Obtaining Unbiased Math and Science Achievement Effect Estimates from Nonrandomized Studies

从非随机研究中获得公正的数学和科学成绩效果估计

• 批准号: 1438331
• 负责人: Mark Lipsey
• 金额: $ 23.37万
• 依托单位: Vanderbilt University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1438331&HistoricalAwards=false
• 关键词: Obtaining; Unbiased; Math; Science; Achievement

The researchers in this project will use large longitudinal datasets that include rich arrays of descriptive variables as well as measures of science and mathematics achievement to identify promising covariates that may eliminate or greatly reduce selection bias in nonrandomized estimates of program effects in research and evaluations of interventions or programs for K-12 students. Simulated nonrandomized intervention and comparison groups with selection bias will be created from a selection of data from five de-identified, publically available longitudinal datasets. The researchers will then create propensity scores for each simulation based on the examination of the magnitude of the multiple correlations between covariates and the outcome and use these scores to assess how much of the selection bias they might eliminate. The identification of covariates that sufficiently account for enough selection bias so that the remaining selection bias might be ignored will strengthen the use of quasi-experimental study design in STEM education.Randomized control designs in STEM education are well known as being the strongest causal design with the ability to provide unbiased estimates of the effects of programs or interventions on intended outcomes. With an adequate sample size to minimize the likelihood of chance differences, random assignment can be expected to equate the experimental groups on all baseline characteristics that might influence the outcomes of interest. However, randomized experiments are not always possible in situations where the effects of STEM education programs are of interest. For many practical and occasionally, ethical reasons, nonrandomized comparison group designs are frequently used in STEM education research and evaluation studies. The findings of this study will provide empirical evidence of what covariate data should be collected to reduce selection bias in quasi-experimental designs.

该项目的研究人员将使用大型纵向数据集，其中包括丰富的描述性变量阵列以及科学和数学成就的测量，以确定有希望的协变量，这些协变量可以消除或大大减少K-12学生干预措施或计划研究和评估中计划效果的非随机估计的选择偏差。 模拟非随机化干预组和具有选择偏倚的比较组将从5个去识别化的、医学上可用的纵向数据集中选择数据创建。然后，研究人员将根据协变量和结果之间多重相关性的大小，为每个模拟创建倾向分数，并使用这些分数来评估他们可能消除多少选择偏差。识别出能够充分解释选择偏差的协变量，从而可以忽略剩余的选择偏差，这将加强准实验研究设计在STEM教育中的应用。STEM教育中的随机对照设计被认为是最强因果设计，能够提供项目或干预措施对预期结果影响的无偏估计。有了足够的样本量，以尽量减少机会差异的可能性，随机分配可以预期等同于实验组的所有基线特征，可能会影响感兴趣的结果。然而，在STEM教育计划的影响令人感兴趣的情况下，随机实验并不总是可能的。由于许多实际的和偶尔的伦理原因，非随机对照组设计经常用于STEM教育研究和评估研究。本研究的结果将提供经验证据，协变量数据应收集，以减少选择偏差的准实验设计。