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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

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Experiment Design 5: Variables & Levels

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  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,..

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