International and Interstate Gaps in Value-Added Math Achievement: Multilevel Instrumental Variable Analysis of Age Effect and Grade Effect

Approaches to academic growth assessment

“…Indeed, researchers are now able to merge data from multiple nations to examine growth across countries. For example, Lee and Fish () merged data from the National Assessment of Educational Progress (NAEP) in the United States with data from the Trends in International Mathematics and Science Study to compare growth across a variety of nations.…”

The significance of student age variance in estimating value added measures for Polish lower secondary schools

“…A review of studies carried out on various teaching levels shows that during consecutive years of school education, the strength of the correlation of achievement with age of the main age cohort decreases (Smith, 2009). This situation is found in many countries (Lee and Fish, 2010). The results of studies quite consistently show that the decrease in the strength of the correlation of achievement and students' age is greater during the first years of education than when students are older (Bell and Daniels, 1990;Hutchison and Sharp, 1999;Langer et al, 1984;Verachtert and others, 2010).…”

“…Many studies show that students who are older in the year group and attend the same class outperform younger students in school achievement (Lee and Fish, 2010;Morrison, Griffith and Alberts, 1997;Sharp, 2002;Sweetland and De Simone, 1987). This effect is observed both with regard to various subject skills (Bell and Daniels, 1990;Hutchison and Sharp, 1999;Konarzewski, 2013;Martin, Foels, Clanton and Moon, 2004;Smith, 2009), as well as broadly understood teaching results, such as motivation, involvement in learning or attitude towards school and teaching (Martin, 2009).…”

Introduction to instrumental variables and their application to large-scale assessment data

“…Interestingly, the effect did not differ significantly from estimates based on classical OLS models showing small but significant effects of schooling on earnings. The date of birth was also used as an IV for estimating the effect of age on cognitive proficiency in large-scale assessment data showing significant positive effects on educational outcomes, such as PIRLS (Weber and Puhani 2006), TIMSS (Bedard and Dhuey 2006;Lee and Fish 2010), and NAEP (Lee and Fish 2010). The estimation of the causal path between age and cognitive proficiency is problematic because of several factors, such as grade retention, delayed entry, and entry grade acceleration.…”