The Use of Discrete Data in PCA: Theory, Simulations, and Applications to Socioeconomic Indices

By Stanislav KolenikovGustavo Angeles. 2004

359 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/09405427891f2d75084abed709ddf74d3b6cd598

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Citations in the corpus

Weather Shocks, Agricultural Production and Migration: Evidence from Tanzania (2016, methodology): “Wealth tertiles based on asset-based index based on the polychoric principal component analysis (Kolenikov & Angeles, 2004).”
Entrepreneurial Intention of Engineering Students: The Role of Social Norms and Entrepreneurial Self-Efficacy (2019, methodology): “In order to perform this analysis, we used a polychoric correlation matrix, since the standard methods of factorial analysis assume that the variables are continuous and follow a normal multivariate distribution [53].”
Rotation Forest: A New Classifier Ensemble Method (2006, methodology): “It consists in splitting the feature set into K subsets, running principal component analysis (PCA) separately on each subset and then reassembling a new extracted feature set while keeping all the components.”
Characterizing Weights in the Measurement of Multidimensional Poverty: An Application of Data-Driven Approaches to Cameroonian Data (2008, methodology): “To specify the polychoric PCA model, we follow Kolenikov and Angeles (2004).”
Accounting Restatements, Governance and Municipal Debt Financing (2013, unknown): “Factor analysis is primarily designed for continuous variables; the use of categorical variables introduces negative spurious correlation which renders factors inferior to indices used in this study (Kolenikov and Angeles 2004).”
Analysing behavioural differences of farm households: An example of income diversification strategies based on European farm survey data (2017, methodology): “A detailed derivation is provided in Kolenikov and Ángeles (2004).”
The importance of socio-economic status in determining educational achievement in South Africa (2009, methodology): “…Analysis as to whether most of the common variation amongst the variables is caused by the correlation amongst the dummies due to the coding technique or by the unobserved factor, such as SES, which is what one aims to isolate with Principal Components Analysis (Kolenikov and Angeles, 2004: 5).”
Stakeholders prioritization of climate-smart agriculture interventions: Evaluation of a framework (2019, methodology): “Overall CSA technology, practice and service implementation feasibility (CSA-IF) was computed using Principal Component Analysis (PCA) based on a polychoric correlation matrix (Kolenikov and Angeles, 2004).”