Using Survey Participants to Estimate the Impact of Nonparticipation

Lin, I‐Fen; Schaeffer, Nora Cate

doi:10.1086/269471

Cited by 304 publications

(205 citation statements)

References 21 publications

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“…We then proceed with the same logic as above for the panel. There is some evidence suggesting this continuum of resistance assumption may not hold (Lin and Schaeffer 1995), and so we place more emphasis on the results from the panel analyses than the cross-sectional analyses if the two produce different results.…”

Section: Some Terminology and The Logic Behind Our Analysismentioning

confidence: 99%

Do low survey response rates bias results? Evidence from Japan

Rindfuss¹,

Choe²,

Tsuya³

et al. 2015

DemRes

168

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: Some Terminology and The Logic Behind Our Analysismentioning

confidence: 99%

Do low survey response rates bias results? Evidence from Japan

Rindfuss¹,

Choe²,

Tsuya³

et al. 2015

DemRes

168

114

View full text Add to dashboard Cite

show abstract

“…Second, little of this work has information for both respondents and nonrespondents (for an exception, see Abraham et al, 2008, which used data from the Current Population Study to predict non-response to the American Time Use Survey). Rather these studies rely on comparing reports to survey questions between cooperative and reluctant respondents, but such an approach does not consistently reflect the characteristics of nonrespondents (e.g., Lin and Schaeffer, 1995;Smith, 1984). Third, questions about social participation have known measurement errors related to social desirability.…”

Section: Social Network Isolationmentioning

confidence: 99%

Social isolation, survey nonresponse, and nonresponse bias: An empirical evaluation using social network data within an organization

Watanabe

Olson

Falci

2017

Social Science Research

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Survey researchers have long hypothesized that social isolation negatively affects the probability of survey participation and biases survey estimates. Previous research, however, has relied on proxy measures of isolation, such as being a marginalized group member within a population. We re-examine the relationship between social isolation and survey participation using direct measures of social isolation derived from social network data; specifically, instrumental research and expressive friendship connections among faculty within academic departments. Using a reconceptualization of social isolation, we find that social network isolation is negatively associated with unit response. Among women (a numerical minority group within the organization), we further find that social group isolation (i.e., lacking instrumental network connections to men, the majority group in the organization) is negatively associated with survey participation. Finally, we show that some survey estimates are systematically biased due to nonparticipation from socially isolated people.

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“…"Response bias occurs when individuals who respond to a survey differ systematically from those that were invited to participate but did not respond" (Menachemi, 2010, p. 5) in which "the participants do not represent non-participants" (Thompson et al, 2014). Methods to adjust for nonparticipation are complicated and the impact of nonparticipation on the total sample is difficult to evaluate since researchers seldom have knowledge about nonparticipants (Lin and Schaeffer, 1995). In our research model, we did ignore the impact of non-response bias since the sample has been collected from white collar professionals and the data mainly consist of face to face data and the rest is online survey with compulsory fields.…”

Section: Non-response Biasmentioning

confidence: 99%

An Empirical Examination of Supply Chain Sustainability in Turkish Automotive Sector: Using the PLS-SEM Approach

Kacar¹,

Sezen²,

Kitapçı³

2017

IBR

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In today's dynamic environment, it has become a necessity for firms to better control their supply chain operations against supply chain vulnerabilities. Little consideration has been paid to the relationship between business environment, supply chain risks, supply chain vulnerability, supply chain performance and sustainability. Hence, we aim with this study to unveil the influence of supply chain variables on supply chain performance and sustainability. The proposed model consists of 10 hypotheses to disclose the relationship between 6 main constructs; Supply Chain Uncertainty, Supply Chain Risks, Supply Chain Performance, Collaborative Planning Systems, Vulnerability, and Supply Chain Sustainability. The hypotheses are validated by empirical study with 213 domestic and foreign automotive companies operating in Turkey. The results of this study indicate that supply chain sustainability is primarily affected by supply chain performance and collaborative planning systems. The findings of this study could provide the necessary point of view for the managers working in supply chain management area to comprehend the dynamics behind sustainable supply chains.

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Using Survey Participants to Estimate the Impact of Nonparticipation

Cited by 304 publications

References 21 publications

Do low survey response rates bias results? Evidence from Japan

Do low survey response rates bias results? Evidence from Japan

Social isolation, survey nonresponse, and nonresponse bias: An empirical evaluation using social network data within an organization

An Empirical Examination of Supply Chain Sustainability in Turkish Automotive Sector: Using the PLS-SEM Approach

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