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How to analyze data

How to analyze data. What do we do with the collected data? By Yun Jin Rho. Contents. 1. Overview of data analysis MasteringAstronomy 2. Case studies - Group comparison (MyITLab) - Causal relationship (MyMathTest) - Item level analysis (MasteringEngineering)

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How to analyze data

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  1. How to analyze data • What do we do with the collected data? • By Yun Jin Rho

  2. Contents • 1. Overview of data analysis • MasteringAstronomy • 2. Case studies • - Group comparison (MyITLab) • - Causal relationship (MyMathTest) • - Item level analysis (MasteringEngineering) • 3. Closing comments How to analyze data l 09/24/11

  3. Overview of data analysis • 1 How to analyze data l 09/24/11

  4. Raw data (MasteringAstronomy) How to analyze data l 09/24/11

  5. Descriptive statistics Improvement from the Pre scores (M = 7.812) to the Post scores (M = 11.825) was observed.  Is this amount of improvement statistically significant? How to analyze data l 09/24/11

  6. Distributions of test scores How to analyze data l 09/24/11

  7. Hypothesis testing Let’s assume the distributions of test scores are all normal. How can we test if this difference of 4.263 is statistically significant?  Dependent (paired) t-test Hypotheses - H0: the improvement is not different from 0 (Pre = Post) - Ha: the improvement is significantly different from 0 (Pre ≠Post) Results 95% Confidence Interval (C.I.) of the mean difference: [3.889, 4.637] t(250) = -22.44, p = 0.000 < 0.05(significance level, ) Conclusion The improvement of 4.263 was statistically significant. How to analyze data l 09/24/11

  8. Cohen’s d < 0.3: small effect Cohen’s d  0.5: medium effect Cohen’s d > 0.8: large effect Effect size Then, how big is this improvement or effect? • Effect size : measure of distance between the two different distributions Cohen’s d = 1.36 Conclusion There was a large learning effect. How to analyze data l 09/24/11

  9. Pretest Posttest Pretest Posttest Reading textbooks, Homework, TA’s help, Quality of teaching, Self-studying… Reading textbooks, Homework, TA’s help, Quality of teaching, Self-studying, Mastering… Historical / Control without Mastering Experimental with Mastering What does this large learning effect mean? Was the students’ improvement in their test scores because of using Mastering? Is this improvement solely from using Mastering? How to analyze data l 09/24/11

  10. Case studies • 2 How to analyze data l 09/24/11

  11. Group comparison MyITLab ....... 2011 1st semester using the labs 2009 2nd semester without the labs ....... How to analyze data l 09/24/11

  12. Group comparison With the labs vs. Without the labs How to analyze data l 09/24/11

  13. Path analysis (causal relationship) MyMathTest How to analyze data l 09/24/11

  14. Path analysis Causal model How to analyze data l 09/24/11

  15. Item level analysis MasteringEngineering How to analyze data l 09/24/11

  16. Item level analysis: Pre-test How to analyze data l 09/24/11

  17. Item level analysis: Post-test How to analyze data l 09/24/11

  18. Item level analysis: Posttest - Pretest How to analyze data l 09/24/11

  19. Summary • 3 How to analyze data l 09/24/11

  20. Closing comments To analyze the data for efficacy studies, the most important thing is the study design to collect data. Educational interpretation will be more important than the data analysis result. yunjin.rho@pearson.com How to analyze data l 09/24/11

  21. Thank you How to analyze data l 09/24/11

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