Handbook of Nonresponse in Household Surveys

Bethlehem, Jelke; Cobben, Fannie; Schouten, Barry

doi:10.1002/9780470891056

Cited by 160 publications

(134 citation statements)

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“…Declines in respondent cooperation in developed countries have been widely reported in the survey research literature (e.g., Groves 2011), including government-conducted surveys (e.g., Atrostic et al 2001;Bethlehem, Cobben, and Schouten 2011;Brick and Williams 2013;de Leeuw and de Heer 2002), and such declines have been going on for many years (Steeh 1981). These declines are easiest to see in cross-sectional surveys that have been repeated over a long period.…”

Section: Decline In Respondent Cooperationmentioning

confidence: 97%

“…By using the data from the basic residence registration system, unlike the typical cross-sectional survey in the United States (e.g., Lessler and Kalsbeek 1992), we know the age, sex, and place of residence of both responders and non-responders. To evaluate bias in survey data it is crucial to have auxiliary data for both non-respondents and respondents (Bethlehem, Cobben, and Schouten 2011). Japan's basic residence registration provides auxiliary data on age, sex, and geographic location.…”

Section: Datamentioning

confidence: 99%

“…One type of data available to those doing survey research and which was used here is paradata, defined as information about the data collection process itself (Bethlehem, Cobben, and Schouten 2011;Couper 1997Couper , 2000Couper and Lyberg 2005;Wagner 2010). The two paradata variables used above were number of visits required to obtain a completed questionnaire and the date the completed questionnaire was collected.…”

Section: Paradatamentioning

confidence: 99%

See 2 more Smart Citations

Do low survey response rates bias results? Evidence from Japan

Rindfuss¹,

Choe²,

Tsuya³

et al. 2015

DemRes

167

114

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BACKGROUNDIn developed countries, response rates have dropped to such low levels that many in the population field question whether the data can provide unbiased results. OBJECTIVEThe paper uses three Japanese surveys conducted in the 2000s to ask whether low survey response rates bias results. A secondary objective is to bring results reported in the survey response literature to the attention of the demographic research community. METHODSUsing a longitudinal survey as well as paradata from a cross-sectional survey, a variety of statistical techniques (chi square, analysis of variance (ANOVA), logistic regression, ordered probit or ordinary least squares regression (OLS), as appropriate) are used to examine response-rate bias. RESULTSEvidence of response-rate bias is found for the univariate distributions of some demographic characteristics, behaviors, and attitudinal items. But when examining relationships between variables in a multivariate analysis, controlling for a variety of background variables, for most dependent variables we do not find evidence of bias from low response rates. COMMENTSThe results have two implications. First, demographers should not presume the presence or absence of low response-rate bias; rather they should test for it in the context of a specific substantive analysis. Second, demographers should lobby data gatherers to collect as much paradata as possible so that rigorous tests for low response-rate bias are possible. IntroductionSample surveys (cross-sectional and longitudinal) have become the dominant data source used by population researchers. Response rates, both the initial response rate and attrition rates in longitudinal studies, have historically been an important rough-andready yardstick to judge data quality. Response rates 6 have been declining in urbanized, high-income countries to the point where many cross-sectional surveys now have response rates below 50% (Atrostic et al. 2001;Brick and Williams 2013;de Leeuw and de Heer 2002;Groves 2011;Singer 2006). The survey literature has long recognized that low response rates only indicate potential bias (e.g., Lessler and Kalsbeck 1992), yet the almost automatic response among most in the population field has been to equate low response rates with poor data quality. Low response rates produce bias only to the extent that there are differences between responders and non-responders on the estimate(s) of interest, and then only if such differences cannot be eliminated or controlled for through the use of observable and available characteristics of responders and non-responders. The difficulty in evaluating non-response bias is that measures of the variable(s) of interest and characteristics of non-responders are generally not observed, and hence the population field has tended to rely on the presumption that low response rates necessarily mean low data quality.In this paper, using surveys designed by demographers and variables that have often been used as dependent variables in demographic research, we examine this 6 ...

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Section: Decline In Respondent Cooperationmentioning

confidence: 97%

Section: Datamentioning

confidence: 99%

Section: Paradatamentioning

confidence: 99%

See 1 more Smart Citation

Do low survey response rates bias results? Evidence from Japan

Rindfuss¹,

Choe²,

Tsuya³

et al. 2015

DemRes

167

114

View full text Add to dashboard Cite

show abstract

“…One similar approach is reweighting to adjust the initial sample weights of a data set to match (the margins of) one or several tables of auxiliary variables. When the complete population distribution of the auxiliary variables is available, this approach is usually called poststratification (Bethlehem et al 2011). If only partial population information is available about the lower-dimensional margins of tables of auxiliary variables, it is possible to use linear or multiplicative weighting.…”

Section: Methodsmentioning

confidence: 99%

Models for Combining Aggregate-Level Administrative Data in the Absence of a Traditional Census

Yıldız

Smith

2015

Journal of Official Statistics

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Administrative data sources are an important component of population data collection and they have been used in census data production in the Nordic countries since the 1960s. A large amount of information about the population is already collected in administrative data sources by governments. However, there are some challenges to using administrative data sources to estimate population counts by age, sex, and geographical area as well as population characteristics. The main limitation with the administrative data sources is that they only collect information from a subset of the population about specific events, and this may result in either undercoverage or overcoverage of the population. Another issue with the administrative data sources is that the information may not have the same quality for all population groups. This research aims to correct an inaccurate administrative data source by combining aggregate-level administrative data with more accurate marginal distributions or two-way marginal information from an auxiliary data source and produce accurate population estimates in the absence of a traditional census. The methodology developed is applied to estimate population counts by age, sex, and local authority area in England and Wales. The administrative data source used is the Patient Register which suffers from overcoverage, particularly for people between the ages of 20 and 50.

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“…Table 1 presents the distributions of the three response variables used in the analysis. The literature has identified a range of variables associated with household nonresponse, including sociodemographic characteristics of respondents, physical impediments of the property, social and environmental attributes of households, interviewer observations about households as well as call timing and frequency of calls (Bethlehem et al 2011;Groves and Couper 1996;Groves and Couper 1998;Durrant et al 2010;Durrant and Steele 2009;West 2013;Groves 2006). Groves and Couper (1998) concluded that physical impediments to accessing the household, at-home patterns of the household (larger household sizes, presence of elderly persons or young children), and the timing (day of the week and time of the day) and number of interviewer visits to the unit as well as residence of household (urban or rural) were significantly associated with household contactability.…”

Section: Response and Explanatory Variablesmentioning

confidence: 99%

Using Prior Wave Information and Paradata: Can They Help to Predict Response Outcomes and Call Sequence Length in a Longitudinal Study?

Durrant

Maslovskaya

Smith

2017

Journal of Official Statistics

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In recent years the use of paradata for nonresponse investigations has risen significantly. One key question is how useful paradata, including call record data and interviewer observations, from the current and previous waves of a longitudinal study, as well as previous wave survey information, are in predicting response outcomes in a longitudinal context. This article aims to address this question. Final response outcomes and sequence length (the number of calls/visits to a household) are modelled both separately and jointly for a longitudinal study. Being able to predict length of call sequence and response can help to improve both adaptive and responsive survey designs and to increase efficiency and effectiveness of call scheduling. The article also identifies the impact of different methodological specifications of the models, for example different specifications of the response outcomes. Latent class analysis is used as one of the approaches to summarise call outcomes in sequences. To assess and compare the models in their ability to predict, indicators derived from classification tables, ROC (Receiver Operating Characteristic) curves, discrimination and prediction are proposed in addition to the standard approach of using the pseudo R 2 value, which is not a sufficient indicator on its own. The study uses data from Understanding Society, a large-scale longitudinal survey in the UK. The findings indicate that basic models (including geographic, design and survey data from the previous wave), although commonly used in predicting and adjusting for nonresponse, do not predict the response outcome well. Conditioning on previous wave paradata, including call record data, interviewer observation data and indicators of change, improve the fit of the models slightly. A significant improvement can be observed when conditioning on the most recent call outcome, which may indicate that the nonresponse process predominantly depends on the most current circumstances of a sample unit.

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Handbook of Nonresponse in Household Surveys

Cited by 160 publications

References 0 publications

Do low survey response rates bias results? Evidence from Japan

Do low survey response rates bias results? Evidence from Japan

Models for Combining Aggregate-Level Administrative Data in the Absence of a Traditional Census

Using Prior Wave Information and Paradata: Can They Help to Predict Response Outcomes and Call Sequence Length in a Longitudinal Study?

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