1. Statistics: What do I need to know? What are Chi squares, t-tests, ANOVA, correlations and regressions? How do I know if the researchers used the correct statistical tests?

2. How do statstics relate to an indivudal participant  Difference between climate and weather  Can predict exactly what this person will do!

3. Participants: 65 Pregnant Adolescents in Teen Parent Programs Means: age: 16.12, GPA: 2.04, age/grade lag:.17, Grade: 10.8, FOB/MOB age difference: 3.17 year Percentage: AA (13%), Hispanic (42%), Caucasian (45%). Refusal rate: 2% Attrition: 6% (fetal demise, diagnosis of CA) 7 Sites: Relationship of demographics to school attendance: site, ethnicity, SES, age, grade, GPA P values:.13 -.95  Was I happy?

4. Statistical tests: Were the right ones used?  Demographics: 7 groups: ANOVA used.. not 42 T-Tests  Linear Regression: Not single order correlations. Symbol is r  DV: school attendance: interval data: 1-21 days.  Variables entered into the computer in a step-wise manner based on the TPB: 1 st : Demographics then TPB concepts

5. Theory of Planned Behavior DemographicsTBP Constructs Intention predicts behavior Outcome AgeSN School successPCIntentionBehavior SES GPA Fx Hx Attitude

6. What are correlations?  The relationship between the IV (x axis) and the DV (the y axis)  R=.6: for every 1 on the Y axis the x axis line goes up.6

7. What does Regression mean?  All the little dots are each data point (i.e each score)  r refers to how much y (DV) changes in relationship to X (IV)  The solid line is the line of “best fit”

8. Ven Diagrams and correlations  IV Red (attitude)  IV Blue (social norm)  DV Yellow (attendance)  Orange: what attitude contributes uniquely to attendance (beta wt)  R2 = orange + white + green

9. DV: C = School attendance: IV: A= Attitude: r = AC + ABC R2= AC beta = AC IV: B= Social Norm r = BC+ABC beta= BC Visualizing Shared and Unique variance: How much do we understand about the DV?

10. Single order correlations can be deceiving! IVsbetaFp Attitude.006.96 Perceived Control.098.45 Social Norm.159.17 Intention.234.63 Full Model 3.07.023 APCSNI School (DV).06 (.67).25 (.09).29 (.04).33 (.01) A.10 (.52).00 (.99).06 (.69) PC.28 (.05).38 (.00) SN.22 (.10)

11. Did the theory (model) work? How do you know? Why are the R2 and Adjusted R2 different (think sample size!) R2Adjusted R2FP (.05).140.0933.007.023

12. Does the TBP help us understand school attendance? IVs: Demographics: Did not predict school attendance: P =.28 -.62 IVs: Attitude + Social Norm + Perceived Control + Intention Predicted DV: School Attendance Why is this a helpful thing to know?

13. Confidence Intervals  If crosses 0 or 1 then results are not significant  The larger dot is the mean  The line relates SD: p value (p =.05 then line =.95)

14. Effect Size: Chi Square  Small Effect size: Don’t smoke: CA  Med Effect size: smoke: CA  Lge ES: smoke/emphsyema/fx hx Df N-1SmMedLGE 17958726 296410739 3109012144

15. Effect Size: t test/ANOVA  Small Effect:  Med Effect:  Lge Effect Df N-1SmallMedLarge 23936726 33225221 42744518

16. ANOVA: More than 2 groups  Where is the difference??  Post Hoc will tell you!  i.e.: Significant difference between groups 1& 3 and 1 & 4; 3 & 4 GroupX= Minutes of exercise X = Weight loss (lbs) P value 10-.1.04 230.2.02 315.3.05 445.8.01

17. Did they use the right test? Why we need p values Chi Square: comparing two or more groups using %

18. Did they use the right tests: means  T test: comparing the means of two groups  ANOVA comparing the means of three or more groups Did they do a post hoc test

19. Did they do the right tests: comparisons  Correlations: comparing two variables ( 1 IV & 1 DV) on a continuum  Regression: there is more then one IV and there is one DV IV 1 + IV2 + IV3 = DV

20. P values and fishing expeditions What does a p value of.05 mean? So… if I do 100 comparisons.. How many will be related by chance alone?

21. How much confidence should I have in statistics?  Statistics don’t lie.. Liars use statistics  Based on what I know about this subject (people, disease) does this make sense  You can have statistical significance, but not clinical significance, but not the other way around!