Abstract: Empirical Analyses for Development of Material Hardship Measures in Poverty Studies (Society for Social Work and Research 14th Annual Conference: Social Work Research: A WORLD OF POSSIBILITIES)

128P Empirical Analyses for Development of Material Hardship Measures in Poverty Studies

Saturday, January 16, 2010
* noted as presenting author
Youngmi Kim, MSW , Washington University in Saint Louis, Doctoral Student, St. Louis, MO
Purpose: Traditional income measures have been criticized for not fully capturing economic well-being. Accordingly, material hardship is discussed to expand our understanding on the natures of poverty. Although families experiencing income-poverty and material hardship are expected to be homogeneous in terms of economic deprivation, specific measures of hardships differentiate two populations. However, the hardship measures tend to be inconsistently employed and the quality of measures are rarely examined. In order to fill this gap and improve the measures, this study reviews the hardship measures by using the 2002 National Survey of American Families data.

Method: NASF has three widely used dimensions for material hardship measures: food insecurity, inadequacy of medical care, and housing insecurity. There are three questions asking frequency and concerns related to food shortage, after a screening question, regarding food insecurity; three questions about unmet needs for dental, prescriptive drugs, and any medical care to measure inadequate access to medical care; two questions about payment failure and move-out experience for housing insecurity. All of the questions are asked whether it occurred due to economic difficulties. Several empirical analyses are done based on the responses from 18028 families. Correlation and internal consistency reliability are employed to see whether indicators are related one another for the theoretical construct of material hardship and respondents consistently answer all the indicators as intended. Also, exploratory factor analysis (EFA) is utilized to test validity of the concept. EFA is chosen instead of confirmatory factor analysis, given few studies examine the validity and this study mainly purposes to explore potential strengths and weaknesses of existing measures. Each indicator with 3-4 ordinal responses is dichotomously coded. This coding is performed consistently given prior empirical research. WLSMV estimator is used in Mplus program because all indicators are binary variables. In addition, descriptive statistics are compared between hardship measures and income poverty by household type to investigate what is similar and different.

Result: Findings demonstrate that hardship indicators are all correlated, and those in the same type of hardships are more strongly correlated; Cronbach alpha coefficient, 0.739, indicates that those indicators are well-designed to measure the same concept of material hardship. According to EFA models [one- to three- factor models], one-factor and two-factor models are supported in terms of substantive and statistical interpretations. One-factor model presents that all indicators serve well for the construct material hardship. However, in two-factor models, model fits better and two factors are clearly shown (medical insecurity VS. other hardships). It remains interesting why indicators of food and housing insecurity are clustered against medical care indicators.

Conclusion: This study suggests that either one- or two- factor EFA models are reasonable to accept in NASF, depending on the substantive knowledge and main interest of researchers. This conclusion is tentative partially because of the limited number of indicators per dimension available in NSAF and insufficient information from previous studies in this area. Therefore, the findings encourage more studies to actively discuss valid as well as reliable measures of hardships by using diverse data and different populations.