@book{MunzertRubbaMeissneretal., author = {Munzert, Simon and Rubba, Christian and Meißner, Peter and Nyhuis, Dominic}, title = {Automated Data Collection with R: A Practical Guide to Web Scraping and Text Mining}, publisher = {John Wiley \& Sons}, address = {Chichester}, isbn = {978-1-118-83481-7}, doi = {10.1002/9781118834732}, publisher = {Hertie School}, pages = {474}, abstract = {A hands on guide to web scraping and text mining for both beginners and experienced users of R Introduces fundamental concepts of the main architecture of the web and databases and covers HTTP, HTML, XML, JSON, SQL. Provides basic techniques to query web documents and data sets (XPath and regular expressions). An extensive set of exercises are presented to guide the reader through each technique. Explores both supervised and unsupervised techniques as well as advanced techniques such as data scraping and text management. Case studies are featured throughout along with examples for each technique presented. R code and solutions to exercises featured in the book are provided on a supporting website.}, language = {en} } @article{BernauerMunzert, author = {Bernauer, Julian and Munzert, Simon}, title = {Loyal to the Game? Strategic Policy Representation in Mixed Electoral Systems}, series = {Representation. Journal of Representative Democracy}, volume = {50}, journal = {Representation. Journal of Representative Democracy}, number = {1}, doi = {10.1080/00344893.2014.902221}, pages = {83 -- 97}, abstract = {In Germany's compensatory mixed electoral system, alternative electoral routes lead into parliament. We study the relationship between candidates' electoral situations across both tiers and policy representation, fully accounting for candidate, party and district preferences in a multi-actor constellation and the exact electoral incentives for candidates to represent either the party or the district. The results (2009 Bundestag election data) yield evidence of an interactive effect of closeness of the district race and list safety on candidates' positioning between their party and constituency.}, language = {en} } @article{Munzert, author = {Munzert, Simon}, title = {Big Data in der Forschung! Big Data in der Lehre? Ein Vorschlag zur Erweiterung der bestehenden Methodenausbildung}, series = {Zeitschrift f{\"u}r Politikwissenschaft}, volume = {24}, journal = {Zeitschrift f{\"u}r Politikwissenschaft}, number = {1-2}, issn = {1430-6387}, doi = {10.5771/1430-6387-2014-1-2-205}, pages = {207 -- 222}, language = {de} } @incollection{ShikanoMunzertSchuebeletal., author = {Shikano, Susumu and Munzert, Simon and Sch{\"u}bel, Thomas and Herrmann, Michael and Selb, Peter}, title = {Eine empirische Sch{\"a}tzmethode f{\"u}r Valenz-Issues auf der Basis der Kandidatenbeurteilung am Beispiel der Konstanzer Oberb{\"u}rgermeisterwahl 2012}, series = {Jahrbuch f{\"u}r Handlungs- und Entscheidungstheorie}, booktitle = {Jahrbuch f{\"u}r Handlungs- und Entscheidungstheorie}, editor = {Linhart, Eric and Kittel, Bernhard and B{\"a}chtiger, Andr{\´e}}, publisher = {Springer}, address = {Wiesbaden}, isbn = {978-3-658-05007-8}, doi = {10.1007/978-3-658-05008-5_4}, publisher = {Hertie School}, pages = {113 -- 131}, abstract = {Bei der Entwicklung der r{\"a}umlichen Modelle des Parteienwettbewerbs spielt die Valenz eine wichtige Rolle. Trotz der theoretischen Relevanz bleibt die Mess- und Sch{\"a}tzmethode der Valenz unterentwickelt. Angesichts dieser Forschungsl{\"u}cke schl{\"a}gt dieser Beitrag ein statistisches Modell vor, das die gleichzeitige Sch{\"a}tzung der Kandidatenpositionen und der Valenz erm{\"o}glicht. Ein wichtiger Vorzug dieses Modells liegt darin, dass man nur die Kandidatenbeurteilungen per Skalometer ben{\"o}tigt, der in den meisten Umfragedaten verf{\"u}gbar ist. Dieses Modell wird auf Daten angewendet, die in Rahmen der Konstanzer Oberb{\"u}rgermeisterwahl 2012 erhoben wurden.}, language = {de} } @misc{Munzert, author = {Munzert, Simon}, title = {XML and Web Technologies for Data Sciences with R}, series = {Journal of Statistical Software}, volume = {81}, journal = {Journal of Statistical Software}, doi = {10.18637/jss.v061.b01}, language = {de} }