1. Day 6: Non-Experimental & Experimental Design
Where are the beakers??

2. What kind of research is considered the “gold standard” by the Institute of Education Sciences?
Descriptive
Causal-Comparative
Correlational
Experimental
Why?

3. Why does most educational research use non-experimental designs?

4. What is the purpose of non-experimental designs?

5. Causal-Comparative Example
Green & Jaquess (1987)
Interested in the effect of high school students’ part-time employment on their academic achievement.
Sample: 477 high school juniors who were unemployed or employed > 10 hours/wk.

6. Causal-Comparative Design
A study in which the researcher attempts to determine the cause, or reason, for pre-existing differences in groups of individuals
At least two different groups are compared on a dependent variable or measure of performance (called the “effect”) because the independent variable (called the “cause”) has already occurred or cannot be manipulated

7. Causal-Comparative Design
Ex-post facto
Causes studied after they have exerted their effect on another variable.

8. Causal-Comparative Design
Drawbacks
Difficult to establish causality based on collected data.
Unmeasured variables (confounding variables) are always a source of potential alternative causal explanations.

9. Some Thought Questions…

10. Correlational Design
Determines whether and to what degree a relationship exists between two or more quantifiable variables.

11. Example of Correlation

12. Correlational Design
The degree of the relationship is expressed as a coefficient of correlation
Examples
Relationship between math achievement and math attitude
Relationship between degree of a school’s racial diversity and student use of stereotypical language
Your topics?

13. Correlation coefficient…
-1.00
0.00
+1.00
strong positive
strong negative
no relationship

14. Advantages of Correlational Design
Analysis of relationships among a large number of variables in a single study
Information about the degree of the relationship between the variables being studied

15. Cautions
A relationship between two variables does not mean one causes the other (Think about the reading achievement and body weight correlations)
Possibility of low reliability of the instruments makes it difficult to identify relationships

16. Cautions
Lack of variability in scores (e.g. everyone scoring very, very low; everyone scoring very, very high; etc.) makes it difficult to identify relationships
Large sample sizes and/or using many variables can identify significant relationships for statistical reasons and not because the relationships really exist (Avoid shotgun approach)

17. Cautions
Need to identify your sample to know what is actually being compared.
If using predictor variables, time interval between collecting the predictor and criterion variable data is important.

18. Correlational Designs
Guidelines for interpreting the size of correlation coefficients
Much larger correlations are needed for predictions with individuals than with groups
Crude group predictions can be made with correlations as low as .40 to .60
Predictions for individuals require correlations above .75

19. Correlational Designs
Guidelines for interpreting the size of correlation coefficients
Exploratory studies
Correlations of .25 to .40 indicate the need for further research
Much higher correlations are needed to confirm or test hypotheses

20. Correlational Designs
Criteria for evaluating correlational studies
Causation should not be inferred from correlational studies
Practical significance should not be confused with statistical significance
The size of the correlation should be sufficient for the use of the results (individuals vs groups)

21. Think…
If you were going to take your action research topic, and create a causal-comparative study, what would it look like?
--OR--
If you were going to take your action research project, and create a correlational study, what would it look like?

22. Experimental Design
The Gold Standard?

23. To Review
Why is most educational research comprised of non-experimental research designs?

24. To Review
What is the purpose of non-experimental research?

25. To Review
How does the independent variable function in non-experimental research?

26. To Review
Can non-experimental research claim causality?

27. An example
Read the example given in class and in pairs respond to the questions

28. Experimental Research
Purpose
To make causal inferences about the relationship between the independent and dependent variables
Characteristics
Direct manipulation of the independent variable
Control of extraneous variables

29. Experimental Designs Examples Single Group Post-test
Single Group Pre-test Post-test
Non-Equivalent Groups Post-test
Quasi-Experimental Design
Randomized Post-test only
Randomized Pre-test Post-test
Factorial
Examples

30. Experimental Validity
Internal validity
The extent to which the independent variable, and not other extraneous variables , produced the observed effect on the dependent variable
External validity
The extent to which the results are generalizable

31. Internal Validity
Threats that reduce the level of confidence in any causal conclusions
Key Question: Is this a plausible threat to the internal validity of the study?

32. Threats to Internal Validity
History
Extraneous events have an effect on the subjects’ performance on the dependent variable
Ex - The crash of the stock market, 9-11, the invasion of Iraq, etc.
Selection
Groups that are initially not equal due to differences in the subjects in those groups
Ex - Positive and negative attitudes, high and low achievers, etc.

33. Threats to Internal Validity
Maturation
Changes experienced within the subject over time
Pretesting
The effect of having taken a pretest
Instrumentation
Poor technical quality (i.e. validity, reliability) or changes in instrumentation

34. Threats to Internal Validity
Subject attrition
Differential loss of subjects from groups
Statistical regression
The natural movement of extreme scores toward the mean
Diffusion of treatment
The treatment is given to the control group
Experimenter effects
Different characteristics or expectations of those implementing the treatments across groups

35. Threats to Internal Validity
Subject effects
The effects of being aware that one is involved in a study
Four types
Hawthorne effect
John Henry effect
Resentful demoralization
Novelty effect

36. Internal Validity
Key Point: Ultimately, validity is a matter of judgment. Ask if it is reasonable that possible threats are likely to affect the results.

37. External Validity
The extent to which results can be generalized from a sample to a particular population.
Question – Why would really good internal validity often result in poor external validity?

38. External Validity Factors affecting external validity Subjects
Representativeness of the sample in comparison to the population
Personal characteristics of the subjects
Situations - characteristics of the setting
Specific environment
Special situation
Particular school

39. External Validity
Importance of explanation of sampling procedures

40. Experimental Designs Examples Single Group Post-test
Single Group Pre-test Post-test – Libby, Deb
Non-Equivalent Groups Post-test – Mary, Cheryl
Quasi-Experimental Design – Pete, Laura
Randomized Post-test only – Amanda, Nicole, Tam
Randomized Pre-test Post-test – Karen, Jen, Justin
Examples

41. Your Task
Based on the topic of your proposal, design an experimental study using the design you were assigned.
Write a research question and hypothesis.
Sketch out the methods.
Identify strengths and weaknesses of each design.

42. Experimental Designs Notation
R indicates random selection or random assignment
O indicates an observation
Test
Observation score
Scale score
X indicates a treatment
A, B, C, ... indicates a group

43. Pre-Experimental Designs
No pre-experimental design controls internal validity threats well
Single group pretest only
A X O
Internal validity threats
History, maturation, attrition, experimenter effects, subject effects, and instrumentation are viable threats
Useful only when the research is sure of the status of the knowledge, skill, or attitude being changed and there are no extraneous variables affecting the results

44. Pre-Experimental Designs
Single group pretest post-test
A O X O
Internal validity threats
Maturation and pretesting are threats
History and instrumentation are potential threats
Useful when subject effects will not influence the results, history effects can be minimized, and multiple pretests and post-tests are used

45. Pre-Experimental Designs
Non-equivalent groups post-test only
A X O
B O
Internal validity threats
Definite Threat: Selection
Potential Threats: History, maturation, and instrumentation
Useful when groups are comparable and subjects can be assumed to be about the same at the beginning of the study

46. Quasi-Experimental Designs
Types
Non-equivalent pretest/post-test, experimental control groups
A O X O B O O
Non-equivalent pretest/post-test, multiple treatment groups
A O X1 O B O X2 O
Useful when subjects are in pre-existing groups (e.g. classes, schools, teams, etc.)

47. Quasi-Experimental Designs
Threats to internal validity
Selection is the major concern
Likely to control for most other threats, provided the groups are not significantly different from one another
See Table 9.2 for specific threats related to each design

48. True Experimental Designs
Important terminology
Random assignment
Subjects placed into groups by random
Ensures equivalency of the groups
Random selection of subjects
Subjects chosen from population by random
Ensures generalizability to the population from which the subjects were selected (i.e. external validity)

49. True Experimental Designs
Types
Randomized post-test only experimental control groups
R A X O R B O
Randomized post-test only multiple treatment groups
R A X1 O R B X2 O

50. True Experimental Designs
Types (continued)
Randomized pretest/post-test multiple treatment groups
R A O X1 O R B O X2 O
Randomized pretest/post-test experimental control groups
R A O X O R B O O

51. True Experimental Designs
Threats to internal validity
Controls for selection, maturation, and statistical regression
Likely to control for most other threats
See Table 9.2 for specific threats related to each design

52. Evaluating Experimental Designs
Criteria for evaluating experimental research
The primary purpose is to test causal hypotheses
There should be direct manipulation of the independent variable
There should be clear identification of the specific research design

53. Evaluating Experimental Designs
Criteria for evaluating experimental research
The design should provide maximum control of extraneous variables
Treatments are substantively different from one another
The number of subjects is dependent on or equal to the number of treatment replications