1. Chapter 10 Causal Inference and Correlational Designs
PowerPoint presentation developed by:
Sarah E. Bledsoe & Lin Fang

2. Overview Introduction to Causal Inference and Correlational Designs
Criteria for Inferring Causality
Internal Validity
External Validity
Correlational Designs
The Elaboration Model

3. Introduction to Causal Inference and Correlational Designs
A conclusion that can be drawn logically given the research design and findings
Causal Inference
Implies that the independent variable has a causal impact on the dependent variable
Research Design
Refers to the decisions made in planning and conducting research
Often used in connection with whether logical arrangements permit causal inferences

4. Criteria for Inferring Causality
Cause (independent variable) must precede the effect (dependent variable) in time
The two variables are empirically correlated with one another
The observed empirical correlation between the two variables can not be due to the influence of a third variable that causes the two under consideration

5. Internal Validity
Depends on the extent to which the 3 criteria for causality are met
Determines whether or not causal inferences can be drawn from the results
Prominent threats to internal validity:
History
Maturation
Testing
Instrumentation changes
Statistical regression
Selection bias
Ambiguity regarding the direction of causal inference

6. External Validity Determines generalizability Influenced by:
Refers to the extent that we can generalize the findings of a study to other settings and larger populations than those represented in the study
Influenced by:
Representativeness of sample, setting, and procedures
Ambiguity or vagueness in reporting study results in the literature

7. Correlational Designs
Often used when experimental designs are not feasible
Weak on internal validity due to limited ability to control for prominent threats
External validity is enhanced by studying people in the “real world”

8. Cross-Sectional Studies
Study based on observations that represents a single point in time
May have exploratory, explanatory or descriptive purposes
Explanatory cross-sectional studies attempt to understand causal processes by showing associations between variables
Can not make causal inferences because observations are made at one point in time
Speak only to the plausibility of causal relationships

9. Case-Control Studies
Compares groups of cases with contrasting outcomes and collects retrospective data that might explain the differences in outcome
Popular because of feasibility as data can be collected at one point in time
Useful in generating hypotheses
Inference and generalizability are limited
Representativeness of cases
Memory may be faulty

10. Longitudinal Studies
Involves the collection of data observations at different points in time
Internal validity is strengthened by the ability to:
Control for time order
Detect correlation between variables
Other threats to internal validity limit ability to make causal inference

11. The Elaboration Model
A way to better understand the meaning of a relationship (or lack of a relationship) between two variables by examining multivariate frequency tables to study the effects on the original bivariate relationship that are produced by introducing additional variables into the tables

12. The Elaboration Model Basic Steps:
A relationship is observed to exist between two variables
A third variable is held constant by subdividing the cases according to the attributes of the third variable
The original two variable relationship is recomputed within each subgroup
The comparison of the original relationship with the relationship within subgroups provides a fuller understanding of the original relationship

13. Replication
The original bivariate relationship appears to be essentially the same in the multivariate analysis as it was in the bivariate analysis
Hypothetical data relating case management provision to rehospitalization, controlling for practitioners degree

14. Explanation
The original bivariate relationship is explained away as spurious
Hypothetical data relating case management provision to rehospitalization, controlling for impairment

15. Interpretation
A control variable is discovered to be the mediating factor through which an independent variable affects a dependent variable
Hypothetical data relating case management provision to rehospitalization, controlling for medication compliance

16. Specification
The observed relationship between two variables is replicated among some subgroups created by the control variable and not among others specifying the conditions under which the relationship exists
Hypothetical data relating case management provision to rehospitalization, controlling for adequacy of care

17. Suppressor Variables
Control variable shows that two variables that appear unrelated or weakly related in the bivariate analysis are more strongly related than they appeared
Hypothetical data relating case management provision to rehospitalization, controlling for type of impairment

18. Distorter Variables
Control variable shows that the direction of a bivariate relationship reverses, for example:
A study found that female received higher starting salaries than males because females were hired relatively recently – The relationship between variable gender and variable salary is affected by the distorter variable time of first hire