A Meta-Analysis of the Relation between Class Size and Achievement

McGiverin, Jennifer; Gilman, David Alan; Tillitski, Chris

doi:10.1086/461601

Cited by 45 publications

(20 citation statements)

References 4 publications

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“…Studies on the relationship between class size and students' achievement, as opposed to mathematics self-efficacy, suggest that class size is a significant variable that affects students' achievement. While some studies (Boozer & Rouse, 2001;Hedges & Stock, 1983;McGiverin, Gilman, & Tillitski, 1989) suggest that lower class size has a positive contribution on achievement, some emphasize that although lower class size provides advantages for teachers and students, achievement cannot be increased effectively by simply decreasing class size ( Hanushek 1999;Lockheed & Komenan, 1989;Molnar et al, 1999). Furthermore, it is also suggested that for students attending classes with lower size, there is lower probability to fail the class or to drop out of school and that there are more advantages for students in classes of smaller size (Hattie, 2002).…”

Section: Discussionmentioning

confidence: 99%

Analysis of Student and School Level Variables Related to Mathematics Self-Efficacy Level Based on PISA 2012 Results for China-Shanghai, Turkey, and Greece

Usta¹

2016

EDUC SCI-THEOR PRACT

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This study aims to analyze the student and school level variables that affect students' self-efficacy levels in mathematics in China-Shanghai, Turkey, and Greece based on PISA 2012 results. In line with this purpose, the hierarchical linear regression model (HLM) was employed. The interschool variability is estimated at approximately 17% in China-Shanghai, approximately 22% in Turkey, and approximately 23% in Greece.This study showed a positive association between variables of self-confidence, teacher support, and attitude toward school, all of which are among Level 1 variables, and mathematics self-efficacy in all three countries.A negative association was observed to exist between the variables socio-cultural index and educational opportunities at home and mathematics self-efficacy in all three countries. While pre-school education in ChinaShanghai and Turkey were negatively associated with students' mathematics self-efficacy levels, the same variable was positively associated with students' mathematics self-efficacy in Greece. While the variable mathematical anxiety was negatively associated with students' mathematics self-efficacy in China-Shanghai and Greece, it was positively associated with students' mathematics self-efficacy in Turkey. The variable interest in mathematics, in turn, was negatively associated with mathematics self-efficacy solely in ChinaShanghai. Regarding the association between mathematics self-efficacy levels and the school level variables, a near-zero positive association was found between class size, deemed significant for Turkey, and self-efficacy levels. The association between teacher to student ratio in school and self-efficacy levels was found to be negative in all three countries. The variable teacher' s morale, however, was positively associated with self-efficacy level in China-Shanghai and Turkey.

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Section: Discussionmentioning

confidence: 99%

Analysis of Student and School Level Variables Related to Mathematics Self-Efficacy Level Based on PISA 2012 Results for China-Shanghai, Turkey, and Greece

Usta¹

2016

EDUC SCI-THEOR PRACT

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“…Experimental and control teachers and their classrooms were preselected to be similar and we largely controlled for student inequivalence and other threats to internal validity using a non-equivalent pre-test-post-test control group design. Research on class size (Canadian Council on Learning, 2005;McGiverin, Gilman, & Tillitski, 1989;Smith & Glass, 1980) shows positive effects on learning only when class sizes are quite small. Finally, using technology for learning, especially when the technology is intended to support the process of student self-regulation, is not a straightforward teaching strategy to master and its difficulty may help to explain teacher attrition and why some effects were insignificant.…”

Section: Cautions and Limitationsmentioning

confidence: 99%

Improving literacy and metacognition with electronic portfolios: Teaching and learning with ePEARL

Meyer

Abrami

Wade

et al. 2010

Computers & Education

132

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“…In the field of education, meta-analysis has been used to assess the relation between classroom size and achievement (e.g., McGiverin, Gilman, & Tillitski, 1989) and to examine the efficacy of mainstreaming programs for special education students (e.g., Wang & Baker, 1985). In the medical sciences, meta-analysis has been used in multiple areas including, for example, an examination of the relationship between breast cancer risk and mammography screening (McCaul, Branstetter, Schroeder, Glasgow, 1996).…”

Section: Popularity and Increased Usage Of Meta-analysismentioning

confidence: 99%

Meta-Analytic Methods of Pooling Correlation Matrices for Structural Equation Modeling Under Different Patterns of Missing Data.

Furlow¹,

Beretvas²

2005

Psychological Methods

108

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Three methods of synthesizing correlations for meta-analytic structural equation modeling (SEM) under different degrees and mechanisms of missingness were compared for the estimation of correlation and SEM parameters and goodness-of-fit indices by using Monte Carlo simulation techniques. A revised generalized least squares (GLS) method for synthesizing correlations, weighted-covariance GLS (W-COV GLS), was compared with univariate weighting with untransformed correlations (univariate r) and univariate weighting with Fisher's z-transformed correlations (univariate z). These 3 methods were crossed with listwise and pairwise deletion. Univariate z and W-COV GLS performed similarly, with W-COV GLS providing slightly better estimation of parameters and more correct model rejection rates. Missing not at random data produced high levels of relative bias in correlation and model parameter estimates and higher incorrect SEM model rejection rates. Pairwise deletion resulted in inflated standard errors for all synthesis methods and higher incorrect rejection rates for the SEM model with univariate weighting procedures.

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A Meta-Analysis of the Relation between Class Size and Achievement

Cited by 45 publications

References 4 publications

Analysis of Student and School Level Variables Related to Mathematics Self-Efficacy Level Based on PISA 2012 Results for China-Shanghai, Turkey, and Greece

Analysis of Student and School Level Variables Related to Mathematics Self-Efficacy Level Based on PISA 2012 Results for China-Shanghai, Turkey, and Greece

Improving literacy and metacognition with electronic portfolios: Teaching and learning with ePEARL

Meta-Analytic Methods of Pooling Correlation Matrices for Structural Equation Modeling Under Different Patterns of Missing Data.

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