Multiple Comparison Tests
Many research projects involve testing multiple research hypotheses. These research hypotheses could be evaluated using comparisons of means, bivariate correlations, regressions, and so forth, and in fact most studies consist of a mixture of different types of test statistics. An important consideration when conducting multiple tests of significance is how to deal with the increased likelihood (relative to conducting a single test of significance) of falsely declaring one (or more) hypotheses statistically significant, titled the multiple comparisons problem. This multiple comparisons problem is especially relevant to the topic of research design because the issues associated with the multiple comparisons problem relate directly to designing studies (i.e., number and nature of variables to include) and deriving a data analysis strategy for the study. This entry introduces ...
Looks like you do not have access to this content.
Reader's Guide
Descriptive Statistics
Distributions
Graphical Displays of Data
Hypothesis Testing
Important Publications
Inferential Statistics
Item Response Theory
Mathematical Concepts
Measurement Concepts
Organizations
Publishing
Qualitative Research
Reliability of Scores
Research Design Concepts
Research Designs
Research Ethics
Research Process
Research Validity Issues
Sampling
Scaling
Software Applications
Statistical Assumptions
Statistical Concepts
Statistical Procedures
Statistical Tests
Theories, Laws, and Principles
Types of Variables
Validity of Scores
- All
- A
- B
- C
- D
- E
- F
- G
- H
- I
- J
- K
- L
- M
- N
- O
- P
- Q
- R
- S
- T
- U
- V
- W
- X
- Y
- Z