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Measuring, explaining and adjusting for cross-country differences in unit nonresponse : what can process data contribute?
Blom, Annelies G.
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URL:
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http://survex.de/fileadmin/user_upload/PhD_thesis_...
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Dokumenttyp:
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Dissertation
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Erscheinungsjahr:
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2009
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Ort der Veröffentlichung:
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Colchester
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Hochschule:
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University of Essex
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Datum der mündl. Prüfung:
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November 2009
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Sprache der Veröffentlichung:
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Englisch
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Einrichtung:
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Fakultät für Sozialwissenschaften > Data Science (Blom 2017-2022)
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Fachgebiet:
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300 Sozialwissenschaften, Soziologie, Anthropologie
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Normierte Schlagwörter (SWD):
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Empirische Sozialforschung
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Freie Schlagwörter (Englisch):
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Applied Social and Economic Research
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Abstract:
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The analysis of cross-national survey data can be hindered by unit non-response. It is not
uncommon for the countries in a cross-national surv
ey to have very different response rates. This
raises awareness amongst analysts of the potential for differential non-response errors, which might bias estimates of differences between countries. Research on cross-national differences in
nonresponse and nonresponse bias, however, is still scarce, partly due to a scarcity of auxiliary data permitting such analyses. Process data are auxiliary data about the data collection process and can be suitable for nonresponse analyses in cross-national surveys if they are available across countries for both respondents and nonrespondents. The process data discussed in this
thesis are contact (or call-record) data and interviewer observations of the neighbourhood.
This thesis investigates the role that process data play in the measurement, analysis and
adjustment of unit nonresponse in cross-national surveys. I first provide an overview of existing
studies of nonresponse in the cross-national setting and the role that contact data have played therein. Quality concerns raised in these studies led to the development of equivalence criteria for cross-national contact data. The second chapter investigates the comparative collection and measurement of response outcomes. I develop a conceptual framework of influences on the
response outcomes available to the survey researcher, design a codeframe of response outcomes
for cross-national implementation and compare the effect of two coding strategies on deriving final case outcomes. In the third chapter I use decomposition methods to explain whether cross-
country differences in contact rates are due to dif
ferential sample unit characteristics, differential
fieldwork characteristics or a differential effect of these characteristics on contact propensity.
Finally, chapter four assesses the effect of weights based on process data on reducing relative nonresponse bias. All analyses are based on data from the European Social Survey.
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 | Dieser Datensatz wurde nicht während einer Tätigkeit an der Universität Mannheim veröffentlicht, dies ist eine Externe Publikation. |
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