Jost Reinecke

Uwe Engel

Advanced Techniques in Panel Data Analysis

Analysis of Change

Measurement error and nonresponse represent real threats to the quality of survey data. Unit and item nonresponse, survey modes, question wordings and response formats are potential sources of error. In addition, survey methods are developing further to keep step with a continuously changing world. The identification and proper handling of threats to data quality requires thus continued efforts and research "on" survey methods. The volume contributes to this demand recent developments and research findings. Its 12 chapters address issues of data quality with respect to survey questions and different kinds of surveys and report developments in the field of missing data handling. Key topics include motivated misreporting, audio-recording of open-ended questions, framing effects, MTMM modeling, web, mobile web and mixed-mode research, experience sampling, estimates of change, missing data handling and multiple imputation. Contributions come from Germany, the Netherlands, Spain, the United Kingdom and the United States.

Techniques, Data Quality and Sources of Error

Survey Measurements

Anabel Quan-Haase

Sunny Xun Liu

Lars E Lyberg

The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches. The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This first volume focuses on the scope of computational social science, ethics, and case studies. It covers a range of key issues, including open science, formal modeling, and the social and behavioral sciences. This volume explores major debates, introduces digital trace data, reviews the changing survey landscape, and presents novel examples of computational social science research on sensing social interaction, social robots, bots, sentiment, manipulation, and extremism in social media. The volume not only makes major contributions to the consolidation of this growing research field but also encourages growth in new directions. With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates, and researchers engaging with computational methods across the social sciences, as well as those within the scientifi c and engineering sectors.

Theory, Case Studies and Ethics

Handbook of Computational Social Science, Volume 1

By making both causal and process analyses possible, panel data has enjoyed increasing popularity in empirical science. In this compilation, several statistical techniques are presented in the face of a growing need to analyze panel data. Measurement error, missing data, heterogeneous populations and particular requirements for causal interference make the analysis of change more difficult. Readers will find up-to-date approaches covering a wide range of topics. Among these are loglinear and probit models, state space models, and structural equation and multilevel growth curve models of panel data.

Uwe Engel

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