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Class 6 Hepp et al -15 Cha 13 Quant Descriptive Designs .

Class 6 Hepp et al -15 Cha 13 Quant Descriptive Designs. Research Designs:. Kazdin Gelso. Research Designs: Kazdin. Experimental Quasi-Experimental Correlational. Internal Vs. External Validity. Internal Validity Experimental Control Random Assignment to Groups Manipulation of IV

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Class 6 Hepp et al -15 Cha 13 Quant Descriptive Designs .

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  1. Class 6Hepp et al -15 Cha 13 Quant Descriptive Designs.

  2. Research Designs: • Kazdin • Gelso

  3. Research Designs: Kazdin • Experimental • Quasi-Experimental • Correlational

  4. Internal Vs. External Validity • Internal Validity • Experimental Control • Random Assignment to Groups • Manipulation of IV • External Validity • Generalizability

  5. Internal & ExternalValidity Clinical Trials

  6. KerlingerMAXMINCON Principle • Maximize • Minimize • Control

  7. KerlingerMAXMINCON Principle • Maximize • Variability in constructs of interest • Experimental:Treatment groups • Correlational: Measured variables • Minimize • Error/random variance • Experimental: Within treatment groups • Assessment/observation procedures • Participants’ characteristics not examined • Control • External Confounds

  8. Quant. descriptive Designs

  9. Can Survey Research Be Used To: • Test Cause-Effect Relations ? • YES ------ NO • Test a Hypothesis ? • YES ------ NO

  10. Survey Research • Describe, document frequency, characteristics, patterns of behaviors, attitudes, events- Epidemiological • Identify variables that may explain behaviors, attitudes, events • Test hypothesis about the association of behaviors, attitudes, events • Generate a causal hypotheses • cholera example Chap. 13 p. 289 • Cook, Alegria, Lin and Guo’s Study

  11. Quant. descriptive Designs

  12. Latent vs. Observed Constructs

  13. Variable Centered Research • Bi-variate Correlation • Extent to which 2 or more continuous psychological constructs vary together • Pearson correlation coefficient r- bivariate correlation: strength and direction • r = .35 r = -.35 r =. 50 r2 coefficient of determination • Regressionmultiple IV’s one DV • Examine combined contribution of • several IVs to one DV • Examine unique contribution of each IV to the DV while controlling for the other IVs

  14. Pina-Watson et al Hypothesis

  15. Pina-Watson, et al. 2014

  16. Bivariate relation (Pearson’s r)of x, y, and zto Life Satisfaction Independ. SC r = .46 Life Satisf. Barriers r =-.32 Career D Self- Efficacy r = .44

  17. Coefficient of Determination • Correlation of Life Satisfaction with ISC r = .46 r2 = .21 21% Barriers r = -.32 r2= .10 10% CDSE. r = .44 r2=.19 19% Variance explained (shared)

  18. Regression Hypothesis

  19. Examine the unique() and collective(R2) relation of predictorsx, y, and z to Life Satisfaction controlling for S & G Independ. SC Barriers SES Life Satisf. Career D Self- Efficacy GEN

  20. 1 SES + 1GEN +1ISC+ 2Bar+ 3CSE+ C = Life SatisfactionR2 = .31*** Independ. SC r=.46 .26 SES -.18 Barriers r=-.32 Life Satisf. Career D Self- Efficacy r=.44 .29 GEN

  21. Pina-Watson, et al. 2013

  22. Categorical Continuous ????

  23. Multiple Linear Regression • Objective: examine the unique and collective association of several independent variables (or predictorvariables) to one dependent variable (also called criterion variable). • Both IVs -predictor(s) and criterion-DVs are continuous. • Categorical variables with only two levels can be used as predictors-IVs (e.g. gender, depressed/not depressed; treatment/control group)

  24. Fill in the Blanks • The best research design is: • What type of design is used in the randomized controlled clinical trials used to examine therapy outcome?

  25. Multiple Linear Regression • It is best when: • Predictors (IVs) are not highly correlated among themselves and • Predictors (IVs) are correlated to the Criterion (DV)

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