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Understanding Experimental Design: Key Concepts and Techniques

Explore the world of experimental design, from the basics like how and why we conduct experiments to advanced topics such as statistical analysis and decision errors. Learn about different types of designs and methods to ensure validity in your research.

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Understanding Experimental Design: Key Concepts and Techniques

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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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