scholarly journals Estimating the Concomitant-Variable Latent-Class Model With the EM Algorithm

1996 ◽  
Vol 21 (3) ◽  
pp. 215-229 ◽  
Author(s):  
Peter G. M. van der Heijden ◽  
Jos Dessens ◽  
UIf Bockenholt
Keyword(s):  
Latent Class Analysis ◽  
Latent Variable ◽  
Latent Class ◽  
Latent Class Model ◽  
Estimation Procedure ◽  
Class Analysis ◽  
Class Model ◽  
Explanatory Variables ◽  
Concomitant Variable ◽  
The Em Algorithm

Latent class analysis assumes the existence of a categorical latent variable that explains the relations between a set of categorical manifest variables. Simultaneous latent class analysis deals with sets of multiway contingency tables simultaneously. In this way an explanatory categorical grouping variable is related to latent class results. In this article we discuss a tool called the concomitant-variable latent-class model, which generalizes this work to continuous explanatory variables. An EM estimation procedure to estimate the model is worked out in detail, and the model is applied to an example on juvenile delinquency.

Introduction to Latent Class Analysis With Applications

2016 ◽  
Vol 37 (1) ◽  
pp. 129-158 ◽  
Author(s):  
Mariano Porcu ◽  
Francesca Giambona
Keyword(s):  
Latent Class Analysis ◽  
Latent Variable ◽  
Latent Class ◽  
Latent Class Model ◽  
Fifth Grade ◽  
Class Analysis ◽  
Class Model ◽  
Online Appendix ◽  
Data Files ◽  
Primary School Pupils

Latent class analysis (LCA) is a statistical method used to group individuals (cases, units) into classes (categories) of an unobserved (latent) variable on the basis of the responses made on a set of nominal, ordinal, or continuous observed variables. In this article, we introduce LCA in order to demonstrate its usefulness to early adolescence researchers. We provide an application of LCA to empirical data collected from a national survey carried out in 2010 in Italy to assess mathematics and reading skills of fifth-grade primary school pupils (10 years in age). The data were used to measure pupils’ supplies of cultural capital by specifying a latent class model. This article aims to describe and interpret results of LCA, allowing users to replicate the analysis. All LCA examples included in the text are illustrated using the Latent GOLD package, and command files needed to reproduce all analyses with SAS and R are available as supplemental online appendix files along with the example data files.

Politieke voorkeur en antwoordgedrag van respondenten in politieke opiniepeilingen

Res Publica ◽  
1994 ◽  
Vol 36 (2) ◽  
pp. 143-152
Author(s):  
Geert Loosveldt
Keyword(s):  
Latent Class Analysis ◽  
Latent Class ◽  
Latent Class Model ◽  
Response Category ◽  
The Political ◽  
Class Analysis ◽  
Background Characteristics ◽  
Class Model ◽  
Political Preference

In this article a typology of respondent's ability to participate in a survey interview is created by means of a latent class analysis. The indicators in the analysis are: the interviewer's evaluation of the respondent's ability, the use of the "don't know" response category and inconsistent answers. It was possible to fit a latent class model with three classes or types of respondents. The three types are clearly differentiated concerning ability. As expected, this typology is related to respondent's education and age. Ability to participate is higher for better educated and younger respondents. Given the fact that political preference is also related to these two background characteristics, there is a relationship between the respondent's typology and the political preference of the respondents.

scholarly journals Estimating the Concomitant-Variable Latent-Class Model with the EM Algorithm

1996 ◽  
Vol 21 (3) ◽  
pp. 215 ◽  
Author(s):  
Peter G. M. van der Heijden ◽  
Jos Dessens ◽  
Ulf Bockenholt
Keyword(s):  
Em Algorithm ◽  
Latent Class ◽  
Latent Class Model ◽  
Class Model ◽  
Concomitant Variable ◽  
The Em Algorithm

Evaluating students' abilities to construct mathematical models from data using latent class analysis

2018 ◽  
Vol 19 (1) ◽  
pp. 375-391 ◽  
Author(s):  
Alexandra Brandriet ◽  
Charlie A. Rupp ◽  
Katherine Lazenby ◽  
Nicole M. Becker
Keyword(s):  
Latent Class Analysis ◽  
Large Scale ◽  
Latent Class ◽  
Latent Class Model ◽  
Similar Response ◽  
Class Analysis ◽  
Concentration Data ◽  
Class Model ◽  
Chemistry Students ◽  
Rate Laws

Analyzing and interpreting data is an important science practice that contributes toward the construction of models from data; yet, there is evidence that students may struggle with making meaning of data. The study reported here focused on characterizing students’ approaches to analyzing rate and concentration data in the context of method of initial rates tasks, a type of task used to construct a rate law, which is a mathematical model that relates the reactant concentration to the rate. Here, we present a large-scale analysis (n= 768) of second-semester introductory chemistry students’ responses to three open-ended questions about how to construct rate laws from initial concentration and rate data. Students’ responses were coded based on the level of sophistication in their responses, and latent class analysis was then used to identify groups (i.e.classes) of students with similar response patterns across tasks. Here, we present evidence for a five-class model that included qualitatively distinct and increasingly sophisticated approaches to reasoning about the data. We compared the results from our latent class model to the correctness of students’ answers (i.e.reaction orders) and to a less familiar task, in which students were unable to use the control of variables strategy. The results showed that many students struggled to engage meaningfully with the data when constructing their rate laws. The students’ strategies may provide insight into how to scaffold students’ abilities to analyze data.

The EM algorithm for latent class analysis with equality constraints

Psychometrika ◽  
1992 ◽  
Vol 57 (2) ◽  
pp. 261-269 ◽  
Author(s):  
Ab Mooijaart ◽  
Peter G. M. van der Heijden
Keyword(s):  
Em Algorithm ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Equality Constraints ◽  
Class Analysis ◽  
The Em Algorithm

scholarly journals Using Latent Class Analysis to Identify Different Risk Patterns for Patients With Masked Hypertension

2021 ◽  
Vol 8 ◽  
Author(s):  
Ming Fu ◽  
Xiangming Hu ◽  
Shixin Yi ◽  
Shuo Sun ◽  
Ying Zhang ◽  
...  
Keyword(s):  
High Risk ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Latent Class Model ◽  
Fasting Blood Glucose ◽  
Low Risk ◽  
Biological Information ◽  
Masked Hypertension ◽  
Class Analysis ◽  
The Impact

Background: There is controversy whether masked hypertension (MHT) requires additional intervention. The aim of this study is to evaluate whether MHT accompanied with high-risk metabolic syndrome (MetS), as the subphenotype, will have a different prognosis from low-risk MetS.Methods: We applied latent class analysis to identify subphenotypes of MHT, using the clinical and biological information collected from High-risk Cardiovascular Factor Screening and Chronic Disease Management Programme. We modeled the data, examined the relationship between subphenotypes and clinical outcomes, and further explored the impact of antihypertensive medication.Results: We included a total of 140 patients with MHT for analysis. The latent class model showed that the two-class (high/low-risk MetS) model was most suitable for MHT classification. The high-risk MetS subphenotype was characterized by larger waist circumference, lower HDL-C, higher fasting blood glucose and triglycerides, and prevalence of diabetes. After four years of follow-up, participants in subphenotype 1 had a higher non-major adverse cardiovascular event (MACE) survival probability than those in subphenotype 2 (P = 0.016). There was no interaction between different subphenotypes and the use of antihypertensive medications affecting the occurrence of MACE.Conclusions: We have identified two subphenotypes in MHT that have different metabolic characteristics and prognosis, which could give a clue to the importance of tracing the clinical correlation between MHT and metabolic risk factors. For patients with MHT and high-risk MetS, antihypertensive therapy may be insufficient.

scholarly journals Applying latent class analysis in the identification of occupational accident patterns

2020 ◽  
Vol 2020 (146) ◽  
pp. 339-355
Author(s):  
Marzena NOWAKOWSKA ◽  
◽  
Michał PAJĘCKI ◽  
Keyword(s):  
Latent Class Analysis ◽  
Latent Class ◽  
Latent Class Model ◽  
Severe Accident ◽  
Occupational Accidents ◽  
Occupational Accident ◽  
Class Analysis ◽  
Data Set ◽  
Pilot Studies ◽  
Individual Record

Purpose: The objective of the study is to use selected data mining techniques to discover patterns of certain recurring mechanisms related to the occurrence of occupational accidents in relation to production processes. Design/methodology/approach: The latent class analysis (LCA) method was employed in the investigation. This statistical modeling technique enables discovering mutually exclusive homogenous classes of objects in a multivariate data set on the basis of observable qualitative variables, defining the class homogeneity in terms of probabilities. Due to a bilateral agreement, Statistics Poland provided individual record-level real data for the research. Then the data were preprocessed to enable the LCA model identification. Pilot studies were conducted in relation to occupational accidents registered in production plants in 2008-2017 in the Wielkopolskie voivodeship. Findings: Three severe accident patterns and two light accident patterns represented by latent classes were obtained. The classes were subjected to descriptive characteristics and labeling, using interpretable results presented in the form of probabilities classifying categories of observable variables, symptomatic for a given latent class. Research limitations/implications: The results from the pilot studies indicate the necessity to continue the research based on a larger data set along with the analysis development, particularly as regards selecting indicators for the latent class model characterization. Practical implications: The identification of occupational accident patterns related to the production process can play a vital role in the elaboration of efficient safety countermeasures that can help to improve the prevention and outcome mitigation of such accidents among workers. Social implications: Creating a safe work environment comprises the quality of life of workers, their families, thus affirming the enterprises' principles and values in the area of corporate social responsibility. Originality/value: The investigation showed that latent class analysis is a promising tool supporting the scientific research in discovering the patterns of occupational accidents. The proposed investigation approach indicates the importance for the research both in terms of the availability of non-aggregated occupational accident data as well as the type of value aggregation of the variables taken for the analysis.

scholarly journals Hispanic/Latino Acculturation Profiles and Telomere Length: Latent Class Analysis on a Nationally Representative Sample

2021 ◽  
Vol 9 ◽  
Author(s):  
Francisco Alejandro Montiel Ishino ◽  
Philip McNab ◽  
Kevin Villalobos ◽  
Jeffrey H. Cohen ◽  
Anna M. Nápoles ◽  
...  
Keyword(s):  
Latent Class Analysis ◽  
Telomere Length ◽  
Representative Sample ◽  
Latent Variable ◽  
Latent Class ◽  
The United States ◽  
Public Health Research ◽  
Class Analysis ◽  
Foreign Born ◽  
The Us

Background: Acculturation profiles and their impact on telomere length among foreign-born Hispanics/Latinos living in the United States (US) are relatively unknown. The limited research available has linked acculturation with shortened telomere length.Objectives: To identify acculturation profiles among a US representative sample of Hispanics/Latinos and to then examine telomere length differences between profiles.Methods: We conducted a latent class analysis among a non-institutionalized US-representative sample of Hispanics/Latinos using the 1999–2002 National Health and Nutrition Examination Survey (N = 2,292). The latent variable of acculturation was assessed by length of time in the US and language used as a child, read and spoken, usually spoken at home, used to think, and used with friends (i.e., Spanish and/or English). Telomere length assessed from leukocytes was used as the distal continuous outcome.Results: We identified five profiles: (1) low acculturated [33.2% of sample]; (2) partially integrated [18.6% of sample]; (3) integrated [19.4% of sample]; (4) partially assimilated [15.1% of sample]; and (5) assimilated [13.7% of sample]. Acculturation profiles revealed nuanced differences in conditional probabilities with language use despite the length of time spent in the US. While telomere length did vary, there were no significant differences between profiles.Conclusion: Profiles identified revealed that possible life-course and generational effects may be at play in the partially assimilated and assimilated profiles. Our findings expand public health research using complex survey data to identify and assess the dynamic relationship of acculturation profiles and health biomarkers, while being among the first to examine this context using a person-centered approach.

Latent class analysis identification of syndromes in Alzheimer's disease

2006 ◽  
Vol 3 (1) ◽  
Author(s):  
Cathal Walsh
Keyword(s):  
Social Sciences ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Latent Class Analysis ◽  
Posterior Distribution ◽  
Latent Variable ◽  
Latent Class ◽  
Bayesian Framework ◽  
Class Analysis ◽  
The Social

Latent variable models have been used extensively in the social sciences. In this work a latent class analysis is used to identify syndromes within Alzheimer's disease. The fitting of the model is done in a Bayesian framework, and this is examined in detail here. In particular, the label switching problem is identified, and solutions presented. Graphical summaries of the posterior distribution are included.

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