1 / 22

Experiment Design 5: Variables & Levels

Experiment Design 5: Variables & Levels. Martin, Ch 8, 9,10. Recap. Different kinds of variables Independent, dependent, confounding, control, and random Different kinds of validity Internal, construct, statistical, external Each associated with a question Randomization

palila
Télécharger la présentation

Experiment Design 5: Variables & Levels

An Image/Link below is provided (as is) to download presentation 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. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Experiment Design 5:Variables & Levels Martin, Ch 8, 9,10

  2. Recap • Different kinds of variables • Independent, dependent, confounding, control, and random • Different kinds of validity • Internal, construct, statistical, external • Each associated with a question • Randomization • Random sampling: generalization • Random assignment: causation

  3. Picking a design • Choosing how to assign participants to levels of an independent variable • Between vs. within • Choosing how many levels of an independent variable • Choosing how many independent variables

  4. Condition 1: Fred Ginger Mary Condition 2: Ed Mabel George Condition 1: Fred Ginger Mary Condition 2: Fred Ginger Mary Between vs. Within designs 5 8 6 5 8 6 6 9 7 6 9 7

  5. Within vs. Between Subjects • Cost • Between: More participants • Within: More time per participant • Confounding variables • Between: Group differences possible • Use randomization, many subjects, matching • Within: Order effects possible • Use counterbalancing

  6. Transfer effects (order effects) • Definition: • When taking part in earlier trials changes performance in the later trials • Types • Learning • Fatigue • Range or context effect • Problem: • Makes within-subjects designs difficult to interpret

  7. Counterbalancing • Adjust condition order to unconfound transfer effects with condition effects • A,B,C • A,C,B • B,A,C • B,C,A • C,A,B • C,B,A

  8. Counter-balancing either within- or between- subjects • Between: • Joe: A,B • Mary: B,A • Within: • Joe: ABBA • Mary: ABBA

  9. Things to worry about in counter-balancing • If within-subjects counter-balancing: • Linear transfer effects? • Is the transfer from the 1st position to the 2nd position the same as the transfer from 2nd to 3rd position? • E.g., sometimes most learning happens in 1st trials • Always worry about asymmetrical transfer • Does A influence B more than B influences A?

  10. Quiet Quiet Noisy Noisy Asymmetrical transfer • Effect of noise depends on order • People stick with the strategy they pick first • Or mix strategies % trigrams remembered Time 1 Time 2

  11. Partial counterbalancing: Latin Square • Every condition appears in every position equally: • Joe: A B C • Mary: C A B • John: B C A

  12. Matching • Try to reduce between-group differences • E.g., rank hearing as Good, Fair, Poor • Unmatched, could get • Noisy: Poor1, Poor2, Fair1 • Quiet: Good1, Good2, Fair2 • Matched, get: • Noisy: Poor1, Fair2, Good1 • Quiet: Poor2, Fair1, Good2

  13. Matching • Match variable(s) and DV’s should be strongly correlated • Caveat: Match test should not affect DV • e.g., use existing match variable (SAT-M) • Note: Within-subjects designs “match” automatically

  14. Number of levels • How many different groups or conditions that change just one independent variable • Two: • Experimental vs. control • Massed vs. Distributed practice • More: • Drug vs. Placebo vs. No pill • # of times an item is studied: 1,2,4,8, or 16 times

  15. ? ? Inter- and extra-polating inside outside

  16. Floor & Ceiling Effects

  17. Single Variable vs.Multiple Variables • Single Variable: • Only one independent variable • Cannot look at interactions • Multiple Variables: • Two or more independent variables • If use factorial design, can look at interactions • Can require a lot of participants (between) or time (within)

  18. PrepLevel Manuscript Draft Interactions 100 • Who finds more errors, author or editor? • How to spot the interaction graphically? % errors detected 0 Author Editor Proofreader

  19. Interactions • Two independent variables interact when the effect of one depends on the level of the other • Independent vs. Control vs. Random • What if PrepLevel had been a control variable? • What if PrepLevel had been a random variable? • Make it an independent variable if there is reason to believe its effect might depend

  20. Factorial design • Do all combinations of factors (cells) • E.g., Language learning • A factor can be within or between

  21. Converging Operations(≠converging series) • Using more than one method to test the same hypothesis • E.g., using experimental and observational methods • E.g., using cross-sectional and longitudinal designs

  22. Baseline procedure • Example 1: Clinical • No drug, drug, no drug, drug,... • Example 2: Education • Regular class, new format, regular class, new format,..

More Related