| Research Design and Methods: A Process Approach, 5/e Kenneth S. Bordens,
Indiana University/Purdue University - Fort Wayne Bruce Barrington Abbott,
Indiana University/Purdue University
Chapter 13 OutlineInferential Statistics: Basic Concepts
Sampling Distribution
Sampling Error
Degrees of Freedom
Parametric Versus Nonparametric Statistics
The Logic Behind Inferential Statistics
Statistical Errors
Statistical Significance
One-Tailed Versus Two-Tailed Tests
Parametric Statistics
Assumptions Underlying a Parametric Statistic
Inferential Statistics with Two Samples
The t test
The t test for Independent Samples
The t test for Correlated Samples
Contrasting Two Groups: An Example from the Literature
The z-test for the Difference Between Two Proportions
Beyond Two Groups: Analysis of Variance (ANOVA)
Partitioning Variation
The F ratio
The One-Factor Between-Subjects ANOVA
Interpreting Your F ratio
Planned Comparisons
Unplanned Comparisons
Sample Size
Unweighted-Means Analysis
Weighted-Means Analysis
The One-Factor Within-Subjects ANOVA
The Latin Square ANOVA
Interpreting Your F ratio
The Two-Factor Between-Subjects ANOVA
Main Effects and Interactions
Sample Size
ANOVA for a Two-Factor Between-Subjects Design: An Example
Interpreting the Results
The Two-Factor Within-Subjects ANOVA
Mixed Designs
Higher Order and Special Case ANOVAs
Nonparametric Statistics
Chi-Square
Chi-Square for Contingency Tables
Limitations of Chi-Square
The Mann-Whitney U Test
Parametric Versus Nonparametric Statistics
Special Topics in Inferential Statistics
Power of a Statistical Test
Alpha Level
Sample Size
One-Tailed Versus Two-Tailed Tests
Effect Size
Determining Power
Statistical Versus Practical Significance
The Meaning of the Level of Significance
Data Transformations
Alternatives to Inferential Statistics
Summary
Key Terms |
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