External validity

1 / 22

# External validity - PowerPoint PPT Presentation

External validity. Generalization based upon representation If you can’t see it you can’t talk about it. EXTERNAL VALIDITY FOUR ISSUES. GENERALIZATION. One can only generalize to that which one has examined. If ya haven’t looked it ya can’t talk about it!

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.

## External validity

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
1. External validity • Generalization based upon representation • If you can’t see it you can’t talk about it.

2. EXTERNAL VALIDITYFOUR ISSUES

3. GENERALIZATION • One can only generalize to that which one has examined. • If ya haven’t looked it ya can’t talk about it! • It “it” compares with what you’re examining you can apply what you found. • Note the “judgment” which must enter in here. • Hence science is tentative.

4. Setting • Are other variables combining with X which influence the X-Y relationship so that there is more than one relationship (statistical interaction; context)? • Can we / do we see that X acts differently on Y in diffferent situations (i.e. in combination with additional variables)?

5. Setting • EX of pretest. • R OXO Classical design • R O O • Vs. • R XO Posttest only design • R O

6. Solomon 4 Design • R OXO • R O O • R XO • R O • Look at the XY effect when there is a pretest vs one when there isn’t.

7. R OXO R O O R XO R O O and Z are Sam Ting R ZXO R Z O R XO R O Pretest is just example of any second variable Z Factorial

8. Factorial • We can now see how X and Z interact.

9. Example • Drug rehab program • Negotiating in neutral territory vs. under threat… • Driving on dry vs. icy road.

10. Statistical partitioning • Same as factorial without randomizing the Zs. • Without randomizing Zs we don’t know what other variables may be confounded with Z and be the real causal culprit.

11. Subjects • We’ve already considered generalization when looking at sampling…We know our odds of being wrong in rejecting no relationship in a population on the basis of a sample. • also…whatever you want to study…make certain that these are the types of people you are studying!

12. Subjects • Because of statistical interaction, we won’t know how a given experiment generalizes without specifically examining each “Z” that can influence the X-Y relationship. • This is an issue raised by experimental psychologists that sociologists “know” but don’t often pass on to their students.

13. Subjects • …so for each type of subject to which we want to generalize…we must examine the X-Y relationship within different categories of the relevant subject characteristic. • Note also…whatever you want to study…make certain that these are the types of people you are studying!

14. How? • Factorial • Statistical partitioning • Note also…whatever you want to study…make certain that these are the types of people you are studying!

15. Operationalizations • Manipulations …we “play god” and “decide” what amount of X the subject will receive by using random assignment…the subject doesn’t decide the amount…so we’re doing more than measuring the independent variable.

16. Operationalizations • We measure the effect of Y. • X manipulates Y we don’t.

17. X • Type • Example • Aggression • Pain pill • Counseling • Tutoring • Diet program • Drug rehab program

18. X • Amount • Total hours of counseling • Total hours of tutoring • Total hours of studying • Total amount of penicillin

19. Y • Type • (amount is measured but we don’t give different amounts as we could with X) • Aggression • Hitting Bobo doll vs. person • Tutoring • One on one vs. group

20. timING • We’re not talking about the “times”…that’s a setting issue. • We’re not talking about time…that’s a manipulation issue. • We’re talking about when we administer the total amount of X…how we dole X out. • & we’re talking about when we assess the effects of X…do we measure Y right after X occurs or later after it’s had time to have an effect or lose its effect?

21. Timing of X • …all at once or in measured doses? • E.g. tutoring, studying, counseling, exercising, dieting, drugging…etc

22. Timing of Y • Right after or later? • Some effects occur immediately and then fade…e.g.: diet programs, inspirational speeches or sales pitches • Others have a sleeper effect…they don't “kick in” ‘till later…e.g.: your appreciation of this great class,