Table 1.

Previous studies addressing academic achievement.

References	Methods	St	Pa	Sc
(Hanushek and Kimko, 2000)	Regression models	x		x
(Hoxby, 2000)	Regression models	x		x
(Fan and Chen, 2001)	General linear model	x	x
(Barnett et al., 2002)	Linear Programming techniques			x
(Driessen et al., 2005)	Frequency, Variance, and Structural models	x	x	x
(Rivkin et al., 2005)	Regression models			x
(Archibald, 2006)	Hierarchical linear models	x		x
(Jackson et al., 2006)	Internet recorded	x
(Lee and Bowen, 2006)	Hierarchical linear model	x	x
(Marks et al., 2006)	Item Response Theory; Regressions models	x	x	x
(Jeynes, 2007)	Regression models		x
(Codjoe, 2007)	Interviews	x
(Croninger et al., 2007)	Hierarchical linear models	x
(Lee, 2007)	Hierarchical linear models; Regression models	x	x	x
(Lei and Zhao, 2007)	Hierarchical linear models; ANOVA tests	x
(Steinmayr and Spinath, 2008)	Regression models	x
(Caro et al., 2009)	Hierarchical linear models; Panel data models	x
(Mensah and Kiernan, 2010)	Tobit regression models; Univariate and Multivariate analyses	x	x
(Hartas, 2011)	Univariate analyses of variance; Chi-square tests		x
(Patterson and Pahlke, 2011)	Regression models	x	x
(Hanushek and Woessmann, 2012)	Regression models	x		x
(S. Huang and Fang, 2013)	Regression model, Artificial Neural Networks, Radial Basis Function, and Support Vector Machines.	x
(Brunner et al., 2013)	Multiple group factor analytic models; Full maximum likelihood	x
(Wally-Dima and Mbekomize, 2013)	Descriptive statistics T-tests	x
(Bosworth, 2014)	Regression models	x		x
(Krassel and Heinesen, 2014)	Regression discontinuity design; Control for school fixed effects; Regression models	x	x	x
(Vigdor et al., 2014)	Probit regression; Regression models	x
(Hodis et al., 2015)	Hierarchical linear models	x
(Lee and Mallik, 2015)	Ordinary least squares	x
(Miguéis et al., 2018)	Random Forests, decision trees, support vector machines and naïve Bayes	x	x	x
(Yağci and Çevik, 2019)	Artificial neural networks	x	x	x