Measurement biases in panel data

Meurs, Henk; Wissen, Leo van; Visser, Jacqueline

doi:10.1007/bf00163114

Cited by 48 publications

(31 citation statements)

References 12 publications

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“…Also, trip attributes such as origin, destination, and duration, were imputed wherever logically possible using information available in the reported trips. This effort was undertaken to account as much as possible for the potential effects of trip reporting errors, which are common in multi-day panel travel diary surveys of this type [Golob and Meurs, 1986;Meurs, et al, 1989;and Pas, 1986].…”

Section: List Ofmentioning

confidence: 99%

Impact of telecommuting on spatial and temporal patterns of household travel

1991

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Section: List Ofmentioning

confidence: 99%

Impact of telecommuting on spatial and temporal patterns of household travel

1991

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“…The issue of reporting accuracy in the Dutch panel survey is addressed in Meurs et al (1989). As in Golob and Meurs (1986), they found trip reporting decline gradually within each diary period.t Using the average number of trips reported on the first diary day as a reference, Meurs et al, estimate that trip under-reporting amounted to an average of 2.27 trips per week per person in the first survey that a respondent participated.…”

Section: Panel Surveymentioning

confidence: 99%

“…In the case of attrition, a probabilistic model of attrition, constructed using measurement from the earlier survey wave(s), can be used to formulate more efficient weight that takes advantage of mobility and other available information (Kitamura and Bovy, 1987). For discussions on panel conditioning and reporting accuracy, see Golob (1990), Golob and Meurs (1986), Meurs et al (1989), andvan de Pol (1987).…”

Section: Panel Surveymentioning

confidence: 99%

Panel analysis in transportation planning: An overview

Kitamura

1990

Transportation Research Part A: General

136

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“…While Williams (1970), Williams and Mallows (1970), and Meurs et al (1989) showed that systematic biases occur in panel data sets, due to attrition as well as panel conditioning, Coombs (1973) found differences in knowledge due to re-interviewing, i.e., panel conditioning, but little impact on behavior or attitudes. Waterton and Lievesley (1989) found some evidence that respondents are influenced by re-interviewing, especially respondents with low knowledge scores.…”

Section: Introductionmentioning

confidence: 98%

“…This may be a good thing and reduce measurement error, if respondents learn how to interpret questions and make fewer errors. On the other hand, experienced respondents may become strategic and learn, e.g., that answering 3 "no" reduces the burden of their task, avoiding follow up questions (see, e.g., Meurs et al 1989). Sturgis et al (2007) expand on the main theory behind panel conditioning: the cognitive stimulus hypothesis.…”

Section: Introductionmentioning

confidence: 99%

Can I Use a Panel? Panel Conditioning and Attrition Bias in Panel Surveys

2007

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Over the past decades there has been an increasing use of panel surveys at the household or individual level, instead of using independent cross-sections. Panel data have important advantages, but there are also two potential drawbacks: attrition bias and panel conditioning effects. Attrition bias can arise if respondents drop out of the panel non-randomly, i.e., when attrition is correlated to a variable of interest. Panel conditioning arises if responses in one wave are influenced by participation in the previous wave(s). The experience of the previous interview(s) may affect the answers of respondents in a next interview on the same topic, such that their answers differ systematically from the answers of individuals who are interviewed for the first time. The literature has mainly focused on estimating attrition bias; less is known on panel conditioning effects.In this study we discuss how to disentangle the total bias in panel surveys due to attrition and panel conditioning into a panel conditioning and an attrition effect, and develop a test for panel conditioning allowing for non-random attrition. First, we consider a fully nonparametric approach without any assumptions other than those on the sample design, leading to interval identification of the measures for the attrition and panel conditioning effect. Second, we analyze the proposed measures under additional assumptions concerning the attrition process, making it possible to obtain point estimates and standard errors for both the attrition bias and the panel conditioning effect.We illustrate our method on a variety of questions from two-wave surveys conducted in a Dutch household panel. We found a significant bias due to panel conditioning in knowledge questions, but not in other types of questions. The examples show that the bounds can be informative if the attrition rate is not too high. Point estimates of the panel conditioning effect do not vary a lot with the different assumptions on the attrition process. * CentERdata, Tilburg University * * Department of Econometrics and Operations Research, Faculty of Economics and Business Administration, Tilburg University 1 Please address correspondence to: Marcel Das, CentERdata, Tilburg University, P.O. Box 90153, 5000 LE Tilburg, The Netherlands. E-mail: das@uvt.nl. We are very grateful to Ramon van den Akker for his help and comments.

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Measurement biases in panel data

Cited by 48 publications

References 12 publications

Impact of telecommuting on spatial and temporal patterns of household travel

Impact of telecommuting on spatial and temporal patterns of household travel

Panel analysis in transportation planning: An overview

Can I Use a Panel? Panel Conditioning and Attrition Bias in Panel Surveys

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