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

作者：Sartipi Majid; Nedjat Saharnaz^*; Mansournia Mohammad Ali; Baigi Vali; Fotouhi Akbar

来源：Archives of Iranian Medicine, 2016, 19(11): 791-796.

• Index
• latent class analysis
• principal components analysis
• socioeconomic status

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: 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: 0.86-0.58).
Conclusion: 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.

• 出版日期2016-11