AI Summary
[DOCUMENT_TYPE: instructional_content]
**What This Document Is**
These are lecture notes from EVAL 6970, a graduate-level course on Research on Evaluation at Western Michigan University. The notes focus on critical aspects of experimental and quasi-experimental research designs, specifically delving into the nuances of statistical power, validity, and hypothesis testing. This comprehensive set of notes covers foundational concepts essential for designing and interpreting rigorous evaluation studies. The material appears to be from a Spring 2012 course offering, representing established principles in the field.
**Why This Document Matters**
This resource is invaluable for graduate students in evaluation, research methods, or related fields. It’s particularly helpful for those preparing to design their own research projects, critically appraise published studies, or understand the statistical underpinnings of evaluation findings. Researchers and practitioners seeking a refresher on core concepts in experimental design will also find this material beneficial. It’s best used as a companion to coursework or as a reference guide during the research process.
**Common Limitations or Challenges**
These notes represent a specific instructor’s presentation of the material and do not substitute for a complete course or textbook. While the concepts are foundational, applying them to real-world research scenarios requires further study and practice. The notes do not include worked examples or detailed statistical calculations; they focus on conceptual understanding. Access to this resource will not provide completed analyses or study designs.
**What This Document Provides**
* An overview of different types of research hypotheses (superiority, equivalence, etc.).
* A detailed exploration of Type I and Type II errors in statistical testing.
* Discussion of factors influencing statistical power and design sensitivity.
* Examination of key determinants of power, including sample size, alpha levels, and effect size.
* An introduction to approaches for working with statistical power, including *a priori* and *post hoc* methods.
* Coverage of construct validity and its importance in evaluation research.
* Discussion of the challenges of measuring abstract constructs.