Ex-post-facto designs ("after the fact")

Much less able to determine causality than true experiments but these are necessary and important research designs.

Necessity for:1. ethical reasons or 2. an interest in organismic variables

Two main types:
Prospective and Retrospective designs: find naturally occurring groups (thus, "after the fact") and follow them forward (prospective) or trace their histories (retrospective)

  1. subjects are not randomly assigned to treatments, as a result there will be inherent confounds in the populations studied (this is the most serious problem)
  2. sampling problems (often a convenient sample):
  3. dropouts in prospective studies
  4. detection bias (equally likely to detect in both groups?)
Partial solutions:
1) subject for subject (preferable but more difficult) or
2) distribution by distribution Measuring: so will:
1) know if potential confounds (uncontrolled or extraneous variables) are confounded,  &
2) to statistically control for these variables (See later sections on multiple regression and partial correlations)

Retrospective studies have additional problems in that they rely on memory so the partial solutions are more difficult to employ successfully

Have the advantage (over prosepective designs) in that they are more efficient (cheaper and faster) May be necessary with very rare grouping variables of interest (e.g., rare diseases)

Note that even with measurement and matching, internal validity is still questionable. [The additional problems of retrospective designs are well illustrated by McFarland's (1988) study of cyclical variability in moods)].

DVs used in Ex-post-facto studies Problem with both in that absolute risks are hidden, both (absolute and relative risks) should be reported. 

Causality and ex-post-facto designs. Although no one (or few) quasi-analytic experiment will unambiguously show a causal relationship, with converging evidence from many such studies (5, 10 or 100?) can make causal statements (like "smoking causes cancer").