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SAS® Software Companion for Sampling: Design and Analysis, Third Edition [Pehme köide]

  • Formaat: Paperback / softback, 248 pages, kõrgus x laius: 254x178 mm, kaal: 435 g, 60 Tables, black and white; 4 Line drawings, black and white; 4 Illustrations, black and white
  • Ilmumisaeg: 30-Nov-2021
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-10: 0367748517
  • ISBN-13: 9780367748517
Teised raamatud teemal:
SAS® Software Companion for Sampling: Design and Analysis, Third EditionSuurem pilt
  • Formaat: Paperback / softback, 248 pages, kõrgus x laius: 254x178 mm, kaal: 435 g, 60 Tables, black and white; 4 Line drawings, black and white; 4 Illustrations, black and white
  • Ilmumisaeg: 30-Nov-2021
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-10: 0367748517
  • ISBN-13: 9780367748517
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780367748517.html
Märksõnad:
The SAS® Software Companion for Sampling: Design and Analysis, designed to be read alongside Sampling: Design and Analysis, Third Edition by Sharon L. Lohr (SDA; 2022, CRC Press), shows how to use the survey selection and analysis procedures of SAS® software to perform calculations for the examples in SDA.

No prior experience with SAS software is needed. Chapter 1 tells you how to access the software, introduces basic features, and helps you get started with analyzing data.

Each subsequent chapter provides step-by-step guidance for working through the data examples in the corresponding chapter of SDA, with code, output, and interpretation. Tips and warnings help you develop good programming practices and avoid common survey data analysis errors.

Features of the SAS software procedures are introduced as they are needed so you can see how each type of sample is selected and analyzed. Each chapter builds on the knowledge developed earlier for simpler designs; after finishing the book, you will know how to use SAS software to select and analyze almost any type of probability sample.

All code is available on the book website and is easily adapted for your own survey data analyses. The website also contains all data sets from the examples and exercises in SDA to help you develop your skills through analyzing survey data from social and public opinion research, public health, crime, education, business, agriculture, and ecology
Preface xi
1 Getting Started
1(12)
1.1 Windows in SAS Software
3(1)
1.2 Reading Data
4(3)
1.3 Saving Output
7(2)
1.4 Saving Data Sets
9(1)
1.5 Missing Data
9(2)
1.6 Summary, Tips, and Warnings
11(2)
2 Simple Random Sampling
13(14)
2.1 Selecting a Simple Random Sample
13(4)
2.2 Computing Statistics from an SRS
17(5)
2.3 Estimating Proportions from an SRS
22(1)
2.4 Additional Code for Exercises
23(1)
2.5 Summary, Tips, and Warnings
24(3)
3 Stratified Sampling
27(14)
3.1 Selecting a Stratified Random Sample
27(8)
3.1.1 Allocation Methods
29(4)
3.1.2 Additional Helpful Options for Selecting Stratified Samples
33(1)
3.1.3 Drawing a Stratified Sample without a Population Listing
34(1)
3.2 Computing Statistics from a Stratified Random Sample
35(2)
3.3 Estimating Proportions from a Stratified Random Sample
37(1)
3.4 Additional Code for Exercises
38(1)
3.5 Summary, Tips, and Warnings
39(2)
4 Ratio and Regression Estimation
41(14)
4.1 Ratio Estimation
41(2)
4.2 Regression Estimation
43(2)
4.3 Domain Estimation
45(2)
4.4 Poststratification
47(2)
4.5 Ratio Estimation with Stratified Sampling
49(1)
4.6 Model-Based Ratio and Regression Estimation
50(3)
4.7 Summary, Tips, and Warnings
53(2)
5 Cluster Sampling with Equal Probabilities
55(14)
5.1 Estimating Means and Totals from a Cluster Sample
55(6)
5.1.1 One-Stage Cluster Sampling
55(2)
5.1.2 Multi-Stage Cluster Sampling
57(4)
5.2 Estimating Proportions from a Cluster Sample
61(1)
5.3 Model-Based Design and Analysis for Cluster Samples
62(2)
5.4 Additional Code for Exercises
64(2)
5.5 Summary, Tips, and Warnings
66(3)
6 Sampling with Unequal Probabilities
69(14)
6.1 Selecting a Sample with Unequal Probabilities
69(5)
6.1.1 Sampling with Replacement
69(2)
6.1.2 Sampling without Replacement
71(3)
6.2 Selecting a Two-Stage Cluster Sample
74(3)
6.3 Computing Estimates from an Unequal-Probability Sample
77(4)
6.3.1 Estimates from With-Replacement Samples
78(2)
6.3.2 Estimates from Without-Replacement Samples
80(1)
6.4 Summary, Tips, and Warnings
81(2)
7 Complex Surveys
83(24)
7.1 Selecting a Stratified Multistage Sample
83(1)
7.2 Estimating Quantiles
84(1)
7.3 Computing Estimates from Stratified Multistage Samples
85(4)
7.4 Univariate Plots from Complex Surveys
89(2)
7.5 Scatterplots from Complex Surveys
91(11)
7.6 Additional Code for Exercises
102(3)
7.7 Summary, Tips, and Warnings
105(2)
8 Nonresponse
107(6)
8.1 How the Survey Analysis Procedures Treat Missing Data
107(2)
8.2 Poststratification and Weighting Class Adjustments
109(1)
8.3 Imputation
110(2)
8.4 Summary, Tips, and Warnings
112(1)
9 Variance Estimation in Complex Surveys
113(24)
9.1 Linearization (Taylor Series) Methods
113(1)
9.2 Replicate Samples and Random Groups
113(5)
9.3 Constructing Replicate Weights
118(14)
9.3.1 Balanced Repeated Replication
118(5)
9.3.2 Jackknife
123(5)
9.3.3 Bootstrap
128(2)
9.3.4 Replicate Weights and Nonresponse Adjustments
130(2)
9.4 Computing Estimates with Replicate Weights
132(2)
9.5 Domain Estimates with Replicate Weights
134(1)
9.6 Variance Estimation for Quantiles
135(1)
9.7 Summary, Tips, and Warnings
136(1)
10 Categorical Data Analysis in Complex Surveys
137(12)
10.1 Contingency Tables and Odds Ratios
137(4)
10.2 Chi-Square Tests
141(2)
10.3 Loglinear Models
143(4)
10.4 Summary, Tips, and Warnings
147(2)
11 Regression with Complex Survey Data
149(24)
11.1 Straight Line Regression in an SRS
149(2)
11.2 Linear Regression with Complex Survey Data
151(5)
11.2.1 Straight Line Regression
152(1)
11.2.2 Multiple Linear Regression
153(3)
11.3 Using Regression to Compare Domain Means
156(7)
11.4 Logistic Regression
163(7)
11.4.1 Logistic Regression with a Simple Random Sample
163(2)
11.4.2 Logistic Regression with a Complex Survey
165(5)
11.5 Additional Resources and Code for Exercises
170(1)
11.6 Summary, Tips, and Warnings
170(3)
12 Additional Topics for Survey Data Analysis
173(8)
12.1 Two-Phase Sampling
173(2)
12.2 Estimating the Size of a Population
175(4)
12.2.1 Ratio Estimation of Population Size
175(2)
12.2.2 Loglinear Models with Multiple Lists
177(2)
12.3 Small Area Estimation
179(1)
12.4 Evolving Capabilities of SAS Software
180(1)
A Data Set Descriptions
181(34)
B Jackknife Macros
215(8)
B.1 Using Replicate Weights with Non-Survey Procedures
215(4)
B.2 Jackknife for Two-Phase Sampling
219(4)
Bibliography 223(8)
Index 231
Sharon L. Lohr, the author of Measuring Crime: Behind the Statistics, has published widely about survey sampling and statistical methods for education, public policy, law, and crime. She is a Fellow of the American Statistical Association and an elected member of the International Statistical Institute, and has received the Gertrude M. Cox, Morris Hansen, and Deming Awards. Formerly Deans Distinguished Professor of Statistics at Arizona State University and a Vice President at Westat, she is now a statistical consultant and writer.