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Why do we conduct experiments anyway?

Why do we conduct experiments anyway?. I dunno!. How do we conduct experiments? One answer…. Independent Groups Designs. Other Questions???????. When is “manipulation” a “good” thing? What makes a “good” experiment? What allows decisions re: cause and effect?. Experimental Control.

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Why do we conduct experiments anyway?

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  1. Why do we conduct experiments anyway? I dunno! How do we conduct experiments? One answer… Independent Groups Designs

  2. Other Questions??????? • When is “manipulation” a “good” thing? • What makes a “good” experiment? • What allows decisions re: cause and effect?

  3. Experimental Control • Covariation of IV and DV • Time-order relationship: IVDV • Elimination of confounds • Holding conditions constant • Balancing

  4. A First Independent Groups Design • Random Groups Design • Random selection versus • Random assignment…comparable groups • A technique: Block randomization

  5. The Great Halloween Candy “Caper” • How many participants in each of 4 conditions: ( ) M & Ms ( ) candy corn ( ) Kit Kats ( ) Mary Janes ????????? • Block 1 • Block 2 • Block 3 • Block 4 • Block ? 1-5-6-6-4-1-0-4-9-3-2-0-4-9-2-3-8-3-9-1-9-1-1-3-2-2-1-9-9-9-5-9-5-1-6-8-1-6-5-2-2-7-1-9-5-4-8-2-2-3-4-6-7-5-1-2-2-9-2-3-8-7-5-0-2-4-6-6-1

  6. Steps: Block Randomization • Assign a number from 1 to 4 to the respective conditions, if there are 4 conditions 1=M & Ms, 2=candy corn, 3=Kit Kats, 4=Mary Janes • Use random numbers to select 4 sequences of the numbers from 1 to 4 to obtain 4 sequences for 4 randomized blocks • Skip numbers GT 4 • Skip numbers that repeat a number already in sequence • Result is order of testing the conditions for the first 16 participants

  7. Order of Testing

  8. Issues of Validity….Optimizing vs. “no-nos” • External? • Replication • Does random assignment produce a representative sample?

  9. Issues of Validity….Optimizing vs. “no-nos” • Internal? • The problem of Intact Groups • Subjective subject loss versus • Mechanical subject loss • Demand characteristics? • Placebo controls • Double-blind experiments • Experimenter effects? • Double-blind experiments

  10. Another Design….Matched Groups • Matching task (a “pre-test”) • Split-litter technique

  11. A Third Design…Natural Groups • Correlation or causation? • Problems with causal inferences • Subject variables can’t be manipulated • Subject variables can’t be randomly assigned • Solution: complex designs • E.g., 2 x 2: Age x amount of dosage • IV? IV? • DV?

  12. Summary: Avoiding Problems Common to All Independent Designs • Eliminate confounding (internal validity) • Select appropriate DV (construct validity) • Replicate to increase external validity (convergent validity)

  13. Analysis of Experiments • Descriptive statistics: to summarize results, only • Inferential statistics: to determine reliability, IVDV • Confidence Intervals • Null-Hypothesis Testing

  14. Confidence Intervals • Sample mean,   • CI—range of values around , at ?% confidence • Question: Do CIs for different study samples (conditions, groups) overlap? • No overlap difference between samples • Yes, overlap NO difference between samples

  15. Constructing CI • For a 95% CI • Upper limit: + (t .05)( ) • Upper limit: - (t .05)( )

  16. Null-hypothesis Testing and Decision Errors • Focus on mean differences • Assume no effect for the null (“ no difference”) • Use probability theory • Decision errors • Limitations • Statistical significance vs. real significance (meaningfulness) • Internal validity • Truth of the null • Reliability

  17. Null-hypothesis Testing and Decision Errors • Focus on mean differences • Assume no effect for the null (“ no difference”) • Use probability theory • Decision errors • Limitations • Statistical significance vs. real significance (meaningfulness) • Internal validity • Truth of the null • Reliability

  18. Null-hypothesis Testing and Decision Errors • Focus on mean differences • Assume no effect for the null (“ no difference”) • Use probability theory • Decision errors • Limitations • Statistical significance vs. real significance (meaningfulness) • Internal validity • Truth of the null • Reliability

  19. Null-hypothesis Testing and Decision Errors • Focus on mean differences • Assume no effect for the null (“ no difference”) • Use probability theory • Decision errors • Limitations • Statistical significance vs. real significance (meaningfulness) • Internal validity • Truth of the null • Reliability

  20. Null-hypothesis Testing and Decision Errors • Focus on mean differences • Assume no effect for the null (“ no difference”) • Use probability theory • Decision errors • Limitations • Statistical significance vs. real significance (meaningfulness) • Internal validity • Truth of the null • Reliability

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