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Introduction to Research Design. Threats to Internal Validity One Group Pretest-Posttest Design.  O   X   O. Campbell & Stanley “pre-experimental.” Wuensch “experimental.” OK design if can achieve “experimental isolation,” as in the chemistry lab.

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introduction to research design

Introduction to Research Design

Threats to Internal Validity

One Group Pretest-Posttest Design

o x o
 O   X   O
  • Campbell & Stanley “pre-experimental.”
  • Wuensch “experimental.”
  • OK design if can achieve “experimental isolation,” as in the chemistry lab.
  • Correlated samples t test or nonparametric equivalent.
history
History

Events other than X between pretest and posttest

  • Pre = subjects’ energy consumption.
  • X = Education on importance of conserving resources.
  • Post = subjects’ energy consumption.
  • History = price of energy increases 50% between pre and post.
maturation
Maturation

Processes that normally cause subjects to change across time.

  • Subjects = newly hired employees
  • Pre = Test of morale
  • X = Six month program to elevate morale
  • Post = Test of morale
  • Maturation = end of honeymoon effect
testing
Testing

Pretesting subjects can change them.

  • Pre = frequency of conservation behaviors
    • have you installed a low-flow shower head?
    • et cetera
  • X = Education on importance of conserving resources
  • Post = frequency of conservation behaviors
  • Testing = just (pre) asking them about certain behaviors might cause them to try them.
instrumentation
Instrumentation

The measuring instrument changes across time.

  • The $1.99 scale for our fishing experiment.
    • AM versus PM weight of our catch
    • Spring stretched
  • The human observer as instrument
    • effect of treatment on number of problems in computer lab
    • changes in observers from pre to post
statistical regression
Statistical Regression

When scores have an error component, both high and low scores regress towards the mean upon retesting.

  • My ESP demo in PSYC 2101
  • Educational research at Miami Univ.
    • mean IQ in school district = 102
    • mean of selected students = 80
    • how much regression is expected?
slide8
Percent of regression towards the mean, PRM = 100(1 – ρ).
  • If pretest-posttest corr = .8,
  • PRM = 100(1 - .8) = 20%.
  • Expect regression (up) of .2(102 - 80) = 4.4 points.
  • That may be enough to get significant pre-post change.
mortality
Mortality

Subjects who drop out of the experiment may differ from those who stay in.

  • Subjects = patients with wasting disease
  • Pre, Post = body weight, X = New drug
  • 20 patients at pre, 10 at post
  • Pre mean weight = 97 lb, post = 125.
  • Was the drug effective?
slide10
Perhaps the sickest (and lightest) 10 dropped out.
  • Maybe positive effect from some, who stay in, negative for others, who drop out.
  • Correct by computing means only for those who complete study.
    • creating a problem of external validity.