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| 020 | $z3319489402 | |
| 024 | 7_ | $a10.1007/978-3-319-48941-4$2doi |
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| 082 | 04 | $a300.1/5195$223 |
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| 245 | 00 | $aGroup processes :$bdata-driven computational approaches /$cAndrew Pilny, Marshall Scott Poole, editors. |
| 264 | _1 | $aCham, Switzerland :$bSpringer,$c[2017] |
| 264 | _4 | $c©2017 |
| 300 | $a1 online resource :$billustrations. | |
| 336 | $atext$btxt$2rdacontent | |
| 337 | $acomputer$bc$2rdamedia | |
| 338 | $aonline resource$bcr$2rdacarrier | |
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| 490 | 1_ | $aComputational social sciences |
| 505 | 0_ | $aChapter 1: Introduction; References; Chapter 2: Response Surface Models to€Analyze Nonlinear Group Phenomena; 2.1 Introduction to€Response Surface Methodology; 2.2 Brief Background of€RSM; 2.3 Basic Processes Underlying RSM; 2.3.1 Step 1: Second-Order Regression Modeling; 2.3.2 Step 2: Lack of€Fit; 2.3.3 Step 3: Coding of€Variables; 2.3.4 Step 4: Canonical Analysis of€the€Response System; 2.3.5 Step 5: Conduct Ridge Analysis if Needed; 2.4 RSM in€Context; 2.4.1 About the€Game; 2.5 Dependent Variable; 2.5.1 Team Performance; 2.6 Independent Variables; 2.6.1 Complexity. |
| 505 | 8_ | $a2.6.2 Difficulty2.7 Control Variables; 2.7.1 Group Size; 2.8 Data Analysis; 2.8.1 Controlling for€Group Size; 2.8.2 Experience Points: A€Minimum Stationary Point; 2.9 Results; 2.9.1 Model for€Deaths: A€Saddle Point; 2.10 Conclusion; References; Chapter 3: Causal Inference Using Bayesian Networks; 3.1 Introduction; 3.2 Scenario; 3.2.1 Variables; 3.2.2 Data Preparation; 3.3 Description of€Weka Environment; 3.4 Running Bayesian Network Analysis in€Weka; 3.4.1 Analysis with€All Variables; 3.4.2 Understanding Weka Output; 3.4.3 Assessing Information Gain; 3.4.4 Re-run with€Selected Variables. |
| 505 | 8_ | $a3.4.5 Probability Distribution3.4.6 Re-run with€Two Parent Nodes; 3.5 Conclusion; References; Chapter 4: A Relational Event Approach to€Modeling Behavioral Dynamics; 4.1 Representing Interaction: From€Social Networks to€Relational Events; 4.1.1 Prefatory Notes; 4.2 Overview of€the€Relational Event Framework; 4.3 Sample Cases; 4.3.1 Butts et€al.'s WTC Data; 4.3.2 McFarland's Classroom Data; 4.4 Tutorial; 4.4.1 Ordinal Time Event Histories; 4.4.2 A First Model: Exploring ICR Effects; 4.4.3 Bringing in€Endogenous Social Dynamics; 4.4.4 Assessing Model Adequacy; 4.5 Exact Time Histories. |
| 505 | 8_ | $a4.5.1 Modeling with€Covariates4.5.2 Modeling Endogenous Social Dynamics; 4.5.3 Interpretation of€a€Fitted Model; 4.5.4 Assessing Model Adequacy; 4.6 Conclusion; References; Chapter 5: Text Mining Tutorial; 5.1 Introduction; 5.2 Overview of€Text Mining; 5.3 Text Mining Tutorial; 5.3.1 Data Collection; 5.3.2 Data Preparation; 5.3.3 Preprocessing; 5.3.4 Data Analysis; 5.3.5 Interpretation; 5.4 Contributions; References; Chapter 6: Sequential Synchronization Analysis; 6.1 Introduction; 6.2 Sequence Analysis; 6.2.1 Sequence Data; 6.2.2 Analyzing Sequences; 6.2.2.1 Whole Sequence Analysis. |
| 505 | 8_ | $a6.2.2.2 Subsequence Analysis6.3 Sequential Synchronization Analysis; 6.3.1 Individual Sequences into Group Processes; 6.3.2 Entrainment; 6.4 A Step-by-Step Guide to€Sequential Synchronization Analysis; 6.4.1 Step 1: Theoretically Define the€Units of€Interest; 6.4.2 Step 2: Extract Subsequences from€Data; 6.4.3 Step 3: Revisit Theoretically Defined Subsequences in€Light of€Sequence Mining Results; 6.4.4 Step 4: Aggregate Frequency Counts of€Subsequences for€Data Segments; 6.4.5 Step 5: Compute Synchronization Scores; 6.5 Example; 6.6 Discussion; References. |
| 504 | $aIncludes bibliographical references. | |
| 588 | 0_ | $aVendor-supplied metadata. |
| 520 | $aThis volume introduces a series of different data-driven computational methods for analyzing group processes through didactic and tutorial-based examples. Group processes are of central importance to many sectors of society, including government, the military, health care, and corporations. Computational methods are better suited to handle (potentially huge) group process data than traditional methodologies because of their more flexible assumptions and capability to handle real-time trace data. Indeed, the use of methods under the name of computational social science have exploded over the years. However, attention has been focused on original research rather than pedagogy, leaving those interested in obtaining computational skills lacking a much needed resource. Although the methods here can be applied to wider areas of social science, they are specifically tailored to group process research. A number of data-driven methods adapted to group process research are demonstrated in this current volume. These include text mining, relational event modeling, social simulation, machine learning, social sequence analysis, and response surface analysis. In order to take advantage of these new opportunities, this book provides clear examples (e.g., providing code) of group processes in various contexts, setting guidelines and best practices for future work to build upon. This volume will be of great benefit to those willing to learn computational methods. These include academics like graduate students and faculty, multidisciplinary professionals and researchers working on organization and management science, and consultants for various types of organizations and groups. | |
| 650 | _0 | $aSocial sciences$xStatistical methods$xData processing. |
| 650 | _7 | $aSOCIAL SCIENCE$xEssays.$2bisacsh |
| 650 | _7 | $aSOCIAL SCIENCE$xReference.$2bisacsh |
| 650 | _7 | $aSocial sciences$xStatistical methods$xData processing.$2fast$0(OCoLC)fst01122985 |
| 700 | 1_ | $aPilny, Andrew,$eeditor. |
| 700 | 1_ | $aPoole, Marshall Scott,$d1951-$eeditor. |
| 776 | 08 | $iPrint version:$tGroup processes.$dCham, Switzerland : Springer, [2017]$z3319489402$z9783319489407$w(OCoLC)959951241 |
| 830 | _0 | $aComputational social sciences |
| 856 | 40 | $uhttp://link.springer.com/10.1007/978-3-319-48941-4 |