Statistical survey design and evaluating impact
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. Sam...
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| 245 | 1 | 0 | |a Statistical survey design and evaluating impact |c Tarun Kumar Roy, Rajib Acharya, Arun Kumar Roy |
| 246 | 1 | 3 | |a Statistical Survey Design & Evaluating Impact |
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| 520 | |a 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 | ||
| 650 | 4 | |a Surveys / Methodology | |
| 650 | 4 | |a Sampling (Statistics) | |
| 700 | 1 | |a Acharya, Rajib |e Sonstige |4 oth | |
| 700 | 1 | |a Roy, Arun Kumar |e Sonstige |4 oth | |
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Datensatz im Suchindex
| _version_ | 1819654004845576192 |
|---|---|
| adam_text | Titel: Statistical survey design and evaluating impact
Autor: Roy, T. K
Jahr: 2016
Contents
Figures xv
Tables xoi
Foreword xvii
Preface xix
Acknowledgments xxiii
1. Introduction to Sample Survey Designs
1.1 Introduction 1
1.2 Population, Units and Sampling Units 1
1.3 Sampling Design 3
1.4 Probability and Purposive Sampling 3
1.4.1 Probability sampling 3
1.4.2 Purposive sampling 4
1.5 Frame 5
1.6 Bias and Error 6
1.7 Few Guidelines for a Desirable Sampling Design 11
2. Basic Sampling Designs
2.1 Introduction 13
2.2 Simple Random Sampling 14
2.2.1 Description 14
2.2.2 Methods of selection 15
2.2.3 Estimation of mean, total and need for weights 16
2.2.3.1 Normalization of weights 19
2.2.3.2 Role of weights 21
2.2.4 Estimation of proportion 22
2.2.5 Subclass estimates 22
2.2.6 Sampling variance of estimates 23
2.2.6.1 Sampling variance of a sample mean 23
2.2.6.2 Sampling variance of estimated population total 25
2.2.6.3 Sampling variance of proportion 25
Contents
2.2.6.4 Sampling variance of subclass estimates 25
2.2.7 Determination of sample size 28
2.3 Stratified Sampling 31
2.3.1 Description 31
2.3.2 Estimation of parameters 33
2.3.2.1 Estimation of mean 34
2.3.2.2 Estimation of total 35
2.3.2.3 Estimation of proportion 35
2.3.3 Weighting and its similarity with standardization 35
2.3.4 Sampling variance of estimates 37
2.3.4.1 Sampling variance of mean 38
2.3.4.2 Sampling variance of total 38
2.3.4.3 Sampling variance of proportion 38
2.3.5 Allocation and selection of units 38
2.3.5.1 Proportional allocation 39
2.3.5.2 Optimum allocation 40
2.3.5.3 Practical guidelines for allocation 41
2.3.6 Some advantages of stratification 42
2.3.7 Post-stratification 43
2.4 Systematic Sampling 45
2.4.1 Description 45
2.4.2 Method of selection 45
2.4.2.1 Decimal interval method 46
2.4.3 Advantages of systematic sampling 46
2.4.4 Disadvantages of systematic sampling 47
2.4.4.1 Monotonie trend 47
2.4.4.2 Periodicity 47
2.4.5 Estimation of parameters and their sampling variances 48
2.4.5.1 Two consecutive units per stratum 49
2.5 Probability Proportional to Size Sampling 50
2.5.1 Description 50
2.6 Cluster Sampling 54
2.6.1 Description 54
2.6.1.1 Preparation of artificial clusters 55
2.6.2 Method of selection 56
2.6.3 Estimation of parameters and sampling variances 57
2.6.3.1 Clusters of equal size 57
2.6.3.2 Clusters of unequal size 58
2.7 Key Points 60
3. Multi-stage Designs
3.1 Introduction 62
3.2 Two-stage Design with Equal Size Clusters 63
3.2.1 Components of overall variation 64
3.2.2 Two-stage design for selection of units with equal probability 69
3.2.2.1 Procedure of selection in a two-stage EPSEM design 69
Contents
3.2.2.2 Estimation of mean and variance 70
3.2.2.3 Clustering, design effect and choice of number of PSUs/cluster 73
3.3 Two-Stage Design with Unequal Cluster Size 79
3.3.1 Estimation of mean and sampling variance 80
3.3.2 Two desirable properties of the design 84
3.3.3 Guidelines for attaining desired property 85
3.3.4 Ways to control variations in cluster size 87
3.3.4.1 Controlling size of clusters 87
3.3.4.2 Alternative selection procedure 87
3.3.4.2.1 Alternatives when information on size of PSUs
refers to a past period 89
3.3.4.3 Stratification of PSUs to reduce variations in cluster size 96
3.4 Stratification in Multistage Design 96
3.4.1 Estimation of parameters in unequal cluster size 97
3.4.2 Estimation of parameters in equal cluster size 100
3.5 Selection of Sampling Units at Different Stages 103
3.5.1 Selection of PSUs 103
3.5.2 Selection of second-stage units 104
3.5.3 Selection of individuals within a household 105
3.6 Key Points 107
4. Probability Sampling under Imperfect Frame
4.1 Introduction 109
4.2 Sampling Populations Having Specific Attributes 110
4.2.1 Sampling when target population is not rare 111
4.2.1.1 Sampling without screening 111
4.2.1.2 Sampling with screening 112
4.2.1.3 Relative advantages with screening and without screening 112
4.2.1.4 Facilitating screening 113
4.2.2 Sampling for rare attributes 114
4.2.2.1 Household-based sampling of rare population 114
4.3 Defective Frame 117
4.3.1 Duplications 118
4.3.1.1 Estimation in presence of duplications 118
4.3.1.2 Procedure to deal with duplicate listing 119
4.3.1.3 Incompleteness or omissions in a frame 121
4.4 Sampling in Absence of a Frame 122
4.4.1 Facilitating a cluster design 123
4.4.2 Selection, data collection and estimation 125
4.5 Household Listing 128
4.5.1 Two alternatives if listing is to be avoided 129
4.6 Key Points 131
Contents
5. Tackling Non-Sampling Errors
5.1 Introduction 133
5.2 Coverage Error 134
5.2.1 Discrepancy between study and target population 134
5.2.2 Omission of areas to reduce cost 135
5.2.3 Tackling small PSUs 135
5.2.4 Error in identification of a PSU 136
5.2.5 Error in segmentation of a PSU 136
5.2.6 Error in listing a PSU/segment 137
5.3 Non-response Error 137
5.3.1 Remedy for non-response 139
5.3.1.1 Adjustment for non-response when error is
randomly distributed 140
5.3.1.2 Adjustment for non-response when error is
not completely random 141
5.3.2 Item non-response error 142
5.4 Response Error 143
5.4.1 Questionnaire construction 143
5.4.1.1 Factual questions 143
5.4.1.2 Non-factual questions 144
5.4.2 Errors due to investigators 146
5.4.2.1 Training 147
5.4.2.2 Supervision 147
5.5 Key Points 148
6. Introduction to Evaluation Design
6.1 Background 149
6.2 Bias and Error 150
6.2.1 Bias 150
6.2.2 Bias elimination 153
6.2.3 Error 155
6.3 Types of Evaluation Designs 155
6.3.1 Evaluation designs with random allocation of units 155
6.3.2 Evaluation designs with clusters allocated randomly 156
6.3.3 Evaluation designs with unit level matching 156
6.3.4 Evaluation designs with cluster matching 157
6.3.5 Observational and case-control studies 157
7. Designs for Causal Effects: Setting Comparison Groups
7.1 Introduction 158
7.2 Measuring Main and Interaction Effects 159
7.3 Bias and Error in Measurement of Treatment Effect 163
7.3.1 Sources of bias 164
7.3.2 Internal and external validity 167
7.4 Three Basic Designs for Estimating Treatment Effect 168
7.4.1 One sample each from T and C at two different times (before-after design) 168
Contents
7.4.1.1 Description and estimation of effect 168
7.4.1.2 Biasing effects and remedies 169
7.4.1.3 Estimation of standard error of estimated impact 173
7.4.2 One sample each from T and C observed at one point of time
(treatment-control design) 174
7.4.2.1 Description and estimation of effect 174
7.4.2.2 Biasing effects and remedies 175
7.4.2.3 Estimation of standard error of estimated impact 175
7.4.3 Two samples each from T and C observed at two points in time
(beiore-after and treatment-control design) 175
7.4.3.1 Description and estimation of effect 175
7.4.3.2 Biasing effects and remedies 177
7.4.3.3 Estimation of standard error of estimated impact 178
7.5 Output and Its Timing 179
7.6 Key Points 179
8. Designs for Causal Effects: Allocation of Study Units
8.1 Introduction 181
8.2 Alternative Tools to Attain Balance 181
8.2.1 Randomization 182
8.2.2 Stratification 183
8.2.3 Pair matching 184
8.3 Advantages and Disadvantages of Three Tools 184
8.3.1 Randomization 185
8.3.2 Matching 185
8.3.2.1 Stratification 185
8.3.2.2 Pair matching 186
8.4 Choice of Study Units 187
8.4.1 Procedure of allocation of units /clusters 188
8.4.1.1 Randomization 188
8.4.1.1.1 Restricted randomization 189
8.4.1.2 Stratification 191
8.4.1.3 Pair matching 191
8.5 Potential Outcome Framework 191
8.5.1 Propensity score matching 192
8.6 Choice of a Design 196
8.7 Key Points 198
9. Statistical Tests for Measuring Impact
9.1 Introduction 199
9.1.1 Two different ways to estimate impact 200
9.2 Impact when Units are Allocated Randomly 201
9.2.1 Testing difference between two means 202
9.2.1.1 Testing means from two different populations 203
9.2.1.2 Large-sample z-test 204
9.2.1.3 Testing several means: Application of ANOVA 205
xii I Contents
9.2.1.4 Non-parametric tests 207
9.2.2 Testing difference between two proportions 213
9.2.2.1 Chi-square test of independence 213
9.2.2.2 Testing odds ratio 215
9.3 Impact when Clusters are Allocated Randomly 216
9.3.1 Analysis at cluster level 216
9.3.2 Analysis at individual level 217
9.4 Impact when Stratification is used before allocation 218 •
9.4.1 When units are allocated 218
9.4.1.1 Two-way ANOVA test 219
9.4.2 When clusters are allocated 222
9.5 Impact in Pair Matching 222
9.5.1 Variables measured in interval scale 223
9.5.2 Dichotomous variable 223
9.5.2.1 Exact binomial test 224
9.5.3 Non-parametric test 226
9.6 Model-Based Analysis 227
9.6.1 Multiple regression analysis 228
9.6.1.1 Modifications in the case of cluster sampling 229
9.6.2 Logistic regression 230
9.6.3 Assumptions in regressions 232
9.7 Key Points 233
10. Case Studies
10.1 Introduction 234
Part I: Sample Survey Designs 234
10.2 National Family Health Surveys, India (NFHS, India) 234
10.3 Sampling Design of NFHS 234
10.3.1 Sample size 234
10.3.2 Choice of PSU 236
10.3.3 Design for rural area 236
10.3.3.1 Merging of small villages 236
10.3.3.2 Stratification 237
10.3.3.3 Selection of sampling units 239
10.3.3.4 An alternative two-stage selection 243
10.3.4 Design for urban area 243
10.3.5 Estimation 244
10.3.5.1 Computation of weights 244
10.3.5.2 Estimation of parameters 245
10.4 Other Global Large-Scale Surveys 247
10.4.1 Use of master sample in survey designs 247
10.4.2 Example of GATS sample design in Nigeria 249
10.5 National Sample Surveys (NSS) in India 249
Part II: Evaluation Design 250
10.6 Illustration of Evaluation Designs 250
Contents I xiii
10.6.1 Impact evaluation of a life skills education intervention on
adolescent girls empowerment 250
10.6.2 Fisher s tea test 252
10.6.3 Impact of an intervention to promote use of Intra Uterine Device
in a population 253
10.6.4 An experiment to test effectiveness of a medicine 253
10.6.5 An intervention to reduce post-partum haemorrhage 254
References 255
Index 260
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| id | DE-604.BV043940350 |
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| indexdate | 2024-12-24T05:33:57Z |
| institution | BVB |
| isbn | 9781316550892 |
| language | English |
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| spellingShingle | Roy, Tarun Kumar Statistical survey design and evaluating impact Surveys / Methodology Sampling (Statistics) |
| title | Statistical survey design and evaluating impact |
| title_alt | Statistical Survey Design & Evaluating Impact |
| title_auth | Statistical survey design and evaluating impact |
| title_exact_search | Statistical survey design and evaluating impact |
| title_full | Statistical survey design and evaluating impact Tarun Kumar Roy, Rajib Acharya, Arun Kumar Roy |
| title_fullStr | Statistical survey design and evaluating impact Tarun Kumar Roy, Rajib Acharya, Arun Kumar Roy |
| title_full_unstemmed | Statistical survey design and evaluating impact Tarun Kumar Roy, Rajib Acharya, Arun Kumar Roy |
| title_short | Statistical survey design and evaluating impact |
| title_sort | statistical survey design and evaluating impact |
| topic | Surveys / Methodology Sampling (Statistics) |
| topic_facet | Surveys / Methodology Sampling (Statistics) |
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