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Confidence Intervals
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Confidence Intervals

Volume: 140
November 2002 | 104 pages | SAGE Publications, Inc
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Smithson first introduces the basis of the confidence interval framework and then provides the criteria for "best" confidence intervals, along with the trade-offs between confidence and precision. Next, using a reader-friendly style with lots of worked out examples from various disciplines, he covers such pertinent topics as: the transformation principle whereby a confidence interval for a parameter may be used to construct an interval for any monotonic transformation of that parameter; confidence intervals on distributions whose shape changes with the value of the parameter being estimated; and, the relationship between confidence interval and significance testing frameworks, particularly regarding power.

 
Ch 1 Introduction and Overview
 
Ch 2 Confidence Statements and Interval Estimates
Why Confidence Intervals?

 
 
Ch 3 Central Confidence Intervals
Central and Standardizable versus Noncentral Distributions

 
Confidence Intervals Using the Central t and Normal Distributions

 
Confidence Intervals Using the Central Chi-Square and F Distributions

 
Transformation Principle

 
 
Ch 4 Noncentral Confidence Intervals for Standardized Effect Sizes
Noncentral Distributions

 
Computing Noncentral Confidence Intervals

 
 
Ch 5 Applications in Anova and Regression
Fixed-Effects ANOVA

 
Random-Effects ANOVA

 
A Priori and Post-Hoc Contrasts

 
Regression: Multiple, Partial, and Semi-Partial Correlations

 
Effect-Size Statistics for MANOVA and Setwise Regression

 
Confidence Interval for a Regression Coefficient

 
Goodness of Fit Indices in Structural Equations Models

 
 
Ch 6 Applications in Categorical Data Analysis
Odds Ratio, Difference between Proportions and Relative Risk

 
Chi-Square Confidence Intervals for One Variable

 
Two-Way Contingency Tables

 
Effects in Log-Linear and Logistic Regression Models

 
 
Ch 7 Significance Tests and Power Analysis
Significance Tests and Model Comparison

 
Power and Precision

 
Designing Studies Using Power Analysis and Confidence Intervals

 
Confidence Intervals for Power

 
 
Concluding Remarks
 
References
 
About the Author
Key features
  • Introduces the basis of the confidence interval framework and then provides the criteria for "best" confidence intervals, along with the tradeoffs between confidence and precision.
  • Uses a reader-friendly style with lots of worked out examples from various disciplines
  • Covers such pertinent topics as: the transformation principle whereby a confidence interval for a parameter may be used to construct an interval for any monotonic transformation of that parameter; confidence intervals on distributions whose shape changes with the value of the parameter being estimated; and, the relationship between confidence interval and significance testing frameworks, particularly regarding power.

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