Family background as a predictor of reading comprehension performance: An examination of the contributions of human, financial, and social capital Endia J. Lindo University of North Texas, 1155 Union Circle #311335, Denton, TX 76203-5017, United States abstract article info Article history: Received 6 August 2013 Received in revised form 30 January 2014 Accepted 24 March 2014 Keywords: Social factors Predictors Reading comprehension At-risk This study examines the relationship between students' family background, (i.e., human, social and financial cap- ital), and the comprehension of struggling readers in grades 2–6. Decades of research have worked to further un- derstand the relationship between background factors and achievement. However, few studies have focused on comprehension outcomes, or accounted for parent cognitive ability and intergenerational effects. Family back- ground surveys and assessments of cognitive and reading skills were administered to the parents of struggling readers (N = 51). Correlation and regression analyses examined the relationship between family background variables and students' comprehension scores, identifying a significant relationship accounting for as much or more variability than the traditional socioeconomic measures. These findings have implications regarding how we currently examine the influence of socioeconomic status in intervention research and its role in identifying students at-risk for reading failure and their differential response to intervention. © 2014 Elsevier Inc. All rights reserved. The ability to understand and gain knowledge from text is vital for success in school and everyday life. As students progress through school, the demand for independent reading and extraction of informa- tion increases (Snow, Porche, Tabors, & Harris, 2007); placing those experiencing comprehension difficulties at a serious disadvantage. The 2011 Nation's Report Card found 63% of U.S. fourth graders were not proficient in reading comprehension, a number that increased to 83% for low-income students and 89% for students with disabilities (National Center for Educational Statistics, 2011). Unfortunately, re- search has shown that these early reading difficulties often plague students throughout their academic careers (Lee & Burkam, 2002; Nation, Cocksey, Taylor, & Bishop, 2010; Snow et al., 2007), highlighting the need to better understand and address the factors leading to reading difficulties and disabilities. Discussion regarding effective predictors of reading outcomes has been on-going on for several decades. However, legislation such as the Elementary and Secondary Education Act (2002) and the Individuals with Disabilities Education Improvement Act and its reauthorization (IDEIA, 2004, 2006) placed the need for effective predictive measures and interventions at the forefront of the educational research agenda. There is mounting evidence that early intervention can prevent reading difficulties in many children (Denton et al., 2010; Yell, Shriner, & Katsiyannis, 2006). However, in order to effectively intervene we must be able to accurately identify those students in need of intervention. Elbro and Scarborough (2003) note that 25–69% of students identified as at-risk for reading failure never develop reading difficulties and up to 9% of those who are not identified as at-risk display reading problems. This suggests that there is still work to be done in regard to developing efficient measures for identifying those at-risk for reading failure. 1. Socioeconomic status as a predictor of reading achievement Low family income is frequently used in education to identify students at-risk for reading difficulty (Lubienski & Crane, 2010; Weinstein, Stiefel, Schwartz, & Chalico, 2009). Billions of dollars are spent annually on educational programming targeting children in poverty (e.g., Title 1 programs) with questionable results (see McDill & Natriello, 1998; Weinstein et al., 2009). Though social scientists have emphasized the link between parental socioeconomic status (SES) and student achievement (Berliner, 2005; Callahan & Eyberg, 2010; Lee & Burkam, 2002; Sirin, 2005) questions remain about the na- ture and magnitude of the relationship (Jeynes, 2002; Kieffer, 2012; Sirin, 2005). Sirin's (2005) and White's (1982) meta-analytic reviews reported a moderate, mean correlation between SES and achievement — .29 and .35, respectively. However, both meta-analyses note that studies have found the relationship between these variables to range from having no significant relation to a strong correlation. One explana- tion for the wide discrepancy is the lack of consensus with regard to how best to conceptualize and measure SES (Oakes & Rossi, 2003). White's (1982) review found that over 70 different variables employed individually or in combination were used as indicators of SES. In order to Learning and Individual Differences 32 (2014) 287–293 E-mail address: Endia.Lindo@unt.edu. http://dx.doi.org/10.1016/j.lindif.2014.03.021 1041-6080/© 2014 Elsevier Inc. All rights reserved. Contents lists available at ScienceDirect Learning and Individual Differences journal homepage: www.elsevier.com/locate/lindif