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Statistical analysis of a one- semester general chemistry approach for students entering the pharmacy field Taylor Owings Marcy Towns Purdue University.

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Presentation on theme: "Statistical analysis of a one- semester general chemistry approach for students entering the pharmacy field Taylor Owings Marcy Towns Purdue University."— Presentation transcript:

1 Statistical analysis of a one- semester general chemistry approach for students entering the pharmacy field Taylor Owings Marcy Towns Purdue University 1

2 Goal The goal of this research was to evaluate the newly introduced CHM 109 on its effectiveness for preparing students for organic chemistry 2

3 Introduction Change in the MCAT encourages more biochemistry for premedical and pre-pharmacy students ▫AAMC mandated Recommendation for a 1-2-1 format, with inclusion of Biochemistry in place of a second semester of general chemistry CHM 109 implemented at Purdue to accommodate the one semester general chemistry requirement ▫5 credit hours, 3 lectures a week 3

4 Background on the CHM 109 course 5 credit hour course Created to replace the 2 semester equivalent (CHM 115 and CHM 116) Designed to be part of new 1-2-1 chemistry series for students ▫1 general chemistry course CHM 109 ▫2 organic courses (MCMP (Medicinal Chemistry & Molecular Pharmacology) 204-205) ▫1 biochemistry course 4

5 Participants Students enrolled in MCMP 204/205 from fall 2002 - fall 2011 ▫Classified by year enrolled in MCMP 204 ▫Information collected on all chemistry classes students had enrolled in 5

6 Data collection Data collected included ▫Demographic information ▫SAT/ACT scores ▫Grades in all Purdue chemistry courses 6

7 Research question How do students enrolled in MCMP 204/205 perform in organic chemistry based on prior enrollment in CHM 109 and CHM 115-116? 7

8 Statistical analysis Analysis to determine if comparisons can be made between student groups Analysis to determine if students course outcomes in MCMP 204/205 varied based on prior enrollment in CHM 115-116 or CHM 109 8

9 Explaining differences Significant differencePractical difference Difference is determined by significance test ▫ANOVA, t-test, etc… Answers the question “are they different” Degrees of freedom included in equation Different tests that accompany significance tests ▫Cohen’s d, eta squared, etc… Looks at how different values are ▫Small, medium, or large Answers the question “Does the difference matter” Degrees of freedom not included in equation 9

10 Formulas 10

11 SAT test scores 11

12 Preliminary standardized test scores MeanF-statisticEffect size (η 2 ) SAT Verbal5766.79.0338 SAT Math62310.8.0524 ACT English26.49.16.0809 ACT Math27.95.20.0476 ACT English/Writing26.07.41.00010 12 Effect Size: Small ~.01, Medium ~.06, Large ~.14 All listed standardized test scores were significantly different (p <.001)

13 Demographics analysis of chemistry GPA F-Statisticp-valueEffect Size (η 2 ) Gender.852.427.00062 Ethnicity5.46<.001.0125 13 Effect size: Small ~.01, Medium ~.06, Large ~.14 Significant difference: p <.05

14 Average course grades CourseMeanStandard deviation F-statisticEffect size (η 2 ) CHM 1093.10.80616.6.0342 CHM 1153.26.69912.8.0556 CHM 1163.32.75719.7.0768 14 CHM 109 data is from 2010-2011 CHM 115/116 data is from 2002-2011 Effect size: Small ~.01, Medium ~.06, Large ~.14 All courses had a significant difference (p <.001)

15 Student performance in MCMP 204 MeanStandard deviation 115 Students2.781.11 116 Students2.841.08 109 Students2.761.03 15

16 Student performance in MCMP 204 16 Effect size: Small =.1, Medium =.25, Large =.4 Significant difference: p <.05 t-statisticp-valueEffect size (Cohen’s d) CHM 115 vs. CHM 109.289.772.00001 CHM 116 vs. CHM 1091.08.278.0745

17 Student performance in MCMP 205 MeanStandard deviation 115 Students2.831.02 116 Students2.87.999 109 Students2.87.978 17

18 Student performance in MCMP 205 18 Effect size: Small =.1, Medium =.25, Large =.4 Significant difference: p <.05 t-statisticp-valueEffect size (Cohen’s d) CHM 115 vs. CHM 109-.372.711.0394 CHM 116 vs. CHM 109.007.994.0018

19 Conclusion Analysis demonstrates no significant or practical differences exist in performance in MCMP 204/205 based upon general chemistry preparation in CHM 109 and CHM 115/116. Thus, the new course sequence (one semester general chemistry) supports student success in Purdue’s pre-pharmacy curriculum. 19

20 Implications Demonstrates efficacy of one semester gen chemistry course in pre-medical and pre- pharmacy curriculum Research finding provide research based support for curriculum augmentation 20

21 Acknowledgements HHMI Nexus team at Purdue ▫Dr. Marc Loudon ▫Dr. Chris Hrycyna Research and assessment grant 2011 Towns research group ▫Dr. Marcy Towns 21

22 Participant demographics N Male1059 Female1707 White Non-Hispanic1680 Hispanic/ Latino43 African American87 Asian American/ Pacific Islander289 Native American5 Other65 22

23 Formulas 23

24 CHM 109 MeanStandard DeviationN 2010-20113.27.7387204 2011-20122.97.8315266 24

25 CHM 109 MeanStandard Deviation N Male3.05.7986168 Female3.12.8096302 Other demographics were unavailable for a significant portion of population 25

26 CHM 109 F-StatisticP-valueEffect Size (η 2 ) Gender.922.337.00197 EthnicityN/A 26

27 CHM 115 MeanStandard DeviationN 2002-20032.99.718220 2003-20043.13.716234 2004-20053.17.632205 2005-20063.15.696185 2006-20073.28.729233 2007-20083.51.623264 2008-20093.36.650221 2009-20103.38.665188 2010-20113.80.42210 2011-20122.331.1553 27

28 CHM 115 MeanStandard Deviation N Male3.29.668665 Female3.24.7171095 White Non- Hispanic 3.28.6861323 Hispanic/ Latino3.30.87730 African American2.89.65773 Asian American/ Pacific Islander 3.16.735208 Other3.506.7940 28

29 CHM 115 F-StatisticP-valueEffect Size (η 2 ) Gender1.231.267.00080 Ethnicity4.973.000.01681 29

30 CHM 116 MeanStandard DeviationN 2002-20033.04.788186 2003-20043.33.746216 2004-20053.08.806190 2005-20063.15.762183 2006-20073.25.763235 2007-20083.44.739256 2008-20093.56.604207 2009-20103.68.556195 2010-20113.4.8945 2011-20121.52.1212 30

31 CHM 116 MeanStandard Deviation N Male3.35.735646 Female3.31.7701027 White Non- Hispanic 3.34.7441257 Hispanic/ Latino3.17.96629 African American2.92.80261 Asian American/ Pacific Islander 3.28.774204 Other3.53.72645 31

32 CHM 116 F-StatisticP-valueEffect Size (η 2 ) Gender1.036.309.00065 Ethnicity5.734.000.01421 32

33 MCMP 204 MeanStandard DeviationN 2002-20032.331.320222 2003-20042.731.048230 2004-20052.851.152259 2005-20062.751.175238 2006-20072.761.183208 2007-20083.031.013266 2008-20092.691.263270 2009-20102.99.983238 2010-20112.751.011417 2011-20122.0002 33

34 MCMP 204 MeanStandard Deviation N Male2.801.16896 Female2.751.121449 White Non- Hispanic 2.801.131596 Hispanic/ Latino2.711.2037 African American2.431.0981 Asian American/ Pacific Islander 2.631.21280 Other3.051.1861 34

35 MCMP 204 F-StatisticP-valueEffect Size (η 2 ) Gender1.749.186.00085 Ethnicity4.141.002.0080 35

36 MCMP 205 MeanStandard DeviationN 2002-20032.65.894169 2003-20042.79.983184 2004-20052.76.978195 2005-20062.751.031175 2006-20072.91.9713148 2007-20083.031.043216 2008-20093.07.897185 2009-20102.691.134188 2010-20112.911.117169 2011-20122.86.989174 36

37 MCMP 205 MeanStandard Deviation N Male2.871.012700 Female2.821.0181098 White Non- Hispanic 2.87.9981213 Hispanic/ Latino2.71.93728 African American2.331.20360 Asian American/ Pacific Islander 2.771.048212 Other3.14.83350 37

38 MCMP 205 F-StatisticP-valueEffect Size (η 2 ) Gender.796.372.00059 Ethnicity5.478.000.01387 38

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