# Survey methodology and missing data : tools and techniques for practitioners /

**Saved in:**

Author / Creator: | Laaksonen, Seppo, author. |
---|---|

Imprint: | Cham, Switzerland : Springer, [2018] ©2018 |

Description: | 1 online resource |

Language: | English |

Subject: | Surveys -- Methodology. Surveys -- Data processing. Social sciences -- Research -- Statistical methods. REFERENCE -- Questions & Answers. Social research & statistics. Econometrics. Psychological testing & measurement. Probability & statistics. Social sciences -- Research -- Statistical methods. Surveys -- Data processing. Surveys -- Methodology. Electronic books. |

Format: | E-Resource Book |

URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/11736846 |

**Table of Contents:**

- Intro; Preface; Contents; 1: Introduction; References; 2: Concept of Survey and Key Survey Terms; 2.1 What Is a Survey?; 2.2 Five Populations in Surveys; A Multiframe Example; 2.3 The Purpose of Populations; 2.4 Cross-Sectional Survey Micro Data; 2.4.1 Specific Examples of Problems in the Data File; 2.5 X Variables-Auxiliary Variables in More Detail; 2.6 Summary of the Terms and the Symbols in Chap. 2; 2.7 Transformations; Example 2.1 Summary Variable with Linear Transformations; Example 2.2 Summary/Compound Variable Using Exploratory Factor Analysis and Factor Scores; References.
- 3: Designing a Questionnaire and Survey Modes3.1 What Is Questionnaire Design?; 3.2 One or More Modes in One Survey?; Examples of Mixed-Mode Surveys; Estonian Pilot Mixed-Mode Survey 2012 for the ESS; Mode Effects in Estimates; 3.3 Questionnaire and Questioning; 3.4 Designing Questions for the Questionnaire; 3.5 Developing Questions for the Survey; Example 3.1 Instance in Which the Scale Was Kept Similar to Earlier Social Surveys; Example 3.2 Screening Example of the Finnish Security Survey; 3.6 Satisficing; 3.7 Straightlining; Example 3.3 Textual Versus Coded Categories.
- 3.8 Examples of Questions and ScalesExample 3.4 Two Alternative Lengths of Scales; Example 3.5 Different Scales for `Happiness ́in the Two Questionnaires; References; 4: Sampling Principles, Missingness Mechanisms, and Design Weighting; 4.1 Basic Concepts for Both Probability and Nonprobability Sampling; 4.2 Missingness Mechanisms; 4.3 Nonprobability Sampling Cases; 4.4 Probability Sampling Framework; 4.5 Sampling and Inclusion Probabilities; Implicit Stratification; PPS with Replacement, with a Valid Inclusion Probability; Example 4.1 ESS Sampling of Dwellings.
- Example 4.2 Inclusion Probabilities and Weights of the Test Data with Three-Stage Cluster Design4.6 Illustration of Stratified Three-Stage Sampling; 4.7 Basic Weights of Stratified Three-Stage Sampling; Example 4.3 Basic Weights of the Test Data for the Cluster Domain (see Sect. 6.2); 4.8 Two Types of Sampling Weights; Example 4.4 The Weights of the 2012 PISA Survey; References; 5: Design Effects at the Sampling Phase; 5.1 DEFF Because of Clustering, DEFFc; 5.2 DEFF Because of Varying Inclusion Probabilities, DEFFp.
- Example 5.1 Design Effects Because of Unequal Weights in the Test Data, by Eight StrataExample 5.2 Design Effects Because of Unequal Weights Based on the Design Weights in Some Countries of the ESS, Round 6; Count ... ; 5.3 The Entire Design Effect: DEFF and Gross Sample Size; 5.4 How Should the Sample Size Be Decided, and How Should the Gross Sample Be Allocated into Strata?; Example 5.3 Components of the Design Effect for the Variable `Plausible Value of Science Literacy ́in PISA, 2015; References; 6: Sampling Design Data File; 6.1 Principles of the Sampling Design Data File.