1. Chapter 9: Non-Experimental Designs

2. Correlation and Regression: The Basics
Finding the relationship between two variables without being able to infer causal relationships
Correlation is a statistical technique used to determine the degree to which two variables are related
Three types of [linear] correlations:
Positive correlation
Negative correlation
No correlation

3. Correlation and Regression: The Basics
Positive correlation
Higher scores on one variable associated with higher scores on a second variable

4. Correlation and Regression: The Basics
Negative correlation
Higher scores on one variable associated with lower scores on a second variable

5. Correlation and Regression: The Basics
Correlation coefficient Pearson’s r
Statistical tests include:
Pearson’s r, Spearman’s rho
Ranges from –1.00 to +1.00
Numerical value = strength of correlation
Closer to or +1.00, the stronger the correlation
Sign = direction of correlation
Positive or Negative

6. Correlation and Regression: The Basics
Scatterplots
Graphic representations of data from your two variables
One variable on X- axis, one on Y-axis
Examples:

7. Correlation and Regression: The Basics
Scatterplots
Creating a scatterplot from data
Each point represents an individual subject

8. Correlation and Regression: The Basics
Scatterplots from the hypothetical GPA data for positive (top) and negative (bottom) correlations

9. Correlation and Regression: The Basics
Scatterplots
Correlation assumes a linear relationship, but scatterplot may show otherwise
Curvilinear  correlation coefficient will be close to zero
Left half  strong positive
Right half  strong negative

10. Correlation and Regression: The Basics
Coefficient of determination
Equals value of Pearson’s r2
Proportion of variability in one variable that can be accounted for (or explained) by variability in the other variable
The remaining proportion can be explained by factors other than your variables
r = .60  r2 = .36
36% of the variability of one variable can be explained by the other variable
64% of the variability can be explained by other factors

11. Correlation and Regression: The Basics
Regression Analysis – Making Predictions
The process of predicting individual scores AND estimating the accuracy of those predictions
Regression line – straight line on a scatterplot that best summarizes a correlation
Y = bX + a
Y = dependent variable—the variable that is being predicted
Predicting GPA from study hours  Y = GPA
X = independent variable—the variable doing the predicting
Predicting GPA from study hours  X = study hours
a = point where regression line crosses Y axis
b = the slope of the line
Use the independent variable (X) to predict the dependent variable (Y)

12. Correlation and Regression: The Basics
Regression lines for the GPA scatterplots
Study time (X) of 40 predicts GPA (Y) of 3.5
Goof-off time (X) of 40 predicts GPA (Y) of 2.1

13. Interpreting Correlations
Correlations and causality
Directionality problem
Given correlation between A and B, A could cause B, or B could cause A
Third variable problem
Given correlation between A and B
uncontrolled third variable could cause both A and B to occur
Partial correlations “partial out” possible third variables

15. Interpreting Correlations
Caution: correlational statistics vs. correlational research
Not identical
Correlational research could involve t tests
Experimental research could examine relationship between IV and DV
Using correlations
The need for correlational research
Some IVs cannot be manipulated
Subject variables
Practical/ethical reasons
e.g., brain damage

16. Combining Correlational and Experimental Research
Research example 27: Loneliness and anthropomorphism
Study 1: correlation between loneliness and tendency to anthropomorphize
r = .53
Studies 2 & 3: manipulated loneliness to tests its effects on likelihood to anthropomorphize
IVstudy1 = [false] personality feedback (will be lonely, will have many connections with others)
DVstudy1 = degree of belief in supernatural beings (e.g., God, Devil, ghosts)
IVstudy2 = induce feeling of connection or disconnection
DVstudy1 = anthropomorphic ratings of own pets and others’ pets
Results  feelings of disconnection (loneliness) increased likelihood to anthropomorphize

17. Multivariate Analysis
Bivariate vs. multivariate analyses
Multiple regression
One dependent variable
More than one independent variable
Relative influence of each predictor variable can be weighted
Examples:
predicting school success (GPA) from (a) SAT scores and (b) high school grades
predicting susceptibility to colds from (a) negative life events, (b) perceived stress, and (c) negative affect

18. Multivariate Analysis
Factor analysis
After correlating all possible scores, factor analysis identifies clusters of intercorrelated scores
First cluster  factor could be called verbal fluency
Second cluster  factor could be called spatial skill
Often used in psychological test development

19. Quasi-Experimental Designs
no causal conclusions, less than complete control, no random assignment
From prior chapters:
Single-factor nonequivalent groups designs
Nonequivalent groups factorial designs
subject x manipulated variable factorial designs
All the correlational research

20. Quasi-Experimental Designs
Nonequivalent control group designs
Typically (but not necessarily) include pretests and posttests
Experimental  O1 T O2
Nonequiv control  O1 O2
Random assignment to groups not possible for practical reasons
Two groups may initially be different at O1
Thus, nonequivalent groups and inevitably confounded with Treatment/No Treatment
Pre-test  Flextime  Post-test
Pittsburgh
Pre-test  Nothing  Post-test
Cleveland

21. Quasi-Experimental Designs
Nonequivalent control group designs
Research example 29
IV  whether coaches given “coach effectiveness” training
Nonequivalent groups – coaches from two different leagues
DV  player self-esteem (preseason and postseason)

22. Quasi-Experimental Designs
Nonequivalent control group designs
Research example: NO PRE-TEST?????
IV  living distance from SF earthquake
Experimental  California
Nonequivalent control  Arizona
DV  nightmare frequency
Results
California > Arizona
Ruled out alternative explanation that those in California would always have more earthquake nightmares
Beebe, B. (2011) Introduction to mothers, infants, and young children of September 11, 2001: A primary prevention project. Journal of Infant, Child, and Adolescent Psychotherapy, 10(2-3),

23. Quasi-Experimental Designs
Interrupted time series designs

24. Quasi-Experimental Designs
Interrupted time series designs
Useful for evaluating overall trends
Basic design  O1 O2 O3 O4 O5 T O6 O7 O8 O9 O10
Outcomes: Best outcome  d (lower right)

25. Quasi-Experimental Designs
Interrupted time series designs
Research example 31
Effect of incentive plan on productivity
Ruled out effects of history, instrumentation, and subject selection

26. Quasi-Experimental Designs
Interrupted time series designs
Variations on the basic time series design
Add a control group
O1 O2 O3 O4 O5 T O6 O7 O8 O9 O10
O1 O2 O3 O4 O O6 O7 O8 O9 O10
Add a “switching” replication
Second treatment, but at a different time
O1 O2 O3 T O4 O5 O6 O7 O8 O9 O10
O1 O2 O3 O4 O5 O6 O7 T O8 O9 O10

27. Quasi-Experimental Designs
Research using archival data
Data previously collected for some other purpose
Often undergoes content analysis
Susceptible to missing data and bias, but no reactivity
Research example 32
IV  patient recovering room
Experimental  pleasant view of park
Nonequivalent control  brick wall
DV  recovery & other factors (better for room with a view)