– Results for eighth graders indicated no differences between students in single-sex and coeducational schools in mathematics and science achievement.
– Results from the 2003 TIMSS data replicated the finding: students’ mathematics and science achievement was unrelated to the gender composition of their school.
– For both the 2007 and the 2003 data sets, students’ performance was consistently significantly predicted by factors related to socioeconomic status; students (both boys and girls) performed better on the mathematics and science exams when their fathers had more education, their families had more resources, and a lower proportion of their schoolmates came from economically disadvantaged families.
– Both boys’ and girls’ mathematics performance was predicted by the amount of time spent on homework; students do worse when they spend relatively more time on mathematics homework (or students spend more time on homework when they are performing poorly).
2013 - The Effects of Single-Sex Compared With Coeducational Schooling on Mathematics and Science Achievement: Data From Korea
The data for the present study came from the Korean sample of the TIMSS from 2007 and 2003. In both waves, nationally representative samples of Korean eighth graders were created with a multistage sampling frame. The sample design included explicit stratification by 16 provinces and implicit stratification by urbanization (urban, suburban, rural) and school gender composition (coeducational, all girls, all boys).
In 2007, participants were 4,240 eighth-grade students from 150 schools in the Republic of Korea. In 2003, participants were 5,309 eighth-grade students from 149 schools in the Republic of Korea.
DV: Math and science achievement (TIMSS 2007 math content domains are number, algebra, geometry, and data and chance; science content domains are biology, chemistry, physics, and earth science)
IV: School gender composition (coeducational, all-girls, or all-boys)
Controls: Mother’s and father’s education; The total number of items served as a proxy measure for family income.); time spent on math and science homework; expected educational attainment; percentage of disadvantaged students; size of community; number of instructional hours per school year; number of students in the school; mathematics and science instructional resources; teacher experience; teacher education.
The authors used hierarchical linear modeling (HLM) to account for the nesting of students within schools.