Identifying Factors Affecting Longitudinal Survey Response

Watson, Nicole; Wooden, Mark

doi:10.1002/9780470743874.ch10

Cited by 193 publications

(220 citation statements)

References 20 publications

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“…The full modelling results for the final models (Model 8) for the two binary outcomes and the multinomial outcome are given in the Appendix Tables A3 and A4 (available online at: http://dx.doi.org/10.1515/JOS-2017-0037). Although the inclusion of survey variables as seen earlier does not improve the ability of the models to predict the outcomes of interest by very much, many of the variables are significant or highly significant (e.g., highest qualification), supporting well known correlates of nonresponse in longitudinal surveys (Lepkowski and Couper 2002;Watson and Wooden 2009;De Leeuw and De Heer 2002;Campanelli and O'Muircheartaigh 1999;Pickery et al 2001;Haunberger 2010). We noted earlier that paradata from the previous wave increase the ability of the models for prediction.…”

Section: 4supporting

confidence: 55%

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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Section: 4supporting

confidence: 55%

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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“…One of our main concerns stemmed from the potential change in interviewer workforce. It has, for example, been consistently demonstrated, both in the HILDA Survey and in other longitudinal surveys, that interviewer continuity matters for response (Watson and Wooden 2009a). Actively pursuing the employment of experienced HILDA Survey interviewers was thus made a priority for RMR, something that was facilitated by both the casual nature of the interviewer workforce and the cooperation received from Nielsen.…”

Section: New Fieldwork Providermentioning

confidence: 99%

Data Survey: The HILDA Survey: Progress and Future Developments

Watson

Wooden

2010

Australian Economic Review

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“…Demographic characteristics have been shown to be related to panel attrition, with older people, minorities and males generally more likely to attrite (Aneshensel et al, 1989;Burkam and Lee, 1998;Fitzgerald et al, 1998;Gray et al, 1996;Loosveldt et al, 2002;Lynn et al, 2005;MaCurdy et al, 1998;Mirowsky and Reynolds, 2000;Peracchi, 2002;Sharot, 1991;Watson and Wooden, 2009). Persons with more education and higher income are less likely to attrite (Fitzgerald et al, 1998;Gray et al, 1996;Loosveldt et al, 2002;Lynn et al, 2005;MaCurdy et al, 1998;Mirowsky and Reynolds, 2000;Watson and Wooden, 2009), and persons living in urban areas are more likely to attrite (Gray et al, 1996;Lynn et al, 2005;Watson and Wooden, 2009). Attitudinal measures have also been examined as predictors of panel attrition, including social (Waterton and Lievesley, 1987) and political attitudes (Lepkowski and Couper, 2002;Loosveldt and Carton, 1997;Loosveldt et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

“…As such, characteristics of the previous survey interview also predict panel attrition. Item nonresponse to questions in a previous wave of the study (Burkam and Lee, 1998;Lepkowski and Couper, 2002;Loosveldt and Carton, 1997;Loosveldt et al, 2002;Watson and Wooden, 2009) is associated with higher attrition rates, while the longer interviews in prior waves are associated with higher retention rates (Bogen, 1996).…”

Section: Introductionmentioning

confidence: 99%

Are we keeping the people who used to stay? Changes in correlates of panel survey attrition over time

Olson

Witt

2011

Social Science Research

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As survey response rates decline, correlates of survey participation may also be changing. Panel studies provide an opportunity to study a rich set of correlates of panel attrition over time. We look at changes in attrition rates in the American National Election Studies from 1964 to 2004, a repeated panel survey with a two-wave pre-post election design implemented over multiple decades. We examine changes in attrition rates by three groups of variables: sociodemographic and ecological characteristics of the respondent and household, party affiliation and political and social attitudes recorded at the first interview, and paradata about the first wave interview. We find relatively little overall change in the pre-post election panel attrition rates, but important changes in demographic correlates of panel attrition over time. We also examine contact and cooperation rates from 1988 to 2004.

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Identifying Factors Affecting Longitudinal Survey Response

Cited by 193 publications

References 20 publications

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

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

Data Survey: The HILDA Survey: Progress and Future Developments

Are we keeping the people who used to stay? Changes in correlates of panel survey attrition over time

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