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Correlation between Age and Monitoring on Risk Behaviors in Children

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This study examines the impact of age and monitoring on risk behaviors in 12-year-old children. Results show the correlation between age and risk behaviors controlling for monitoring, as well as the correlation between monitoring and risk behaviors controlling for age.

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Correlation between Age and Monitoring on Risk Behaviors in Children

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Presentation Transcript

  1. Practice • N = 130 • Risk behaviors (DV; Range 0 – 4) • Age (IV; M = 10.8) • Monitoring (IV; Range 1 – 4)

  2. How many risk behaviors would a child likely engage in if they are 12 years old and were monitored “1”?

  3. How many risk behaviors would a child likely engage in if they are 12 years old and were monitored “1”? = 1.72 behaviors

  4. How many risk behaviors would a child likely engage in if they are 12 years old and were monitored “4”?

  5. How many risk behaviors would a child likely engage in if they are 12 years old and were monitored “4”? .51 behaviors

  6. What has a bigger “effect” on risk behaviors – age or monitoring?

  7. Did the entire model significantly predict risk behaviors?

  8. Significance testing for Multiple R p = number of predictors N = total number of observations

  9. Significance testing for Multiple R p = number of predictors N = total number of observations

  10. What is the correlation between age and risk controlling for monitoring? What is the correlation between monitoring and risk controlling for age?

  11. Quick Review • Predict using 2 or more IVs • Test the fit of this overall model • Multiple R; Significance test • Standardize the model • Betas • Compute correlations controlling for other variables • Semipartical correlations

  12. Testing for Significance • Once an equation is created (standardized or unstandardized) typically test for significance. • Two levels • 1) Level of each regression coefficient • 2) Level of the entire model

  13. Testing for Significance • Note: Significance tests are the same for • Unstandarized Regression Coefficients • Standardized Regression Coefficients • Semipartial Correlations

  14. Remember • Y = Salary • X1 = Years since Ph.D.; X2 = Publications • rs(P.Y) = .17

  15. Remember • Y = Salary • X1 = Years since Ph.D.; X2 = Publications • rs(P.Y) = .17

  16. Significance Testing • H1 = sr, b, or β is not equal to zero • Ho = sr, b, or β is equal to zero

  17. Significance Testing sr = semipartial correlation being tested N = total number of people p = total number of predictors R = Multiple R containing the sr

  18. Multiple R

  19. Significance Testing N = 15 p = 2 R2 = .53 sr = .17

  20. Significance Testing • t critical • df = N – p – 1 • df = 15 – 2 – 1 = 12 • t critical = 2.179 (two-tailed)

  21. t distribution tcrit = -2.179 tcrit = 2.179 0

  22. t distribution tcrit = -2.179 tcrit = 2.179 0 .85

  23. If tobs falls in the critical region: • Reject H0, and accept H1 • If tobs does not fall in the critical region: • Fail to reject H0 • sr, b2, and β2 are not significantly different than zero

  24. Practice • Determine if $977 increase for each year in the equation is significantly different than zero.

  25. Significance Testing N = 15 p = 2 R2 = .53 sr = .43

  26. Practice • Determine if $977 increase for each year in the equation is significantly different than zero.

  27. Significance Testing • t critical • df = N – p – 1 • df = 15 – 2 – 1 = 12 • t critical = 2.179 (two-tailed)

  28. t distribution tcrit = -2.179 tcrit = 2.179 0

  29. t distribution tcrit = -2.179 tcrit = 2.179 0 2.172

  30. If tobs falls in the critical region: • Reject H0, and accept H1 • If tobs does not fall in the critical region: • Fail to reject H0 • sr, b2, and β2 are not significantly different than zero

  31. Remember • Calculate t-observed b = Slope Sb = Standard error of slope

  32. Significance Test • It is possible (as in this last problem) to have the entire model be significant but no single predictor be significant – how is that possible?

  33. Common Applications of Regression

  34. Common Applications of Regression • Mediating Models Teaching Evals Candy

  35. Common Applications of Regression • Mediating Models Happy Teaching Evals Candy

  36. Mediating Relationships • How do you know when you have a mediating relationship? • Baron & Kenny (1986)

  37. Mediating Relationships Mediator b a c DV IV

  38. Mediating Relationships Mediator a IV 1. There is a relationship between the IV and the Mediator

  39. Mediating Relationships Mediator b DV 2. There is a relationship between the Mediator and the DV

  40. Mediating Relationships c DV IV 3. There is a relationship between the IV and DV

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