This open access book explores how machine learning can enhance both quantitative and qualitative research in sociology. By developing algorithms tailored to specific data, machine learning enables social scientists to uncover patterns, generate new theories, calibrate indicators, and strengthen causal inference. The book offers an accessible introduction to the principles and applications of supervised and unsupervised learning (Part I), followed by empirical case studies across key areas of sociological research. In the social prediction section (Parts II–IV), it illustrates how supervised learning can 1) impute missing indicators, 2) derive theories directly from data, and 3) improve causal inference through counterfactual construction. In the culture modeling section (Parts V–VI), it shows how unsupervised machine learning can map the structure of large-scale cultural texts―such as online novels and film databases―making complex cultural patterns visible across time and space.
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Yunsong Chen is Changjiang Distinguished Professor of sociology at the Department of Sociology, Nanjing University. He earned a D.Phil. in sociology from University of Oxford, Nuffield College.
Zhuo Chen is Postdoctoral Research Fellow in sociology at the Department of Sociology, Nanjing University. She earned a Ph.D. in sociology from Nanjing University.
Wen Ma is Research Associate at the School of Journalism and Communication, Nanjing University. She earned a Ph.D. in sociology from Nanjing University.
Guodong Ju is Postdoctoral Research Fellow in social attitudes at the China Institute, University of Alberta. He earned a Ph.D. from London School of Economics and Political Science (LSE).
This open access book explores how machine learning can enhance both quantitative and qualitative research in sociology. By developing algorithms tailored to specific data, machine learning enables social scientists to uncover patterns, generate new theories, calibrate indicators, and strengthen causal inference. The book offers an accessible introduction to the principles and applications of supervised and unsupervised learning (Part I), followed by empirical case studies across key areas of sociological research. In the social prediction section (Parts II-IV), it illustrates how supervised learning can 1) impute missing indicators, 2) derive theories directly from data, and 3) improve causal inference through counterfactual construction. In the culture modeling section (Parts V-VI), it shows how unsupervised machine learning can map the structure of large-scale cultural texts--such as online novels and film databases--making complex cultural patterns visible across time and space.
Yunsong Chen is Changjiang Distinguished Professor of sociology at the Department of Sociology, Nanjing University. He earned a D.Phil. in sociology from University of Oxford, Nuffield College.
Zhuo Chen is Postdoctoral Research Fellow in sociology at the Department of Sociology, Nanjing University. She earned a Ph.D. in sociology from Nanjing University.
Wen Ma is Research Associate at the School of Journalism and Communication, Nanjing University. She earned a Ph.D. in sociology from Nanjing University.
Guodong Ju is Postdoctoral Research Fellow in social attitudes at the China Institute, University of Alberta. He earned a Ph.D. from London School of Economics and Political Science (LSE).
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Hardcover. Condición: new. Hardcover. This open access book explores how machine learning can enhance both quantitative and qualitative research in sociology. By developing algorithms tailored to specific data, machine learning enables social scientists to uncover patterns, generate new theories, calibrate indicators, and strengthen causal inference. The book offers an accessible introduction to the principles and applications of supervised and unsupervised learning (Part I), followed by empirical case studies across key areas of sociological research. In the social prediction section (Parts IIIV), it illustrates how supervised learning can 1) impute missing indicators, 2) derive theories directly from data, and 3) improve causal inference through counterfactual construction. In the culture modeling section (Parts VVI), it shows how unsupervised machine learning can map the structure of large-scale cultural textssuch as online novels and film databasesmaking complex cultural patterns visible across time and space. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9789819564644
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This open access book explores how machine learning can enhance both quantitative and qualitative research in sociology. By developing algorithms tailored to specific data, machine learning enables social scientists to uncover patterns, generate new theories, calibrate indicators, and strengthen causal inference. The book offers an accessible introduction to the principles and applications of supervised and unsupervised learning (Part I), followed by empirical case studies across key areas of sociological research. In the social prediction section (Parts II IV), it illustrates how supervised learning can 1) impute missing indicators, 2) derive theories directly from data, and 3) improve causal inference through counterfactual construction. In the culture modeling section (Parts V VI), it shows how unsupervised machine learning can map the structure of large-scale cultural texts such as online novels and film databases making complex cultural patterns visible across time and space. 363 pp. Englisch. Nº de ref. del artículo: 9789819564644
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Hardcover. Condición: new. Hardcover. This open access book explores how machine learning can enhance both quantitative and qualitative research in sociology. By developing algorithms tailored to specific data, machine learning enables social scientists to uncover patterns, generate new theories, calibrate indicators, and strengthen causal inference. The book offers an accessible introduction to the principles and applications of supervised and unsupervised learning (Part I), followed by empirical case studies across key areas of sociological research. In the social prediction section (Parts IIIV), it illustrates how supervised learning can 1) impute missing indicators, 2) derive theories directly from data, and 3) improve causal inference through counterfactual construction. In the culture modeling section (Parts VVI), it shows how unsupervised machine learning can map the structure of large-scale cultural textssuch as online novels and film databasesmaking complex cultural patterns visible across time and space. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Nº de ref. del artículo: 9789819564644
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