Data Collection in a Probability-Based Internet Panel: How the LISS Panel Was Built and How It Can Be Used

Scherpenzeel, Annette

doi:10.1177/0759106310387713

Cited by 138 publications

(115 citation statements)

References 1 publication

Supporting

Mentioning

108

Contrasting

Unclassified

Order By: Relevance

“…The LISS panel is administered by CentERdata, a survey research institute affiliated with Tilburg University, and follows close to 8,000 individuals from 5,000 households. Scherpenzeel (2011) provides details on the design and sample. Surveys are distributed over the internet every week.…”

Section: The Liss Panelmentioning

confidence: 99%

Can survey participation alter household saving behavior?

Crossley¹,

Bresser²,

Delaney³

et al. 2014

View full text Add to dashboard Cite

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may AbstractMuch empirical research in economics is based on data from household surveys. Panel surveys are particularly valuable for understanding dynamics and heterogeneity. A possible concern with panel surveys is that survey participation itself may alter subsequent behavior. We provide novel evidence of survey effects on household financial behavior in a developed country. We exploit randomized assignment to survey modules within the LISS-panel, an internet panel survey which is representative of the Dutch population. Our saving measure is based on linked administrative wealth data, allowing us to distinguish changes in saving behavior from changes in reporting behavior. We find that households that respond to detailed questions on expenditures and needs in retirement reduced their non-housing saving rate by 3 percentage points. The size of the effect is increasing in the education level of the household. One interpretation is that the survey acted as a salience shock.

show abstract

Section: The Liss Panelmentioning

confidence: 99%

Can survey participation alter household saving behavior?

Crossley¹,

Bresser²,

Delaney³

et al. 2014

View full text Add to dashboard Cite

show abstract

“…It has been related to a number of properties, such as inequality (Dewilde and Decker 2016), employment insecurity (Lersch and Dewilde 2015) and government redistribution (André and Dewilde 2016). Therefore, we apply the MILC method on a composite data set that brings together survey data from the LISS (Longitudinal Internet Studies for the Social sciences) panel from 2013 (Scherpenzeel 2011), which is administered by CentERdata (Tilburg University, The Netherlands) and a population register from Statistics Netherlands from 2013. Because samples for LISS were drawn by Statistics Netherlands, we were very well able to link these surveys and registers.…”

Section: Datamentioning

confidence: 99%

Estimating Classification Errors Under Edit Restrictions in Composite Survey-Register Data Using Multiple Imputation Latent Class Modelling (MILC)

Boeschoten

Oberski

Waal

2017

Journal of Official Statistics

View full text Add to dashboard Cite

Both registers and surveys can contain classification errors. These errors can be estimated by making use of a composite data set. We propose a new method based on latent class modelling to estimate the number of classification errors across several sources while taking into account impossible combinations with scores on other variables. Furthermore, the latent class model, by multiply imputing a new variable, enhances the quality of statistics based on the composite data set. The performance of this method is investigated by a simulation study, which shows that whether or not the method can be applied depends on the entropy R 2 of the latent class model and the type of analysis a researcher is planning to do. Finally, the method is applied to public data from Statistics Netherlands.

show abstract

“…If the base wave is successful at "training" respondents to use a computerized instrument such as through the Internet and/or a smart phone for conducting these follow-ups, then frequent (perhaps monthly) collection could be a short set of items that takes no more than 5 to 8 minutes to complete, such as in the Longitudinal Internet Studies for the Social Sciences (LISS) panel in the Netherlands [66]. One benefit of this approach is better tracking of respondents, lowering attrition.…”

Section: Frequency Of Data Collectionmentioning

confidence: 99%

Methodological considerations for a new household panel survey

Olson

Brick

2015

JEM

View full text Add to dashboard Cite

This paper identifies new opportunities for innovation and expansion on current survey practice in the design of a new household panel survey, including an increased use of new and mobile technologies, more frequent data collection, modified clustering, and use of non-traditional survey measures such as administrative data, planned missing/matrix sampling questionnaire design, real-time data collection, and biomarkers. These innovative data collection methods require rethinking traditional panel survey methods, but can help reduce respondent burden and expand on current social science knowledge. The paper concludes that a new household panel survey would improve knowledge about important social, economic and health issues facing the US, and would provide a useful test bed for new hypotheses and innovative methods of data collection.

show abstract

Data Collection in a Probability-Based Internet Panel: How the LISS Panel Was Built and How It Can Be Used

Cited by 138 publications

References 1 publication

Can survey participation alter household saving behavior?

Can survey participation alter household saving behavior?

Estimating Classification Errors Under Edit Restrictions in Composite Survey-Register Data Using Multiple Imputation Latent Class Modelling (MILC)

Methodological considerations for a new household panel survey

Contact Info

Product

Resources

About