Jeff Gill – författare

This book is your own personal PhD coach. It’s not just about surviving your PhD, it’s about thriving in the experience. Taking a unique self-coaching approach, this book will enable you to understand how to navigate your way through the various challenges posed by PhD study with resilience, self-sufficiency, and the determination to succeed.

Each chapter contains self-coaching challenges so that you can discover what works for you and generate fresh ideas to enable you to move forwards, banish self-doubt, bust procrastination, and realise your full potential to make the most of your PhD experience.

Your PhD Coach will enable you to:

Build resilience and self-leadershipChallenge yourself to be focused and achieve aspiring goalsFind the right balance and nurture stronger relationshipsDevelop your confidence, creativity and motivationUsing a coaching philosophy that helps you realize your own solutions and develop your own strategies to move forward, the book is packed full of techniques, tips and tricks applicable to a wide range of circumstances both within the PhD process and beyond - an essential book for all those taking on the challenge of PhD research.

"Medd and Gill go deep into the under the skin of what it is like to do a PhD, pull out the reality of the operation and offer some sound advice. They provide effective techniques to bust the 'gremlins' – the voices in the head - that can haunt postgraduate researchers and diminish the research experience. I urge all research students (and supervisors) to read this very accessible book. It will help them reflect deeper into their research experience and help build confidence in themselves and (re)gain satisfaction in their work and studies."Dr Richard Hinchcliffe, Academic Development, Centre for Lifelong Learning, University of Liverpool, UK

"Coaching empowered me to reclaim my PhD and this book will enable many more students to do the same. Will Medd and Jeff Gill have a deep understanding of the PhD process and students reading this book, at any stage in their PhD, will find themselves thinking 'Wow, that is exactly how I feel!' The book challenges the inevitability of the 'PhD steam-roller' and its comfortable and chatty tone creates a friendly guide for those struggling with the demands of a PhD and inspiration for those who want to get the most from the whole experience." Beth Brockett, PhD Student, Lancaster Environment Centre, Lancaster University, UK

This book is your own personal PhD coach. It’s not just about surviving your PhD, it’s about thriving in the experience. Taking a unique self-coaching approach, this book will enable you to understand how to navigate your way through the various challenges posed by PhD study with resilience, self-sufficiency, and the determination to succeed.

Each chapter contains self-coaching challenges so that you can discover what works for you and generate fresh ideas to enable you to move forwards, banish self-doubt, bust procrastination, and realise your full potential to make the most of your PhD experience.

Your PhD Coach will enable you to:

Build resilience and self-leadershipChallenge yourself to be focused and achieve aspiring goalsFind the right balance and nurture stronger relationshipsDevelop your confidence, creativity and motivationUsing a coaching philosophy that helps you realize your own solutions and develop your own strategies to move forward, the book is packed full of techniques, tips and tricks applicable to a wide range of circumstances both within the PhD process and beyond - an essential book for all those taking on the challenge of PhD research.

"Medd and Gill go deep into the under the skin of what it is like to do a PhD, pull out the reality of the operation and offer some sound advice. They provide effective techniques to bust the ''gremlins'' – the voices in the head - that can haunt postgraduate researchers and diminish the research experience. I urge all research students (and supervisors) to read this very accessible book. It will help them reflect deeper into their research experience and help build confidence in themselves and (re)gain satisfaction in their work and studies."Dr Richard Hinchcliffe, Academic Development, Centre for Lifelong Learning, University of Liverpool, UK

"Coaching empowered me to reclaim my PhD and this book will enable many more students to do the same. Will Medd and Jeff Gill have a deep understanding of the PhD process and students reading this book, at any stage in their PhD, will find themselves thinking ''Wow, that is exactly how I feel!'' The book challenges the inevitability of the ''PhD steam-roller'' and its comfortable and chatty tone creates a friendly guide for those struggling with the demands of a PhD and inspiration for those who want to get the most from the whole experience." Beth Brockett, PhD Student, Lancaster Environment Centre, Lancaster University, UK

At last—a social scientist's guide through the pitfalls of modern statistical computing Addressing the current deficiency in the literature on statistical methods as they apply to the social and behavioral sciences, Numerical Issues in Statistical Computing for the Social Scientist seeks to provide readers with a unique practical guidebook to the numerical methods underlying computerized statistical calculations specific to these fields. The authors demonstrate that knowledge of these numerical methods and how they are used in statistical packages is essential for making accurate inferences. With the aid of key contributors from both the social and behavioral sciences, the authors have assembled a rich set of interrelated chapters designed to guide empirical social scientists through the potential minefield of modern statistical computing.Uniquely accessible and abounding in modern-day tools, tricks, and advice, the text successfully bridges the gap between the current level of social science methodology and the more sophisticated technical coverage usually associated with the statistical field.Highlights include: A focus on problems occurring in maximum likelihood estimationIntegrated examples of statistical computing (using software packages such as the SAS, Gauss, Splus, R, Stata, LIMDEP, SPSS, WinBUGS, and MATLAB®)A guide to choosing accurate statistical packagesDiscussions of a multitude of computationally intensive statistical approaches such as ecological inference, Markov chain Monte Carlo, and spatial regression analysisEmphasis on specific numerical problems, statistical procedures, and their applications in the fieldReplications and re-analysis of published social science research, using innovative numerical methodsKey numerical estimation issues along with the means of avoiding common pitfallsA related Web site includes test data for use in demonstrating numerical problems, code for applying the original methods described in the book, and an online bibliography of Web resources for the statistical computationDesigned as an independent research tool, a professional reference, or a classroom supplement, the book presents a well-thought-out treatment of a complex and multifaceted field.

At last—a social scientist''s guide through the pitfalls of modern statistical computing

Addressing the current deficiency in the literature on statistical methods as they apply to the social and behavioral sciences, Numerical Issues in Statistical Computing for the Social Scientist seeks to provide readers with a unique practical guidebook to the numerical methods underlying computerized statistical calculations specific to these fields. The authors demonstrate that knowledge of these numerical methods and how they are used in statistical packages is essential for making accurate inferences. With the aid of key contributors from both the social and behavioral sciences, the authors have assembled a rich set of interrelated chapters designed to guide empirical social scientists through the potential minefield of modern statistical computing.

Uniquely accessible and abounding in modern-day tools, tricks, and advice, the text successfully bridges the gap between the current level of social science methodology and the more sophisticated technical coverage usually associated with the statistical field.

Highlights include:

A focus on problems occurring in maximum likelihood estimation Integrated examples of statistical computing (using software packages such as the SAS, Gauss, Splus, R, Stata, LIMDEP, SPSS, WinBUGS, and MATLAB®) A guide to choosing accurate statistical packages Discussions of a multitude of computationally intensive statistical approaches such as ecological inference, Markov chain Monte Carlo, and spatial regression analysis Emphasis on specific numerical problems, statistical procedures, and their applications in the field Replications and re-analysis of published social science research, using innovative numerical methods Key numerical estimation issues along with the means of avoiding common pitfalls A related Web site includes test data for use in demonstrating numerical problems, code for applying the original methods described in the book, and an online bibliography of Web resources for the statistical computation

Designed as an independent research tool, a professional reference, or a classroom supplement, the book presents a well-thought-out treatment of a complex and multifaceted field.

Essential Mathematics for Political and Social Research addresses an educational deficiency in the social and behavioral sciences. This 2006 book was the first of its kind to specifically address the comprehensive introduction to the mathematical principles needed by modern social scientists. The material introduces basic mathematical principles necessary to do analytical work in the social sciences, starting from first principles, but without unnecessary complexity. The core purpose is to present fundamental notions in standard notation and standard language with a clear, unified framework throughout. Through examples and exercises, this book is intended to not only motivate specific mathematical principles and practices, but also introduce the way that social science researchers use these tools. The intended emphasis is on conceptual understanding of key principles and their subsequent application.

Essential Mathematics for Political and Social Research addresses an educational deficiency in the social and behavioral sciences. This 2006 book was the first of its kind to specifically address the comprehensive introduction to the mathematical principles needed by modern social scientists. The material introduces basic mathematical principles necessary to do analytical work in the social sciences, starting from first principles, but without unnecessary complexity. The core purpose is to present fundamental notions in standard notation and standard language with a clear, unified framework throughout. Through examples and exercises, this book is intended to not only motivate specific mathematical principles and practices, but also introduce the way that social science researchers use these tools. The intended emphasis is on conceptual understanding of key principles and their subsequent application.

This Element introduces the basics of Bayesian regression modeling using modern computational tools. This Element only assumes that the reader has taken a basic statistics course and has seen Bayesian inference at the introductory level of Gill and Bao (2024). Some matrix algebra knowledge is assumed but the authors walk carefully through the necessary structures at the start of this Element. At the end of the process readers will fully understand how Bayesian regression models are developed and estimated, including linear and nonlinear versions. The sections cover theoretical principles and real-world applications in order to provide motivation and intuition. Because Bayesian methods are intricately tied to software, code in R and Python is provided throughout.

In this Element, the authors introduce Bayesian probability and inference for social science students and practitioners starting from the absolute beginning and walk readers steadily through the Element. No previous knowledge is required other than that in a basic statistics course. At the end of the process, readers will understand the core tenets of Bayesian theory and practice in a way that enables them to specify, implement, and understand models using practical social science data. Chapters will cover theoretical principles and real-world applications that provide motivation and intuition. Because Bayesian methods are intricately tied to software, code in both R and Python is provided throughout.

In this Element, the authors introduce Bayesian probability and inference for social science students and practitioners starting from the absolute beginning and walk readers steadily through the Element. No previous knowledge is required other than that in a basic statistics course. At the end of the process, readers will understand the core tenets of Bayesian theory and practice in a way that enables them to specify, implement, and understand models using practical social science data. Chapters will cover theoretical principles and real-world applications that provide motivation and intuition. Because Bayesian methods are intricately tied to software, code in both R and Python is provided throughout.

This Element introduces the basics of Bayesian regression modeling using modern computational tools. This Element only assumes that the reader has taken a basic statistics course and has seen Bayesian inference at the introductory level of Gill and Bao (2024). Some matrix algebra knowledge is assumed but the authors walk carefully through the necessary structures at the start of this Element. At the end of the process readers will fully understand how Bayesian regression models are developed and estimated, including linear and nonlinear versions. The sections cover theoretical principles and real-world applications in order to provide motivation and intuition. Because Bayesian methods are intricately tied to software, code in R and Python is provided throughout.

An Update of the Most Popular Graduate-Level Introductions to Bayesian Statistics for Social ScientistsNow that Bayesian modeling has become standard, MCMC is well understood and trusted, and computing power continues to increase, Bayesian Methods: A Social and Behavioral Sciences Approach, Third Edition focuses more on implementation details of th

This book is about you. It's about how you can shape your student experience based on what matters to you. It helps you to 'get sorted', take control and make the most of your time at college or university. This unique and practical self-coaching guide bridges the gap between student life, academic performance and employability skills. It actively encourages students to discover ways to shape their outlooks and experiences. - There are a host of useful illustrations, diagrams, checklists, exercises and real student stories to help you get the most from the book.- It's easy to find your way around the chapters so you can pick the bits that are most relevant for you. Regardless of your subject, level of study or reasons for doing a degree, Get Sorted will prove invaluable in helping you to navigate the challenges of your student journey, and beyond.

This book is about you. It''s about how you can shape your student experience based on what matters to you. It helps you to ''get sorted'', take control and make the most of your time at college or university. This unique and practical self-coaching guide bridges the gap between student life, academic performance and employability skills. It actively encourages students to discover ways to shape their outlooks and experiences. - There are a host of useful illustrations, diagrams, checklists, exercises and real student stories to help you get the most from the book.- It''s easy to find your way around the chapters so you can pick the bits that are most relevant for you. Regardless of your subject, level of study or reasons for doing a degree, Get Sorted will prove invaluable in helping you to navigate the challenges of your student journey, and beyond.

This book is about you. It''s about how you can shape your student experience based on what matters to you. It helps you to ''get sorted'', take control and make the most of your time at college or university. This unique and practical self-coaching guide bridges the gap between student life, academic performance and employability skills. It actively encourages students to discover ways to shape their outlooks and experiences. - There are a host of useful illustrations, diagrams, checklists, exercises and real student stories to help you get the most from the book.- It''s easy to find your way around the chapters so you can pick the bits that are most relevant for you. Regardless of your subject, level of study or reasons for doing a degree, Get Sorted will prove invaluable in helping you to navigate the challenges of your student journey, and beyond.

An Update of the Most Popular Graduate-Level Introductions to Bayesian Statistics for Social ScientistsNow that Bayesian modeling has become standard, MCMC is well understood and trusted, and computing power continues to increase, Bayesian Methods: A Social and Behavioral Sciences Approach, Third Edition focuses more on implementation details of the procedures and less on justifying procedures. The expanded examples reflect this updated approach.New to the Third Edition A chapter on Bayesian decision theory, covering Bayesian and frequentist decision theory as well as the connection of empirical Bayes with James–Stein estimationA chapter on the practical implementation of MCMC methods using the BUGS softwareGreatly expanded chapter on hierarchical models that shows how this area is well suited to the Bayesian paradigmMany new applications from a variety of social science disciplines Double the number of exercises, with 20 now in each chapter Updated BaM package in R, including new datasets, code, and procedures for calling BUGS packages from RThis bestselling, highly praised text continues to be suitable for a range of courses, including an introductory course or a computing-centered course. It shows students in the social and behavioral sciences how to use Bayesian methods in practice, preparing them for sophisticated, real-world work in the field.

An Update of the Most Popular Graduate-Level Introductions to Bayesian Statistics for Social ScientistsNow that Bayesian modeling has become standard, MCMC is well understood and trusted, and computing power continues to increase, Bayesian Methods: A Social and Behavioral Sciences Approach, Third Edition focuses more on implementation details of th