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Analyzing Experimental Designs: Between and Within Groups Approaches in Educational Research

This review delves into experimental designs, focusing on the crucial aspects of manipulation of conditions, control of confounding variables, and treatment types—between subjects and within subjects. It highlights the use of random assignment to treatment groups and counterbalancing to reduce bias effects such as order and carry-over. The text compares the statistical power of within-group designs against between-group designs, demonstrating the impact of sample size (N) on observation accuracy. Statistical analyses, including ANOVA, are employed to evaluate main and interaction effects in a study of instructional methods (Tech vs. Lecture) and gender differences in knowledge scores.

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Analyzing Experimental Designs: Between and Within Groups Approaches in Educational Research

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Presentation Transcript

  1. Review

  2. Experimental Designs • Requirements: • Manipulation of Conditions or Treatments • Control for confounding variables • Types • Between Subjects • Within Subjects • Larger N • Small n

  3. Between Groups IV • Random assignment to treatment groups • Distribute evenly across levels of IV (e.g. treatment groups) individual differences among participants • Minimize impact if these difference in DV

  4. Within Groups IV • All participants receive all levels of IV • No individual differences across participants as potential confounds, therefore Randomnization is not needed (or possible) • Bias: Order effects: carry-over, fatigue • Counterbalancing (randomly assigned

  5. Within Groups Design • More statistical power than Between Groups: • With same sample size, more observation per condition N=40 Treat 1 Treat 2 • Between Groups 20 20 • Within Groups: 40 40 • Less variability across groups, therefore les sampling error (same individuals) and the higher the chance that p.alpha • Source of bias: crossover effects- order and fatigue

  6. question 22 • IVs • Treatment: Tech vs. lecture –True IV, BW- random assignment • Gender : M F Quasi-Exp BG • DVs Knowledge Score in test • Design 2x2 factorial, between groups- quota

  7. Analyses • ANOVA P values • Main Effect 1 p<.05 Gender • Main Effect2 p>.05 Lesson Type • Interaction Effect p<.05 Interaction

  8. Main Effects • Girls scored better on test than boys (regardless of type of instruction) • Boys score = 75Girls score = 86 p= <.05 • There is no difference in test scores between the Tech and Lecture lesson groups (regardless of gender) • Tech Avg 82.5 Lecture Avg 78.1 p>.05 ANOVA – for main effects

  9. Interaction effect • Boys • boys in Tech G > boys in Lecture group • Girls • girls in Tech G = girls in Lecture group Tech Lecture Boys 80 70 p<.025 Girls 85 87 p>.025 ANOVA for interaction effect --- followed by Test of simple effects – two T-tests; one per gender

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