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Misuses of Quantitative Research

Misuses of Quantitative Research. V. Darleen Opfer. Competing Notions of the Use of Quantitative Data. You can prove anything with statistics Versus There are three kinds of lies: lies, damned lies, and statistics. Statistical Pitfalls. Sources of bias Errors in method

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Misuses of Quantitative Research

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  1. Misuses of Quantitative Research V. Darleen Opfer

  2. Competing Notions of the Use of Quantitative Data You can prove anything with statistics Versus There are three kinds of lies: lies, damned lies, and statistics

  3. Statistical Pitfalls • Sources of bias • Errors in method • Problems with interpretation

  4. Sources of bias • Representative sampling • Statistical assumptions

  5. Gender & Achievement on GCSEs

  6. Girls performance has increased more rapidly than that of boys. Speed, 1998 • Female students have improved their performance markedly, whereas male students have not shown a similar improvement. Arnot, 1996 • There are increasing gender differences in performance. Stobart, 1992

  7. Errors in Method • Multiple comparisons • Measurement error

  8. Problems with interpretation • Confusion over significance • Precision and accuracy • Causality • Unequal comparisons

  9. Definitions of School Effects • School effects as the absolute effects of schooling, using naturally occurring “control” groups of students who receive no schooling • School effects as the unadjusted average achievement of all students in a school • School effects as the impact of schooling on the average achievement of all students in a school, adjusted for family background and/or prior achievement • School effects as measuring the extent of ‘between schools’ variation in the total variation of their students’ individual test scores • School effects as measuring the unique effect of each school on their students’ outcomes • School effects as measuring the impact of schools on student performance over time

  10. What is Good Statistical Practice? • Be sure your sample is representative of the population in which you’re interested • Be sure you understand the assumptions of your statistical procedures and be sure they are satisfied • Be sure the sample size is not misleading you about the amount of significance • Be sure to use the best measurement/collection tools • Be aware of impact of multiple comparisons and the lack of an a priori analysis plan • Be clear about what you’re trying to discover • Use numerical notation in a rational way • Be sure you understand the conditions for causal inference • Be explicit about the measures you are comparing

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