Confidence Intervals

Michael Smithson

Volume: 140

Series:
Quantitative Applications in the Social Sciences

November 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.

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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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Confidence Intervals

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