Atnaujinkite slapukų nuostatas

Introduction to Power Analysis: Two-Group Studies [Minkštas viršelis]

  • Formatas: Paperback / softback, 160 pages, aukštis x plotis: 215x139 mm, weight: 200 g
  • Serija: Quantitative Applications in the Social Sciences
  • Išleidimo metai: 28-Feb-2018
  • Leidėjas: SAGE Publications Inc
  • ISBN-10: 1506343120
  • ISBN-13: 9781506343129
Kitos knygos pagal šią temą:
Introduction to Power Analysis: Two-Group StudiesDidesnis paveikslėlis
  • Formatas: Paperback / softback, 160 pages, aukštis x plotis: 215x139 mm, weight: 200 g
  • Serija: Quantitative Applications in the Social Sciences
  • Išleidimo metai: 28-Feb-2018
  • Leidėjas: SAGE Publications Inc
  • ISBN-10: 1506343120
  • ISBN-13: 9781506343129
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9781506343129.html
Raktažodžiai:

Introduction to Power Analysis: Two-Group Studies provides readers with the background, examples, and explanation they need to read technical papers and materials that include complex power analyses.  This clear and accessible guide explains the components of test statistics and their sampling distributions, and author Eric Hedberg walks the reader through the simple and complex considerations of this research question. Filled with graphics and examples, the reader is taken on a tour of power analyses from covariates to clusters, seeing how the complicated task of comparing two groups, and the power analysis, can be made easy.

Recenzijos

"Introduction to Power Analysis provides detailed coverage of the topic in a succinct and concise way. Graduate students and others (including faculty who are also researchers) can benefit from this resource as it outlines the steps to conduct and evaluate power analysis to produce rigorous quantitative research in the social sciences, as well as why power analysis and effects are important to understand and apply in research." -- Stephanie Jones "Although there are a number of software programs available for power analysis, this volume teaches the reader how to employ power analysis using a popular software program (R) that can also be used to perform the desired statistical analyses on the data." -- Leslie Echols

Series Editor's Introduction xi
Preface xiii
About the Author xv
Acknowledgments xvii
1 The What, Why, and When of Power Analysis
1(9)
What is Statistical Power?
1(2)
Why Should Power Be a Consideration When Planning Studies?
3(3)
When Should You Perform a Power Analysis?
6(1)
Significance and Effect
7(1)
What Do You Need to Know to Perform a Power Analysis?
8(1)
The Structure of the Volume
9(1)
Summary
9(1)
2 Statistical Distributions
10(8)
Normally Distributed Random Variables
10(2)
The Χ2 Distribution
12(3)
The t Distribution
15(1)
The F Distribution
15(1)
F to t
16(1)
Summary
17(1)
3 General Topics in Hypothesis Testing and Power Analysis When the Population Standard Deviation is Known: The Case of Two Group Means
18(18)
The Difference in Means as a Normally Distributed Random Variable When the Population Standard Deviation is Known
18(1)
Hypothesis Testing With the Difference Between Two Group Means When the Population Standard Deviation is Known
19(5)
Power Analysis for Testing the Difference Between Two Group Means When the Population Standard Deviation is Known
24(4)
Scale-Free Parameters
28(1)
Balanced or Unbalanced?
29(1)
Types of Power Analyses
30(4)
Power Tables
34(1)
Summary
35(1)
4 The Difference Between Two Groups in Simple Random Samples Where the Population Standard Deviation Must Be Estimated
36(18)
Data-Generating Process
37(1)
Testing the Difference Between Group Means With Samples
38(8)
Power Analysis for Samples Without Covariates
46(6)
Summary
52(2)
5 Using Covariates When Testing the Difference in Sample Group Means for Balanced Designs
54(17)
Example Analysis
55(1)
Tests Employing a Covariate (ANCOVA) With Balanced Samples
56(5)
Power Analysis With a Covariate Correlated With the Treatment Indicator
61(6)
Power Analysis With a Covariate Uncorrelated to the Treatment Indicator
67(3)
Summary
70(1)
6 Multilevel Models I: Testing the Difference in Group Means in Two-Level Cluster Randomized Trials
71(18)
Example Data
71(1)
Understanding the Single Level Test as an ANOVA
72(4)
The Hierarchical Mixed Model for Cluster Randomized Trials
76(4)
Power Parameters for Cluster Randomized Trials
80(2)
Example Analysis of a Cluster Randomized Trial
82(3)
Power Analyses for Cluster Randomized Trials
85(3)
Summary
88(1)
7 Multilevel Models II: Testing the Difference in Group Means in Two-Level Multisite Randomized Trials
89(10)
Power Parameters for Multisite Randomized Trials
92(2)
Example Analysis of a Multisite Randomized Trial
94(1)
Power Analyses for Multisite Randomized Trails
95(3)
Summary
98(1)
8 Reasonable Assumptions
99(10)
Power Analyses Are Arguments
99(3)
Strategies for Using the Literature to Make Reasonable Assumptions
102(6)
Summary
108(1)
9 Writing About Power
109(6)
What to Include
109(1)
Examples
110(4)
Summary
114(1)
10 Conclusions, Further Reading, and Regression
115(8)
The Case Study of Comparing Two Groups
115(1)
Further Reading
116(2)
Observational Regression
118(4)
Summary
122(1)
Appendix 123(4)
References 127(4)
Index 131
E. C. Hedberg is a Senior Associate at Abt Associates. Previously he was a Senior Research Scientist at NORC at the University of Chicago. He received his undergraduate degree in Sociology from the University of Minnesota and his PhD in Sociology from the University of Chicago. His work is primarily focused on estimating design parameters useful for power analysis, multilevel modeling, social capital theory, and evaluation research.