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Confidence Intervals
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Confidence Intervals



© 2002 | 104 pages | SAGE Publications, Inc
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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