09 SES 02 C, Improving Measures of Socio-Economic Status to Support Educational Research in Low- and Middle-Income Countries
Large-scale educational surveys, typically include international as well as country-specific student-response items about home possessions as part of a suite of SES measures. For school-age children, these are considered a more reliable measure of wealth than traditional indicators (e.g., asking about household income). Home possession measures, however, appear to work better in some countries compared to others (Rutkowski &Rutkowski, 2013). One cause of this is the selection of inappropriate items: countries may choose home possession items that most children have or that no children have and these items provide little information to a measure of household wealth. This study undertakes a novel investigation of TIMSS 2011 and PIRLS 2011 home-possession items to propose: (i) a psychometrically sound home-possessions scale, and (ii) a method for countries to select appropriate home-possession items based on national profiling based on World Bank Development Indicators. First, Item Reponses Modelling (IRM) is used to construct a measure of household wealth using a “common set” of home possession items asked in TIMSS and PIRLS by different countries nationally and internationally. This measure is compared to more traditional composite measure (e.g., just international items) in terms of reliability, predictive validity, and external validity (including World Bank Development Indicators). Then the results of IRM are used to make recommendations about whether better targeting of “more difficult” or “easier” items would improve the measurement of family wealth within countries and internationally. The study demonstrates that the measurement of the household wealth can be improved by simply selecting items optimally targeted to the national profile of each country. Improved measurement is a simple way to improve explanatory power of statistical models. This is important because SES indicators are key variables considered in relation to academic achievement.
Rutkowski, D., Rutkowski, L. (2013). Measuring Socioeconomic Background in PISA: One Size Might not fit all. Research in Comparative and International Education September 2013 8: 259-278, http://rci.sagepub.com/content/8/3/259.full.pdf Saegert, S. C., Adler, N. E., Bullock, H. E., Cauce, A. M., Liu, W. M., & F.Wyche, K. (2007). Report of the APA Task Force on Socioeconomic Status. Retrieved from Washington, DC: https://www.apa.org/pi/ses/resources/publications/task-force-2006.pdf
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