Urbanicity and Self-Concept-1 Urbanicity and Academic Self-Concept

The main focus of this study was the relationships between school urbanicity (size of community in which the school is located) and fifth-grade students’ academic self-concepts. Using multi-level modeling methodology (HLM) we were able to investigate “school effects”, net of individual students’ characteristics. School urbanicity had no effect on reading, math, or general academic self-concept. School-level effects were found consistently for aggregate school achievement in reading and math, congruent with Marsh’s Big-Fish-Little-Pond effect. Less consistent school-level effects were found for proportion of minority students and school-average SES. Individual level effects mirrored those reported in other literature with tested achievement having the greatest effect. Educational researchers increasingly have become attuned to the possibility of “school effects” on educational outcomes. “School effects” refers to the idea that various outcomes may be, in part, due to school-wide characteristics, over and above individual student or teacher characteristics. For example, Lee and Smith (1997) found that mid-size high schools produce larger achievement gains from students’ freshman to senior years than do either small or large high schools, net of individual student characteristics, and that achievement in these schools was more equitably distributed across students’ SES. Similarly, Lee and Smith (1996) found that high schools in which teachers, as a group, believed in collective responsibility for student achievement produced stronger achievement gains than did schools whose teachers, as a group, held different attitudes. “School effects” are just one example of an “ecological effect”, in which features of the larger environment have demonstrable effects on various outcomes net of individual variables. The school psychology literature contains little research that truly explores ecological effects, although some recent studies (e.g., Stage, 2001) have included such variables. Testing for ecological effects also raises methodological issues. Most such research has used ordinary least-squares regression (OLS) to assess school effects net of individual variables through the straightforward practice of using ordered regression procedures in which individual student characteristics are entered as a block on the first step, and school-wide variables are entered on the second step. A significant increase in R 2 then serves as the measure of school-wide variables while controlling for individual variables. Alternatively, one might use an ANOVA and use covariates to partial out various effects. While these procedures have intuitive appeal and are reasonably easily understood, Lee (2000) delineates the methodological shortcomings of this approach: (a) aggregation bias – in which the same variable (e.g., SES) may have different meanings at different levels of aggregation (e.g., individual vs. school levels), (b) non-independence of cases (e.g., an individual student’s achievement may be related to the achievement levels of other students in the school), and (c) heterogeneity of regression. To counter these shortcomings, researchers may use multi-level modeling (MLM) methodology that allows for the modeling all of these effects. The most commonly used statistical program for performing such analyses is Hierarchical Linear Modeling (HLM) (Raudenbush, Byrk, Cheong, Congdon, & du Toit, 2004). Studies using HLM have begun to appear in the school psychology literature (Rhodes, Roffman, Reddy, & Fredriksen, 2004; Stage, 2001). Most recently Clements, Bolt, Hoyt, & Kratochwill (2007) have championed the use of MLM methodology to study school-based interventions. Research on school effects has focused primarily on achievement as outcomes, but such logic and Urbanicity and Self-Concept 3 analysis can be applied to social-emotional outcomes as well. Over a 20-year period, Herbert Marsh and his colleagues (e.g., Marsh & Hau, 2003) have conducted a substantial amount of research investigating the effects of school average ability on student’s academic self-concepts (ASC). Marsh and his coworkers discovered that school characteristics moderated the relationship between ASC and achievement. Humorously dubbed the “big-fish-little-pond effect (BFLPE)”, Marsh found that students with the same academic achievement level had somewhat lower ASCs when embedded in schools with higher average student ability, although the effect size is relatively small. Other recent research has further explored school effects on ASC. Trautwein, Ludtke, Koller, and Baumert (2006) found that the learning environment moderates the development of self-concept for a set of seventh grade students. Specifically, “meritocratic” schools produced more accurate ASCs than did “ego-protective” school environments. School average ability and meritocratic structure are excellent examples of “school effects”. None of these studies, above, investigated school urbanicity (size of community). Ozturk (2007) investigated math self-concept and other variables and found complex interactions between schools’ urbanicity, minority concentration, and poverty concentration with reference to high school students’ mathematics course-taking. Signer, Beasley, and Bauer, (1997) found that urban students’ academic selfconcepts were influenced by a set of interactions between ethnicity and the type of educational program in which they were engaged. The goal of the present study was to use HLM methodology on data from the Early Childhood Longitudinal Study, Kindergarten – Fifth Grade (ECLS-K) to address the following questions: 1. Are there “school effects” on the levels of elementary school students’ self-concepts in English, math, and “all subjects”, after accounting for relevant individual child characteristics. Particularly, does school urbanicity have a significant effect on individual student academic self concept? 2. Are there “cross-level interactions”, such that school characteristics modify the relationships among various student characteristics and ASCs?