David Bartholomew – författare

Drawing on the authors’ varied experiences researching and teaching in the field, Analysis of Multivariate Social Science Data: Statistical Machine Learning Methods, Third Edition enables a basic understanding of how to use key multivariate methods in the social sciences. With minimal mathematical and statistical knowledge required, this third edition expands its topics to include graphical modelling, models for longitudinal data, structural equation models for categorical variables, and latent class analysis for ordinal, nominal, and continuous variables. It also connects the topics to terminology and principles of machine learning, intended to help readers grasp the links between methods of multivariate analysis and advancements in the field of data science.After describing methods for the summarisation of data in the first part of the book, the authors consider regression analysis. This chapter provides a link between the two halves of the book, signalling the move from descriptive to inferential methods. The remainder of the text deals with model-based methods that primarily make inferences about processes that generate data.Relying heavily on numerical examples from a range of disciplines, the authors provide insight into the purpose and working of the methods as well as the interpretation of results from analyses. Many of the same examples are used throughout to illustrate connections between the methods. In most chapters, the authors present suggestions for further work that go beyond conventional practice, encouraging readers to explore new ground in social science research.FeaturesContains new chapters on undirected graphical modelling and models for longitudinal data, as well as new material such as K-means, cross-validation, structural equation models for categorical variables, latent class analysis for categorical, nominal and continuous variables, and treatment of missing data.Connects topics with terminology and principles of machine learning.Presents numerous examples of real-world applications, including voting preferences, social attitudes, educational assessment, recidivism, and health.Covers methods that summarise, describe, and explore multivariate datasets, including longitudinal data.Establishes a unified approach to latent variable modelling by providing detailed coverage of methods such as item response theory, factor analysis for continuous and categorical data, and models for categorical latent variables.Covers models for hierarchical and longitudinal data and their connections to latent variable models.Offers a full version of the data sets in the text or the book’s website, with software code for implementing the analyses on the website.The book offers a balanced and accessible resource for students and researchers with limited mathematical and statistical training. It serves as a practical resource for courses in multivariate analysis and as a guide for applying these techniques in applied research.

Drawing on the authors’ varied experiences researching and teaching in the field, Analysis of Multivariate Social Science Data: Statistical Machine Learning Methods, Third Edition enables a basic understanding of how to use key multivariate methods in the social sciences. With minimal mathematical and statistical knowledge required, this third edition expands its topics to include graphical modelling, models for longitudinal data, structural equation models for categorical variables, and latent class analysis for ordinal, nominal, and continuous variables. It also connects the topics to terminology and principles of machine learning, intended to help readers grasp the links between methods of multivariate analysis and advancements in the field of data science.After describing methods for the summarisation of data in the first part of the book, the authors consider regression analysis. This chapter provides a link between the two halves of the book, signalling the move from descriptive to inferential methods. The remainder of the text deals with model-based methods that primarily make inferences about processes that generate data.Relying heavily on numerical examples from a range of disciplines, the authors provide insight into the purpose and working of the methods as well as the interpretation of results from analyses. Many of the same examples are used throughout to illustrate connections between the methods. In most chapters, the authors present suggestions for further work that go beyond conventional practice, encouraging readers to explore new ground in social science research.FeaturesContains new chapters on undirected graphical modelling and models for longitudinal data, as well as new material such as K-means, cross-validation, structural equation models for categorical variables, latent class analysis for categorical, nominal and continuous variables, and treatment of missing data.Connects topics with terminology and principles of machine learning.Presents numerous examples of real-world applications, including voting preferences, social attitudes, educational assessment, recidivism, and health.Covers methods that summarise, describe, and explore multivariate datasets, including longitudinal data.Establishes a unified approach to latent variable modelling by providing detailed coverage of methods such as item response theory, factor analysis for continuous and categorical data, and models for categorical latent variables.Covers models for hierarchical and longitudinal data and their connections to latent variable models.Offers a full version of the data sets in the text or the book’s website, with software code for implementing the analyses on the website.The book offers a balanced and accessible resource for students and researchers with limited mathematical and statistical training. It serves as a practical resource for courses in multivariate analysis and as a guide for applying these techniques in applied research.

Drawing on the authors' varied experiences researching and teaching in the field, Analysis of Multivariate Social Science Data: Statistical Machine Learning Methods, Third Edition enables a basic understanding of how to use key multivariate methods in the social sciences. With minimal mathematical and statistical knowledge required, this third edition expands its topics to include graphical modelling, models for longitudinal data, structural equation models for categorical variables, and latent class analysis for ordinal, nominal, and continuous variables. It also connects the topics to terminology and principles of machine learning, intended to help readers grasp the links between methods of multivariate analysis and advancements in the field of data science.After describing methods for the summarisation of data in the first part of the book, the authors consider regression analysis. This chapter provides a link between the two halves of the book, signalling the move from descriptive to inferential methods. The remainder of the text deals with model-based methods that primarily make inferences about processes that generate data.Relying heavily on numerical examples from a range of disciplines, the authors provide insight into the purpose and working of the methods as well as the interpretation of results from analyses. Many of the same examples are used throughout to illustrate connections between the methods. In most chapters, the authors present suggestions for further work that go beyond conventional practice, encouraging readers to explore new ground in social science research.Features Contains new chapters on undirected graphical modelling and models for longitudinal data, as well as new material such as K-means, cross-validation, structural equation models for categorical variables, latent class analysis for categorical, nominal and continuous variables, and treatment of missing data. Connects topics with terminology and principles of machine learning. Presents numerous examples of real-world applications, including voting preferences, social attitudes, educational assessment, recidivism, and health. Covers methods that summarise, describe, and explore multivariate datasets, including longitudinal data. Establishes a unified approach to latent variable modelling by providing detailed coverage of methods such as item response theory, factor analysis for continuous and categorical data, and models for categorical latent variables. Covers models for hierarchical and longitudinal data and their connections to latent variable models. Offers a full version of the data sets in the text or the book's website, with software code for implementing the analyses on the website. The book offers a balanced and accessible resource for students and researchers with limited mathematical and statistical training. It serves as a practical resource for courses in multivariate analysis and as a guide for applying these techniques in applied research.

Drawing on the authors' varied experiences researching and teaching in the field, Analysis of Multivariate Social Science Data: Statistical Machine Learning Methods, Third Edition enables a basic understanding of how to use key multivariate methods in the social sciences. With minimal mathematical and statistical knowledge required, this third edition expands its topics to include graphical modelling, models for longitudinal data, structural equation models for categorical variables, and latent class analysis for ordinal, nominal, and continuous variables. It also connects the topics to terminology and principles of machine learning, intended to help readers grasp the links between methods of multivariate analysis and advancements in the field of data science.After describing methods for the summarisation of data in the first part of the book, the authors consider regression analysis. This chapter provides a link between the two halves of the book, signalling the move from descriptive to inferential methods. The remainder of the text deals with model-based methods that primarily make inferences about processes that generate data.Relying heavily on numerical examples from a range of disciplines, the authors provide insight into the purpose and working of the methods as well as the interpretation of results from analyses. Many of the same examples are used throughout to illustrate connections between the methods. In most chapters, the authors present suggestions for further work that go beyond conventional practice, encouraging readers to explore new ground in social science research.Features Contains new chapters on undirected graphical modelling and models for longitudinal data, as well as new material such as K-means, cross-validation, structural equation models for categorical variables, latent class analysis for categorical, nominal and continuous variables, and treatment of missing data. Connects topics with terminology and principles of machine learning. Presents numerous examples of real-world applications, including voting preferences, social attitudes, educational assessment, recidivism, and health. Covers methods that summarise, describe, and explore multivariate datasets, including longitudinal data. Establishes a unified approach to latent variable modelling by providing detailed coverage of methods such as item response theory, factor analysis for continuous and categorical data, and models for categorical latent variables. Covers models for hierarchical and longitudinal data and their connections to latent variable models. Offers a full version of the data sets in the text or the book's website, with software code for implementing the analyses on the website. The book offers a balanced and accessible resource for students and researchers with limited mathematical and statistical training. It serves as a practical resource for courses in multivariate analysis and as a guide for applying these techniques in applied research.

This guide shows design practices and other construction professionals how to manage knowledge successfully. It explains how to develop and implement a knowledge management strategy, and how to avoid the pitfalls, focusing on the techniques of learning and knowledge sharing that are most relevant in professional practice. Expensive IT-based ‘solutions’ bought off-the-shelf rarely succeed in a practice context, so the emphasis here is on people-centred techniques, which recognise and meet real business knowledge needs and fit in with the organisational culture. Knowledge is supplanting physical assets as the dominant basis of capital value and an understanding of how knowledge is acquired, shared and used is increasingly crucial in organisational success. Most business leaders recognise this, but few have yet succeeded in making it the pervasive influence on management practice that it needs to become; that has turned out to be harder than it looks. Construction professionals are among those who have furthest to go, and most to gain. Design is a knowledge-based activity, and project managers, contractors and clients, as well as architects and engineers, have always learned from experience and shared their knowledge with immediate colleagues. But the intuitive processes they have traditionally used break down alarmingly quickly as organisations grow; even simply dividing the office over two floors can noticeably reduce communication. At the same time, increasingly sophisticated construction technology and more demanding markets are making effective management of knowledge ever more important. Other knowledge-intensive industries (such as management consultancy, pharmaceuticals, and IT), are well ahead in adopting a more systematic approach to learning and sharing knowledge, and seeing the benefits in improved technical capacity, efficiency, customer satisfaction and reduced risk.

This guide shows design practices and other construction professionals how to manage knowledge successfully. It explains how to develop and implement a knowledge management strategy, and how to avoid the pitfalls, focusing on the techniques of learning and knowledge sharing that are most relevant in professional practice. Expensive IT-based ‘solutions’ bought off-the-shelf rarely succeed in a practice context, so the emphasis here is on people-centred techniques, which recognise and meet real business knowledge needs and fit in with the organisational culture.

Knowledge is supplanting physical assets as the dominant basis of capital value and an understanding of how knowledge is acquired, shared and used is increasingly crucial in organisational success. Most business leaders recognise this, but few have yet succeeded in making it the pervasive influence on management practice that it needs to become; that has turned out to be harder than it looks.

Construction professionals are among those who have furthest to go, and most to gain. Design is a knowledge-based activity, and project managers, contractors and clients, as well as architects and engineers, have always learned from experience and shared their knowledge with immediate colleagues. But the intuitive processes they have traditionally used break down alarmingly quickly as organisations grow; even simply dividing the office over two floors can noticeably reduce communication. At the same time, increasingly sophisticated construction technology and more demanding markets are making effective management of knowledge ever more important. Other knowledge-intensive industries (such as management consultancy, pharmaceuticals, and IT), are well ahead in adopting a more systematic approach to learning and sharing knowledge, and seeing the benefits in improved technical capacity, efficiency, customer satisfaction and reduced risk.