NDLI: Analyzing data streams for social scientists

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Analyzing data streams for social scientists

Content Provider	Scilit
Author	Ippel, Lianne Kaptein, Maurits Vermunt, Jeroen K.
Copyright Year	2021
Description	The technological developments of the last decades have created opportunities to efficiently collect data of many individuals over time. While these technologies provide exciting research opportunities, they also provide challenges: data sets collected using these technologies grow increasingly large or are continuously augmented with new observations. These data streams make the standard computation of well-known estimators inefficient, as computations are repeated each time new data enter. This chapter details online learning, an analysis method that updates parameter estimates instead of re-estimating them to analyze large and/or streaming data. The chapter presents several simple (and exact) examples of the online estimation for independent observations. Additionally, social scientists are often faced with nested data: pupils are nested within schools, or repeated measurements are nested within individuals. Nested data are typically analyzed using multilevel models. Estimating multilevel models, however, can be challenging in data streams: the standard algorithms used to fit these models repeatedly revisit all data points, which becomes infeasible in a data stream context. We present a solution to this problem by introducing the streaming expectation maximization approximation (SEMA) algorithm for fitting multilevel models online. We end this chapter with a discussion of the methodological challenges that remain. Book Name: Handbook of Computational Social Science, Volume 2
Related Links	https://api.taylorfrancis.com/content/chapters/edit/download?identifierName=doi&identifierValue=10.4324/9781003025245-6&type=chapterpdf
Ending Page	81
Page Count	11
Starting Page	71
DOI	10.4324/9781003025245-6
Language	English
Publisher	Informa UK Limited
Publisher Date	2021-11-10
Access Restriction	Open
Subject Keyword	Book Name: Handbook of Computational Social Science, Volume 2 Multilevel Models Data Streams Nested Challenges Analyzing
Content Type	Text
Resource Type	Chapter

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