scholarly journals Classical latent variable models for medical research

2008 ◽  
Vol 17 (1) ◽  
pp. 5-32 ◽  
Author(s):  
Sophia Rabe-Hesketh ◽  
Anders Skrondal
Keyword(s):  
Medical Research ◽  
Latent Variable ◽  
Structural Equation ◽  
Latent Class ◽  
Structural Equation Models ◽  
Real Data ◽  
Latent Variable Models ◽  
Latent Class Models ◽  
Forced Expiratory Flow ◽  
Class Models

Latent variable models are commonly used in medical statistics, although often not referred to under this name. In this paper we describe classical latent variable models such as factor analysis, item response theory, latent class models and structural equation models. Their usefulness in medical research is demonstrated using real data. Examples include measurement of forced expiratory flow, measurement of physical disability, diagnosis of myocardial infarction and modelling the determinants of clients' satisfaction with counsellors' interviews.

Visualizing Latent Class Models with Analysis-of-distance BIPLOTS

2017 ◽  
Vol 47 (1) ◽  
pp. 345-378 ◽  
Author(s):  
Zsuzsa Bakk ◽  
Niel J. le Roux
Keyword(s):  
Relative Density ◽  
Latent Class ◽  
Real Data ◽  
Latent Class Models ◽  
Three Dimensions ◽  
Categorical Analysis ◽  
Class Models

The authors propose using categorical analysis-of-distance biplots to visualize the posterior classifications arising from a latent class (LC) model. Using this multivariate plot, it is possible to visualize in two (or three) dimensions the profile of multiple LCs, specifically both the within- and between-class variation, and the overlap or separation of the classes together with the class weights. Furthermore, visualization of the relative density of each of the data patterns associated with a class is possible. The authors illustrate this approach with real data examples of LC models with three and more classes.

scholarly journals Latent Variable Models for Integrated Analysis of Credit and Point Usage History Data on Rewards Credit Card System

2020 ◽  
Vol 13 (3) ◽  
pp. 106
Author(s):  
Ryotaro Shimizu ◽  
Haruka Yamashita ◽  
Masao Ueda ◽  
Ranna Tanaka ◽  
Tetsuya Tachibana ◽  
...  
Keyword(s):  
Credit Card ◽  
Latent Variable ◽  
Latent Class ◽  
Credit Cards ◽  
Latent Class Models ◽  
Integrated Analysis ◽  
Purchasing Behavior ◽  
History Of ◽  
Card System ◽  
Class Models

Recently, credit cards with point rewards functions (rewards credit cards) are widely used. Credit card companies can collect the users’ usage log data of various stores in multiple industries. The purposes of possessing a credit card varies depending on each user such as to use only the credit function, to use both the credit and point rewards functions, etc. Moreover, credit cards can be used in various situations in users’ lives, and the purchase history of each user is diverse. Focusing on the diversity of both card possessing purposes and purchasing behavior for each user, we propose two latent class models representing these diversities in this research.

scholarly journals Generalized ridge estimators adapted in structural equation models

2020 ◽  
Vol 43 ◽  
pp. e49929
Author(s):  
Gislene Araujo Pereira ◽  
Mariana Resende ◽  
Marcelo Ângelo Cirillo
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Real Data ◽  
Alternative Methods ◽  
Estimation Methods ◽  
Specification Error ◽  
Maximum Likelihood Methods ◽  
Pls Method

Multicollinearity is detected via regression models, where independent variables are strongly correlated. Since they entail linear relations between observed or latent variables, the structural equation models (SEM) are subject to the multicollinearity effect, whose numerous consequences include the singularity between the inverse matrices used in estimation methods. Given to this behavior, it is natural to understand that the suitability of these estimators to structural equation models show the same features, either in the simulation results that validate the estimators in different multicollinearity degrees, or in their application to real data. Due to the multicollinearity overview arose from the fact that the matrices inversion is impracticable, the usage of numerical procedures demanded by the maximum likelihood methods leads to numerical singularity problems. An alternative could be the use of the Partial Least Squares (PLS) method, however, it is demanded that the observed variables are built by assuming a positive correlation with the latent variable. Thus, theoretically, it is expected that the load signals are positive, however, there are no restrictions to these signals in the algorithms used in the PLS method. This fact implies in corrective areas, such as the observed variables removal or new formulations of the theoretical model. In view of this problem, this paper aimed to propose adaptations of six generalized ridge estimators as alternative methods to estimate SEM parameters. The conclusion is that the evaluated estimators presented the same performance in terms of accuracy, precision while considering the scenarios represented by model without specification error and model with specification error, different levels of multicollinearity and sample sizes.

scholarly journals Bayesian Multilevel Latent Class Models for the Multiple Imputation of Nested Categorical Data

2018 ◽  
Vol 43 (5) ◽  
pp. 511-539 ◽  
Author(s):  
Davide Vidotto ◽  
Jeroen K. Vermunt ◽  
Katrijn van Deun
Keyword(s):  
Multiple Imputation ◽  
Mixture Model ◽  
Categorical Data ◽  
Latent Class ◽  
Real Data ◽  
Latent Class Models ◽  
Parameter Estimates ◽  
Complex Interactions ◽  
Analysis Models ◽  
Class Models

With this article, we propose using a Bayesian multilevel latent class (BMLC; or mixture) model for the multiple imputation of nested categorical data. Unlike recently developed methods that can only pick up associations between pairs of variables, the multilevel mixture model we propose is flexible enough to automatically deal with complex interactions in the joint distribution of the variables to be estimated. After formally introducing the model and showing how it can be implemented, we carry out a simulation study and a real-data study in order to assess its performance and compare it with the commonly used listwise deletion and an available R-routine. Results indicate that the BMLC model is able to recover unbiased parameter estimates of the analysis models considered in our studies, as well as to correctly reflect the uncertainty due to missing data, outperforming the competing methods.

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 Quality management, job-related contentment and performance

2015 ◽  
Vol 3 (2) ◽  
pp. 106-129 ◽  
Author(s):  
Lilian M. de Menezes ◽  
Stephen Wood
Keyword(s):  
Quality Management ◽  
Labor Productivity ◽  
Latent Variable ◽  
Management Practices ◽  
Latent Class ◽  
Structural Equation Models ◽  
Latent Class Models ◽  
Content Type ◽  
Motivational Support ◽  
And Performance

Purpose – The purpose of this paper is to investigate whether a quality management (QM) philosophy underlies the joint use of operations and human resource management practices, and the relationships with job-related contentment and performance. Design/methodology/approach – Data from an economy-wide survey are used to test hypotheses via latent variable analyses (latent trait and latent class models) and structural equation models. The sensitivity of each path is then assessed using regression models. Findings – Different elements rather than a unified philosophy are identified. A managerial approach that integrates total QM and just-in-time procedures is rare, but is associated with the quality of the product or service delivered. Labor productivity and quality are independent of the level of job-related contentment in the workplace. Although the average workforce is content, high involvement management and motivational support practices are associated with job anxiety. On the positive side, job enrichment is linked to labor productivity, thus suggesting potential gains through job design. Originality/value – The study adds evidence from a national sample about a comprehensive range of management practices, and suggests distinct outcomes from different elements of QM. Additionally, it shows that performance expectations based on previous studies may not hold in large nationwide heterogeneous samples.

On the robustness of latent class models for diagnostic testing with no gold standard

2021 ◽  
Author(s):  
Matthew R. Schofield ◽  
Michael J. Maze ◽  
John A. Crump ◽  
Matthew P. Rubach ◽  
Renee Galloway ◽  
...  
Keyword(s):  
Gold Standard ◽  
Latent Class ◽  
Diagnostic Testing ◽  
Latent Class Models ◽  
No Gold Standard ◽  
Class Models
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