Homework 3

Experimental Design in Education

Author
Affiliation

Jihong Zhang*, Ph.D

Educational Statistics and Research Methods (ESRM) Program*

University of Arkansas

Published

February 10, 2025

1 Homework 2: One-Way ANOVA Analysis

1.1 Objective

This assignment aims to guide students through the complete process of conducting a One-Way ANOVA, ensuring they understand the fundamental assumptions, hypothesis formulation, statistical execution, and interpretation of results.

1.2 Instructions

  1. Dataset Selection
    • Choose an appropriate dataset containing at least one categorical independent variable (with at least three levels) and one continuous dependent variable (DV).
    • Provide a brief description of the dataset, including its source and relevant background information.
  2. Research Question and Hypothesis
    • Clearly state your research question.
    • Formulate both the null and alternative hypotheses.
  3. Assumption Checking
    • Evaluate ANOVA assumptions (normality, homogeneity of variance, and independence).
    • Provide diagnostic plots and justify whether the assumptions are met.
  4. ANOVA Execution
    • Conduct a One-Way ANOVA using R.
    • Report the ANOVA table and interpret the results.
  5. Post-Hoc Analysis and Contrasts
    • Perform an omnibus test to assess the overall significance.
    • Design and test planned orthogonal contrasts.
    • If necessary, conduct post-hoc pairwise comparisons.
  6. Effect Size Calculation
    • Compute and report an appropriate effect size (e.g., eta-squared or omega-squared).
  7. Results and Interpretation
    • Summarize key findings in a concise and structured format.
    • Interpret statistical significance, effect size, and practical implications.
  8. R Code Submission
    • Include well-documented R code for all analyses.

1.3 Submission Requirements

  • Submit a 2-3 page document (excluding references) consisting of:
    • A 1-page report summarizing the analysis and findings.
    • 1-2 pages of R code with comments explaining key steps.

1.4 Evaluation Criteria

Your submission will be evaluated based on the following criteria through AI-assisted and peer review:

  1. Coverage (30%): Completeness of the analysis, ensuring all required components are addressed.
  2. Structure and Clarity (40%): Logical organization, coherence, and readability of the report.
  3. Statistical and Coding Accuracy (30%): Correct use of R functions and appropriate interpretation of results.

2 Guideline for peer review

Evaluate the student’s One-Way ANOVA assignment based on the following criteria. Provide a score ranging from 0.0 to 10.0 for each criterion, using the following scale:

  • 1.0 - 3.9 (Not Okay): Significant issues, missing key components, incorrect methodology, or poor presentation.
  • 4.0 - 5.9 (Okay): Some correct elements, but lacks clarity, has errors, or is incomplete.
  • 6.0 - 8.9 (Satisfied): Mostly correct and well-structured, with minor issues or room for improvement.
  • 9.0 - 10.0 (Perfect): Excellent execution with clear, correct, and well-documented work.

Score_{Peer1} = Coverage * .3 + Structure * .4 + Coding * .3

Score_{HW2} = \frac{Score_{AI}+Score_{Peer1}+Score_{Peer2}}{3}

2.1 R Code for Shuffling Raters

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