Appendix 2 Literature Review Summary Relationship between learning behaviours and student achievement Year Name and other details Variables considered Methodology incl. sample size and duration Correlation and/or predictor/ Results 2010 Michael A. Gottfried, Wharton Student attendance Philadelphia, USA. Longitudinal (1996-2001) This study evaluates the hypothesis that the Business School. and relationship to individual level, n=86,000 students in number of days a student is present in school student achievement. kindergarten through grade 8. An instrumental positively affects learning outcomes. This paper variables strategy is utilised. This uses a two-stage confirms that student performance can be least squares format, in which there are two influenced by higher levels of attendance. separate regression equations for each stage. Stage one is necessary is because unobserved, time-variant influences are affecting both independent and dependent variables. The results would yield biased estimates. Here a one-year lagged GPA score serves as a proxy for individual student fixed effects. However, there may also be unobservable factors that are time-variant, and the use of implementing fixed effects would not necessarily remedy this problem. Stage two employed an instrumental variable - in this case geographic distance from school - as the exogenous independent variable, as this is not directly correlated to student achievement, except through its relationship with student attendance.
Appendix 2 Literature Review Summary Relationship between learning behaviours and student achievement Year Name and other details Variables considered Methodology incl. sample size and duration Correlation and/or predictor/ Results 2010 Michael A. Gottfried, Wharton Business School. Student attendance and relationship to student achievement. Philadelphia, USA. Longitudinal (1996-2001) individual level, n=86,000 students in kindergarten through grade 8. An instrumental variables strategy is utilised. This uses a two-stage least squares format, in which there are two separate regression equations for each stage. Stage one is necessary is because unobserved, time-variant influences are affecting both independent and dependent variables. The results would yield biased estimates. Here a one-year lagged GPA score serves as a proxy for individual student fixed effects. However, there may also be unobservable factors that are time-variant, and the use of implementing fixed effects would not necessarily remedy this problem. Stage two employed an instrumental variable - in this case geographic distance from school - as the exogenous independent variable, as this is not directly correlated to student achievement, except through its relationship with student attendance. This study evaluates the hypothesis that the number of days a student is present in school positively affects learning outcomes. This paper confirms that student performance can be influenced by higher levels of attendance.
hps-impact-targeted-student-outcomes-analysis-report-sep17.docx