Raising response rates: What works?

Porter, Stephen R.

doi:10.1002/ir.97

Cited by 147 publications

(101 citation statements)

References 44 publications

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“…While much advice and many studies suggest keeping the survey length itself as short as possible (so as to increase completion rates) (Porter, 2004), little has been written about the impact that the invitation message might have on survey response. One example is the study made by Klofstand et al (2008) where it compared two different email lengths.…”

Section: Invitation Emailmentioning

confidence: 99%

Effects of Personalization and Invitation Email Length on Web-Based Survey Response Rates

Trespalacios

Perkins

2016

TechTrends

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This is an author-produced, peer-reviewed version of this article. AbstractIndividual strategies to increase response rate and survey completion have been extensively researched. Recently, efforts have been made to investigate a combination of interventions to yield better response rates for web-based surveys. This study examined the effects of four different survey invitation conditions on response rate. From a large metropolitan university in the West, a group of 1,598 selected students were randomly assigned to four groups, each of which received a different version of the invitation email to participate in a survey of campus technology needs. Findings show that neither the degree of personalization nor the length of the invitation email impacted survey response or completion. Additionally, the outcomes demonstrated the impact of research-based "best practices" and their impact on overall response rate.

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Section: Invitation Emailmentioning

confidence: 99%

Effects of Personalization and Invitation Email Length on Web-Based Survey Response Rates

Trespalacios

Perkins

2016

TechTrends

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“…In addition, the difference in responses between online and telephonic surveys was discussed. Although the literature discusses many strategies to improve response rates to online surveys (e.g., see Porter [2004b], Nair, Adams andMertova [2008], Nulty [2008], and Bennett and Nair [2010], as well as Alderman, Towers and Bannah [2012, 272] for additional references), such strategies pertain mostly to student evaluation surveys conducted shortly before, during or immediately after graduation, while graduate destination surveys pose the difficulty of tracing students several years after graduation. The purpose here is therefore not to repeat a discussion of strategies already known to improve response rates, but to conclude with three methodological suggestions that add to the literature on student surveys in general and graduate destination surveys in particular.…”

Section: Resultsmentioning

confidence: 99%

From study to work: methodological challenges of a graduate destination survey in the Western Cape, South Africa

Toit

Kraak

Favish

et al. 2014

Assessment & Evaluation in Higher Education

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“…As survey response rates have been dropping (e.g., (Porter 2004;Shih and Fan 2008)), researchers have been looking at how to motivate respondents to fill out these surveys, to avoid having survey results not represent the full population. Commercial surveys often pay respondents, but compensation does not necessarily ensure thoughtful responsesparticipants still exhibit satisficing behavior in paid surveys (e.g., (Barge and Gehlbach 2012;Kapelner and Chandler 2010)).…”

Section: Survey Burden Respondent Engagement and Data Qualitymentioning

confidence: 99%

Dynamic Question Ordering in Online Surveys

Early

Mankoff

Fienberg

2017

Journal of Official Statistics

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Online surveys have the potential to support adaptive questions, where later questions depend on earlier responses. Past work has taken a rule-based approach, uniformly across all respondents. We envision a richer interpretation of adaptive questions, which we call Dynamic Question Ordering (DQO), where question order is personalized. Such an approach could increase engagement, and therefore response rate, as well as imputation quality. We present a DQO framework to improve survey completion and imputation. In the general survey-taking setting, we want to maximize survey completion, and so we focus on ordering questions to engage the respondent and collect hopefully all information, or at least the information that most characterizes the respondent, for accurate imputations. In another scenario, our goal is to provide a personalized prediction. Since it is possible to give reasonable predictions with only a subset of questions, we are not concerned with motivating users to answer all questions. Instead, we want to order questions to get information that reduces prediction uncertainty, while not being too burdensome. We illustrate this framework with two case studies, for the prediction and survey-taking settings. We also discuss DQO for national surveys and consider connections between our statistics-based question-ordering approach and cognitive survey methodology.

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Raising response rates: What works?

Abstract: This chapter discusses the theoretical literature on why people choose to respond to a survey and reviews the latest empirical research on how survey administration and the characteristics of a survey affect response rates.

Cited by 147 publications

References 44 publications

Effects of Personalization and Invitation Email Length on Web-Based Survey Response Rates

Effects of Personalization and Invitation Email Length on Web-Based Survey Response Rates

From study to work: methodological challenges of a graduate destination survey in the Western Cape, South Africa

Dynamic Question Ordering in Online Surveys

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