1. Class 6 Hepp 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 & External Validity
E I
Experimental
Field
E i
Correlational
Descriptive
e I
Laboratory
e i
Clinical Trials

6. Kerlinger MAXMINCON Principle
Maximize
Minimize
Control

7. Kerlinger MAXMINCON 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
Purpose: Describe Phenomena
Types of Studies
Occurrence
Characteristics
Relationship among variables
Survey epidemiological
Variable Centered
Person Centered

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

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
Purpose: Describe Phenomena
Types of Studies
Occurrence
Characteristics
Relationship among variables
Survey epidemiological
Variable Centered
Person Centered

13. Latent vs. Observed Constructs
Cole et al. (1987) assessed social skills and depression from 4 perspectives:
Self-report Researchers’ behavioral ratings
Interviews Significant others’ report

15. 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 = r = r = r2 coefficient of determination
Regression multiple 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

16. Pina-Watson et al Hypothesis

17. Pina-Watson, et al. 2014

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

19. Coefficient of Determination
Correlation of Life Satisfaction with
ISC r = r2 = %
Barriers r = r2 = %
CDSE r = r2 = %
Variance explained (shared)

20. Regression Hypothesis

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

22. 1 SES + 1 GEN +1 ISC + 2 Bar + 3 CSE+ C = Life Satisfaction R2 =
Independ. SC
r =
SES
-.18
Barriers
r =-.32
Life
Satisf.
Career D
Self-
Efficacy
r =
GEN

23. Pina-Watson, et al. 2013

24. Categorical Continuous ????

25. Multiple Linear Regression
Objective: examine the unique and collective association of several independent variables (or predictor variables) 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)

26. 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?

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