scholarly journals Latent class cluster analysis

2019 ◽  
Author(s):  
Ali Ünlü
Keyword(s):  
Cluster Analysis ◽  
Latent Class ◽  
Graphical Representation ◽  
Latent Class Model ◽  
General Social Survey ◽  
Information Criteria ◽  
Survey Respondent ◽  
Latent Class Cluster Analysis ◽  
Gold Classification

This paper describes the technique of exploratory latent class cluster analysis. The classical analysis is a model-based statistical approach for identifying unobserved subgroups from observed categorical data and for classifying cases into the identified subgroups based on membership probabilities estimated directly from the statistical model. In the first part on mathematical modeling of the paper, we introduce the data and the sampling distribution for the data as required in the analysis of latent classes, the fundamental model assumptions are reviewed, and the general unrestricted latent class model is presented. Classification of cases into the clusters using modal assignment is discussed. In the second part on inferential statistics of the paper, we briefly review the classical maximum likelihood methodology related to parameter estimation and model testing, and the information criteria AIC and SIC for model selection. In the third part on case study of the paper, the General Social Survey data are analyzed using the software Latent GOLD. We present the Latent GOLD profile plot and tri plot options for the graphical representation of the results. The Latent GOLD classification output illustrating the assignment of respondents to the latent survey respondent types is also shown.

scholarly journals Latent class cluster analysis

2019 ◽  
Vol 3 (10) ◽  
Author(s):  
Ali Ünlü
Keyword(s):  
Cluster Analysis ◽  
Latent Class ◽  
Graphical Representation ◽  
Latent Class Model ◽  
General Social Survey ◽  
Information Criteria ◽  
Survey Respondent ◽  
Latent Class Cluster Analysis

This paper describes the technique of exploratory latent class cluster analysis. The classical analysis is a model-based statistical approach for identifying unobserved subgroups from observed categorical data and for classifying cases into the identified subgroups based on membership probabilities estimated directly from the statistical model. In the first part on mathematical modeling of the paper, we introduce the data and the sampling distribution for the data as required in the analysis of latent classes, the fundamental model assumptions are reviewed, and the general unrestricted latent class model is presented. Classification of cases into the clusters using modal assignment is discussed. In the second part on inferential statistics of the paper, we briefly review the classical maximum likelihood methodology related to parameter estimation and model testing, and the information criteria AIC and SIC for model selection. In the third part on case study of the paper, the General Social Survey data are analyzed using the software Latent GOLD®. We present the Latent GOLD® profile plot and tri plot options for the graphical representation of the results. The Latent GOLD® classification output illustrating the assignment of respondents to the latent survey respondent types is also shown.

scholarly journals Latent class cluster analysis identified hidden headache phenotypes in COVID-19: impact of pulmonary infiltration and IL-6

2021 ◽  
Author(s):  
Ömer Karadaş ◽  
Bilgin Öztürk ◽  
Ali Rıza Sonkaya ◽  
Bahar Taşdelen ◽  
Aynur Özge ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Latent Class ◽  
Latent Class Cluster Analysis ◽  
Pulmonary Infiltration

scholarly journals Negative psychotic symptoms and impaired role functioning predict transition outcomes in the at-risk mental state: a latent class cluster analysis study

2013 ◽  
Vol 43 (11) ◽  
pp. 2311-2325 ◽  
Author(s):  
L. R. Valmaggia ◽  
D. Stahl ◽  
A. R. Yung ◽  
B. Nelson ◽  
P. Fusar-Poli ◽  
...  
Keyword(s):  
At Risk ◽  
Cluster Analysis ◽  
Mental State ◽  
Latent Class ◽  
Mental States ◽  
Psychotic Symptoms ◽  
Role Functioning ◽  
Latent Class Cluster Analysis ◽  
At Risk Mental State

BackgroundMany research groups have attempted to predict which individuals with an at-risk mental state (ARMS) for psychosis will later develop a psychotic disorder. However, it is difficult to predict the course and outcome based on individual symptoms scores.MethodData from 318 ARMS individuals from two specialized services for ARMS subjects were analysed using latent class cluster analysis (LCCA). The score on the Comprehensive Assessment of At-Risk Mental States (CAARMS) was used to explore the number, size and symptom profiles of latent classes.ResultsLCCA produced four high-risk classes, censored after 2 years of follow-up: class 1 (mild) had the lowest transition risk (4.9%). Subjects in this group had the lowest scores on all the CAARMS items, they were younger, more likely to be students and had the highest Global Assessment of Functioning (GAF) score. Subjects in class 2 (moderate) had a transition risk of 10.9%, scored moderately on all CAARMS items and were more likely to be in employment. Those in class 3 (moderate–severe) had a transition risk of 11.4% and scored moderately severe on the CAARMS. Subjects in class 4 (severe) had the highest transition risk (41.2%), they scored highest on the CAARMS, had the lowest GAF score and were more likely to be unemployed. Overall, class 4 was best distinguished from the other classes on the alogia, avolition/apathy, anhedonia, social isolation and impaired role functioning.ConclusionsThe different classes of symptoms were associated with significant differences in the risk of transition at 2 years of follow-up. Symptomatic clustering predicts prognosis better than individual symptoms.

Cluster Analysis with General Latent Class Model

2011 ◽  
pp. 225-230
Author(s):  
Dingxi Qiu ◽  
Edward C. Malthouse
Keyword(s):  
Cluster Analysis ◽  
Statistical Models ◽  
Latent Variable ◽  
Latent Class ◽  
Latent Class Model ◽  
Teaching Style ◽  
Teaching Styles ◽  
Binary Responses ◽  
Cluster Membership ◽  
Wide Range

Cluster analysis is a set of statistical models and algorithms that attempt to find “natural groupings” of sampling units (e.g., customers, survey respondents, plant or animal species) based on measurements. The observable measurements are sometimes called manifest variables and cluster membership is called a latent variable. It is assumed that each sampling unit comes from one of K clusters or classes, but the cluster identifier cannot be observed directly and can only be inferred from the manifest variables. See Bartholomew and Knott (1999) and Everitt, Landau and Leese (2001) for a broader survey of existing methods for cluster analysis. Many applications in science, engineering, social science, and industry require grouping observations into “types.” Identifying typologies is challenging, especially when the responses (manifest variables) are categorical. The classical approach to cluster analysis on those data is to apply the latent class analysis (LCA) methodology, where the manifest variables are assumed to be independent conditional on the cluster identity. For example, Aitkin, Anderson and Hinde (1981) classified 468 teachers into clusters according to their binary responses to 38 teaching style questions. This basic assumption in classical LCA is often violated and seems to have been made out of convenience rather than it being reasonable for a wide range of situations. For example, in the teaching styles study two questions are “Do you usually allow your pupils to move around the classroom?” and “Do you usually allow your pupils to talk to one another?” These questions are mostly likely correlated even within a class.

scholarly journals Using Two-Step Cluster Analysis and Latent Class Cluster Analysis to Classify the Cognitive Heterogeneity of Cross-Diagnostic Psychiatric Inpatients

2020 ◽  
Vol 11 ◽  
Author(s):  
Mariagrazia Benassi ◽  
Sara Garofalo ◽  
Federica Ambrosini ◽  
Rosa Patrizia Sant’Angelo ◽  
Roberta Raggini ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Latent Class ◽  
Psychiatric Inpatients ◽  
Latent Class Cluster Analysis ◽  
Cognitive Heterogeneity
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