1. Easy (and not so easy) questions to ask about adolescent health data J. Dennis Fortenberry MD MS Indiana University School of Medicine

2. Four types of questions about health data About data users About data production About data quality About data inferences

3. Questions about users

4. Questions about the users Who are the end-users What are the data skills of the end-users What are the conditions of use

5. Questions about data production

6. Why were the data collected Who collected the data How were the data collected How were the data processed

7. Questions about data production Who is represented by the data Who is left out – and why Are there issues of privacy and confidentiality

8. Questions about data quality

9. Missing data Incorrect data Coded data Out of range data Accuracy

10. Precision and Accuracy True Value Accurate & Precise Accurate only Neither Accurate nor Precise Precise only

11. Questions about data inferences

12. What type of data is available Nominal Ordinal Interval Ratio

13. Measurement Scales Nominal Ordinal Interval Ratio Interval and Ratio scales produce continuous variables A nominal scale produces categorical variables

14. Examples of Measurement Scales Nominal Temperature ( 0 F) Ordinal Blood Pressure Interval Tanner Stage Ratio Gender

15. What type of descriptive statistics are needed Mean Median Shape of distribution Variation – standard deviation Proportion

16. Mean + 2 SD-2 SD -1 SD+1 SD -1.96 SD+1.96 SD For a normal curve, a traditional alpha is nearly two standard deviation units from the mean

17. Standard Deviation A measure of variability within a sample Positive square root of variance Area between - 1SD and +1 SD represents 68% of area under the curve Between -2 SD and +2 SD is 95.4%

18. Confidence Intervals Range of values containing true mean with a given level of certainty 95% CI commonly used 95% CI = mean  1.96 SE

19. The Null Hypothesis H 0 : A does not differ from B H 1 : A is different than B Where A and B are two variables of interest

20. Types of Error in Statistical Testing Type 1: Rejection of a ‘true’ null hypothesis Type 2: Acceptance of a ‘false’ null hypothesis

21. One-Tail versus Two-Tails One-Tailed tests are used to assess a directional hypothesis One-tailed tests have greater power One-tailed tests can be used when there is solid theoretical or empirical basis

22. Elements of Statistical Power The statistical test Level of Alpha 1-Tailed / 2-Tailed Sample Size The difference to be detected

23. What type of inferential statistics are appropriate Correlation Chi square t test Risk ratio and Odds ratio

24. What is a Risk Ratio

25. What is an Odds Ratio

26. Who do the data represent and Can the data be applied to other groups Representativeness Generalizability

27. Questions?