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

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

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

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

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

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

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”

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

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

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

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

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

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

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

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

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

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

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