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Adequate experimental control Lack of artificiality Basis for comparison Adequate information from the data Uncontaminated data No confounding of relevant variables Representativeness Parsimony. Pretest-posttest control group design Posttest only control group design

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quantitative research
Adequate experimental control

Lack of artificiality

Basis for comparison

Adequate information from the data

Uncontaminated data

No confounding of relevant variables

Representativeness

Parsimony

Pretest-posttest control group design

Posttest only control group design

Solomon four group design

Counterbalanced design

Factorial design

Quantitative Research

Criteria

Experimental Designs

criteria 1 adequate experimental control
Criteria #1Adequate Experimental Control
  • There are adequate restraints, or controls, on the conditions of the experiment.
  • With controls in place the researcher should be able to accurately interpret the results.
  • The experimental design should be structured in such a way that the experimental variable (or treatment) effect can be detected.
criteria 2 lack of artificiality
Criteria #2Lack of Artificiality
  • The experiment must be conducted in such a way that the results will apply to the real educational world.
criteria 3 basis for comparison
Criteria #3Basis for Comparison
  • There must be some way to make a comparison to determine whether or not there is an experimental effect.
  • In some cases a control group is used (control groups do not receive the experimental treatment).
  • In some cases multiple treatments may be studied against an external criterion (i.e. Different reading techniques may be examined to assess their relative impact on student reading scores).
criteria 4 adequate information from the data
Criteria #4Adequate Information From the Data
  • There must be adequate data for testing the hypothesis.
  • The data must be such that statistical testing will be precise enough for the researcher to make a decision about the hypothesis.
criteria 5 uncontaminated data
Criteria #5Uncontaminated Data
  • The data should adequately reflect the experimental effects.
  • The data should not be affected by poor measurement or errors in the experimental procedures.
  • Example: subjects in an experiment should not communicate across groups (control and experimental).
criteria 6 no confounding variables
Criteria #6No Confounding Variables
  • Confounding of variables occur when two or more variables cannot be separated.
  • Experimental controls should be such that variables can be isolated.
  • Translation: the relationship between independent variable and the dependant variable can be measured with confidence in that another variable is not contributing to the effect.
criteria 7 parsimony
Criteria #7Parsimony
  • Keep it simple!
  • This criterion means that, with all other factors being equal, a simple design is preferred to a more complex one.
  • Note: simple designs are usually easier to implement, and may yield data that is easier to interpret.
quantitative experimental designs
QuantitativeExperimental Designs

This family of research designs is characterized by random selection of subjects.

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The following experimental designs are based on the problem statement: The effects of teaching method(s) on history achievement.

experimental design 5 factorial design
Experimental Design #5Factorial Design

Diagram

Independent Variable #1 Teaching Method

Independent Variable #2

Aptitude