Data science, statistical modelling, and machine learning methods
著者
書誌事項
Data science, statistical modelling, and machine learning methods
(European Association of Methodology series, . Handbook of computational social science ; v. 2)
Routledge, 2022
- : hbk
大学図書館所蔵 全3件
  青森
  岩手
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  福島
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  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
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注記
Other editors: Anabel Quan-Haase, Sunny Xun Liu, Lars Lyberg
Includes bibliographical references and index
内容説明・目次
内容説明
1. Very comprehensive and extensive coverage (stresses the relevance of the entire research cycle, from design to data collection to analysis to interpretation). 2. Highlights the multidisciplinary nature of CSS, drawing from research in computer science, statistics, and the social and behavioural sciences. 3. Takes a holistic approach to CSS methods. Instead of focusing on simply harvesting data, the editors emphasise the importance of a carefully crafted research design containing key milestone checks. 4. Covers important and emergent topics in the field like the relationship between CSS, AI and machine learning.
目次
Preface
Introduction to the Handbook of Computational Social Science
Uwe Engel, Anabel Quan-Haase, Sunny Xun Liu and Lars Lyberg
Section I. Data in CSS: Collection, Management, and Cleaning
A Brief History of APIs: Limitations and Opportunities for Online Research
Jakob Junger
Application Programming Interfaces and Web Data For Social Research
Dominic Nyhuis
Web Data Mining: Collecting Textual Data from Web Pages Using R
Stefan Bosse, Lena Dahlhaus and Uwe Engel
Analyzing Data Streams for Social Scientists
Lianne Ippel, Maurits Kaptein and Jeroen Vermunt
Handling Missing Data in Large Data Bases
Martin Spiess and Thomas Augustin
A Primer on Probabilistic Record Linkage
Ted Enamorado
Reproducibility and Principled Data Processing
John McLevey, Pierson Browne and Tyler Crick
Section II. Data Quality in CSS Research
Applying a Total Error Framework for Digital Traces to Social Media Research
Indira Sen, Fabian Floeck, Katrin Weller, Bernd Weiss and Claudia Wagner
Crowdsourcing in Observational and Experimental Research
Camilla Zallot, Gabriele Paolacci, Jesse Chandler and Itay Sisso
Inference from Probability and Nonprobability Samples
Rebecca Andridge and Richard Valliant
Challenges of Online Non-Probability Surveys
Jelke Bethlehem
Section III. Statistical Modelling and Simulation
Large-scale Agent-based Simulation and Crowd Sensing with Mobile Agents
Stefan Bosse
Agent-based Modelling for Cultural Networks: Tagging by Artificial Intelligent Cultural Agents
Fernando Sancho-Caparrini and Juan Luis Suarez
Using Subgroup Discovery and Latent Growth Curve Modeling to Identify Unusual Developmental Trajectories
Axel Mayer, Christoph Kiefer, Benedikt Langenberg and Florian Lemmerich
Disaggregation via Gaussian Regression for Robust Analysis of Heterogeneous Data
Nazanin Alipourfard, Keith Burghardt and Kristina Lerman
Section IV: Machine Learning Methods
Machine Learning Methods for Computational Social Science
Richard D. De Veaux and Adam Eck
Principal Component Analysis
Andreas Poege and Jost Reinecke
Unsupervised Methods: Clustering Methods
Johann Bacher, Andreas Poege and Knut Wenzig
Text Mining and Topic Modeling
Raphael H. Heiberger and Sebastian Munoz-Najar Galvez
From Frequency Counts to Contextualized Word Embeddings: The Saussurean Turn in Automatic Content Analysis
Gregor Wiedemann and Cornelia Fedtke
Automated Video Analysis for Social Science Research
Dominic Nyhuis, Tobias Ringwald, Oliver Rittmann, Thomas Gschwend and Rainer Stiefelhagen
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