Constructing socio-economic status indices: how to use principal components analysis.

By S. Vyas, L. Kumaranayake. Health Policy and Planning, 2006

3,344 citations recorded by the source corpus.

Subfields: Health, Social Psychology, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/e6e353c1f379e177bda6e79d1d482e0b8f4ded56

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In Health

1. Jemal Abafita
2. F. V. D. van de Vijver
3. Manfred Zeller
4. Nóra Bunford
5. Veronika Konok
6. A. Keil
7. M. Schachner
8. Á. Miklósi

In Social Psychology

1. Jemal Abafita
2. F. V. D. van de Vijver
3. Manfred Zeller
4. Nóra Bunford
5. Veronika Konok
6. A. Keil
7. C. Murendo
8. M. Schachner

In Sociology and Political Science

1. Mathieu J. P. Poirier
2. Karen A. Grépin
3. Michel Grignon
4. Jemal Abafita
5. A. Wangombe
6. G. Ángeles
7. F. V. D. van de Vijver
8. N. Khadioli

Citations in the corpus

• Measuring Ethiopian farmers' vulnerability to climate change across regional states: (2008, background): “The use of assetbased indices for measurements of wealth across different social groups is a good example (Filmer and Pritchett 2001; Langyintuo 2005; Sumarto, Suryadarma, and Suryahadi 2006; Vyas and Kumaranayake 2006).”
• Dual Principal Component Pursuit (2015, background): “lysis (PCA) is one of the oldest (Pearson, 1901; Hotelling, 1933) and most fundamental techniques in data analysis, with ubiquitous applications in engineering (Moore, 1981), economics and sociology (Vyas and Kumaranayake, 2006), chemistry (Ku et al., 1995), physics (Loyd et al., 2014) and genetics ”
• Approaches and Alternatives to the Wealth Index to Measure Socioeconomic Status Using Survey Data: A Critical Interpretive Synthesis (2019, background): “Others counter that this is not misclassification, but an accurate representation of the relative affluence of urban households (Vyas and Kumaranayake 2006).”
• How All Students Can Belong and Achieve: Effects of the Cultural Diversity Climate Amongst Students of Immigrant and Nonimmigrant Background in Germany (2019, methodology): “The four items were merged to form a combined SES index by using principal component analysis and saving factor scores for each student (Vyas & Kumaranayake, 2006).”
• Socioeconomic Status Measurement with Discrete Proxy Variables (2009, background): “Vyas and Kumaranayake (2006) reviewed issues related to choice of variables and data preparation, and problems such as data clustering were addressed.”
• Determinants of Household Food Security in Rural Ethiopia: An Empirical Analysis (2014, methodology): “A detailed account of the use of PCA for constructing socio-economic status indices has been outlined in Vyas and Kumaranayake (2006).”
• Financial literacy and savings decisions by adult financial consumers in Zimbabwe (2017, background): “(Sahn and Stifel, 2000; Vyas and Kumaranayake, 2006; Field, 2013).”
• The International Wealth Index (IWI) (2015, background): “(c) Clumping and truncation Differentiating well between wealthier and less wealthy households is important to prevent clumping and truncation, two problems of which asset based wealth indices may suffer (McKenzie, 2005; Vyas & Kumaranayake, 2006).”
• Factors affecting the choices of coping strategies for climate extremes: The case of farmers in the Nile Basin of Ethiopia (2010, background): “Ownership of radio and better-quality houses are indicators of wealth in rural Africa (Langyintuo 2005; Vyas and Kumaranayake 2006), meaning that wealthier farmers can better cope.”
• Using panel data to estimate the effect of rainfall shocks on smallholders food security and vulnerability in rural Ethiopia (2011, methodology): “…x2, x3, . . ., xn), PCA creates uncorrelated indices or components whereby each component is a linear weighted combination of the initial variables (Vyas and Kumaranayake 2006) as follows: PCm=am1x1 + am2x2 + am3x3 + . . . + amnxn (1) Where amn represents the weight for the mth principal component…”
• Associations between child mobile use and digital parenting style in Hungarian families (2019, methodology): “Although PCA is generally suggested for continuous data, it is considered appropriate for binary data as well (Gower, 1966; Jolli ff e, 2002; Vyas & Kumaranayake, 2006).”
• A Logistic Regression Model to Identify Key Determinants of Poverty Using Demographic and Health Survey Data (2010, background): “Similarly with the expenditure data the limitation is the extensive data collection which is time- consuming and costly as stated by Vyas and Kumaranayake (2006).”