Suchergebnisse

Introduction / Uwe Engel -- Motivated misreporting : shaping answers to reduce survey burden / Roger Tourangeau, Frauke Kreuter, and Stephanie Eckman -- Audio-recording of open-ended survey questions : a solution to the problems of interviewer transcription? / Patrick Sturgis and Rebekah Luff -- Framing effects / Uwe Engel and Britta Köster -- Estimating and comparing the quality of different scales of an online survey using an MTMM approach / Melanie Revilla and Willem E. Saris -- Collecting MTMM data on satisfaction with life / Laura Burmeister and Uwe Engel -- On the quality of web panels / Jelke Bethlehem -- Online surveys and the burden of mobile responding / Marika de Bruijne and Marije Oudejans -- Well-being, survey attitudes, and readiness to report on everyday life events in an experience sampling study / Laura Burmeister, Uwe Engel, and Björn Oliver Schmidt -- Nonresponse, measurement error, and estimates of change : lessons from the German PPSM panel / Suat Can and Uwe Engel -- Handling of missing data in statistical analyses / Daniel Salfrán and Martin Spiess -- Multiple imputation of overdispersed multilevel count data / Kristian Kleinke and Jost Reinecke

Wissenschaftliche Umfragen können keine aussagekräftigen Ergebnisse liefern, wenn ihre Datenqualität durch fehlende oder verfälschte Antworten beeinträchtigt wird. Eine Herausforderung der Sozialforschung besteht darin, solche Fehlerquellen zu erkennen und zu kontrollieren. Der Band präsentiert Erkenntnisse und Methoden zur Behandlung von Unit Nonresponse, Missing Data und verschiedene Arten von Messfehlern im Kontext von Web und Mixed-Mode Panel, Mobile Web und Faceto-Face-Befragungen. Uwe Engel ist Professor für Soziologie mit dem Schwerpunkt Statistik und empirische Sozialforschung an der Universität Bremen.

Contents -- Contents, Volume 2 -- Preface -- Part I. Background -- 1. Introduction -- 1.1 Origins of the Study -- 1.2 Domain of the Study: Objective and Subjective Phenomena -- 1.3 Subjectivity and ""Social Facts"" -- 1.4 Putting Surveys and Their Problems in Perspective -- 1.5 Organization of the Volume -- Part II. Uses and Abuses of Surveys -- 2. The Development and Contemporary Use of Subjective Surveys -- 2.1 The Development of the Survey Method -- 2.2 The Survey Enterprise -- 2.3 The Use of Subjective Survey Measurements -- 2.4 The Effects of Surveys and Polls

Contents -- Contents, Volume 1 -- Preface -- Part I. Measurement of Subjective Phenomena in the Social Sciences -- 1. Attitude Measurement in Psychology and Sociology: The Early Years - Jean M. Converse -- 2. Utility in Economics: A Survey of the Literature - J. G. Tulip Meeks -- 3. The Use of Survey Data in Basic Research in the Social Sciences - Stanley Presser -- Part II. Quasi-Facts -- 4. The Subjectivity of Ethnicity - Tom W. Smith -- 5. Measuring Employment and Unemployment - Barbara A. Bailar and Naomi D. Rothwell

This case study is based on the research project I undertook for my PhD dissertation. The goal of the project was to quantitatively analyze the effects of socially constructed identities on the political attitudes and behavior of sexual minorities in the United States. Achieving this goal necessitated the collection of individual-level data about the identity and political experiences of sexual minorities. This case study focuses on the construction of an online survey instrument and its administration to a representative sample of more than 1,200 lesbian, gay, bisexual, and transgender (LGBT) people in the United States. The incorporation of intersectional theory into the research design allowed me to robustly measure heterogeneity within a group often considered politically and behaviorally homogeneous. My experience highlights the difficulties of LGBT survey research, the complexity of quantitative intersectional analysis, and the imperative of survey researchers to justify the use of categorical indicators in survey instruments to prevent the continued marginalization of intersectional identities in survey research.

Basic principles -- Surveys and societies -- Planning a survey -- Measurement -- Sampling -- Data collection -- Preparing data for use -- Assessing and improving data quality -- Further issues

Empirical analysis of rural credit market failure has been of key scientific and political interest in recent years. The aim of this paper is to give an overview of various methods for measuring credit rationing of farms employed in the literature. Furthermore, based on a common analytical framework entailing a formal model of a credit rationed farm household, the methods are subjected to a comparativeevaluation of their specific strengths or shortcomings. Six approaches are distinguished: measurement of loan transaction costs, analysis of qualitative information collected in interviews, analysis of quantitative information collected in interviews by using the credit limit concept, analysis of spill-over effects with regard to secondary credit sources, econometric household modelling, and the econometric analysis of dynamic investment decisions. The first approach defines credit rationing as the impossibility to take a loan due to prohibitively high, measurable transaction costs on loan markets, which is a price rationing mechanism. All other approaches at least implicitly define credit rationing as a persistent private excess demand in terms of a quantity restriction. The six approaches are more or less closely linked to the neo-classical efficiency concept. An explicit comparison with a first-best solution is impossible in the first three approaches, since they essentially rely on a subjective assessment of borrowers' access to credit, based on qualitative or quantitative indicators. The fifth and sixth approach allow a rigorous interpretation in the framework of neo-classical equilibrium theory. The fourth approach takes an intermediate position, since spill-over on segmented loan markets reveals a willingness to pay with regard to the supposedly less expensive but rationed primary source. Approaches are fairly data demanding in general, usually requiring specific data on loan transactions. Even so, most approaches are applicable to cross-sectional household data. Only dynamic modelling of investment decisions necessitates the availability of panel data, therefore restricting the applicability in low-income and transition countries. With the exception of the first, all methods surveyed might plausibly be used to empirically detect credit rationing.