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.

This comprehensive Handbook is the first to provide a practical, interdisciplinary review of ethical issues as they relate to quantitative methodology including how to present evidence for reliability and validity, what comprises an adequate tested population, and what constitutes scientific knowledge for eliminating biases. The book uses an ethical framework that emphasizes the human cost of quantitative decision making to help researchers understand the specific implications of their choices. The order of the Handbook chapters parallels the chronology of the research process: determining the research design and data collection; data analysis; and communicating findings. Each chapter: Explores the ethics of a particular topic Identifies prevailing methodological issues Reviews strategies and approaches for handling such issues and their ethical implications Provides one or more case examples Outlines plausible approaches to the issue including best-practice solutions. Part 1 presents ethical frameworks that cross-cut design, analysis, and modeling in the behavioral sciences. Part 2 focuses on ideas for disseminating ethical training in statistics courses. Part 3 considers the ethical aspects of selecting measurement instruments and sample size planning and explores issues related to high stakes testing, the defensibility of experimental vs. quasi-experimental research designs, and ethics in program evaluation. Decision points that shape a researchers' approach to data analysis are examined in Part 4 - when and why analysts need to account for how the sample was selected, how to evaluate tradeoffs of hypothesis-testing vs. estimation, and how to handle missing data. Ethical issues that arise when using techniques such as factor analysis or multilevel modeling and when making causal inferences are also explored. The book concludes with ethical aspects of reporting meta-analyses, of cross-disciplinary statistical reform, and of the publication process. This Handbook appeals to researchers and practitioners in psychology, human development, family studies, health, education, sociology, social work, political science, and business/marketing. This book is also a valuable supplement for quantitative methods courses required of all graduate students in these fields.

Originally published in 1976, the authors of six of the most widely quoted works in behavioural science related to education, at the time, here describe in detail their research work, including its origins, planning and implementation. The accounts are unusual, not only for their technical detail but for their candour. The brief was to put the heart and brains back into accounts of research so the authors comment not only on the research design, but on the personal and professional problems they had to overcome. They also reflect on the reception of their work, and the way in which it has been adapted, misunderstood or deliberately distorted to support arguments of widely differing ideological pressure groups. The book shows how ingenuity and persistence as well as technical competence lie at the heart of the research process. The authors do not give the normal depersonalised, streamlined account which gives a false, mechanical picture of research as an occupation, but show it to be a profound personal and professional experience as they comment on the thought that lay behind their work and the way it was finally produced for publication. Dr Shipman has written a short introduction to each chapter, and contributed a concluding chapter relating the six research experiences to conventional views on the research process and to the part played by research evidence in policy making.

Feminist research is informed by a history of breaking silences, of demanding that women's voices be heard, recorded and included in wider intellectual genealogies and histories. This has led to an emphasis on voice and speaking out in the research endeavour. Moments of secrecy and silence are less often addressed. This gives rise to a number of questions. What are the silences, secrets, omissions and and political consequences of such moments? What particular dilemmas and constraints do they represent or entail? What are their implications for research praxis? Are such moments always indicative of voicelessness or powerlessness? Or may they also constitute a productive moment in the research encounter? Contributors to this volume were invited to reflect on these questions. The resulting chapters are a fascinating collection of insights into the research process, making an important contribution to theoretical and empirical debates about epistemology, subjectivity and identity in research. Researchers often face difficult dilemmas about who to represent and how, what to omit and what to include. This book explores such questions in an important and timely collection of essays from international scholars.

Multistate Models for the Analysis of Life History Data provides the first comprehensive treatment of multistate modeling and analysis, including parametric, nonparametric and semiparametric methods applicable to many types of life history data. Special models such as illness-death, competing risks and progressive processes are considered, as well as more complex models. The book provides both theoretical development and illustrations of analysis based on data from randomized trials and observational cohort studies in health research. It features: Discusses a wide range of applications of multistate models, Presents methods for both continuously and intermittently observed life history processes, Gives a thorough discussion of conditionally independent censoring and observation processes, Discusses models with random effects and joint models for two or more multistate processes, Discusses and illustrates software for multistate analysis that is available in R, Target audience includes those engaged in research and applications involving multistate models.

Multi-State Survival Models for Interval-Censored Data introduces methods to describe stochastic processes that consist of transitions between states over time. It is targeted at researchers in medical statistics, epidemiology, demography, and social statistics. One of the applications in the book is a three-state process for dementia and survival in the older population. This process is described by an illness-death model with a dementia-free state, a dementia state, and a dead state. Statistical modelling of a multi-state process can investigate potential associations between the risk of moving to the next state and variables such as age, gender, or education. A model can also be used to predict the multi-state process. The methods are for longitudinal data subject to interval censoring. Depending on the definition of a state, it is possible that the time of the transition into a state is not observed exactly. However, when longitudinal data are available the transition time may be known to lie in the time interval defined by two successive observations. Such an interval-censored observation scheme can be taken into account in the statistical inference. Multi-state modelling is an elegant combination of statistical inference and the theory of stochastic processes. Multi-State Survival Models for Interval-Censored Data shows that the statistical modelling is versatile and allows for a wide range of applications.

In multivariate data analysis, regression techniques predict one set of variables from another while principal component analysis (PCA) finds a subspace of minimal dimensionality that captures the largest variability in the data. How can regression analysis and PCA be combined in a beneficial way? Why and when is it a good idea to combine them? What kind of benefits are we getting from them? Addressing these questions, Constrained Principal Component Analysis and Related Techniques shows how constrained PCA (CPCA) offers a unified framework for these approaches. The book begins with four concrete examples of CPCA that provide readers with a basic understanding of the technique and its applications. It gives a detailed account of two key mathematical ideas in CPCA: projection and singular value decomposition. The author then describes the basic data requirements, models, and analytical tools for CPCA and their immediate extensions. He also introduces techniques that are special cases of or closely related to CPCA and discusses several topics relevant to practical uses of CPCA. The book concludes with a technique that imposes different constraints on different dimensions (DCDD), along with its analytical extensions. MATLAB (R) programs for CPCA and DCDD as well as data to create the book's examples are available on the author's website.

The number of innovative applications of randomization tests in various fields and recent developments in experimental design, significance testing, computing facilities, and randomization test algorithms have necessitated a new edition of Randomization Tests. Updated, reorganized, and revised, the text emphasizes the irrelevance and implausibility of the random sampling assumption for the typical experiment in three completely rewritten chapters. It also discusses factorial designs and interactions and combines repeated-measures and randomized block designs in one chapter. The authors focus more attention on the practicality of N-of-1 randomization tests and the availability of user-friendly software to perform them. In addition, they provide an overview of free and commercial computer programs for all of the tests presented in the book. Building on the previous editions that have served as standard textbooks for more than twenty-five years, Randomization Tests, Fourth Edition includes downloadable resources of up-to-date randomization test programs that facilitate application of the tests to experimental data. This CD-ROM enables students to work out problems that have been added to the chapters and helps professors teach the basics of randomization tests and devise tasks for assignments and examinations.

Practical Handbook of Curve Fitting is a reference work assembled by Arlinghaus and a set of editors with well over a century of combined experience in various disciplines and activities related to curve fitting. The book demonstrates how to analyze World data bases and graph and map the results. Default settings in software packages can produce attractive graphs of data imported into the software. Often, however, the default graph has no equation associated with it and cannot therefore be used as a tool for further analysis or projection of the data. The same software can often be used to generate curves from equations. The reader is shown directly, and in a series of steps, how to fit curves to data using Lotus 1-2-3. There are traditional unbounded curve fitting techniques-lines of least squares, exponentials, logistic curves, and Gompertz curves. There is the bounded curve fitting technique of cubic spline interpolation. Beyond these, there is a detailed application of Feigenbaum's graphical analysis from chaos theory, and there is a hint as to how fractal geometry might come into play. Curve fitting algorithms take on new life when they are actually used on real-world data. They are used in numerous worked examples drawn from electronic data bases of public domain information from the Stars data base of The World Bank and from the WRD data base of the World Resources Institute. The applications are current and reflect a state-of-the-art interest in the human dimensions of global change.

Data Analysis of Asymmetric Structures provides a comprehensive presentation of a variety of models and theories for the analysis of asymmetry and its applications and provides a wealth of new approaches in every section. It meets both the practical and theoretical needs of research professionals across a wide range of disciplines and considers data analysis in fields such as psychology, sociology, social science, ecology, and marketing. In seven comprehensive chapters this guide details theories, methods, and models for the analysis of asymmetric structures in a variety of disciplines and presents future opportunities and challenges affecting research developments and business applications.

This new handbook is the definitive resource on advanced topics related to multilevel analysis. The editors assembled the top minds in the field to address the latest applications of multilevel modeling as well as the specific difficulties and methodological problems that are becoming more common as more complicated models are developed. Each chapter features examples that use actual datasets. These datasets, as well as the code to run the models, are available on the book's website http: //www.hlm-online.com . Each chapter includes an introduction that sets the stage for the material to come and a conclusion. Divided into five sections, the first provides a broad introduction to the field that serves as a framework for understanding the latter chapters. Part 2 focuses on multilevel latent variable modeling including item response theory and mixture modeling. Section 3 addresses models used for longitudinal data including growth curve and structural equation modeling. Special estimation problems are examined in section 4 including the difficulties involved in estimating survival analysis, Bayesian estimation, bootstrapping, multiple imputation, and complicated models, including generalized linear models, optimal design in multilevel models, and more. The book's concluding section focuses on statistical design issues encountered when doing multilevel modeling including nested designs, analyzing cross-classified models, and dyadic data analysis. Intended for methodologists, statisticians, and researchers in a variety of fields including psychology, education, and the social and health sciences, this handbook also serves as an excellent text for graduate and PhD level courses in multilevel modeling. A basic knowledge of multilevel modeling is assumed.

Interviewer Effects from a Total Survey Error Perspective presents a comprehensive collection of state-of-the-art research on interviewer-administered survey data collection. Interviewers play an essential role in the collection of the high-quality survey data used to learn about our society and improve the human condition. Although many surveys are conducted using self-administered modes, interviewer-administered modes continue to be optimal for surveys that require high levels of participation, include difficult-to-survey populations, and collect biophysical data. Survey interviewing is complex, multifaceted, and challenging. Interviewers are responsible for locating sampled units, contacting sampled individuals and convincing them to cooperate, asking questions on a variety of topics, collecting other kinds of data, and providing data about respondents and the interview environment. Careful attention to the methodology that underlies survey interviewing is essential for interviewer-administered data collections to succeed. In 2019, survey methodologists, survey practitioners, and survey operations specialists participated in an international workshop at the University of Nebraska-Lincoln to identify best practices for surveys employing interviewers and outline an agenda for future methodological research. This book features 23 chapters on survey interviewing by these worldwide leaders in the theory and practice of survey interviewing. Chapters include: The legacy of Dr. Charles F. Cannell's groundbreaking research on training survey interviewers and the theory of survey interviewing Best practices for training survey interviewers Interviewer management and monitoring during data collection The complex effects of interviewers on survey nonresponse Collecting survey measures and survey paradata in different modes Designing studies to estimate and evaluate interviewer effects Best practices for analyzing interviewer effects Key gaps in the research literature, including an agenda for future methodological research Chapter appendices available to download from https://digitalcommons.unl.edu/sociw/ Written for managers of survey interviewers, survey methodologists, and students interested in the survey data collection process, this unique reference uses the Total Survey Error framework to examine optimal approaches to survey interviewing, presenting state-of-the-art methodological research on all stages of the survey process involving interviewers. Acknowledging the important history of survey interviewing while looking to the future, this one-of-a-kind reference provides researchers and practitioners with a roadmap for maximizing data quality in interviewer-administered surveys.

MATLAB Blues is an accessible, comprehensive introduction to the MATLAB computer programming language-a powerful and increasingly popular tool for students and researchers. Rosenbaum identifies many of the common mistakes and pitfalls associated with using MATLAB, and shows users how they can learn from these mistakes to be better, happier programmers. Each chapter systematically addresses one of the basic principles of the programming language, like matrices, calculations, contingencies, plotting, input-output, and graphics, and then identifies areas that are problematic, as well as potential errors that can occur. This not only provides the reader with the fundamental "scales and chords" that a MATLAB programmer needs to know, but also with a series of examples and explanations of how to avoid and remedy common mistakes. Accompanied by an array of sample code that can be used and manipulated in conjunction with the textbook, this book is a practical, insightful introduction to MATLAB which provides motivation and encouragement to those with little or no background in programming as well as to those with more advanced concerns. It is an invaluable resource for researchers and students undertaking courses in research methods, statistics, and programming.

The Springer Handbook of Odor is the definitive guide to all aspects related to the study of smell and their impact on human life. For the first time, this handbook aligns the senso-chemo-analytical characterization of everyday smells encountered by mankind, with the elucidation of perceptual, hedonic, behavioral and physiological responses of humans to such odors. From birth onwards we learn to interact with our environment using our sense of smell. Moreover, evolutionary processes have engendered a multi-faceted communication that is supported - even dominated - by olfaction. This compilation examines the responses of humans to odors at different stages of life, thereby building a foundation for a widely overseen area of research with broader ramifications for human life. The expert international authors and editor align aspects, concepts, methodologies and perspectives from a broad range of different disciplines related to the science of smell. These include chemistry, physiology, psychology, material sciences, technology but also disciplines related to linguistics, culture, art and design. This handbook, edited by an internationally renowned aroma scientist with the support of an outstanding team of over 60 authors, is an authoritative reference for researchers in the field of odors both in academia and in industry and is also a useful reference for newcomers to the area.

Collecting and analyzing data on unemployment, inflation, and inequality help describe the complex world around us. When published by the government, such data are called official statistics. They are reported by the media, used by politicians to lend weight to their arguments, and by economic commentators to opine about the state of society. Despite such widescale use, explanations about how these measures are constructed are seldom provided for a non-technical reader. This Measuring Society book is a short, accessible guide to six topics: jobs, house prices, inequality, prices for goods and services, poverty, and deprivation. Each relates to concepts we use on a personal level to form an understanding of the society in which we live: We need a job, a place to live, and food to eat. Using data from the United States, we answer three basic questions: why, how, and for whom these statistics have been constructed. We add some context and flavor by discussing the historical background. This book provides the reader with a good grasp of these measures. Chaitra H. Nagaraja is an Associate Professor of Statistics at the Gabelli School of Business at Fordham University in New York. Her research interests include house price indices and inequality measurement. Prior to Fordham, Dr. Nagaraja was a researcher at the U.S. Census Bureau. While there, she worked on projects relating to the American Community Survey.

Feminist research is informed by a history of breaking silences, of demanding that women's voices be heard, recorded and included in wider intellectual genealogies and histories. This has led to an emphasis on voice and speaking out in the research endeavour. Moments of secrecy and silence are less often addressed. This gives rise to a number of questions. What are the silences, secrets, omissions and and political consequences of such moments? What particular dilemmas and constraints do they represent or entail? What are their implications for research praxis? Are such moments always indicative of voicelessness or powerlessness? Or may they also constitute a productive moment in the research encounter? Contributors to this volume were invited to reflect on these questions. The resulting chapters are a fascinating collection of insights into the research process, making an important contribution to theoretical and empirical debates about epistemology, subjectivity and identity in research. Researchers often face difficult dilemmas about who to represent and how, what to omit and what to include. This book explores such questions in an important and timely collection of essays from international scholars.

One of the greatest changes in the sports world in the past 20 years has been the use of mathematical methods to analyze performances, recognize trends and patterns, and predict results. Analytic Methods in Sports: Using Mathematics and Statistics to Understand Data from Baseball, Football, Basketball, and Other Sports, Second Edition provides a concise yet thorough introduction to the analytic and statistical methods that are useful in studying sports. The book gives you all the tools necessary to answer key questions in sports analysis. It explains how to apply the methods to sports data and interpret the results, demonstrating that the analysis of sports data is often different from standard statistical analyses. The book integrates a large number of motivating sports examples throughout and offers guidance on computation and suggestions for further reading in each chapter. Features Covers numerous statistical procedures for analyzing data based on sports results Presents fundamental methods for describing and summarizing data Describes aspects of probability theory and basic statistical concepts that are necessary to understand and deal with the randomness inherent in sports data Explains the statistical reasoning underlying the methods Illustrates the methods using real data drawn from a wide variety of sports Offers many of the datasets on the author's website, enabling you to replicate the analyses or conduct related analyses New to the Second Edition R code included for all calculations A new chapter discussing several more advanced methods, such as binary response models, random effects, multilevel models, spline methods, and principal components analysis, and more Exercises added to the end of each chapter, to enable use for courses and self-study Full solutions manual available to course instructors.

Statistical power analysis has revolutionized the ways in which we conduct and evaluate research. Similar developments in the statistical analysis of incomplete (missing) data are gaining more widespread applications. This volume brings statistical power and incomplete data together under a common framework, in a way that is readily accessible to those with only an introductory familiarity with structural equation modeling. It answers many practical questions such as:

• How missing data affects the statistical power in a study
• How much power is likely with different amounts and types of missing data
• How to increase the power of a design in the presence of missing data, and
• How to identify the most powerful design in the presence of missing data.

Points of Reflection encourage readers to stop and test their understanding of the material. Try Me sections test one s ability to apply the material. Troubleshooting Tips help to prevent commonly encountered problems. Exercises reinforce content and Additional Readings provide sources for delving more deeply into selected topics. Numerous examples demonstrate the book s application to a variety of disciplines. Each issue is accompanied by its potential strengths and shortcomings and examples using a variety of software packages (SAS, SPSS, Stata, LISREL, AMOS, and MPlus). Syntax is provided using a single software program to promote continuity but in each case, parallel syntax using the other packages is presented in appendixes. Routines, data sets, syntax files, and links to student versions of software packages are found at www.psypress.com/davey. The worked examples in Part 2 also provide results from a wider set of estimated models. These tables, and accompanying syntax, can be used to estimate statistical power or required sample size for similar problems under a wide range of conditions.

Class-tested at Temple, Virginia Tech, and Miami University of Ohio, this brief text is an ideal supplement for graduate courses in applied statistics, statistics II, intermediate or advanced statistics, experimental design, structural equation modeling, power analysis, and research methods taught in departments of psychology, human development, education, sociology, nursing, social work, gerontology and other social and health sciences. The book s applied approach will also appeal to researchers in these areas. Sections covering Fundamentals, Applications, and Extensions are designed to take readers from first steps to mastery.

Practitioner-Based Research is concerned, in particular, with the research which is undertaken by healthcare practitioners and the evidence which they generate as a result of investigating their practice. In so doing it recognizes that, as well as working in academic life, practitioner researchers are often working as practitioners outside the Academy. It argues that the work of practitioner researchers has a significant contribution to make to healthcare research and so needs to be disseminated further in order to create balanced research communities within the healthcare professions. This book will help academic researchers to broaden the limited ontological and epistemological perspectives of their research. It will also encourage healthcare practitioners who have not been trained academically to develop their research skills and to realize that they are actually researching in their practice on a day-to-day basis. Finally, it will provide a degree of transparency about therapeutic processes to help clients and patients to see aspects of professional practice and development which are usually hidden from them.

The development of communication as a discipline has resulted in an explosion of scales tapping various aspects of interpersonal, mass, organizational, and instructional communication. This sourcebook brings together scales that measure a variety of important communication constructs. The scales presented are drawn from areas of interpersonal, mass, organizational, and instructional communication--areas in which the use of formal, quantitative scales is particularly well developed.

Communication Research Measures reflects the recent important emphasis on developing and improving the measurement base of the communication discipline. It results in an equal amount of labor saved on the part of the scholars, students, and practitioners who find this book useful, and it contributes in a significant way to research efforts.
Originally published by Guilford Press in 1994, now available from Routledge.

Emphasizing causation as a functional relationship between variables that describe objects, Linear Causal Modeling with Structural Equations integrates a general philosophical theory of causation with structural equation modeling (SEM) that concerns the special case of linear causal relations. In addition to describing how the functional relation concept may be generalized to treat probabilistic causation, the book reviews historical treatments of causation and explores recent developments in experimental psychology on studies of the perception of causation. It looks at how to perceive causal relations directly by perceiving quantities in magnitudes and motions of causes that are conserved in the effects of causal exchanges.

The author surveys the basic concepts of graph theory useful in the formulation of structural models. Focusing on SEM, he shows how to write a set of structural equations corresponding to the path diagram, describes two ways of computing variances and covariances of variables in a structural equation model, and introduces matrix equations for the general structural equation model. The text then discusses the problem of identifying a model, parameter estimation, issues involved in designing structural equation models, the application of confirmatory factor analysis, equivalent models, the use of instrumental variables to resolve issues of causal direction and mediated causation, longitudinal modeling, and nonrecursive models with loops. It also evaluates models on several dimensions and examines the polychoric and polyserial correlation coefficients and their derivation.

Covering the fundamentals of algebra and the history of causality, this book provides a solid understanding of causation, linear causal modeling, and SEM. It takes readers through the process of identifying, estimating, analyzing, and evaluating a range of models.

How do parenting styles differ globally? How do different, international, parenting practices impact on children's development? Can we bring together and hybridise different international parenting styles? Intercultural Parenting explores the relationship between family, culture and parenting by reviewing established and evolving Western and Eastern parenting styles and their impact on children's development. Authoritarian, authoritative, permissive and neglecting approaches, as well as newer techniques such as helicopter parenting, are compared with filial, tiger and training approaches, and mixed parenting styles. Practical application sections show how cultural understanding can help demonstrate how professionals might use the information and ideas in their clinical work, whilst parental questionnaires encourage self-assessment and reflection. Dr. Foo Koong Hean brings together the traditional and evolving approaches to the art of parenting practices and also showcases relatively neglected research on Eastern parenting practices. This book is important reading for childcare professionals such as health visitors, early years' teachers and those in mental health, as well as students in family studies and developmental psychology.

Statistical Concepts-A Second Course presents the last 10 chapters from An Introduction to Statistical Concepts, Fourth Edition. Designed for second and upper-level statistics courses, this book highlights how statistics work and how best to utilize them to aid students in the analysis of their own data and the interpretation of research results. In this new edition, Hahs-Vaughn and Lomax discuss sensitivity, specificity, false positive and false negative errors. Coverage of effect sizes has been expanded upon and more organizational features (to summarize key concepts) have been included. A final chapter on mediation and moderation has been added for a more complete presentation of regression models. In addition to instructions and screen shots for using SPSS, new to this edition is annotated script for using R. This book acts as a clear and accessible instructional tool to help readers fully understand statistical concepts and how to apply them to data. It is an invaluable resource for students undertaking a course in statistics in any number of social science and behavioral science disciplines.

Brief and inexpensive, this engaging book helps readers identify and then discard 52 misconceptions about data and statistical summaries. The focus is on major concepts contained in typical undergraduate and graduate courses in statistics, research methods, or quantitative analysis. Fun interactive Internet exercises that further promote undoing the misconceptions are found on the book's website.

The author s accessible discussion of each misconception has five parts:

• The Misconception - a brief description of the misunderstanding
• Evidence that the Misconception Exists examples and claimed prevalence
• Why the Misconception is Dangerous consequence of having the misunderstanding
• Undoing the Misconception - how to think correctly about the concept
• Internet Assignment - an interactive activity to help readers gain a firm grasp of the statistical concept and overcome the misconception.

The book's statistical misconceptions are grouped into 12 chapters that match the topics typically taught in introductory/intermediate courses. However, each of the 52 discussions is self-contained, thus allowing the misconceptions to be covered in any order without confusing the reader. Organized and presented in this manner, the book is an ideal supplement for any standard textbook.

Statistical Misconceptions is appropriate for courses taught in a variety of disciplines including psychology, medicine, education, nursing, business, and the social sciences. The book also will benefit independent researchers interested in undoing their statistical misconceptions. "

This book provides an up-to-date review of commonly undertaken methodological and statistical practices that are sustained, in part, upon sound rationale and justification and, in part, upon unfounded lore. Some examples of these "methodological urban legends," as we refer to them in this book, are characterized by manuscript critiques such as: (a) "your self-report measures suffer from common method bias"; (b) "your item-to-subject ratios are too low"; (c) "you can?t generalize these findings to the real world"; or (d) "your effect sizes are too low."

Historically, there is a kernel of truth to most of these legends, but in many cases that truth has been long forgotten, ignored or embellished beyond recognition. This book examines several such legends. Each chapter is organized to address: (a) what the legend is that "we (almost) all know to be true"; (b) what the "kernel of truth" is to each legend; (c) what the myths are that have developed around this kernel of truth; and (d) what the state of the practice should be. This book meets an important need for the accumulation and integration of these methodological and statistical practices.