Kooperativer Bibliotheksverbund

UID:

Umfang: 1 online resource (xxiv, 262 pages) : , digital, PDF file(s).

ISBN: 1-316-75878-8 , 1-316-55089-3

Inhalt: Statistical designs, sample surveys and evaluation designs are fundamental tools for solving queries related to population parameters and the effects of public programs and policies. This book explores the concepts of effective sampling and evaluation techniques in a cohesive and concise manner. Sampling design techniques, including simple random sampling, stratified sampling, systematic sampling and cluster sampling, are presented in detail. These techniques play a vital role when choosing an appropriate sample survey design. The concepts of multistage design, non-sampling errors and evaluation techniques including before-after design, one-time treatment and control design are discussed extensively. The book focuses on different methods of estimation, including multiple regression analysis and logistic regression. It covers the issue of bias in a design, the source of such bias and ways to overcome it. Clear guidelines with remedial measures are outlined to facilitate choosing a suitable sampling design.

Anmerkung: Title from publisher's bibliographic system (viewed on 05 Apr 2016). , Cover -- Statistical Survey Design and Evaluating Impact -- Title -- Copyright -- Dedication -- Contents -- Figures -- Tables -- Foreword -- Preface -- A BRIEF HISTORY OF TWO DESIGNS -- WHY THIS BOOK AND HOW IT IS ORGANIZED -- Acknowledgments -- 1. Introduction to Sample Survey Designs -- 1.1 Introduction -- 1.2 Population, Units and Sampling Units -- 1.3 Sampling Design -- 1.4 Probability and Purposive Sampling -- 1.4.1 Probability sampling -- 1.4.2 Purposive sampling -- 1.5 Frame -- 1.6 Bias and Error -- 1.7 Few Guidelines for a Desirable Sampling Design -- 2. Basic Sampling Designs -- 2.1 Introduction -- 2.2 Simple Random Sampling -- 2.2.1 Description -- 2.2.2 Methods of selection -- 2.2.3 Estimation of mean, total and need for weights -- 2.2.3.1 Normalization of weights -- 2.2.3.2 Role of weights -- 2.2.4 Estimation of proportion -- 2.2.5 Subclass estimates -- 2.2.6 Sampling variance of estimates -- 2.2.6.1 Sampling variance of a sample mean -- 2.2.6.2 Sampling variance of estimated population total -- 2.2.6.3 Sampling variance of proportion -- 2.2.6.4 Sampling variance of subclass estimates -- 2.2.7 Determination of sample size -- 2.3 Stratified Sampling -- 2.3.1 Description -- 2.3.2 Estimation of parameters -- 2.3.2.1 Estimation of mean -- 2.3.2.2 Estimation of total -- 2.3.2.3 Estimation of proportion -- 2.3.3 Weighting and its similarity with standardization -- 2.3.4 Sampling variance of estimates -- 2.3.4.1 Sampling variance of mean -- 2.3.4.2 Sampling variance of total -- 2.3.4.3 Sampling variance of proportion -- 2.3.5 Allocation and selection of units -- 2.3.5.1 Proportional allocation -- 2.3.5.2 Optimum allocation -- 2.3.5.3 Practical guidelines for allocation -- 2.3.6 Some advantages of stratification -- 2.3.7 Post-stratification -- 2.4 Systematic Sampling -- 2.4.1 Description -- 2.4.2 Method of selection. , 2.4.2.1 Decimal interval method -- 2.4.3 Advantages of systematic sampling -- 2.4.4 Disadvantages of systematic sampling -- 2.4.4.1 Monotonic trend -- 2.4.4.2 Periodicity -- 2.4.5 Estimation of parameters and their sampling variances -- 2.4.5.1 Two consecutive units per stratum -- 2.5 Probability Proportional to Size Sampling -- 2.5.1 Description -- 2.6 Cluster Sampling -- 2.6.1 Description -- 2.6.1.1 Preparation of artificial clusters -- 2.6.2 Method of selection -- 2.6.3 Estimation of parameters and sampling variances -- 2.6.3.1 Clusters of equal size -- 2.6.3.2 Clusters of unequal size -- 2.7 Key Points -- 3. Multi-stage Designs -- 3.1 Introduction -- 3.2 Two-stage Design with Equal Size Clusters -- 3.2.1 Components of overall variation -- 3.2.2 Two-stage design for selection of units with equal probability -- 3.2.2.1 Procedure of selection in a two-stage EPSEM design -- 3.2.2.2 Estimation of mean and variance -- 3.2.2.3 Clustering, design effect and choice of number of PSUs/cluster -- 3.3.1 Estimation of mean and sampling variance -- 3.3.2 Two desirable properties of the design -- 3.3.3 Guidelines for attaining desired property -- 3.3.4 Ways to control variations in cluster size -- 3.3.4.1 Controlling size of clusters -- 3.3.4.2 Alternative selection procedure -- 3.3.4.2.1 Alternatives when information on size of PSUs refers to a past period -- 3.3.4.3 Stratification of PSUs to reduce variations in cluster size -- 3.4 Stratification in Multistage Design -- 3.4.1 Estimation of parameters in unequal cluster size -- 3.4.2 Estimation of parameters in equal cluster size -- 3.5 Selection of Sampling Units at Different Stages -- 3.5.1 Selection of PSUs -- 3.5.2 Selection of second-stage units -- 3.5.3 Selection of individuals within a household -- 3.6 Key Points -- 4. Probability Sampling under Imperfect Frame -- 4.1 Introduction. , 4.2 Sampling Populations Having Specific Attributes -- 4.2.1 Sampling when target population is not rare -- 4.2.1.1 Sampling without screening -- 4.2.1.2 Sampling with screening -- 4.2.1.3 Relative advantages with screening and without screening -- 4.2.1.4 Facilitating screening -- 4.2.2 Sampling for rare attributes -- 4.2.2.1 Household-based sampling of rare population -- 4.3 Defective Frame -- 4.3.1 Duplications -- 4.3.1.1 Estimation in presence of duplications -- 4.3.1.2 Procedure to deal with duplicate listing -- 4.3.1.3 Incompleteness or omissions in a frame -- 4.4 Sampling in Absence of a Frame -- 4.4.1 Facilitating a cluster design -- 4.4.2 Selection, data collection and estimation -- 4.5 Household Listing -- 4.5.1 Two alternatives if listing is to be avoided -- 4.6 Key Points -- 5. Tackling Non-Sampling Errors -- 5.1 Introduction -- 5.2 Coverage Error -- 5.2.1 Discrepancy between study and target population -- 5.2.2 Omission of areas to reduce cost -- 5.2.3 Tackling small PSUs -- 5.2.4 Error in identification of a PSU -- 5.2.5 Error in segmentation of a PSU -- 5.2.6 Error in listing a PSU/segment -- 5.3 Non-response Error -- 5.3.1 Remedy for non-response -- 5.3.1.1 Adjustment for non-response when error is randomly distributed -- 5.3.1.2 Adjustment for non-response when error is not completely random -- 5.3.2 Item non-response error -- 5.4 Response Error -- 5.4.1 Questionnaire construction -- 5.4.1.1 Factual questions -- 5.4.1.2 Non-factual questions -- 5.4.2 Errors due to investigators -- 5.4.2.1 Training -- 5.4.2.2 Supervision -- 5.5 Key Points -- 6. Introduction to Evaluation Design -- 6.1 Background -- 6.2 Bias and Error -- 6.2.1 Bias -- 6.2.2 Bias elimination -- 6.2.3 Error -- 6.3 Types of Evaluation Designs -- 6.3.1 Evaluation designs with random allocation of units -- 6.3.2 Evaluation designs with clusters allocated randomly. , 6.3.3 Evaluation designs with unit level matching -- 6.3.4 Evaluation designs with cluster matching -- 6.3.5 Observational and case-control studies -- 7. Designs for Causal Effects: Setting Comparison Groups -- 7.1 Introduction -- 7.2 Measuring Main and Interaction Effects -- 7.3 Bias and Error in Measurement of Treatment Effect -- 7.3.1 Sources of bias -- 7.3.2 Internal and external validity -- 7.4 Three Basic Designs for Estimating Treatment Effect -- 7.4.1 One sample each from T and C at two different times (before-after design) -- 7.4.1.1 Description and estimation of effect -- 7.4.1.2 Biasing effects and remedies -- 7.4.1.3 Estimation of standard error of estimated impact -- 7.4.2 One sample each from T and C observed at one point of time (treatment-control design) -- 7.4.2.1 Description and estimation of effect -- 7.4.2.2 Biasing effects and remedies -- 7.4.2.3 Estimation of standard error of estimated impact -- 7.4.3 Two samples each from T and C observed at two points in time (before-after and treatment-control design) -- 7.4.3.1 Description and estimation of effect -- 7.4.3.2 Biasing effects and remedies -- 7.4.3.3 Estimation of standard error of estimated impact -- 7.5 Output and Its Timing -- 7.6 Key Points -- 8. Designs for Causal Effects: Allocation of Study Units -- 8.1 Introduction -- 8.2 Alternative Tools to Attain Balance -- 8.2.1 Randomization -- 8.2.2 Stratification -- 8.2.3 Pair matching -- 8.3 Advantages and Disadvantages of Three Tools -- 8.3.1 Randomization -- 8.3.2 Matching -- 8.3.2.1 Stratification -- 8.3.2.2 Pair matching -- 8.4 Choice of Study Units -- 8.4.1 Procedure of allocation of units/clusters -- 8.4.1.1 Randomization -- 8.4.1.1.1 Restricted randomization -- 8.4.1.2 Stratification -- 8.4.1.3 Pair matching -- 8.5 Potential Outcome Framework -- 8.5.1 Propensity score matching -- 8.6 Choice of a Design -- 8.7 Key Points. , 9. Statistical Tests for Measuring Impact -- 9.1 Introduction -- 9.1.1 Two different ways to estimate impact -- 9.2 Impact when Units are Allocated Randomly -- 9.2.1 Testing difference between two means -- 9.2.1.1 Testing means from two different populations -- 9.2.1.2 Large-sample z-test -- 9.2.1.3 Testing several means: Application of ANOVA -- 9.2.1.4 Non-parametric tests -- 9.2.2 Testing difference between two proportions -- 9.2.2.1 Chi-square test of independence -- 9.2.2.2 Testing odds ratio -- 9.3 Impact when Clusters are Allocated Randomly -- 9.3.1 Analysis at cluster level -- 9.3.2 Analysis at individual level -- 9.4 Impact when Stratification is used before allocation -- 9.4.1 When units are allocated -- 9.4.1.1 Two-way ANOVA test -- 9.4.2 When clusters are allocated -- 9.5 Impact in Pair Matching -- 9.5.1 Variables measured in interval scale -- 9.5.2 Dichotomous variable -- 9.5.2.1 Exact binomial test -- 9.5.3 Non-parametric test -- 9.6 Model-Based Analysis -- 9.6.1 Multiple regression analysis -- 9.6.1.1 Modifications in the case of cluster sampling -- 9.6.2 Logistic regression -- 9.6.3 Assumptions in regressions -- 9.7 Key Points -- 10. Case Studies -- 10.1 Introduction -- Part I: Sample Survey Designs -- 10.2 National Family Health Surveys, India (NFHS, India) -- 10.3 Sampling Design of NFHS -- 10.3.1 Sample size -- 10.3.2 Choice of PSU -- 10.3.3 Design for rural area -- 10.3.3.1 Merging of small villages -- 10.3.3.2 Stratification -- 10.3.3.3 Selection of sampling units -- 10.3.3.4 An alternative two-stage selection -- 10.3.4 Design for urban area -- 10.3.5 Estimation -- 10.3.5.1 Computation of weights -- 10.4 Other Global Large-Scale Surveys -- 10.4.1 Use of master sample in survey designs -- 10.4.2 Example of GATS sample design in Nigeria -- 10.5 National Sample Surveys (NSS) in India -- Part II: Evaluation Design. , 10.6 Illustration of Evaluation Designs. , English

Weitere Ausg.: ISBN 1-107-14645-3

Sprache: Englisch

URL: https://doi.org/10.1017/CBO9781316550892

URL: https://doi.org/10.1017/CBO9781316550892