the variables of interest, which enables reliable estimation of conditional probabilities and marginal effects, even among subgroups containing fewer than 40 observations. Though here focused on applications to public opinion, the procedure has a wide range of potential uses. I illustrate the benefits of the latent class model—based approach for greatly improved accuracy in estimating and forecasting vote preferences within small demographic subgroups using survey data from the 2004 and 2008 U.S. presidential election campaigns.

scholarly journals Modeling Unobserved Heterogeneity in New York Dairy Farms: One-Stage versus Two-Stage Models

2012 ◽  
Vol 41 (3) ◽  
pp. 275-285 ◽  
Author(s):  
Antonio Alvarez ◽  
Julio del Corral ◽  
Loren W. Tauer
Keyword(s):  
New York ◽  
Latent Class ◽  
Unobserved Heterogeneity ◽  
Latent Class Model ◽  
A Priori ◽  
Dairy Farm ◽  
Latent Class Models ◽  
Class Separation ◽  
Heterogeneous Technologies ◽  
Class Models

Agricultural production estimates have often differentiated and estimated different technologies within a sample of farms. The common approach is to use observable farm characteristics to split the sample into groups and subsequently estimate different functions for each group. Alternatively, unique technologies can be determined by econometric procedures such as latent class models. This paper compares the results of a latent class model with the use of a priori information to split the sample using dairy farm data. Latent class separation appears to be a superior method of separating heterogeneous technologies and suggests that technology differences are multifaceted.

scholarly journals A Note on the Connection Between Knowledge Structures and Latent Class Models

Methodology ◽  
2011 ◽  
Vol 7 (2) ◽  
pp. 63-67 ◽  
Author(s):  
Ali Ünlü
Keyword(s):  
Latent Variable ◽  
Probabilistic Models ◽  
Latent Class ◽  
Latent Class Models ◽  
Knowledge Structures ◽  
Future Research ◽  
Comprehensive Overview ◽  
Additional Information ◽  
Constructing Knowledge ◽  
Class Models

Schrepp (2005) points out and builds upon the connection between knowledge space theory (KST) and latent class analysis (LCA) to propose a method for constructing knowledge structures from data. Candidate knowledge structures are generated, they are considered as restricted latent class models and fitted to the data, and the BIC is used to choose among them. This article adds additional information about the relationship between KST and LCA. It gives a more comprehensive overview of the literature and the probabilistic models that are at the interface of KST and LCA. KST and LCA are also compared with regard to parameter estimation and model testing methodologies applied in their fields. This article concludes with an overview of KST-related publications addressing the outlined connection and presents further remarks about possible future research arising from a connection of KST to other latent variable modeling approaches.

scholarly journals Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence: Navigating the absence of a gold standard

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257743
Author(s):  
Sahar Saeed ◽  
Sheila F. O’Brien ◽  
Kento Abe ◽  
Qi-Long Yi ◽  
Bhavisha Rathod ◽  
...  
Keyword(s):  
Severe Acute Respiratory Syndrome ◽  
Gold Standard ◽  
Latent Class ◽  
Latent Class Model ◽  
Credible Interval ◽  
Latent Class Models ◽  
Igg Antibody ◽  
Cross Sectional ◽  
Class Models ◽  
Over Time

Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence studies bridge the gap left from case detection, to estimate the true burden of the COVID-19 pandemic. While multiple anti-SARS-CoV-2 immunoassays are available, no gold standard exists. Methods This serial cross-sectional study was conducted using plasma samples from 8999 healthy blood donors between April-September 2020. Each sample was tested by four assays: Abbott SARS-Cov-2 IgG assay, targeting nucleocapsid (Abbott-NP) and three in-house IgG ELISA assays (targeting spike glycoprotein, receptor binding domain, and nucleocapsid). Seroprevalence rates were compared using multiple composite reference standards and by a series of Bayesian Latent Class Models. Result We found 13 unique diagnostic phenotypes; only 32 samples (0.4%) were positive by all assays. None of the individual assays resulted in seroprevalence increasing monotonically over time. In contrast, by using the results from all assays, the Bayesian Latent Class Model with informative priors predicted seroprevalence increased from 0.7% (95% credible interval (95% CrI); 0.4, 1.0%) in April/May to 0.7% (95% CrI 0.5, 1.1%) in June/July to 0.9% (95% CrI 0.5, 1.3) in August/September. Assay characteristics varied over time. Overall Spike had the highest sensitivity (93.5% (95% CrI 88.7, 97.3%), while the sensitivity of the Abbott-NP assay waned from 77.3% (95% CrI 58.7, 92.5%) in April/May to 64.4% (95% CrI 45.6, 83.0) by August/September. Discussion Our results confirmed very low seroprevalence after the first wave in Canada. Given the dynamic nature of this pandemic, Bayesian Latent Class Models can be used to correct for imperfect test characteristics and waning IgG antibody signals.

Estimation and Use of Standard Errors of Latent Class Model Parameters

1987 ◽  
Vol 24 (3) ◽  
pp. 298-304
Author(s):  
Rajiv Grover
Keyword(s):  
Latent Class ◽  
Latent Class Model ◽  
Latent Class Models ◽  
Standard Errors ◽  
Model Parameters ◽  
Iterative Proportional Fitting ◽  
Class Model ◽  
Fitting Algorithm ◽  
Marketing Data ◽  
Parsimonious Models

Only recently have latent class models been used effectively to analyze marketing data, though they have been popular for more than a decade in the social sciences. Most research reported in the literture does not include the standard errors of the estimates of the latent class model parameters. The author argues for the usefulness of standard errors while exploring for parsimonious models. He provides an approach to estimating standard errors of all parameters as estimated by the iterative proportional fitting algorithm of Goodman implemented in MLLSA.

scholarly journals Recommendations on the Sample Sizes for Multilevel Latent Class Models

2017 ◽  
Vol 78 (5) ◽  
pp. 737-761 ◽  
Author(s):  
Jungkyu Park ◽  
Hsiu-Ting Yu
Keyword(s):  
Latent Class ◽  
Latent Class Model ◽  
Latent Class Models ◽  
Sample Sizes ◽  
Nested Structures ◽  
Accuracy Parameter ◽  
Multilevel Latent Class Model ◽  
Error Bias ◽  
Design Factors ◽  
Class Models

A multilevel latent class model (MLCM) is a useful tool for analyzing data arising from hierarchically nested structures. One important issue for MLCMs is determining the minimum sample sizes needed to obtain reliable and unbiased results. In this simulation study, the sample sizes required for MLCMs were investigated under various conditions. A series of design factors, including sample sizes at two levels, the distinctness and the complexity of the latent structure, and the number of indicators were manipulated. The results revealed that larger samples are required when the latent classes are less distinct and more complex with fewer indicators. This study also provides recommendations about the minimum required sample sizes that satisfied all four criteria—model selection accuracy, parameter estimation bias, standard error bias, and coverage rate—as well as rules of thumb for sample size requirements when applying MLCMs in data analysis.

1. On the Assignment of Individuals to Latent Classes

2007 ◽  
Vol 37 (1) ◽  
pp. 1-22 ◽  
Author(s):  
Leo A. Goodman
Keyword(s):  
Latent Class ◽  
Response Pattern ◽  
Latent Class Model ◽  
Panel Study ◽  
Latent Classes ◽  
Response Patterns ◽  
Class Model ◽  
Cross Classification ◽  
Dichotomous Variables ◽  
Classification Table

Consider an m-way cross-classification table (for m = 3, 4, …) of m dichotomous variables that describes (1) the 2m possible response patterns to a set of m questions (where the response to each question is binary), and (2) the number of individuals whose responses to the m questions can be described by a particular response pattern, for each of the 2m possible response patterns. Consider the situation where the data in the cross-classification table are analyzed using a particular latent class model having T latent classes (for T = 2, 3, …), and where this model fits the data well. With this latent class model, it is possible to estimate, for an individual who has a particular response pattern, what is the conditional probability that this individual is in a particular latent class, for each of the T latent classes. In this article, the following question is considered: For an individual who has a particular response pattern, can we use the corresponding estimated conditional probabilities to assign this individual to one of the T latent classes? Two different assignment procedures are considered here, and for each of these procedures, two different criteria are introduced to help assess when the assignment procedure is satisfactory and when it is not. In addition, we describe here the particular framework and context in which the two assignment procedures, and the two criteria, are considered. For illustrative purposes, the latent class analysis of a classic set of data, a four-way cross-classification of some survey data, obtained in a two-wave panel study, is discussed; and the two different criteria introduced herein are applied in this analysis to each of the two assignment procedures.

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