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• Preface xi
• Part 1. Symbolic Data 1
• Chapter 1. Explanatory Tools for Machine Learning in the Symbolic Data Analysis Framework 3 Edwin DIDAY
• 1.1. Introduction 4
• 1.2. Introduction to Symbolic Data Analysis 6
• 1.2.1. What are complex data? 6
• 1.2.2. What are "classes" and "class of complex data"? 7
• 1.2.3. Which kind of class variability? 7
• 1.2.4. What are "symbolic variables" and "symbolic data tables"? 7
• 1.2.5. Symbolic Data Analysis (SDA) 9
• 1.3. Symbolic data tables from Dynamic Clustering Method and EM 10
• 1.3.1. The "dynamical clustering method" (DCM) 10
• 1.3.2. Examples of DCM applications 10
• 1.3.3. Clustering methods by mixture decomposition 12
• 1.3.4. Symbolic data tables from clustering 13
• 1.3.5. A general way to compare results of clustering methods by the "explanatory power" of their associated symbolic data table 15
• 1.3.6. Quality criteria of classes and variables based on the cells of the symbolic data table containing intervals or inferred distributions 15
• 1.4. Criteria for ranking individuals, classes and their bar chart descriptive symbolic variables 16
• 1.4.1. A theoretical framework for SDA 16
• 1.4.2. Characterization of a category and a class by a measure of discordance 18
• 1.4.3. Link between a characterization by the criteria W and the standard Tf-Idf 19
• 1.4.4. Ranking the individuals, the symbolic variables and the classes of a bar chart symbolic data table 21
• 1.5. Two directions of research 23
• 1.5.1. Parametrization of concordance and discordance criteria 23
• 1.5.2. Improving the explanatory power of any machine learning tool by a filtering process 25
• 1.6. Conclusion 27
• 1.7. References 28
• Chapter 2. Likelihood in the Symbolic Context 31 Richard EMILION and Edwin DIDAY
• 2.1. Introduction 31
• 2.2. Probabilistic setting 32
• 2.2.1. Description variable and class variable 32
• 2.2.2. Conditional distributions 33
• 2.2.3. Symbolic variables 33
• 2.2.4. Examples 35
• 2.2.5. Probability measures on (?, C), likelihood 37
• 2.3. Parametric models for p = 1 38
• 2.3.1. LDA model 38
• 2.3.2. BLS method 41
• 2.3.3. Interval-valued variables 42
• 2.3.4. Probability vectors and histogram-valued variables 42
• 2.4. Nonparametric estimation for p = 1 45
• 2.4.1. Multihistograms and multivariate polygons 45
• 2.4.2. Dirichlet kernel mixtures 45
• 2.4.3. Dirichlet Process Mixture (DPM) 45
• 2.5. Density models for p
• 2 46
• 2.6. Conclusion 46
• 2.7. References 47
• Chapter 3. Dimension Reduction and Visualization of Symbolic Interval-Valued Data Using Sliced Inverse Regression 49 Han-Ming WU, Chiun-How KAO and Chun-houh CHEN
• 3.1. Introduction 49
• 3.2. PCA for interval-valued data and the sliced inverse regression 51
• 3.2.1. PCA for interval-valued data 51
• 3.2.2. Classic SIR 52
• 3.3. SIR for interval-valued data 53
• 3.3.1. Quantification approaches 54
• 3.3.2. Distributional approaches 56
• 3.4. Projections and visualization in DR subspace 58
• 3.4.1. Linear combinations of intervals 58
• 3.4.2. The graphical representation of the projected intervals in the 2D DR subspace 59
• 3.5. Some computational issues 61
• 3.5.1. Standardization of interval-valued data 61
• 3.5.2. The slicing schemes for iSIR 62
• 3.5.3. The evaluation of DR components 62
• 3.6. Simulation studies 63
• 3.6.1. Scenario
• 1: aggregated data 63
• 3.6.2. Scenario
• 2: data based on interval arithmetic 63
• 3.6.3. Results 64
• 3.7. A real data example: face recognition data 65
• 3.8. Conclusion and discussion 73
• 3.9. References 74
• Chapter 4. On the "Complexity" of Social Reality. Some Reflections About the Use of Symbolic Data Analysis in Social Sciences 79 Frederic LEBARON
• 4.1. Introduction 79
• 4.2. Social sciences facing "complexity" 80
• 4.2.1. The total social fact, a designation of "complexity" in social sciences 80
• 4.2.2. Two families of answers 80
• 4.2.3. The contemporary deepening of the two approaches, "reductionist" and "encompassing" 81
• 4.2.4. Issues of scale and heterogeneity 82
• 4.3. Symbolic data analysis in the social sciences: an example 83
• 4.3.1. Symbolic data analysis 83
• 4.3.2. An exploratory case study on European data 83
• 4.3.3. A sociological interpretation 94
• 4.4. Conclusion 95
• 4.5. References 96
• Part 2. Complex Data 99
• Chapter 5. A Spatial Dependence Measure and Prediction of Georeferenced Data Streams Summarized by Histograms 101 Rosanna VERDE and Antonio BALZANELLA
• 5.1. Introduction 101
• 5.2. Processing setup 103
• 5.3. Main definitions 104
• 5.4. Online summarization of a data stream through CluStream for Histogram data 106
• 5.5. Spatial dependence monitoring: a variogram for histogram data 107
• 5.6. Ordinary kriging for histogram data 110
• 5.7. Experimental results on real data 112
• 5.8. Conclusion 116
• 5.9. References 116
• Chapter 6. Incremental Calculation Framework for Complex Data 119 Huiwen WANG, Yuan WEI and Siyang WANG
• 6.1. Introduction 119
• 6.2. Basic data 122
• 6.2.1. The basic data space 122
• 6.2.2. Sample covariance matrix 123
• 6.3. Incremental calculation of complex data 124
• 6.3.1. Transformation of complex data 124
• 6.3.2. Online decomposition of covariance matrix 125
• 6.3.3. Adopted algorithms 128
• 6.4. Simulation studies 131
• 6.4.1. Functional linear regression 131
• 6.4.2. Compositional PCA 133
• 6.5. Conclusion 135
• 6.6. Acknowledgment 135
• 6.7. References 135
• Part 3. Network Data 139
• Chapter 7. Recommender Systems and Attributed Networks 141 Francoise FOGELMAN-SOULIE, Lanxiang MEI, Jianyu ZHANG, Yiming LI, Wen GE, Yinglan LI and Qiaofei YE
• 7.1. Introduction 141
• 7.2. Recommender systems 142
• 7.2.1. Data used 143
• 7.2.2. Model-based collaborative filtering 145
• 7.2.3. Neighborhood-based collaborative filtering 145
• 7.2.4. Hybrid models 148
• 7.3. Social networks 150
• 7.3.1. Non-independence 150
• 7.3.2. Definition of a social network 150
• 7.3.3. Properties of social networks 151
• 7.3.4. Bipartite networks 152
• 7.3.5. Multilayer networks 153
• 7.4. Using social networks for recommendation 154
• 7.4.1. Social filtering 154
• 7.4.2. Extension to use attributes 155
• 7.4.3. Remarks 156
• 7.5. Experiments 156
• 7.5.1. Performance evaluation 156
• 7.5.2. Datasets 157
• 7.5.3. Analysis of one-mode projected networks 158
• 7.5.4. Models evaluated 160
• 7.5.5. Results 160
• 7.6. Perspectives 163
• 7.7. References 163
• Chapter 8. Attributed Networks Partitioning Based on Modularity Optimization 169 David COMBE, Christine LARGERON, Baptiste JEUDY, Francoise FOGELMAN-SOULIE and Jing WANG
• 8.1. Introduction 169
• 8.2. Related work 171
• 8.3. Inertia based modularity 172
• 8.4. I-Louvain 174
• 8.5. Incremental computation of the modularity gain 176
• 8.6. Evaluation of I-Louvain method 179
• 8.6.1. Performance of I-Louvain on artificial datasets 179
• 8.6.2. Run-time of I-Louvain 180
• 8.7. Conclusion 181
• 8.8. References 182
• Part 4. Clustering 187
• Chapter 9. A Novel Clustering Method with Automatic Weighting of Tables and Variables 189 Rodrigo C. DE ARAUJO, Francisco DE ASSIS TENORIO DE CARVALHO and Yves LECHEVALLIER
• 9.1. Introduction 189
• 9.2. Related Work 190
• 9.3. Definitions, notations and objective 191
• 9.3.1. Choice of distances 192
• 9.3.2. Criterion W measures the homogeneity of the partition P on the set of tables 193
• 9.3.3. Optimization of the criterion W 195
• 9.4. Hard clustering with automated weighting of tables and variables 196
• 9.4.1. Clustering algorithms MND-W and MND-WT 196
• 9.5. Applications: UCI data sets 201
• 9.5.1. Application I: Iris plant 201
• 9.5.2. Application II: multi-features dataset 204
• 9.6. Conclusion 206
• 9.7. References 206
• Chapter 10. Clustering and Generalized ANOVA for Symbolic Data Constructed from Open Data 209 Simona KORENJAK-?ERNE, Natasa KEJ AR and Vladimir BATAGELJ
• 10.1. Introduction 209
• 10.2. Data description based on discrete (membership) distributions 210
• 10.3. Clustering 212
• 10.3.1. TIMSS - study of teaching approaches 215
• 10.3.2. Clustering countries based on age-sex distributions of their populations 217
• 10.4. Generalized ANOVA 221
• 10.5. Conclusion 225
• 10.6. References 226
• List of Authors 229
• Index 233.
• (source: Nielsen Book Data)

Data science unifies statistics, data analysis and machine learning to achieve a better understanding of the masses of data which are produced today, and to improve prediction. Special kinds of data (symbolic, network, complex, compositional) are increasingly frequent in data science. These data require specific methodologies, but there is a lack of reference work in this field. Advances in Data Science fills this gap. It presents a collection of up-to-date contributions by eminent scholars following two international workshops held in Beijing and Paris. The 10 chapters are organized into four parts: Symbolic Data, Complex Data, Network Data and Clustering. They include fundamental contributions, as well as applications to several domains, including business and the social sciences.
(source: Nielsen Book Data)

• Section-1: Data Science - The "What" Chapter-1: IntroductionFirst chapter will set the basic foundation of the subject for students. Like many other books, this introductory level chapter will comprise of the basic concepts. Introduction of the following concepts will be discussed:* Data Science* Importance of data science* Applications of data science* Data Driven Decision Making* Data analysisChapter-2: Widely used techniques in data scienceThis chapter will discuss the concepts required for one to start working on data analysis. Chapter will comprise of the concepts that student should know before performing any task on data analysis and some of the tasks that can be performed as part of data analysis. Following concepts will be discussed.* Supervised vs Unsupervised data* Data understanding* Data preparation* Modeling* Overfitting* Random sampling* Cross Validation* Feature selection* Outlier detection* Rule extractionSection-2: Data science: The "How" Chapter-3: Statistical InferenceEvery part of data analysis involves statistics and statistical inference to properly utilize data and perform decision making. This chapter will provide statistical concepts to support the data analysis tasks performed by students for decision making with real life data. Following topics will be discussed:* Probability theory* Transformations and expectations* Common families of distribution* Random variables* Preparation of random samples* Asymptotic evaluations* Regression and regression models Chapter-4: Supervised Learning In real world, we come across two types of data, supervised and unsupervised. In this chapter, we will discuss the concepts, tools and techniques related to processing of supervised data with examples and decision making out of it. The following concepts will be discussed:* Supervised Learning* Classification and Regression* Generalization, Overfitting and Underfitting* Evaluation models* Supervised learning algorithmsChapter-5: Unsupervised LearningThe unsupervised data forms the other half of the data available in real world applications. Like previous chapter, this chapter will include the concepts, tools and techniques related to unsupervised data with examples. Following contents will be included:* Challenges of unsupervised learning* Processing and scaling* Clustering* Dimensionality reduction, feature extraction and manifold learning* Unsupervised learning algorithmsChapter-6: Natural language processingIn this chapter, we will focus on one particular sort of data that has become extremely common i.e. text data. We will see in this chapter the fundamental principles of natural language processing and will look at one of the common application of NLP that is sentiment analysis. Following contents will be discussed:* Why Text Is Important* Why Text Is Difficult* Representation* Sentiment Analysis* Lexicon-based Approaches for Text MiningSection-3: Data Science - The "Where" Chapter-7: Customers AnalyticsIn this chapter, we will introduce he use of analytics for understanding customers and predicting their behaviour in different situations. This includes the understanding of loyalty programs, market research, understanding customer lifetime value, predicting churn, and identifying potential defaulters. These are few examples of what will be contained in this chapter. Chapter-8: Operations AnalyticsIn this chapter, we will prepare our readers to understand and acknowledge the use of data science for improving business operations. For example, we will discuss how analyzing data can help avoid service outages, or at least predict the service outage in order to prepare contingency plans. Analyzing data can also help in identifying redundancies which can be removed in order to significantly reduce operational costs. We will give examples on how various manufacturing and service industries are using real-time sensor data to track their systems wear and tear. This helps them improve their mean time to repair by forecasting breakdown of different components well ahead in time.
• (source: Nielsen Book Data)

This book comprehensively covers the topic of data science. Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three sections: The first section is an introduction to data science. Starting from the basic concepts, the book will highlight the types of data, its use, its importance and issues that are normally faced in data analytics. Followed by discussion on wide range of applications of data science and widely used techniques in data science. The second section is devoted to the tools and techniques of data science. It consists of data pre-processing, feature selection, classification and clustering concepts as well as an introduction to text mining and opining mining. And finally, the third section of the book focuses on two programming languages commonly used for data science projects i.e. Python and R programming language. Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. The book is suitable for both undergraduate and postgraduate students as well as those carrying out research in data science. It can be used as a textbook for undergraduate students in computer science, engineering and mathematics. It can also be accessible to undergraduate students from other areas with the adequate background. The more advanced chapters can be used by postgraduate researchers intending to gather a deeper theoretical understanding.
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'The Philosophy of Quantitative Methods' undertakes a philosophical examination of a number of important quantitative research methods within the behavioral sciences in order to overcome the non-critical approaches typically provided by textbooks. These research methods are exploratory data analysis, statistical significance testing, Bayesian confirmation theory and statistics, meta-analysis, and exploratory factor analysis. Further readings are provided to extend the reader's overall understanding of these methods.

• SECTION I: An Overview of Research in the Social Sciences: Qualitative Meets Quantitative
• Chapter 1: Understanding the Purpose of Research in the Qualitative and Quantitative Traditions
• Chapter 2: An Overview of the Qualitative Tradition and Connections to the Quantitative Tradition
• Chapter 3: Overview of Research in the Quantitative Tradition
• SECTION II: The Sine Qua Non for Conducting Research in the Quantitative Tradition
• Chapter 4: Choosing Research Participants and Making Generalizations: Sampling and External Validity
• Chapter 5: Measurement and Instrumentation in Quantitative Research
• Chapter 6: Minimizing Alternative Explanations for Research Findings: Internal Validity
• SECTION III: Research Design and Data Analysis in the Quantitative Tradition
• Chapter 7: Non-Experimental Research Designs
• Chapter 8: Experimental Research Designs
• Chapter 9: Descriptive Analyses for Data Generated by Quantitative Research
• Chapter 10: Inferential Analyses for Data Generated by Quantitative Research
• Chapter 11: The Complementary Natures of Quantitative and Qualitative Research.
• (source: Nielsen Book Data)

O'Dwyer and Bernauer promote understanding and appreciation of the quantitative tradition in the social sciences, especially for those who are most familiar with the qualitative tradition. The authors demonstrate that these two research traditions are complementary and that the primary components of good research--problem quality, design quality, evidence quality, and procedural quality--apply equally to both qualitative and quantitative research methodologies. By providing rich pedagogy throughout the book the authors give students ample support and guidance. Each chapter begins with an outline, key terms are defined throughout, and discussion questions conclude each chapter. Connections to Qualitative Research and Review of Published Research sections offer students opportunities to make connections to their current base of knowledge and to see the concepts described in the book being applied in real research.
(source: Nielsen Book Data)

• Part I. Quantitative History:
• 1. History's most significant people--
• 2. Ranking historical figures--
• 3. Who belongs in Bonnie's textbook?--
• 4. Reading through the past--
• 5. Great Americans and the process of canonization--
• 6. The baseball hall of fame--
• 7. Historical time scales-- Part II. Historical Rankings:
• 8. American political figures--
• 9. Modern world leaders--
• 10. Science and technology--
• 11. Religion and philosophy--
• 12. Sports--
• 13. The arts--
• 14. The performing arts--
• 15. Devils and angels-- Part III. Appendices: A. Ranking methodology-- B. Resources-- C. Biographical dictionary.
• (source: Nielsen Book Data)

Is Hitler bigger than Napoleon? Washington bigger than Lincoln? Picasso bigger than Einstein? Quantitative analysts are rapidly finding homes in social and cultural domains, from finance to politics. What about history? In this fascinating book, Steve Skiena and Charles Ward bring quantitative analysis to bear on ranking and comparing historical reputations. They evaluate each person by aggregating the traces of millions of opinions, just as Google ranks webpages. The book includes a technical discussion for readers interested in the details of the methods, but no mathematical or computational background is necessary to understand the rankings or conclusions. Along the way, the authors present the rankings of more than one thousand of history's most significant people in science, politics, entertainment, and all areas of human endeavor. Anyone interested in history or biography can see where their favorite figures place in the grand scheme of things.
(source: Nielsen Book Data)

• Part I: Foundations for Working with Stata
• Chapter 1: Getting to Know Stata 15 What You See Getting Started With Data Files
• Chapter 2: The Essentials Intuition and Stata Commands The Structure of Stata Commands The 5 Essential Commands Nonessential, Everyday Commands
• Chapter 3: Do Files and Data Management What Is a Do File? Data Management Part II: Quantitative Analysis With Stata
• Chapter 4: Descriptive Statistics Frequency Distributions Measures of Central Tendency and Variability
• Chapter 5: Relationships Between Nominal and Ordinal Variables Cross-Tabulations
• Chapter 6: Relationships Between Different Measurement Levels Testing Means Analysis of Variance (ANOVA)
• Chapter 7: Relationships Between Interval-Ratio Variables Correlation Linear Regression
• Chapter 8: Enhancing Your Command Repertoire Stata Help Files Advanced Convenience Commands Expanding Stata's Capabilities.
• (source: Nielsen Book Data)

Using Stata for Quantitative Analysis offers a brief but thorough introduction to analyzing data in undergraduate and graduate level research methods statistics, and data analysis courses using Stata software. Kyle C. Longest teaches the language of Stata from an intuitive perspective, allowing students with no experience in statistical software to start working with data quickly and complete a basic quantitative research project from start to finish. The Third Edition covers the use of Stata 15 and includes more information on data management and non-linear regression techniques. Enhanced layouts make finding important commands even easier.
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This series of books collects a diverse array of work that provides the reader with theoretical and applied information on data analysis methods, models and techniques, along with appropriate applications. Volume 2 begins with an introductory chapter by Gilbert Saporta, a leading expert in the field, who summarizes the developments in data analysis over the last 50 years. The book is then divided into four parts: Part 1 examines (in)dependence relationships, innovation in the Nordic countries, dentistry journals, dependence among growth rates of GDP of V4 countries, emissions mitigation, and five-star ratings; Part 2 investigates access to credit for SMEs, gender-based impacts given Southern Europe's economic crisis, and labor market transition probabilities; Part 3 looks at recruitment at university job-placement offices and the Program for International Student Assessment; and Part 4 examines discriminants, PageRank, and the political spectrum of Germany.

• Introduction-- Part I. Probability:
• 1. Probability theory--
• 2. Summarizing distributions-- Part II. Statistics:
• 3. Learning from random samples--
• 4. Regression--
• 5. Parametric models-- Part III. Identification:
• 6. Missing data--
• 7. Causal inference.
• (source: Nielsen Book Data)

Reflecting a sea change in how empirical research has been conducted over the past three decades, Foundations of Agnostic Statistics presents an innovative treatment of modern statistical theory for the social and health sciences. This book develops the fundamentals of what the authors call agnostic statistics, which considers what can be learned about the world without assuming that there exists a simple generative model that can be known to be true. Aronow and Miller provide the foundations for statistical inference for researchers unwilling to make assumptions beyond what they or their audience would find credible. Building from first principles, the book covers topics including estimation theory, regression, maximum likelihood, missing data, and causal inference. Using these principles, readers will be able to formally articulate their targets of inquiry, distinguish substantive assumptions from statistical assumptions, and ultimately engage in cutting-edge quantitative empirical research that contributes to human knowledge.
(source: Nielsen Book Data)

• A. Working, M. Alqawba, and N. Diawara: Functional Form of Markovian Attribute-level Best-Worst Discrete Choice Modelling
• D. Hitchcock, H. Liu, and S. Zahra Samadi
• Spatial and Spatio-temporal Analysis of Precipitation Data from South Carolina
• D. Musgrove, D. Young, J. Hughes, and L. E. Eberly: A sparse areal mixed model for multivariate outcomes, with an application to zero-inflated Census data
• E. M. Maboudou-Tchao: Wavelet Kernels for Support Matrix Machines
• S. A. Janse and K. L. Thompson: Properties of the number of iterations of a feasible solutions algorithm
• R. Dey and M. S. Mulekar: A Primer of Statistical Methods for Classification
• M. Sheth-Chandra, N. R. Chaganty, and R. T. Sabo: A Doubly-Inflated Poisson Distribution and Regression Model
• J. Mathews, S. Sen, and I. Das: Multivariate Doubly-Inflated Negative Binomial Distribution using Gaussian Copula
• J. Lorio, N. Diawara, and L. Waller: Quantifying spatio-temporal characteristics via Moran's statistics.

This contributed volume features invited papers on current models and statistical methods for spatial and multivariate data. With a focus on recent advances in statistics, topics include spatio-temporal aspects, classification techniques, the multivariate outcomes with zero and doubly-inflated data, discrete choice modelling, copula distributions, and feasible algorithmic solutions. Special emphasis is placed on applications such as the use of spatial and spatio-temporal models for rainfall in South Carolina and the multivariate sparse areal mixed model for the Census dataset for the state of Iowa. Articles use simulated and aggregated data examples to show the flexibility and wide applications of proposed techniques. Carefully peer-reviewed and pedagogically presented for a broad readership, this volume is suitable for graduate and postdoctoral students interested in interdisciplinary research. Researchers in applied statistics and sciences will find this book an important resource on the latest developments in the field. In keeping with the STEAM-H series, the editors hope to inspire interdisciplinary understanding and collaboration.-- Provided by publisher.

• Introduction
• Teaching to the test: Goodhart's Law and the paradox of metrics
• The ins and outs: the logic model and program evaluation
• The long and short of it: intertemporal problems and undervaluing time
• The problem of per: denominator errors
• The forest and the trees: simplifying complex systems
• Apples and oranges: ignoring differing qualities
• Not everything that can be counted counts: the lamppost problem
• Not everything that counts can be counted: measuring what matters
• The measure of metrics
• Gateways not yardsticks

Highlights the pitfalls of data analysis and emphasizes the importance of using the appropriate metrics before making key decisions. Big data is often touted as the key to understanding almost every aspect of contemporary life. This critique of "information hubris" shows that even more important than data is finding the right metrics to evaluate it. The author, an expert in environmental design and city planning, examines the many ways in which we measure ourselves and our world. He dissects the metrics we apply to health, worker productivity, our children's education, the quality of our environment, the effectiveness of leaders, the dynamics of the economy, and the overall well-being of the planet. Among the areas where the wrong metrics have led to poor outcomes, he cites the fee-for-service model of health care, corporate cultures that emphasize time spent on the job while overlooking key productivity measures, overreliance on standardized testing in education to the detriment of authentic learning, and a blinkered focus on carbon emissions, which underestimates the impact of industrial damage to our natural world. He also examines various communities and systems that have achieved better outcomes by adjusting the ways in which they measure data. The best results are attained by those that have learned not only what to measure and how to measure it, but what it all means. By highlighting the pitfalls inherent in data analysis, this illuminating book reminds us that not everything that can be counted really counts.
(source: Nielsen Book Data)

• Preface xi Introduction xiii Gilbert SAPORTA
• Part 1 Applications 1
• Chapter 1 Context-specific Independence in Innovation Study 3 Federica NICOLUSSI and Manuela CAZZARO 1.1 Introduction 3 1.2 Parametrization for CS independencies 4 1.3 Stratified chain graph models 6 1.4 Application on real data 7 1.5 Conclusion 12 1.6 References 12
• Chapter 2 Analysis of the Determinants and Outputs of Innovation in the Nordic Countries 15 Catia ROSARIO, Antonio Augusto COSTA and Ana LORGA DA SILVA 2.1 Introduction 15 2.2 Innovation 16 2.3 Methodology 19 2.4 Results 21 2.5 Conclusion 25 2.6 References 26
• Chapter 3 Bibliometric Variables Determining the Quality of a Dentistry Journal 29 Pilar VALDERRAMA, Manuel ESCABIAS, Evaristo JIMENEZ-CONTRERAS, Mariano JVALDERRAMA and Pilar BACA 3.1 Introduction 29 3.2 Statistical methodology 30 3.3 Results 32 3.4 Conclusions 35 3.5 Acknowledgment 35 3.6 References 36
• Chapter 4 Analysis of Dependence among Growth Rates of GDP of V4 Countries Using Four-dimensional Vine Copulas 37 Jozef KOMORNIK, Magda KOMORNIKOVA and Tomas BACIGAL 4.1 Introduction 37 4.2 Theory 38 4.3 Results 42 4.4 Conclusion and future work 45 4.5 Acknowledgment 47 4.6 References 47
• Chapter 5 Monitoring the Compliance of Countries on Emissions Mitigation Using Dissimilarity Indices 49 Eleni KETZAKI, Stavros RALLAKIS, Nikolaos FARMAKIS and Eftichios SARTZETAKIS 5.1 Introduction 49 5.2 The proposed method 50 5.2.1 Description of method for individual data 51 5.2.2 Description of method for grouped data 52 5.3 Application of method 53 5.3.1 Application of method for individual data 54 5.3.2 Application of method for grouped data 55 5.4 Conclusions 55 5.5
• Appendix 57 5.6 References 58
• Chapter 6 Maximum Entropy and Distributions of Five-Star Ratings 59 Yiannis DIMOTIKALIS 6.1 Introduction 59 6.2 Entropy framework to five-star ratings 60 6.3 Maximum entropy of ratings for values k = 1,2,3, , 30 66 6.3.1 Ratings with two outcomes (k = 1) 66 6.3.2 Ratings with three Outcomes (k=2) 69 6.3.3 Ratings with four outcomes (k=3) 73 6.3.4 Ratings with five outcomes (k = 4) 76 6.3.5 Ratings entropy for outcomes k>4 80 6.3.6 Maximum entropy constraints for the binomial distribution 82 6.4 Application to real five-star rating data 83 6.5 Conclusions 86 6.6 References 86
• Part 2 The Impact of the Economic and Financial Crisis in Europe 89
• Chapter 7 Access to Credit for SMEs after the 2008 Financial Crisis: The Northern Italian Perspective 91 Cinzia COLAPINTO and Mariangela ZENGA 7.1 Introduction 91 7.2 Italian SMEs and access to credit 92 7.3 The data 93 7.4 Methodology 94 7.5 Analysis and discussion 97 7.5.1 The measure for the Great Recession period (2008-2012) 97 7.5.2 The measure for the recovery period (2013-2015) 99 7.5.3 Comparing the two crisis phases 102 7.6 Conclusion 105 7.7 References 105
• Chapter 8 Gender-Based Differences in the Impact of the Economic Crisis on Labor Market Flows in Southern Europe 107 Maria SYMEONAKI, Maria KARAMESSINI and Glykeria STAMATOPOULOU 8.1 Introduction 107 8.2 Data, methods and limitations 108 8.3 Results 111 8.4 Conclusions and discussion 111 8.5 References 119
• Chapter 9 Measuring Labor Market Transition Probabilities in Europe with Evidence from the EU-SILC 121 Maria SYMEONAKI, Maria KARAMESSINI and Glykeria STAMATOPOULOU 9.1 Introduction 121 9.2 Data, methods and limitations 122 9.3 Results 124 9.4 Conclusions 135 9.5 References 135
• Part 3 Student Assessment and Employment in Europe 137
• Chapter 10 Almost Graduated, Close to Employment? Taking into Account the Characteristics of Companies Recruiting at a University Job Placement Office 139 Franca CRIPPA, Mariangela ZENGA and Paolo MARIANI 10.1 Introduction 139 10.2 Recruiters and graduates seeking an HEI common ground 140 10.3 Web survey pitfalls: considerations for data collection 141 10.4 Sampled recruiters: an outline 144 10.5 Conclusion 146 10.6 References 146
• Chapter 11 How Variation of Scores of the Programme for International Student Assessment can be Explained through Analysis of Information 149 Valerie GIRARDIN, Justine LEQUESNE and Olivier THEVENON 11.1 Introduction 149 11.2 Multiplicative models and Zighera's parameterization 151 11.3 Application to PISA surveys 155 11.3.1 Data and variables 155 11.3.2 Analysis of scores in mathematics 157 11.3.3 Conclusion 162 11.4 References 163
• Part 4 Visualization 165
• Chapter 12 A Topological Discriminant Analysis 167 Rafik ABDESSELAM 12.1 Introduction 167 12.2 Topological equivalence 168 12.3 Topological discriminant analysis 171 12.4 Application example 173 12.5 Conclusion and perspectives 175 12.6
• Appendix 176 12.7 References 178
• Chapter 13 Using Graph Partitioning to Calculate PageRank in a Changing Network 179 Christopher ENGSTROEM and Sergei SILVESTROV 13.1 Introduction 179 13.1.1 Computing PageRank 181 13.2 Changes in personalization vector 182 13.3 Adding or removing edges between components 184 13.3.1 Computations in practice 186 13.3.2 Adding or removing an edge inside a component 187 13.3.3 Maintaining the component structure 189 13.4 Conclusions 190 13.5 References 191
• Chapter 14 Visualizing the Political Spectrum of Germany by Contiguously Ordering the Party Policy Profiles 193 Andranik TANGIAN 14.1 Introduction 193 14.2 The model 195 14.3 Conclusions 206 14.4 References 206 List of Authors 209 Index 213.
• (source: Nielsen Book Data)

This series of books collects a diverse array of work that provides the reader with theoretical and applied information on data analysis methods, models and techniques, along with appropriate applications. Volume 2 begins with an introductory chapter by Gilbert Saporta, a leading expert in the field, who summarizes the developments in data analysis over the last 50 years. The book is then divided into four parts: Part 1 examines (in)dependence relationships, innovation in the Nordic countries, dentistry journals, dependence among growth rates of GDP of V4 countries, emissions mitigation, and five-star ratings; Part 2 investigates access to credit for SMEs, gender-based impacts given Southern Europe's economic crisis, and labor market transition probabilities; Part 3 looks at recruitment at university job-placement offices and the Program for International Student Assessment; and Part 4 examines discriminants, PageRank, and the political spectrum of Germany.
(source: Nielsen Book Data)

"One thing for sure: digitization has completely changed the literary archive. People like me used to work on a few hundred nineteenth-century novels; today, we work on thousands of them; tomorrow, hundreds of thousands. This has had a major effect on literary history, obviously enough, but also on critical methodology; because, when we work on 200,000 novels instead of 200, we are not doing the same thing, 1,000 times bigger; we are doing a different thing. The new scale changes our relationship to our object, and in fact it changes the object itself"--Page 1.

• Chapter 1 - Introduction
• Chapter 2 - Lifting the fog
• Chapter 3 - Step away from the spreadsheet - common errors in using spreadsheets, and their ramifications
• Chapter 4 - Starting from scratch
• Chapter 5 - Getting the most out of your raw data
• Chapter 6 - Stop, police!
• Chapter 7 - Pivot magic
• Chapter 8 - Moving beyond basic pivots
• Chapter 9 - How to create your own desktop library cube
• Chapter 10 - Beyond the ordinary.
• (source: Nielsen Book Data)

Have you ever looked at your Library's key performance indicators and said to yourself "so what!"? Have you found yourself making decisions in a void due to the lack of useful and easily accessible operational data? Have you ever worried that you are being left behind with the emergence of data analytics? Do you feel there are important stories in your operational data that need to be told, but you have no idea how to find these stories? If you answered yes to any of these questions, then this book is for you. How Libraries Should Manage Data provides detailed instructions on how to transform your operational data from a fog of disconnected, unreliable, and inaccessible information - into an exemplar of best practice data management. Like the human brain, most people are only using a very small fraction of the true potential of Excel. Learn how to tap into a greater proportion of Excel's hidden power, and in the process transform your operational data into actionable business intelligence.
(source: Nielsen Book Data)
Have you ever looked at your Library's key performance indicators and said to yourself "so what!"? Have you found yourself making decisions in a void due to the lack of useful and easily accessible operational data? Have you ever worried that you are being left behind with the emergence of data analytics? Do you feel there are important stories in your operational data that need to be told, but you have no idea how to find these stories? If you answered yes to any of these questions, then this book is for you. How Libraries Should Manage Data provides detailed instructions on how to transform your operational data from a fog of disconnected, unreliable, and inaccessible information - into an exemplar of best practice data management. Like the human brain, most people are only using a very small fraction of the true potential of Excel. Learn how to tap into a greater proportion of Excel's hidden power, and in the process transform your operational data into actionable business intelligence. Recognize and overcome the social barriers to creating useful operational dataUnderstand the potential value and pitfalls of operational dataLearn how to structure your data to obtain useful information quickly and easilyCreate your own desktop library cube with step-by-step instructions, including DAX formulas.
(source: Nielsen Book Data)

Much of our life is consumed looking for quantitative relationships. For example, How much more sleep do I need at night to make me feel better? How many calories do I need to eliminate to lose weight? How much larger does my budget on the job need to be for me to be more effective? All these quantitative questions are preceded, and depend on, qualitative questions. For example, before I decide how much extra sleep I need at night, I need to determine if extra sleep will actually make me feel better. In another example, I need to determine if a larger budget will make me more effective on the job, before I think about how much more money I will need. What elements influence job performance, and how do they interact? We spend much of our life trying to find answers to such quantitative and qualitative questions. We are, then, in search of a kind of intelligence that includes numbers but is also above and beyond them. We call it "supernumerary" intelligence (SI). To aid our quest for SI, we use Quantitative CyberQuest (QCQ) and the Public Administration Genome Project (PAGP) as useful tools. QCQ is a philosophy as well as an analytic tool that helps in exploring the supernumerary. QCQ is particularly wellsuited for sorting out variables as well as their interrelations. It involves a combination of statistics, systems analysis, research methodology, qualitative research, and artificial intelligence. QCQ also provides a relatively easy to understand but still powerful set of tools and guidancemechanisms to pilot (the "Cyber" part) users in their "Quest" for supernumerary relationships.
(source: Nielsen Book Data)

• Section 1: Measurement Theory
• 1. Psychometrics / Mark Wilson & Perman Gochyyev
• 2. Classical test theory / Ze Wang & Steven J. Osterlind
• 3. Item response theory / Xitao Fan & Shaojing Sun
• Section 2: Methods of analysis
• 4. Multiple regression / Ken Kelley & Jocelyn Holden
• 5. Cluster analysis / Christine DiStefano & Diana Mindrila
• 6. Multivariate analysis of variance: with discriminant function analysis follow-up / Lisa L. Harlow & Sunny R. Duerr
• 7. LoGistic regression / Brian F. French, Jason C. Immekus & Hsiao-Ju Yen
• 8. Exploratory factor analysis / W. Holmes Finch
• 9. A brief introduction to hierarchical linear modeling / Jason W. Osborne & Shevaun D. Neupert
• 10. Longitudinal data analysis / D. Betsy McCoach, John P. Madura, Karen E. Rambo-Hernandez, Ann A. O'Connell & Megan E. Welsh
• 11. Meta-analysis / Spyros Konstantopoulos
• 12. Agent based modelling / Mauricio Salgado & Nigel Gilbert
• 13. Mediation, moderation & interaction: definitions, discrimination & (some) means of testing / James Hall & Pamela Sammons
• Section 3: Structural equation models
• 14. Introduction to confirmatory factor analysis and structural equation modeling / Matthew W. Gallagher & Timothy A. Brown
• 15. Testing measurement and structural invariance: implications for practice / Daniel A. Sass & Thomas A. Schmitt
• 16. Mixture models in education / George A. Marcoulides & Ronald H. Heck
• 17. Selecting SEM computer programs: considerations and comparisons / Barbara Byrne.

• The challenge for educational research
• The importance of theory
• Designing research to address causal questions
• Investigator-designed randomized experiments
• Challenges in designing, implementing, and learning from randomized experiments
• Statistical power and sample size
• Experimental research when participants are clustered within intact groups
• Using natural experiments to provide "arguably exogenous" treatment variability
• Estimating causal effects using a regression-discontinuity approach
• Introducing instrumental-variables estimation
• Using IVE to recover the treatment effect in a quasi-experiment
• Dealing with bias in treatment effects estimated from nonexperimental data
• Methodological lessons from the long quest
• Substantive lessons and new questions.

Educational policy-makers around the world constantly make decisions about how to use scarce resources to improve the education of children. Unfortunately, their decisions are rarely informed by evidence on the consequences of these initiatives in other settings. Nor are decisions typically accompanied by well-formulated plans to evaluate their causal impacts. As a result, knowledge about what works in different situations has been very slow to accumulate. Over the last several decades, advances in research methodology, administrative record keeping, and statistical software have dramatically increased the potential for researchers to conduct compelling evaluations of the causal impacts of educational interventions, and the number of well-designed studies is growing. Written in clear, concise prose, Methods Matter: Improving Causal Inference in Educational and Social Science Research offers essential guidance for those who evaluate educational policies. Using numerous examples of high-quality studies that have evaluated the causal impacts of important educational interventions, the authors go beyond the simple presentation of new analytical methods to discuss the controversies surrounding each study, and provide heuristic explanations that are also broadly accessible. Murnane and Willett offer strong methodological insights on causal inference, while also examining the consequences of a wide variety of educational policies implemented in the U.S. and abroad. Representing a unique contribution to the literature surrounding educational research, this landmark text will be invaluable for students and researchers in education and public policy, as well as those interested in social science.
(source: Nielsen Book Data)

• Principles of statistical inference : likelihood and the Bayesian paradigm / Steve C. Wang
• Resampling methods in paleontology / Michal Kowalewski and Phil Novack-Gottshall
• Fair sampling of taxonomic richness and unbiased estimation of origination and extinction rates / John Alroy
• Estimating rates and probabilities of origination and extinction using taxonomic occurrence data : capture-mark-recapture (CMR) approaches / Lee Hsiang Liow and James D. Nichols
• Diversity partitioning using Shannon's entropy and its relationship to rarefaction / Thomas D. Olszewski
• Using a macroecological approach to study geographic range, abundance and body size in the fossil record / S. Kathleen Lyons and Felisa A. Smith
• Networks, extinction and paleocommunity food webs / Peter D. Roopnarine
• A practical introduction to landmark-based geometric morphometrics / Mark Webster and H. David Sheets
• Probabilistic phylogenetic inference in the fossil record : current and future applications / Peter J. Wagner and Jonathan D. Marcot
• Methods for studying morphological integration and modularity / Anjali Goswami and P. David Polly
• Models and methods for analyzing phenotypic evolution in lineages and clades / Gene Hunt and Matthew T. Carrano
• Brute-force biochronology : sequencing paleobiologic first- and last-appearance events by trial-and-error / Peter M. Sadler
• Using confidence intervals to quantify the uncertainty in the end-points of stratigraphic ranges / Charles R. Marshall.

• Part I. Foundational concepts for quantitative inquiry in music education
• Part II. Advanced concepts for quantitative inquiry in music education
• Appendix : Inferential analysis with nonparametric tests.

In recent years, academics and professionals in the social sciences have forged significant advances in quantitative research methodologies specific to their respective disciplines. Although new and sophisticated techniques for large-scale data analyses have become commonplace in general educational, psychological, sociological, and econometric fields, many researchers in music education have yet to be exposed to such techniques. Design and Analysis of Quantitative Research in Music Education is a comprehensive reference for those involved with research in music education and related fields, providing a foundational understanding of quantitative inquiry methods. Authors Peter Miksza and Kenneth Elpus update and expand the set of resources that music researchers have at their disposal for conceptualizing and analyzing data pertaining to music-related phenomena. This text is designed to familiarize readers with foundational issues of quantitative inquiry as a point of view, introduce and elaborate upon issues of fundamental quantitative research design and analysis, and expose researchers to new, innovative, and exciting methods for dealing with complex research questions and analyzing large samples of data in a rigorous and thorough manner. With this resource, researchers will be better equipped for dealing with the challenges of the increasingly information-rich and data-driven environment surrounding music education. An accompanying companion website provides valuable supplementary exercises and videos.
(source: Nielsen Book Data)