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Published byStewart Willis Modified over 9 years ago
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Using Statistics Effectively in Statistics Education Research Sterling C. Hilton Brigham Young University
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OUTLINE Background on SMER workshops Summary of SMER final report Connections to statistics education research Discussion
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BACKGROUND Richard Scheaffer was PI on ASA sponsored study supported by NSF grant Three workshops –Jan. 2005 –Sept. 2005 –March 2006 11 statisticians and 9 math education researchers
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PURPOSE/MOTIVATION No Child Left Behind Act –“scientifically based research” –“…involves the application of rigorous, systematic, and objective procedures to obtain reliable and valid knowledge…” Call to improve mathematics education in K-12 grades (e.g. TIMMS 3) Widespread use of qualitative research methods in mathematics education research
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REPORT SUMMARY Reporting guidelines for research program not research study Goal to help build a useful and informative body of cumulative research Help establish consistency in –Interventions –Measurement tools –Data collection –Data analysis –Reporting
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REPORT SUMMARY Influential documents –Scientific Research in Education (NRC, 2002) –Advancing Scientific Research in Education (NRC, 2005) –Mathematical Proficiency for All Students (RAND, 2003) Influential perspective –Medical research model (clinical trials)
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REPORT SUMMARY Research program components (with characteristic activities and reporting guidelines) –Generate –Frame Goals and Constructs Measurement Logistics and Feasibility –Examine –Generalize –Extend
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REPORT SUMMARY Generate ideas –Develop a conceptual framework –Consider own values, beliefs, biases Characteristic activities –Identify interesting issues/questions –Search research literature –Build case for importance of issue No reporting guidelines given
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REPORT SUMMARY Framing: Goals and Constructs Characteristic activities –Formulate central ideas and underlying constructs –Formulate a research question –Identify relevant measures or need for new measures –Select and define research methods Reporting guidelines –Define variables and measures used –State conjectures rather than causal statements –Exploratory and descriptive statistics
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REPORT SUMMARY Framing: Measurement –Validity, reliability, fairness Characteristic activities –Examine previously used measures –Develop new measures –Test new measures Reporting guidelines –Describe key details of development process –State validity, reliability and bias of measures
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REPORT SUMMARY Framing: Logistics and Feasibility Characteristic activities –Formulate study design –Develop data collection protocols –Conduct pilot study Reporting guidelines –Describe study design –Describe variables of interest –Describe the pilot tests of instruments / interventions
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REPORT SUMMARY Examine –If warranted, expansion of the pilot study to a larger community (single institution) –Main goal is to establish efficacy Characteristic activities –Explore and define setting –Refine measures and protocols –Identify extraneous sources of variability Reporting guidelines –Provide enough information to allow replication –Give estimates of parameters –Report formal statistical inference
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REPORT SUMMARY Generalize –If warranted, expansion of the study to larger community (multiple institutions) –Generally requires interdisciplinary work –Main goal is to generalize findings to larger population Characteristic activities –Assess portability of measures and protocols –Design a multi-institutional study –Conduct rigorous statistical analysis Reporting guidelines –Describe implementation –Describe efforts to minimize bias –Specify a statistical model
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REPORT SUMMARY Extend –If warranted, extend the study over time and to multiple communities –Generally requires interdisciplinary work –Main goal is to understand long-term effects Characteristic activities –Design and conduct longitudinal study –Document the need for program improvements Reporting guidelines –Describe implementation of longitudinal study –Describe dropout rate and its handling in the analysis
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REPORT SUMMARY Additional issues of interest –Stable unit treatment value assumption: the treatment is not dependent on the person administering it –The use of pre-post scores or gain scores is potentially problematic –Measurement development takes time and effort
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REPORT SUMMARY Connection to medical clinical trial model GeneratePre-clinical FramePre-clinical, Phase I ExaminePhase I, Phase II GeneralizePhase III ExtendPhase IV
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CONNECTIONS Different research methods answer distinct research questions Statistics education research is young (primarily in pre-clinical, phase I stages) Researchers in statistics education need to: –talk the language of statisticians –allow the discipline to develop –not overstate their findings –have patience –persevere in doing quality research
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