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Article Reference Troff document (with manpage macros)Tree-based Machine Learning Methods for Survey Research
Predictive modeling methods from the field of machine learning have become a popular tool across various disciplines for exploring and analyzing diverse data. These methods often do not require specific prior knowledge about the functional form of the relationship under study and are able to adapt to complex non-linear and non-additive interrelations between the outcome and its predictors while focusing specifically on prediction performance. This modeling perspective is beginning to be adopted by survey researchers in order to adjust or improve various aspects of data collection and/or survey management. To facilitate this strand of research, this paper (1) provides an introduction to prominent tree-based machine learning methods, (2) reviews and discusses previous and (potential) prospective applications of tree-based supervised learning in survey research, and (3) exemplifies the usage of these techniques in the context of modeling and predicting nonresponse in panel surveys.
Located in MPRC People / Frauke Kreuter, Ph.D. / Frauke Kreuter Publications
Article Reference Troff document (with manpage macros)Willingness to participate in passive mobile data collection
The rising penetration of smartphones now gives researchers the chance to collect data from smartphone users through passive mobile data collection via apps. Examples of passively collected data include geolocation, physical movements, online behavior and browser history, and app usage. However, to passively collect data from smartphones, participants need to agree to download a research app to their smartphone. This leads to concerns about nonconsent and nonparticipation. In the current study, we assess the circumstances under which smartphone users are willing to participate in passive mobile data collection. We surveyed 1,947 members of a German nonprobability online panel who own a smartphone using vignettes that described hypothetical studies where data are automatically collected by a research app on a participant’s smartphone. The vignettes varied the levels of several dimensions of the hypothetical study, and respondents were asked to rate their willingness to participate in such a study. Willingness to participate in passive mobile data collection is strongly influenced by the incentive promised for study participation but also by other study characteristics (sponsor, duration of data collection period, option to switch off the app) as well as respondent characteristics (privacy and security concerns, smartphone experience).
Located in MPRC People / Christopher Antoun, Ph.D. / Christopher Antoun Publications
Report on Big Data in Survey Research
Frauke Kreuter and colleagues debate key methodological issues in Public Opinion Quarterly article
Located in Research / Selected Research
How does interview methodology affect interviewer variance?
Frauke Kreuter compares the effectiveness of commonly-used face-to-face interview methods
Located in Research / Selected Research
Using propensity scores for causal inference with covariate measurement error
Faculty Associate Frauke Kreuter's project, an R01 funded by the National Institute of Mental Health, seeks to develop and assess new statistical methods
Located in Research / Selected Research
Errors in Housing Unit Listing and their Effects on Survey Estimates
Frauke Kreuter, Joint Program in Survey Methodology
Located in Resources / / Seed Grant Program / Seed Grants Awarded