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Understanding Experimental Design: Principles, Techniques, and Validity Issues

This guide explores the fundamental principles of conducting experiments, focusing on independent groups designs, manipulation in experimentation, and the elements that contribute to a "good" experiment. It addresses key questions about cause-and-effect relationships, experimental control, and the significance of covariation between independent and dependent variables. The text discusses various experimental designs, including random and matched groups, as well as statistical analysis through descriptive and inferential statistics, including confidence intervals and null-hypothesis testing, emphasizing the importance of internal and external validity in research.

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Understanding Experimental Design: Principles, Techniques, and Validity Issues

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Presentation Transcript

  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 Easter Candy “Caper” • How many participants in each of 4 conditions: ( ) jelly beans ( ) chocolate bunnies ( ) Peeps ( ) marshmallow eggs ????????? • 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=jelly beans, 2=chocolate bunnies, 3=peeps, 4=marshmallow eggs • 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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