{"title":"On the difficulties of evaluating the effect of school facility conditions on student outcomes","authors":"Arnt O. Hopland","doi":"10.22361/jfmer/112194","DOIUrl":null,"url":null,"abstract":"\n This paper presents various empirical strategies used to analyze the effect from school facilities on student outcomes, and discusses strengths and weaknesses by the methods. A key challenge in studies of student outcomes is that outcomes are affected by many factors and that many of these factors are correlated with each other. Moreover, some factors are difficult to measure, and cannot be observed in data. Hence, it is difficult to avoid problems related to omitted variables bias and the estimated correlations can thus often not be interpreted as causal effects. It is important to be aware of how difficult it is to move on from a correlation to a causal effect. If researchers wrongfully draw causal inferences one risks misleading policy makers into allocating resources to the wrong factors.","PeriodicalId":168480,"journal":{"name":"Journal of Facility Management Education and Research","volume":"29 4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Facility Management Education and Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22361/jfmer/112194","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper presents various empirical strategies used to analyze the effect from school facilities on student outcomes, and discusses strengths and weaknesses by the methods. A key challenge in studies of student outcomes is that outcomes are affected by many factors and that many of these factors are correlated with each other. Moreover, some factors are difficult to measure, and cannot be observed in data. Hence, it is difficult to avoid problems related to omitted variables bias and the estimated correlations can thus often not be interpreted as causal effects. It is important to be aware of how difficult it is to move on from a correlation to a causal effect. If researchers wrongfully draw causal inferences one risks misleading policy makers into allocating resources to the wrong factors.