The article’s objective is to compare three different statistical software packages that perform latent class cluster analysis, Latent GOLD, MCLUST and poLCA. Latent GOLD is a commercial software package available at Statistical Innovations Inc. that performs a variety of latent class analyses…
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MCLUST and poLCA are free packages developed on R, a command line application.
The article gives an overview of the concept of latent class cluster analysis. A review of the usage of the software to perform the latent analysis is done and the results produced are compared and interpreted. Only a single dataset is used for the purpose of testing the software as it would prevent bias in comparing the different output of the software. The author has indicated that there are no research papers found on comparing and scrutinizing the various latent class cluster analysis software packages available in the market. This article is one such approach to analyze the working of the three software based on a single dataset and differentiate the approachability and presentability of results. The author has also suggested the most comfortable and efficient software among the three based on the study.
The dataset used for the study collected from 160 countries, is arranged in five groups, Digital Development, Economic, Infrastructure, Demographic and Risk. The first group includes data on computer and internet usage. The economic variable captures the income level of the country. Infrastructure includes data on telephone lines and electricity consumption. The demographic variable includes data on population, age and years of schooling. Finally, the Risk Index is used to capture the risk related to the political situation in each country.
The basic description of the cost, guide features, tutorials and installation requirements of the software is provided. The article also provides the description of the usage of the software. The software allows the user to choose the variables that will be used to create the latent cluster and also to designate some variables as covariates to improve classification.
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