Reviewing the use of natural light by architects in the era of electricity, this book aims to show that natural light not only remains a potential source of order in architecture, but that natural lighting strategies impose a usefully creative discipline on design.

Considering an approach to environmental context that sees light as a critical aspect of place, this book explores current attitudes to natural light by offering a series of in-depth studies of recent projects and the particular lighting issues they have addressed. It gives a more nuanced appraisal of these lighting strategies by setting them within their broader topographic, climatic and cultural contexts.

Alan C. Acock's A Gentle Introduction to Stata, Revised Sixth Edition is aimed at new Stata users who want to become proficient in Stata. After reading this introductory text, new users will be able to not only use Stata well but also learn new aspects of Stata. Acock assumes that the user is not familiar with any statistical software. This assumption of a blank slate is central to the structure and contents of the book. Acock starts with the basics; for example, the part of the book that deals with data management begins with a careful and detailed example of turning survey data on paper into a Stata-ready dataset. When explaining how to go about basic exploratory statistical procedures, Acock includes notes that will help the reader develop good work habits. This mixture of explaining good Stata habits and explaining good statistical habits continues throughout the book. Acock is quite careful to teach the reader all aspects of using Stata. He covers data management, good work habits (including the use of basic do-files), basic exploratory statistics (including graphical displays), and analyses using the standard array of basic statistical tools (correlation, linear and logistic regression, and parametric and nonparametric tests of location and dispersion). He also successfully introduces some more advanced topics such as multiple imputation and multilevel modeling in a very approachable manner. Acock teaches Stata commands by using the menus and dialog boxes while still stressing the value of Stata commands and do-files. In this way, he ensures that all types of users can build good work habits. Each chapter has exercises that the motivated reader can use to reinforce the material. The tone of the book is friendly and conversational without ever being glib or condescending. Important asides and notes about terminology are set off in boxes, which makes the text easy to read without any convoluted twists or forward referencing. Rather than splitting topics by their Stata implementation, Acock arranges the topics as they would appear in a basic statistics textbook; graphics and postestimation are woven into the material naturally. Real datasets, such as the General Social Surveys from 2002, 2006, and 2016, are used throughout the book. The focus of the book is especially helpful for those in the behavioral and social sciences because the presentation of basic statistical modeling is supplemented with discussions of effect sizes and standardized coefficients. Various selection criteria, such as semipartial correlations, are discussed for model selection. Acock also covers a variety of commands available for evaluating reliability and validity of measurements. The revised sixth edition is fully up to date for Stata 17, including updated discussion and images of Stata's interface and modern command syntax. In addition, examples include new features such as the table command and collect suite for creating and exporting customized tables as well as the option for creating graphs with transparency.

Max Weber (1864-1920) is generally recognised as one of the founding fathers of modern sociology. His ideas continue to be discussed by sociologists and historians and much homage is paid to his contribution to knowledge. However, such is the awe which the breadth of his knowledge inspires that most general books about Weber contain summaries rather than criticism. This book is the first attempt to evaluate Weber's entire work in the light of historical knowledge available today and of contemporary analytic philosophy. Professor Andreski shows where Weber's true greatness lies, which of Weber's ideas are still valid, which need either correction or modification and which merit rejection. Andreski places Weber in his social and cultural context of the intellectual preeminence of German culture in the second half of the nineteenth century. He examines Weber's most famous theses on objectivity, methodological individualism, ethical neutrality; explanation versus understanding; ideal types; rationalisation; bureaucracy, charisma, power, law and religion; as well as the explanation of the rise of capitalism and uniqueness of Western civilization. Andreski concludes by considering what contemporary scholars should learn from Weber if they want to advance further. He argues that the most important lesson is that comparative study of history (including recent history) is the only method of giving empirical support to an examination of large-scale social processes or a general proposition about them. This book was first published in 1984.

Data Analytics for the Social Sciences is an introductory, graduate-level treatment of data analytics for social science. It features applications in the R language, arguably the fastest growing and leading statistical tool for researchers. The book starts with an ethics chapter on the uses and potential abuses of data analytics. Chapters 2 and 3 show how to implement a broad range of statistical procedures in R. Chapters 4 and 5 deal with regression and classification trees and with random forests. Chapter 6 deals with machine learning models and the "caret" package, which makes available to the researcher hundreds of models. Chapter 7 deals with neural network analysis, and Chapter 8 deals with network analysis and visualization of network data. A final chapter treats text analysis, including web scraping, comparative word frequency tables, word clouds, word maps, sentiment analysis, topic analysis, and more. All empirical chapters have two "Quick Start" exercises designed to allow quick immersion in chapter topics, followed by "In Depth" coverage. Data are available for all examples and runnable R code is provided in a "Command Summary". An appendix provides an extended tutorial on R and RStudio. Almost 30 online supplements provide information for the complete book, "books within the book" on a variety of topics, such as agent-based modeling. Rather than focusing on equations, derivations, and proofs, this book emphasizes hands-on obtaining of output for various social science models and how to interpret the output. It is suitable for all advanced level undergraduate and graduate students learning statistical data analysis.

Requires minimal prerequisites Explained in basic terms Illustrated with binary datasets and real life examples Covers primary concepts and methods Accessible to undergraduates Suitable for a heterogeneous audience

Contains information for using R software with the examples in the textbook Sampling: Design and Analysis, 3rd edition by Sharon L. Lohr.

Why learn functional programming? Isn't that some complicated ivory-tower technique used only in obscure languages like Haskell? In fact, functional programming is actually very simple. It's also very powerful, as Haskell demonstrates by throwing away all the conventional programming tools and using only functional programming features. But it doesn't have to be done that way. Functional programming is a power tool that you can use in addition to all your usual tools, to whatever extent your current mainstream language supports it. Most languages have at least basic support. In this book we use Python and Java and, as a bonus, Scala. If you prefer another language, there will be minor differences in syntax, but the concepts are the same. Give functional programming a try. You may be surprised how much a single power tool can help you in your day-to-day programming.

Spatializing Social Media charts the theoretical and methodological challenges in analyzing and visualizing social media data mapped to geographic areas. It introduces the reader to concepts, theories, and methods that sit at the crossroads between spatial and social network analysis to unpack the conceptual differences between online and face-to-face social networks and the nonlinear effects triggered by social activity that overlaps online and offline. The book is divided into four sections, with the first accounting for the differences between space (the geometrical arrangements that structure and enable forms of interaction) and place (the mechanisms through which social meanings are attached to physical locations). The second section covers the rationale of social network analysis and the ontological differences, stating that relationships, more than individual and independent attributes, are key to understanding of social behavior. The third section covers a range of case studies that successfully mapped social media activity to geographically situated areas and considers the inflection of homophilous dependencies across online and offline social networks. The fourth and last section of the book explores a range of networks and discusses methods for and approaches to plotting a social network graph onto a map, including the purpose-built R package Spatial Social Media. The book takes a non-mathematical approach to social networks and spatial statistics suitable for postgraduate students in sociology, psychology and the social sciences.

* Contains two introductory chapters on how to set up an R environment and do basic imports/manipulation of meta-analysis data, including exercises. * Describes statistical concepts clearly and concisely before applying them in R. * Includes step-by-step guidance through the coding required to perform meta-analyses, and a companion R package for the book.

* 16 accompanying datasets across a wide range of contexts (e.g. academic, corporate, sports, marketing) * Clear step-by-step instructions on executing the analyses. * Clear guidance on how to interpret results. * Primary instruction in R but added sections for Python coders. * Discussion exercises and data exercises for each of the main chapters. * Final chapter of practice material and datasets ideal for class homework or project work.

Design and Analysis in Educational Research Using jamovi is an integrated approach to learning about research design alongside statistical analysis concepts. Strunk and Mwavita maintain a focus on applied educational research throughout the text, with practical tips and advice on how to do high-quality quantitative research. Based on their successful SPSS version of the book, the authors focus on using jamovi in this version due to its accessibility as open source software, and ease of use. The book teaches research design (including epistemology, research ethics, forming research questions, quantitative design, sampling methodologies, and design assumptions) and introductory statistical concepts (including descriptive statistics, probability theory, sampling distributions), basic statistical tests (like z and t), and ANOVA designs, including more advanced designs like the factorial ANOVA and mixed ANOVA. This textbook is tailor-made for first-level doctoral courses in research design and analysis. It will also be of interest to graduate students in education and educational research. The book includes Support Material with downloadable data sets, and new case study material from the authors for teaching on race, racism, and Black Lives Matter, available at www.routledge.com/9780367723088.

This book begins with an introductory outline of the structure of the city politics of the United States. There is a study of the city in the federal system, including the politics of feudal aid. This is followed by four case studies: the political roles of mayor, manager, boss and adminstrator-entrepreneur in the city. Madgwick concludes with some comparative reflections indicating the significance of this study for British local government. This book was first published in 1970.

Through a multi-methodology approach, Principles and Methods of Social Research, Fourth Edition covers the latest research techniques and designs and guides readers toward the design and conduct of social research from the ground up. Applauded for its comprehensive coverage, the breadth and depth of content of this new edition is unparalleled. Explained with updated applied examples useful to the social, behavioral, educational, and organizational sciences, the methods described are relevant to contemporary researchers. The underlying logic and mechanics of experimental, quasi-experimental, and non-experimental research strategies are discussed in detail. Introductory chapters cover topics such as validity and reliability furnish readers with a firm understanding of foundational concepts. The book has chapters dedicated to sampling, interviewing, questionnaire design, stimulus scaling, observational methods, content analysis, implicit measures, dyadic and group methods, and meta-analysis to cover these essential methodologies. Notable features include an emphasis on understanding the principles that govern the use of a method to facilitate the researcher’s choice of the best technique for a given situation; use of the laboratory experiment as a touchstone to describe and evaluate field experiments, correlational designs, quasi experiments, evaluation studies, and survey designs; and coverage of the ethics of social research including the power a researcher wields and tips on how to use it responsibly. The new edition features: Increased attention to the distinction between conceptual replication and exact replication and how each contributes to cumulative science. Updated research examples that clarify the operation of various research design operations. More learning tools including more explanation of the basic concepts, more research examples, and more tables and figures, such as additional illustrations to include internet content like social media. Extensive revisions and expansions of all chapters. A fuller discussion of the dangers of unethical treatment to research participants. Principles and Methods of Social Research, Fourth Edition is intended for graduate or advanced undergraduate courses in research methods in psychology, communication, sociology, education, public health, and marketing, and further appeals to researchers in various fields of social research, such as social psychology and communication.

See How Graphics Reveal Information Graphical Data Analysis with R shows you what information you can gain from graphical displays. The book focuses on why you draw graphics to display data and which graphics to draw (and uses R to do so). All the datasets are available in R or one of its packages and the R code is available at rosuda.org/GDA. Graphical data analysis is useful for data cleaning, exploring data structure, detecting outliers and unusual groups, identifying trends and clusters, spotting local patterns, evaluating modelling output, and presenting results. This book guides you in choosing graphics and understanding what information you can glean from them. It can be used as a primary text in a graphical data analysis course or as a supplement in a statistics course. Colour graphics are used throughout.

Max Weber (1864-1920) is generally recognised as one of the founding fathers of modern sociology. His ideas continue to be discussed by sociologists and historians and much homage is paid to his contribution to knowledge. However, such is the awe which the breadth of his knowledge inspires that most general books about Weber contain summaries rather than criticism. This book is the first attempt to evaluate Weber's entire work in the light of historical knowledge available today and of contemporary analytic philosophy. Professor Andreski shows where Weber's true greatness lies, which of Weber's ideas are still valid, which need either correction or modification and which merit rejection. Andreski places Weber in his social and cultural context of the intellectual preeminence of German culture in the second half of the nineteenth century. He examines Weber's most famous theses on objectivity, methodological individualism, ethical neutrality; explanation versus understanding; ideal types; rationalisation; bureaucracy, charisma, power, law and religion; as well as the explanation of the rise of capitalism and uniqueness of Western civilization. Andreski concludes by considering what contemporary scholars should learn from Weber if they want to advance further. He argues that the most important lesson is that comparative study of history (including recent history) is the only method of giving empirical support to an examination of large-scale social processes or a general proposition about them. This book was first published in 1984.

This core textbook offers an introduction to the basics of statistics that is uniquely suited to behavioural science students. The book offers coverage anchored to real-world stories, a highly visual approach, helpful mathematical support, and useful step-by-step examples. The book focuses on emerging trends that are redefining contemporary behavioural statistics.This textbook helps you get to grips with a challenging subject in an enjoyable and engaging way. The book can also be purchased with the breakthrough online resource, LaunchPad, which offers innovative media content, curated and organised for easy assignability. LaunchPad's intuitive interface presents quizzing, flashcards, animations and much more to make learning actively engaging.

With recent advances in computing power and the widespread availability of preference, perception and choice data, such as public opinion surveys and legislative voting, the empirical estimation of spatial models using scaling and ideal point estimation methods has never been more accessible.The second edition of Analyzing Spatial Models of Choice and Judgment demonstrates how to estimate and interpret spatial models with a variety of methods using the open-source programming language R. Requiring only basic knowledge of R, the book enables social science researchers to apply the methods to their own data. Also suitable for experienced methodologists, it presents the latest methods for modeling the distances between points. The authors explain the basic theory behind empirical spatial models, then illustrate the estimation technique behind implementing each method, exploring the advantages and limitations while providing visualizations to understand the results. This second edition updates and expands the methods and software discussed in the first edition, including new coverage of methods for ordinal data and anchoring vignettes in surveys, as well as an entire chapter dedicated to Bayesian methods. The second edition is made easier to use by the inclusion of an R package, which provides all data and functions used in the book. David A. Armstrong II is Canada Research Chair in Political Methodology and Associate Professor of Political Science at Western University. His research interests include measurement, Democracy and state repressive action. Ryan Bakker is Reader in Comparative Politics at the University of Essex. His research interests include applied Bayesian modeling, measurement, Western European politics, and EU politics. Royce Carroll is Professor in Comparative Politics at the University of Essex. His research focuses on measurement of ideology and the comparative politics of legislatures and political parties. Christopher Hare is Assistant Professor in Political Science at the University of California, Davis. His research focuses on ideology and voting behavior in US politics, political polarization, and measurement. Keith T. Poole is Philip H. Alston Jr. Distinguished Professor of Political Science at the University of Georgia. His research interests include methodology, US political-economic history, economic growth and entrepreneurship. Howard Rosenthal is Professor of Politics at NYU and Roger Williams Straus Professor of Social Sciences, Emeritus, at Princeton. Rosenthal's research focuses on political economy, American politics and methodology.

Individuals with serious and persistent mental illnesses, including schizophrenia and affective disorders, often experience cognitive deficits that make it challenging to perform everyday tasks. For example, they may have difficulty paying attention, remembering and learning, thinking quickly, and solving problems, and this may interfere with functioning at work, school, and in social and living situations. Cognitive remediation is an evidence-based behavioral treatment for people who are experiencing cognitive impairments that interfere with role functioning. Cognitive Remediation for Psychological Disorders contains all the information therapists need to set up a cognitive remediation program that helps clients strengthen the cognitive skills necessary for everyday functioning. The program described is called Neuropsychological and Educational Approach to Remediation (NEAR), an evidence-based approach that utilizes carefully crafted instructional techniques which promote learning. The goals of NEAR are to provide a positive learning experience and to promote independent learning and optimal cognitive functioning in daily life. The second edition of this popular Therapist Guide provides step-by-step instructions on how to implement NEAR techniques with patients. Guidelines for setting up and running a successful cognitive remediation program are laid out in an easy-to-follow format. Therapists will learn how to choose appropriate cognitive exercises, recruit and work with clients, perform intakes, and create treatment plans. This Guide comes complete with all the tools necessary for facilitating treatment, including program evaluation forms and client handouts.

Highly praised for its clear, straightforward approach, Statistics without Maths for Psychology 8th edition provides a comprehensive and accessible introduction to statistics and SPSS. This widely used and trusted textbook is packed with examples, activities and questions to help you to test your learning and deepen your understanding in a practical and manageable way. Statistics without Maths for Psychology, 8e, will help you to gain the confidence to apply statistical concepts and use SPSS to analyse data within your studies and future independent research.

Identifying the sources and measuring the impact of haphazard variations are important in any number of research applications, from clinical trials and genetics to industrial design and psychometric testing. Only in very simple situations can such variations be represented effectively by independent, identically distributed random variables or by random sampling from a hypothetical infinite population.

Components of Variance illuminates the complexities of the subject, setting forth its principles with focus on both the development of models for detailed analyses and the statistical techniques themselves. The authors first consider balanced and unbalanced situations, then move to the treatment of non-normal data, beginning with the Poisson and binomial models and followed by extensions to survival data and more general situations. In the final chapter, they discuss ways of extending and assessing various models, including the study of exceedances, the use of nonlinear representations, the study of transformations of the response variable, and the detailed examination of the distributional form of the underlying random variables.

Careful signposting and numerous examples from genetic data analysis, clinical trial design, longitudinal data analysis, industrial design, and meta-analysis make this book accessible - and valuable - not only to statisticians but to all applied research scientists who use statistical methods.

This book will help undergraduate psychology students to write practical reports of experimental and other quantitative studies in psychology. It is designed to help with every stage of report writing and provides a resource that students can refer to throughout their degree, up-to and including when writing up a final year undergraduate project. Now fully updated in its fourth edition, this book maps to the seventh edition of the APA guidelines and offers more comprehensive advice, guidelines and recommendations than ever before. Students will benefit from: *Coverage of different forms of quantitative study, including online studies and studies that use questionnaires, as well as experiments *A range of handy test yourself questions (with answers at the end of the book) *Self-reflection questions to prompt deeper understanding *Summary sections that articulate the main points and provide a useful revision aid *An Index of Concepts indicating where in the book every concept is introduced and defined *Updated advice on how to find and cite references *Expanded coverage of ethics in quantitative research, including how to write ethically *Common mistake symbols, flagging areas where its easy to be caught out Peter Harris is Emeritus Professor of Psychology at the University of Sussex, UK where he led the Social and Applied Psychology Group. He has taught research design and statistics for many years. He has published extensively in social and health psychology. Matthew J. Easterbrook is Senior Lecturer in Psychology at the University of Sussex, UK. He has taught statistics at a national and international level. Jessica S. Horst is Reader in Psychology at the University of Sussex, UK, where she is also the Director of Teaching and Learning. She has taught research methods in both the USA and the UK.

Highly recommended by the Journal of Official Statistics, The American Statistician, and other journals, Applied Survey Data Analysis, Second Edition provides an up-to-date overview of state-of-the-art approaches to the analysis of complex sample survey data. Building on the wealth of material on practical approaches to descriptive analysis and regression modeling from the first edition, this second edition expands the topics covered and presents more step-by-step examples of modern approaches to the analysis of survey data using the newest statistical software. Designed for readers working in a wide array of disciplines who use survey data in their work, this book continues to provide a useful framework for integrating more in-depth studies of the theory and methods of survey data analysis. An example-driven guide to the applied statistical analysis and interpretation of survey data, the second edition contains many new examples and practical exercises based on recent versions of real-world survey data sets. Although the authors continue to use Stata for most examples in the text, they also continue to offer SAS, SPSS, SUDAAN, R, WesVar, IVEware, and Mplus software code for replicating the examples on the book's updated website.

Routledge is now re-issuing this prestigious series of 204 volumes originally published between 1910 and 1965. The titles include works by key figures such asC.G. Jung, Sigmund Freud, Jean Piaget, Otto Rank, James Hillman, Erich Fromm, Karen Horney and Susan Isaacs. Each volume is available on its own, as part of a themed mini-set, or as part of a specially-priced 204-volume set. A brochure listing each title in the "International Library of Psychology" series is available upon request.

Routledge is now re-issuing this prestigious series of 204 volumes originally published between 1910 and 1965. The titles include works by key figures such asC.G. Jung, Sigmund Freud, Jean Piaget, Otto Rank, James Hillman, Erich Fromm, Karen Horney and Susan Isaacs. Each volume is available on its own, as part of a themed mini-set, or as part of a specially-priced 204-volume set. A brochure listing each title in the "International Library of Psychology" series is available upon request.

This comprehensive book is an introduction to multilevel Bayesian models in R using brms and the Stan programming language. Featuring a series of fully worked analyses of repeated-measures data, focus is placed on active learning through the analyses of the progressively more complicated models presented throughout the book. In this book, the authors offer an introduction to statistics entirely focused on repeated measures data beginning with very simple two-group comparisons and ending with multinomial regression models with many 'random effects'. Across 13 well-structured chapters, readers are provided with all the code necessary to run all the analyses and make all the plots in the book, as well as useful examples of how to interpret and write-up their own analyses. This book provides an accessible introduction for readers in any field, with any level of statistical background. Senior undergraduate students, graduate students, and experienced researchers looking to 'translate' their skills with more traditional models to a Bayesian framework, will benefit greatly from the lessons in this text.