High frequency body mass measurement, feedback, and health behaviors

Kooreman, Peter; Scherpenzeel, Annette

doi:10.1016/j.ehb.2013.12.003

Cited by 11 publications

(18 citation statements)

References 25 publications

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“…Our simulation study is directly inspired by the application presented in Kooreman and Scherpenzeel ( 2014 ). In this study, the authors use a random intercept model with level 1 and level 2 predictors to analyze their longitudinal data regarding fluctuations in people’s weight (for more details, see “SEMA in action” below).…”

Section: Simulation Studymentioning

confidence: 99%

“…In this study, the authors use a random intercept model with level 1 and level 2 predictors to analyze their longitudinal data regarding fluctuations in people’s weight (for more details, see “SEMA in action” below). By carefully extending the model used by Kooreman and Scherpenzeel ( 2014 ), we examine the influence of two important factors on the performance of SEMA.…”

Section: Simulation Studymentioning

confidence: 99%

“…In our simulations, we examine a total of 4 conditions. Inspired by the application presented in Kooreman and Scherpenzeel ( 2014 ), all four conditions consider 15 fixed effects: 5 continuous and one categorical variable with 4 categories (i.e., 3 dummy variables) at level 1, and 3 continuous variables and 2 categorical variables, one consisting of 2 categories (to represent gender) and one variable with 3 categories (education level) at level 2. Next, the four conditions are as follows: Condition A: a simple random intercept model (a model containing a single variance component), Conditions B, C, and D: a random intercepts and slopes model with weak (B), medium (C), and strong (D) associations between these random effects.…”

Section: Simulation Studymentioning

confidence: 99%

“…In this section, the SEMA algorithm is applied to an actual data stream originating from an experiment done by Kooreman and Scherpenzeel ( 2014 ). Using this application, we illustrate the practical issues that occur when analyzing data streams: we need to choose appropriate starting values, decide on an update regime of full SEMA updates, and deal with possible changes in the data-generating process that occur over time.…”

Section: Sema In Action: Predicting Weight Fluctuationsmentioning

confidence: 99%

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Estimating Multilevel Models on Data Streams

2019

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Social scientists are often faced with data that have a nested structure: pupils are nested within schools, employees are nested within companies, or repeated measurements are nested within individuals. Nested data are typically analyzed using multilevel models. However, when data sets are extremely large or when new data continuously augment the data set, estimating multilevel models can be challenging: the current algorithms used to fit multilevel models repeatedly revisit all data points and end up consuming much time and computer memory. This is especially troublesome when predictions are needed in real time and observations keep streaming in. We address this problem by introducing the Streaming Expectation Maximization Approximation (SEMA) algorithm for fitting multilevel models online (or “row-by-row”). In an extensive simulation study, we demonstrate the performance of SEMA compared to traditional methods of fitting multilevel models. Next, SEMA is used to analyze an empirical data stream. The accuracy of SEMA is competitive to current state-of-the-art methods while being orders of magnitude faster. Electronic supplementary material The online version of this article (10.1007/s11336-018-09656-z) contains supplementary material, which is available to authorized users.

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Section: Simulation Studymentioning

confidence: 99%

Section: Simulation Studymentioning

confidence: 99%

Section: Simulation Studymentioning

confidence: 99%

Section: Sema In Action: Predicting Weight Fluctuationsmentioning

confidence: 99%

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Estimating Multilevel Models on Data Streams

2019

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“…This minimizes the respondents' burden to provide the data; they are requested only to step on the scale (once a day, once a week, or at an unspecified frequency). A first empirical analysis is reported in Kooreman and Scherpenzeel (2014), based on almost 80,000 measurements collected in 2011.…”

Section: Innovations In Data Collectionmentioning

confidence: 99%

Experimental and Longitudinal Data for Scientific and Policy Research: Open Access to Data Collected in the Longitudinal Internet Studies for the Social Sciences (LISS) Panel

Das

Knoef

2018

Data-Driven Policy Impact Evaluation

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Empirical research needs data. Scientists are able to collect data through small-scale experiments in laboratories and in many cases by using students as respondents. There are also successful initiatives to collect new data in large-scale longitudinal surveys. The longest-running longitudinal household survey is the Panel Study of Income Dynamics (PSID; https://psidonline.isr.umich.edu/). Other examples include the Health and Retirement Study (HRS); the Survey of Health, Ageing and Retirement in Europe (SHARE); Understanding Society (incorporating the British Household Panel Survey); and the German SocioEconomic Panel Study (SOEP). Access to data is in most cases entirely open, subject to signing a statement governing the use of the data. Empirical researchers benefit from an open-access policy. They have access to a huge number of datasets. However, there are also some problems. Data collection, as in the above examples, is carried out mostly by face-to-face interviewing. This is rather time-consuming, and public release of the data often takes place more than a year after data collection has been completed. In addition, there is little or no room for "outsiders" to add new questions or experimental modules. And although the longitudinal surveys have a multidisciplinary setup, possible new questions must fit the context of the survey. Finally, the large-scale longitudinal surveys all suffer from the problem that face-to-face interviewing has become extremely expensive.

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Open Probability‐Based Panels

Weiß

Das

Kapteyn

et al. 2020

Wiley StatsRef: Statistics Reference Online

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Open probability‐based panels (OPP) aim at collecting high‐quality, population‐representative data either solely or partly through online questionnaires. The most salient feature is that they allow external researchers to apply for cross‐sectional as well as longitudinal data collection about various social science topics. All in all, a panel infrastructure is considered open when it is accessible for all academic researchers from any substantive area to field primary studies and when it provides access to the data collected. In this article, we first describe the essential features of OPPs and give a broad overview of some important OPPs, most notably the Open Probability‐based Panel Alliance (OPPA). We then highlight OPPs' primary and secondary research opportunities and discuss issues of recruitment and refreshment. This article concludes with a list of challenges that affect most OPPs and further research opportunities.

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High frequency body mass measurement, feedback, and health behaviors

Cited by 11 publications

References 25 publications

Estimating Multilevel Models on Data Streams

Estimating Multilevel Models on Data Streams

Experimental and Longitudinal Data for Scientific and Policy Research: Open Access to Data Collected in the Longitudinal Internet Studies for the Social Sciences (LISS) Panel

Open Probability‐Based Panels

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