title: Assets as a Socioeconomic Status Index: Categorical Principal Components Analysis vs. Latent Class Analysis.
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).
  
  
  OBJECTIVE
  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
  This was a cross sectional study in which 1995 respondents filled the questionnaires about their assets in Tehran. The data were analyzed by SPSS 19 (CATPCA command) and SAS 9.2 (PROC LCA command) to estimate their socioeconomic status. The results were compared based on the Intra-class Correlation Coefficient (ICC).
  
  
  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).
  
  
  CONCLUSIONS
  There is no gold standard to measure SES. Therefore, it is not possible to definitely say that a specific method is better than 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.
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
authors.forename: Majid
authors.initials: M
authors.lastname: Sartipi
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran, Knowledge Utilization Research Center, Tehran University of Medical Science, Tehran, Iran.
authors.forename: Saharnaz
authors.initials: S
authors.lastname: Nedjat
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
authors.forename: Mohammad Ali
authors.initials: MA
authors.lastname: Mansournia
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
authors.forename: Vali
authors.initials: V
authors.lastname: Baigi
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
authors.forename: Akbar
authors.initials: A
authors.lastname: Fotouhi
mesh_terms: D000328:Adult; D000704:Analysis of Variance; D003430:Cross-Sectional Studies; D005260:Female; D006801:Humans; D007492:Iran; D008297:Male; D015233:Models, Statistical; D025341:Principal Component Analysis; D012923:Social Class; D011795:Surveys and Questionnaires