scholarly journals Regularized Latent Class Analysis for Polytomous Item Responses: An Application to SPM-LS Data

2020 ◽  
Author(s):  
Alexander Robitzsch
Keyword(s):  
Latent Class Analysis ◽  
Latent Class ◽  
Latent Class Model ◽  
Simulated Data ◽  
Latent Class Models ◽  
Class Analysis ◽  
Polytomous Item ◽  
Item Responses ◽  
Polytomous Item Responses ◽  
Raven's Standard Progressive Matrices

The last series of Raven's standard progressive matrices (SPM-LS) test were studied with respect to its psychometric properties in a series of recent papers. In this paper, the SPM-LS dataset is analyzed with regularized latent class models (RLCM). For dichotomous item response data, an alternative estimation approach for RLCMs is proposed. For polytomous item responses, different alternatives for performing regularized latent class analysis are proposed. The usefulness of the proposed methods is demonstrated in a simulated data illustration and for the SPM-LS dataset. For the SPM-LS dataset, it turned out the regularized latent class model resulted in five partially ordered latent classes.

scholarly journals Regularized Latent Class Analysis for Polytomous Item Responses: An Application to SPM-LS Data

2020 ◽  
Vol 8 (3) ◽  
pp. 30 ◽  
Author(s):  
Alexander Robitzsch
Keyword(s):  
Latent Class ◽  
Latent Class Model ◽  
Simulated Data ◽  
Latent Class Models ◽  
Latent Classes ◽  
Polytomous Item ◽  
Item Functioning ◽  
Item Responses ◽  
Polytomous Item Responses ◽  
Raven's Standard Progressive Matrices

The last series of Raven’s standard progressive matrices (SPM-LS) test was studied with respect to its psychometric properties in a series of recent papers. In this paper, the SPM-LS dataset is analyzed with regularized latent class models (RLCMs). For dichotomous item response data, an alternative estimation approach based on fused regularization for RLCMs is proposed. For polytomous item responses, different alternative fused regularization penalties are presented. The usefulness of the proposed methods is demonstrated in a simulated data illustration and for the SPM-LS dataset. For the SPM-LS dataset, it turned out the regularized latent class model resulted in five partially ordered latent classes. In total, three out of five latent classes are ordered for all items. For the remaining two classes, violations for two and three items were found, respectively, which can be interpreted as a kind of latent differential item functioning.

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 Incentivising doctor attendance in rural Bangladesh: a latent class analysis of a discrete choice experiment

2021 ◽  
Vol 6 (7) ◽  
pp. e006001
Author(s):  
Blake Angell ◽  
Mushtaq Khan ◽  
Raihanul Islam ◽  
Kate Mandeville ◽  
Nahitun Naher ◽  
...  
Keyword(s):  
Latent Class Analysis ◽  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Choice Experiment ◽  
Latent Class ◽  
Latent Class Models ◽  
Class Analysis ◽  
Sustainable Solutions ◽  
Rural Bangladesh ◽  
Incentive Payments

ObjectiveDoctor absenteeism is widespread in Bangladesh, and the perspectives of the actors involved are insufficiently understood. This paper sought to elicit preferences of doctors over aspects of jobs in rural areas in Bangladesh that can help to inform the development of packages of policy interventions that may persuade them to stay at their posts.MethodsWe conducted a discrete choice experiment with 308 doctors across four hospitals in Dhaka, Bangladesh. Four attributes of rural postings were included based on a literature review, qualitative research and a consensus-building workshop with policymakers and key health-system stakeholders: relationship with the community, security measures, attendance-based policies and incentive payments. Respondents’ choices were analysed with mixed multinomial logistic and latent class models and were used to simulate the likely uptake of jobs under different policy packages.ResultsAll attributes significantly impacted doctor choices (p<0.01). Doctors strongly preferred jobs at rural facilities where there was a supportive relationship with the community (β=0.93), considered good attendance in education and training (0.77) or promotion decisions (0.67), with functional security (0.67) and higher incentive payments (0.5 per 10% increase of base salary). Jobs with disciplinary action for poor attendance were disliked by respondents (−0.63). Latent class analysis identified three groups of doctors who differed in their uptake of jobs. Scenario modelling identified intervention packages that differentially impacted doctor behaviour and combinations that could feasibly improve doctors’ attendance.ConclusionBangladeshi doctors have strong but varied preferences over interventions to overcome absenteeism. We generated evidence suggesting that interventions considering the perspective of the doctors themselves could result in substantial reductions in absenteeism. Designing policy packages that take account of the different situations facing doctors could begin to improve their ability and motivation to be present at their job and generate sustainable solutions to absenteeism in rural Bangladesh.

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.

Latent Class Analysis for Marketing Scale Development

2011 ◽  
Vol 53 (2) ◽  
pp. 209-230 ◽  
Author(s):  
Francesca Bassi
Keyword(s):  
Scale Development ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Marketing Research ◽  
Latent Class Models ◽  
Measurement Scale ◽  
Class Analysis ◽  
Validity And Reliability ◽  
Measurement Scales ◽  
Scale Validity

Measurement scales are a crucial instrument in marketing research for measuring unobservable variables such as attitudes, opinions and beliefs. In using, evaluating or developing multi-item scales, a number of guidelines and procedures are recommended, to ensure that the measure applied is psychometrically robust. These procedures have been outlined in the psychometric literature since the late 1970s and are composed of steps that refer to construct and domain definition, scale validity, reliability, dimensionality and generalisability. Various statistical instruments are used in the scale-developing process, almost always referring to metric variables (interval or ratio scales). Instead, items forming scales are rarely measured metrically; items are frequently ordinal and, in some rare cases, nominal. In this paper, it is shown how the implementation of latent class analysis may improve the process of measurement scale development, since it explicitly considers that items generate ordinal or even nominal variables. Specifically, applying appropriate latent class models allows us to assess scale validity and reliability more soundly than traditionally used methods.

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.

scholarly journals Latent class analysis for exploring distribution patterns of primary superficial venous insufficiency

2020 ◽  
Vol 28 (3) ◽  
pp. 474-479
Author(s):  
Nurten Andaç Baltacıoğlu
Keyword(s):  
Latent Class Analysis ◽  
Saphenous Vein ◽  
Latent Class ◽  
Lower Extremities ◽  
Distribution Patterns ◽  
Latent Class Models ◽  
Chronic Venous Disease ◽  
Venous Disease ◽  
Class Analysis ◽  
Saphenofemoral Junction

Background: This study aims to identify specific segmental distribution patterns of lower extremity chronic venous disease based on latent class analysis of Doppler mapping results. Methods: A total of 1,871 lower extremities of 1,218 treatment-naïve patients (536 males, 682 females; mean age 45.4 years; range, 21 to 87 years) with chronic venous disease referred for Doppler examination between September 2009 and August 2018 were included. Refluxing superficial venous segments of the lower extremities were mapped and recorded in database in 10 distinct anatomic locations as follows: saphenofemoral junction and proximal greater saphenous vein, mid and distal thigh greater saphenous vein, anterior and posterior accessory saphenous veins, proximal and distal calf greater saphenous vein, saphenopopliteal junction and proximal lesser saphenous vein, distal lesser saphenous vein, and intersaphenous veins including Giacomini’s vein. Repeated examinations were excluded. The latent class analysis was applied to identify any possible anatomic distribution patterns of chronic venous disease. Results: Bayesian information criteria revealed three latent class models fit for refluxing segment distribution as follows: 58.2% (n=1,089) were above-the-knee greater saphenous vein segments including saphenofemoral junction (pattern 1); 29.3% (n=548) were below-the-knee greater saphenous vein segments (pattern 2); and 12.5% (n=234) were lesser saphenous vein segments and intersaphenous veins including Giacomini’s vein (pattern 3). There was no age- or sex-specific differences in the chronic venous disease distribution patterns. Conclusion: The latent class analysis, by identifying previously unseen subgroups within the sampled population, provides a new approach to classification of reflux patterns in chronic venous disease. Identification of latent classes may provide understanding of different pathophysiological bases of venous reflux and more optimal planning for interventions.

scholarly journals Latent Class Analysis of Obesogenic Behaviors among Korean Adolescents: Associations with Weight-Related Outcomes

2021 ◽  
Vol 18 (21) ◽  
pp. 11059
Author(s):  
Haein Lee ◽  
In-Seo La
Keyword(s):  
Latent Class Analysis ◽  
Weight Status ◽  
Latent Class ◽  
Dietary Habits ◽  
Latent Class Models ◽  
Psychological Status ◽  
Class Analysis ◽  
Weight Control Behaviors ◽  
Obesogenic Behaviors ◽  
Class Models

This study aimed to explore sex-specific latent class models of adolescent obesogenic behaviors (OBs), predictors of latent class membership (LCM), and associations between LCM and weight-related outcomes (i.e., weight status and unhealthy weight control behaviors). We analyzed nationally representative data from the 2019 Korea Youth Risk Behavior Survey. To identify latent classes for boys (n = 29,841) and girls (n = 27,462), we conducted a multiple-group latent class analysis using eight OBs (e.g., breakfast skipping, physical activity, and tobacco product use). Moreover, we performed a multinomial logistic regression analysis and a three-step method to examine associations of LCM with predictors and weight-related outcomes. Among both sexes, the 3-class models best fit the data: (a) mostly healthy behavior class, (b) poor dietary habits and high Internet use class, and (c) poor dietary habits and substance use class. School year, residential area, academic performance, and psychological status predicted the LCM for both sexes. In addition, perceived economic status predicted the LCM for girls. The distribution of weight-related outcomes differed across sex-specific classes. Our findings highlight the importance of developing obesity prevention and treatment interventions tailored to each homogeneous pattern of adolescent OBs, considering differences in their associations with predictors and weight-related outcomes.

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