This book introduces social researchers to all aspects of statistical modelling in an easily accessible but informative way. A website will accompany the book which will provide additional information and exercises. It is the first text to introduce the social researcher to the principles of statistical modelling and to the full range of methods available. This book describes in words rather than mathematical notation the aims and principles of statistical modelling but helpfully remains fully comprehensive.

An accessible introduction to the use of regression analysis in the social sciences Regression with Social Data: Modeling Continuous and Limited Response Variables represents the most complete and fully integrated coverage of regression modeling currently available for graduate-level behavioral science students and practitioners. Covering techniques that span the full spectrum of levels of measurement for both continuous and limited response variables, and using examples taken from such disciplines as sociology, psychology, political science, and public health, the author succeeds in demystifying an academically rigorous subject and making it accessible to a wider audience. Content includes coverage of: Logit, probit, scobit, truncated, and censored regressions Multiple regression with ANOVA and ANCOVA models Binary and multinomial response models Poisson, negative binomial, and other regression models for event-count data Survival analysis using multistate, multiepisode, and interval-censored survival models Concepts are reinforced throughout with numerous chapter problems, exercises, and real data sets. Step-by-step solutions plus an appendix of mathematical tutorials make even complex problems accessible to readers with only moderate math skills. The book’s logical flow, wide applicability, and uniquely comprehensive coverage make it both an ideal text for a variety of graduate course settings and a useful reference for practicing researchers in the field.

Many organizations, whether in the public or private sector, have begun to take advantage of the tools and techniques used for data mining. Utilizing data mining tools, these organizations are able to reveal the hidden and unknown information from available data. Data Mining in Dynamic Social Networks and Fuzzy Systems brings together research on the latest trends and patterns of data mining tools and techniques in dynamic social networks and fuzzy systems. With these improved modern techniques of data mining, this publication aims to provide insight and support to researchers and professionals concerned with the management of expertise, knowledge, information, and organizational development.

This IBM® Redbooks® publication describes how the IBM Big Data Platform provides the integrated capabilities that are required for the adoption of Information Governance in the big data landscape. As organizations embark on new use cases, such as Big Data Exploration, an enhanced 360 view of customers, or Data Warehouse modernization, and absorb ever growing volumes and variety of data with accelerating velocity, the principles and practices of Information Governance become ever more critical to ensure trust in data and help organizations overcome the inherent risks and achieve the wanted value. The introduction of big data changes the information landscape. Data arrives faster than humans can react to it, and issues can quickly escalate into significant events. The variety of data now poses new privacy and security risks. The high volume of information in all places makes it harder to find where these issues, risks, and even useful information to drive new value and revenue are. Information Governance provides an organization with a framework that can align their wanted outcomes with their strategic management principles, the people who can implement those principles, and the architecture and platform that are needed to support the big data use cases. The IBM Big Data Platform, coupled with a framework for Information Governance, provides an approach to build, manage, and gain significant value from the big data landscape.

This is an open access book. As a leading role in the global megatrend of scientific innovation, China has been creating a more and more open environment for scientific innovation, increasing the depth and breadth of academic cooperation, and building a community of innovation that benefits all. Such endeavors are making new contributions to the globalization and creating a community of shared future. The 3rd International Conference on Big Data and Social Sciences (ICBDSS 2022) was held on August 19 – 21, 2022, in Hulunbuir, China. With the support of experts and professors, the ICBDSS 2022 conference successfully held its first conference last year. In order to allow more scholars to have the opportunity to participate in the conference to share and exchange experience. This conference mainly focused on "big data", "social science" and other research fields to discuss. At present, my country has entered the era of "big data cloud migration", that is, the era of big data, the Internet of things, cloud computing and mobile Internet. The market demand for big data talents is also increasing day by day. The purpose of the conference is to provide a way for experts, scholars, engineering technicians, and technical R&D personnel engaged in big data and social science research to share scientific research results and cutting-edge technologies, understand academic development trends, broaden research ideas, strengthen academic research and discussion, and promote the academic achievement industry Platform for chemical cooperation. The conference sincerely invites experts, scholars from domestic and foreign universities, scientific research institutions, business people and other relevant personnel to participate in the conference.

eWork and eBusiness in Architecture, Engineering and Construction 2016 collects the papers presented at the 11th European Conference on Product & Process Modelling (ECPPM 2016, Cyprus, 7-9 September 2016), The contributions cover complementary thematic areas that hold great promise for the advancement of research and technological development in the modelling of complex engineering systems, encompassing a substantial number of high quality contributions on a large spectrum of topics pertaining to ICT deployment instances in AEC/FM, including: • Information and Knowledge Management • Construction Management • Description Logics and Ontology Application in AEC • Risk Management • 5D/nD Modelling, Simulation and Augmented Reality • Infrastructure Condition Assessment • Standardization of Data Structures • Regulatory and Legal Aspects • Multi-Model and distributed Data Management • System Identification • Industrialized Production, Smart Products and Services • Interoperability • Smart Cities • Sustainable Buildings and Urban Environments • Collaboration and Teamwork • BIM Implementation and Deployment • Building Performance Simulation • Intelligent Catalogues and Services

Historical Information Science is an extensive review and bibliographic essay, backed by almost 6,000 citations, detailing developments in information technology since the advent of personal computers and the convergence of several social science and humanities disciplines in historical computing. Its focus is on the access, preservation, and analysis of historical information (primarily in electronic form) and the relationships between new methodology and instructional media, techniques, and research trends in library special collections, digital libraries, data archives, and museums.

Published by WHO, Lucerne University of Applied Sciences and Arts, and the University of New Hampshire, this toolkit provides academics and decision-makers with strategies for conducting national or regional studies of the incidence of and agency response to child maltreatment. These studies are developed based on the collection of administrative data or through surveys of professionals. Such research is important to policy-makers who need information about which agencies have knowledge of the problem of child maltreatment, and their response when they encounter it. Based on this information, they can plan how to improve practices, enhance systems and strengthen professional capacity.--Publisher description.

The complexity of large-scale data sets (“Big Data”) has stimulated the development of advanced computational methods for analysing them. There are two different kinds of methods to aid this. The model-based method uses probability models and likelihood and Bayesian theory, while the model-free method does not require a probability model, likelihood or Bayesian theory. These two approaches are based on different philosophical principles of probability theory, espoused by the famous statisticians Ronald Fisher and Jerzy Neyman. Introduction to Statistical Modelling and Inference covers simple experimental and survey designs, and probability models up to and including generalised linear (regression) models and some extensions of these, including finite mixtures. A wide range of examples from different application fields are also discussed and analysed. No special software is used, beyond that needed for maximum likelihood analysis of generalised linear models. Students are expected to have a basic mathematical background in algebra, coordinate geometry and calculus. Features • Probability models are developed from the shape of the sample empirical cumulative distribution function (cdf) or a transformation of it. • Bounds for the value of the population cumulative distribution function are obtained from the Beta distribution at each point of the empirical cdf. • Bayes’s theorem is developed from the properties of the screening test for a rare condition. • The multinomial distribution provides an always-true model for any randomly sampled data. • The model-free bootstrap method for finding the precision of a sample estimate has a model-based parallel – the Bayesian bootstrap – based on the always-true multinomial distribution. • The Bayesian posterior distributions of model parameters can be obtained from the maximum likelihood analysis of the model. This book is aimed at students in a wide range of disciplines including Data Science. The book is based on the model-based theory, used widely by scientists in many fields, and compares it, in less detail, with the model-free theory, popular in computer science, machine learning and official survey analysis. The development of the model-based theory is accelerated by recent developments in Bayesian analysis.