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Paper Information

Journal:   ARCHIVES OF IRANIAN MEDICINE   November 2016 , Volume 19 , Number 11; Page(s) 791 To 796.
 
Paper: 

Assets as a Socioeconomic Status Index: Categorical Principal Components Analysis vs. Latent Class Analysis

 
DOI: 

0161911/AIM.009

 
Author(s):  SARTIPI MAJID, NEDJAT SAHARNAZ*, MANSOURNIA MOHAMMAD ALI, Baigi Vali, FOTOUHI AKBAR
 
* Knowledge Utilization Research Center, Tehran University of Medical Science, Tehran, Iran
 
Abstract: 
Background: Some variables like Socioeconomic Status (SES) cannot be directly measured, instead, so-called ‘ latent variables’ are measured indirectly through calculating tangible items. There are different methods for measuring latent variables such as data reduction methods e. g. Principal Components Analysis (PCA) and Latent Class Analysis (LCA). Objectives: The purpose of our study was to measure assets index-as a representative of SES-through two methods of Non-Linear PCA (NLPCA) and LCA, and to compare them for choosing the most appropriate model. Methods: 􀀃 􀀷 􀁋 􀁌 􀁖 􀀃 􀁚 􀁄 􀁖 􀀃 􀁄 􀀃 􀁆 􀁕 􀁒 􀁖 􀁖 􀀃 􀁖 􀁈 􀁆 􀁗 􀁌 􀁒 􀁑 􀁄 􀁏 􀀃 􀁖 􀁗 􀁘 􀁇 􀁜 􀀃 􀁌 􀁑 􀀃 􀁚 􀁋 􀁌 􀁆 􀁋 􀀃 􀀔 􀀜 􀀜 􀀘 􀀃 􀁕 􀁈 􀁖 􀁓 􀁒 􀁑 􀁇 􀁈 􀁑 􀁗 􀁖 􀀃 􀂿 􀁏 􀁏 􀁈 􀁇 􀀃 􀁗 􀁋 􀁈 􀀃 􀁔 􀁘 􀁈 􀁖 􀁗 􀁌 􀁒 􀁑 􀁑 􀁄 􀁌 􀁕 􀁈 􀁖 􀀃 􀁄 􀁅 􀁒 􀁘 􀁗 􀀃 􀁗 􀁋 􀁈 􀁌 􀁕 􀀃 􀁄 􀁖 􀁖 􀁈 􀁗 􀁖 􀀃 􀁌 􀁑 􀀃 􀀷 􀁈 􀁋 􀁕 􀁄 􀁑 􀀑 􀀃 􀀷 􀁋 􀁈 􀀃 􀁇 􀁄 􀁗 􀁄 􀀃 were analyzed by SPSS 19 (CATPCA command) and SAS 9. 2 (PROC LCA command) to estimate their socioeconomic status. The results 􀁚 􀁈 􀁕 􀁈 􀀃 􀁆 􀁒 􀁐 􀁓 􀁄 􀁕 􀁈 􀁇 􀀃 􀁅 􀁄 􀁖 􀁈 􀁇 􀀃 􀁒 􀁑 􀀃 􀁗 􀁋 􀁈 􀀃 􀀬 􀁑 􀁗 􀁕 􀁄 􀀐 􀁆 􀁏 􀁄 􀁖 􀁖 􀀃 􀀦 􀁒 􀁕 􀁕 􀁈 􀁏 􀁄 􀁗 􀁌 􀁒 􀁑 􀀃 􀀦 􀁒 􀁈 􀁉 􀂿 􀁆 􀁌 􀁈 􀁑 􀁗 􀀃 􀀋 􀀬 􀀦 􀀦 􀀌 􀀑 Results: The 6 derived classes from LCA based on BIC, were highly consistent with the 6 classes from CATPCA (Categorical PCA) (ICC = 0. 87, 95%CI: 0. 86 – 0. 88). Conclusion: 􀀃 􀀷 􀁋 􀁈 􀁕 􀁈 􀀃 􀁌 􀁖 􀀃 􀁑 􀁒 􀀃 􀁊 􀁒 􀁏 􀁇 􀀃 􀁖 􀁗 􀁄 􀁑 􀁇 􀁄 􀁕 􀁇 􀀃 􀁗 􀁒 􀀃 􀁐 􀁈 􀁄 􀁖 􀁘 􀁕 􀁈 􀀃 􀀶 􀀨 􀀶 􀀑 􀀃 􀀷 􀁋 􀁈 􀁕 􀁈 􀁉 􀁒 􀁕 􀁈 􀀏 􀀃 􀁌 􀁗 􀀃 􀁌 􀁖 􀀃 􀁑 􀁒 􀁗 􀀃 􀁓 􀁒 􀁖 􀁖 􀁌 􀁅 􀁏 􀁈 􀀃 􀁗 􀁒 􀀃 􀁇 􀁈 􀂿 􀁑 􀁌 􀁗 􀁈 􀁏 􀁜 􀀃 􀁖 􀁄 􀁜 􀀃 􀁗 􀁋 􀁄 􀁗 􀀃 􀁄 􀀃 􀁖 􀁓 􀁈 􀁆 􀁌 􀂿 􀁆 􀀃 􀁐 􀁈 􀁗 􀁋 􀁒 􀁇 􀀃 􀁌 􀁖 􀀃 􀁅 􀁈 􀁗 􀁗 􀁈 􀁕 􀀃 􀁗 􀁋 􀁄 􀁑 􀀃 another one. LCA is a complicated method that presents detailed information about latent variables and required one assumption (local independency), while NLPCA is a simple method, which requires more assumptions. Generally, NLPCA seems to be an acceptable method of analysis because of its simplicity and high agreement with LCA.
 
Keyword(s): Index,latent class analysis,principal components analysis,socioeconomic status
 
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