| 000 | 00000cam u2200205 a 4500 | |
| 001 | 000046111563 | |
| 005 | 20220407084945 | |
| 008 | 220331s2019 caua b 001 0 eng c | |
| 010 | ▼a 2018032601 | |
| 020 | ▼a 9781506378053 (paperback) | |
| 035 | ▼a (KERIS)REF000018855191 | |
| 040 | ▼a LBSOR/DLC ▼b eng ▼c LBSOR ▼e rda ▼d DLC ▼d 211009 | |
| 042 | ▼a pcc | |
| 050 | 0 0 | ▼a HA29 ▼b .B315 2019 |
| 082 | 0 0 | ▼a 001.4/22 ▼2 23 |
| 084 | ▼a 001.422 ▼2 DDCK | |
| 090 | ▼a 001.422 ▼b B152p | |
| 100 | 1 | ▼a Bai, Haiyan, ▼e author. |
| 245 | 1 0 | ▼a Propensity score methods and applications / ▼c Haiyan Bai, M.H. Clark. |
| 260 | ▼a Los Angeles : ▼b SAGE, ▼c 2019. | |
| 264 | 1 | ▼a Los Angeles : ▼b SAGE, ▼c [2019] |
| 300 | ▼a xiii, 117 p. : ▼b ill. ; ▼c 22 cm. | |
| 336 | ▼a text ▼2 rdacontent | |
| 337 | ▼a unmediated ▼2 rdamedia | |
| 338 | ▼a volume ▼2 rdacarrier | |
| 490 | 1 | ▼a Quantitative applications in the social sciences ; ▼v 178 |
| 504 | ▼a Includes bibliographical references (p. 105-112) and index. | |
| 520 | ▼a "Researchers often use observational data to estimate treatment effects when randomized controlled trials or experimental designs are not feasible for social, behavioral, and health studies. Unfortunately, using observational data may threaten the internal validity of a study by introducing selection bias. To tackle this problem, Rosenbaum and Rubin (1983) introduced propensity score methods (PSM), which balances the distributions of observed covariates between treatment conditions (i.e., treatment vs. control), to reduce selection bias. Over the past three decades, PSM has become increasingly popular for making causal inferences based on observational studies. This volume provides a concise, introductory text on propensity score methods that is easy to comprehend by those who have limited background in statistics, and is practical enough for researchers to quickly generalize and apply the methods. Although there are other books on PSM, most either focus on advanced topics in PSM or on theories of PSM. This volume covers basic concepts, assumptions, procedures, available software packages, and step-by-step examples for implementing PSM using real world data, with exercises at the end of each chapter. Software code and datasets are available on an accompanying website"-- ▼c Provided by publisher. | |
| 650 | 0 | ▼a Social sciences ▼x Research ▼x Statistical methods. |
| 650 | 0 | ▼a Analysis of variance. |
| 700 | 1 | ▼a Clark, M. H. ▼q (Margaret Hilary), ▼e author. |
| 830 | 0 | ▼a Quantitative applications in the social sciences ; ▼v 178. |
| 945 | ▼a ITMT |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 중앙도서관/서고6층/ | 청구기호 001.422 B152p | 등록번호 111861502 (2회 대출) | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
책소개
A concise, introductory text, Propensity Score Methods and Applications describes propensity score methods (PSM) and how they are used to balance the distributions of observed covariates between treatment conditions as a means to reduce selection bias. This new QASS title specifically focuses on the procedures of implementing PSM for research in social sciences, instead of merely demonstrating the effectiveness of the method.
Using succinct and approachable language to introduce the basic concepts of PSM, authors Haiyan Bai and M. H. Clark present basic concepts, assumptions, procedures, available software packages, and step-by-step examples for implementing PSM using real-world data, with exercises at the end of each chapter allowing readers to replicate examples on their own.
정보제공 :
목차
Series Editor''s Introduction About the Authors Acknowledgments 1. Basic Concepts of Propensity Score Methods 1.1 Causal Inference 1.2 Propensity Scores 1.3 Assumptions 1.4 Summary of the Chapter 2. Covariate Selection and Propensity Score Estimation 2.1 Covariate Selection 2.2 Propensity Score Estimation 2.3 Summary of the Chapter 2.4 An Example 3. Propensity Score Adjustment Methods 3.1 Propensity Score Matching 3.2 Other Propensity Score Adjustment Methods 3.3 Summary of the Chapter 3.4 An Example 4. Covariate Evaluation and Causal Effect Estimation 4.1 Evaluating the Balance of Covariate Distributions 4.2 Causal Effect Estimation 4.3 Sensitivity Analysis 4.4 Summary of the Chapter 4.5 An Example 5. Conclusion 5.1 Limitations of the Propensity Score Methods and How to Address Them 5.2 Summary of Propensity Score Procedures 5.3 Final Comments References Index