![]() ![]() ![]() By manipulating the independent variable, we mean that the researcher controls the independent or experimental variable to evaluate its impact on the dependent variable (s) (outcome(s)). ![]() One of the criteria for conducting an experimental study is to manipulate the experimental independent variable. For further discussion of criteria for experimental and quasi-experimental studies, I refer the interested readers to more extended discussions of quantitative and qualitative methods in medical education research. Given the possibility of methodological errors in experimental or quasi-experimental studies, authors of meta-analyses should first critically appraise the quality of all relevant studies comprised in the meta-analysis. The methodological quality of experimental studies in medical education research may be in error. Threats to internal and external validity can undermine the quality of a meta-analysis which is grounded in a systematic review of the relevant literature. External validity is focused on the extent to which the results of the study can be generalised to the target population. Indeed, these factors may account for the results of the study, not the independent variable (s) (intervention (s)) of interest. Factors that may be considered as threats to internal validity are not part of the independent variables in an experimental study, but they can have a significant effect on the dependent variable (s) (outcome). 1 Internal validity refers to the degree to which changes in the outcome(s) (the dependent variable(s)) of the study can be accounted for by the independent variable(s). Campbell and Stanley described confounding variables as threats to internal and external validity. It is difficult to establish a cause and effect relationship in medical education research because the researcher is unable to control all covariables (confounding/intervening variables) that can influence the outcome of the study. Put another way the medical education researcher manipulates or controls the independent variable (cause) in order to evaluate its impact on the dependent variable or outcome (effect). These studies may be focused on a cause and effect relationship if they are rigorously conducted. According to Hattie the story underlying the data has hardly changed over time even though some effect sizes were updated and we have some new entries at the top, at the middle, and at the end of the list.īelow you can find an updated version of our first, second and third visualization of effect sizes related to student achievement.Interest in and use of experimental and quasi-experimental studies has increased among medical educators. His research is now based on nearly 1200 meta-analyses – up from the 800 when Visible Learning came out in 2009. John Hattie updated his list of 138 effects to 150 effects in Visible Learning for Teachers (2011), and more recently to a list of 195 effects in The Applicability of Visible Learning to Higher Education (2015). He further explained this story in his book “ Visible learning for teachers“. He found that the key to making a difference was making teaching and learning visible. He also tells the story underlying the data. (The updated list also includes the classroom.) But Hattie did not only provide a list of the relative effects of different influences on student achievement. Originally, Hattie studied six areas that contribute to learning: the student, the home, the school, the curricula, the teacher, and teaching and learning approaches. Therefore he decided to judge the success of influences relative to this ‘hinge point’, in order to find an answer to the question “What works best in education?” Hattie found that the average effect size of all the interventions he studied was 0.40. In his ground-breaking study “ Visible Learning” he ranked 138 influences that are related to learning outcomes from very positive effects to very negative effects. John Hattie developed a way of synthesizing various influences in different meta-analyses according to their effect size (Cohen’s d). ![]()
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