Develop a Deep Understanding of the Statistical Issues of APC Analysis Age-Period-Cohort Models: Approaches and Analyses with Aggregate Data presents an introduction to the problems and strategies for modeling age, period, and cohort (APC) effects for aggregate-level data. These strategies include constrained estimation, the use of age and/or period and/or cohort characteristics, estimable functions, variance decomposition, and a new technique called the s-constraint approach. See How Common Methods Are Related to Each Other After a general and wide-ranging introductory chapter, the book explains the identification problem from algebraic and geometric perspectives and discusses constrained regression. It then covers important strategies that provide information that does not directly depend on the constraints used to identify the APC model. The final chapter presents a specific empirical example showing that a combination of the approaches can make a compelling case for particular APC effects. Get Answers to Questions about the Relationships of Ages, Periods, and Cohorts to Important Substantive Variables This book incorporates several APC approaches into one resource, emphasizing both their geometry and algebra. This integrated presentation helps researchers effectively judge the strengths and weaknesses of the methods, which should lead to better future research and better interpretation of existing research.

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.

The normal distribution is widely known and used by scientists and engineers. However, there are many cases when the normal distribution is not appropriate, due to the data being skewed. Rather than leaving you to search through journal articles, advanced theoretical monographs, or introductory texts for alternative distributions, the Handbook of Exponential and Related Distributions for Engineers and Scientists provides a concise, carefully selected presentation of the properties and principles of selected distributions that are most useful for application in the sciences and engineering. The book begins with all the basic mathematical and statistical background necessary to select the correct distribution to model real-world data sets. This includes inference, decision theory, and computational aspects including the popular Bootstrap method. The authors then examine four skewed distributions in detail: exponential, gamma, Weibull, and extreme value. For each one, they discuss general properties and applicability to example data sets, theoretical characterization, estimation of parameters and related inferences, and goodness of fit tests. The final chapter deals with system reliability for series and parallel systems. Presenting methods based on statistical simulations and numerical computations, the Handbook of Exponential and Related Distributions for Engineers and Scientists supplies hands-on tools for applied researchers in need of practical tools for data analysis.

The Modern Kleinian Approach to Psychoanalytic Technique: Clinical Illustrations describes how today's practitioner typically treats a number of types of very disturbed and hard-to-reach patients who, while prone to intense acting out and early termination, are in great need of in-depth psychological reorganization. Many cases barely get off the ground due to levels of pathological conflict and destructive phantasy that make self/object connection extremely fragile. However, the modern Kleinian approach makes it possible to establish analytic contact within even the most chaotic situations and create a therapeutic experience that can be significant and meaningful. In doing so, there can be a healing process and the birth of new object relational experiences and interpersonal exchanges. Robert Waska details a more flexible method of practicing psychoanalysis, Analytic Contact, an approach that brings the healing possibilities of psychoanalysis to the more disturbed patients who tend to fill private practice offices. In addition, Analytic Contact enables the clinician to reach populations that are not usually considered easily treatable by the psychoanalytic method, including psychotic patients, couples who are seeking help with marital issues, and chronic borderline and narcissistic individuals.

This comprehensive volume explores the set of theoretical, methodological, ethical and analytical issues that shape the ways in which visual qualitative research is conducted in psychology. Using visual data such as film making, social media analyses, photography and model making, the book uniquely uses visual qualitative methods to broaden our understanding of experience and subjectivity. In recent years, visual research has seen a growing emphasis on the importance of culture in experience-based qualitative methods. Featuring contributors from diverse research backgrounds including narrative psychology, personal construct theory and psychoanalysis, the book examines the potential for visual methods in psychology. In each chapter of the book, the contributors explore and address how a visual approach has contributed to existing social and psychological theory in their line of research. The book provides up-to-date insights into combining methods to create new multi-modal methodologies, and analyses these with psychology-specific questions in mind. It covers topics such as sexuality, identity, group processes, child development, forensic psychology, race and gender, and would be the ideal companion for those studying or undertaking research in disciplines like psychology, sociology and gender studies.

"Yoder and Symons bring decades of work to bear and it shows.... The book is] presented with broad scholarship and conceptual depth."

-Roger Bakeman, PhD
Professor Emeritus
Georgia State University

"This outstanding volume transcends the typical treatment of behavior observation methods in introductory research texts. Yoder and Symons articulate a set of measurement principles that serve as the foundation for behavior observation as a scientific tool."

-William E. MacLean Jr., PhD
Executive Director
Wyoming Institute for Disabilities University of Wyoming

This comprehensive textbook introduces graduate students to the competent conduct of observational research methods and measurement. The unique approach of this book is that the chapters delineate not only the techniques and mechanics of observational methods, but also the theoretical and conceptual underpinnings of these methods.

The observational methods presented can be used for both single-subject and group-design perspectives, showing students how and when to use both methodologies. In addition, the authors provide many practical exercises within chapters as well as electronic media files of a sample observation session to code with multiple behavior sampling methods.

Key topics:

• Improving measurement of generalized characteristics through direct observation and the generalizability theory
• Developing coding schemes and designing or adapting coding manuals
• Determining sampling methods and metrics for observational variables
• Training observers and assessing their agreement
• Performing sequential analysis on observational data
• Assessing the validity of observational variables

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.

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.

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.

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.

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.

From Subject to Subjectivities profiles the recent debates about the role of qualitative and participatory methods in psychology, a discipline which has traditionally seen itself as a form of positivistic science. Contributors explain how fundamentally different views of the nature of reality and of scientific theory have shaped these debates, and how psychology is being transformed through the use of these methods.

At the heart of the book are 10 exemplars of interpretive and participatory action research which describe the rationale for and process of using these methods in actual cases. They also articulate some of the challenges psychologists may face in adopting them, offering insights into how these complications can be successfully negotiated. Relevant beyond psychology, the models provided can be used within the context of a wide array of social science disciplines, from sociology and anthropology to women's studies and public health.

The contributors represent a veritable "who's who" of qualitative scholars, including Lyn Mikel Brown, Larry Davidson, Michelle Fine, Louise Kidder, M. Brinton Lykes, Jeanne Marecek, Abigail Stewart, and Niobe Way.

No previous book has examined qualitative and participatory methods specifically within the context of psychology. From Subjects to Subjectivities provides a unique and badly needed resource for those interested in learning about the practice of these methods in the field.

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.

Features: Provides an overview of methods for assessing the reliability of generating data Expands a statistic proposed by the author, already widely used in the social sciences Includes many easy to follow numerical examples to illustrate the measures Written to be useful to beginning and advanced researchers from many disciplines, notably linguistics, sociology, psychometric and educational research, and medical science.

Contrasts are statistical procedures for asking focused questions of data. Researchers, teachers of research methods and graduate students will be familiar with the principles and procedures of contrast analysis included here. But they, for the first time, will also be presented with a series of newly developed concepts, measures, and indices that permit a wider and more useful application of contrast analysis. This volume takes on this new approach by introducing a family of correlational effect size estimates. By returning to these correlations throughout the book, the authors demonstrate special adaptations in a variety of contexts from two group comparison to one way analysis of variance contexts, to factorial designs, to repeated measures designs and to the case of multiple contrasts.

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.

Emerging from a qualitative research study on the rehabilitation experiences of adult male probationers with mental health illness, this book describes the treatment and rehabilitation experiences of these individuals and contextualizes their experiences within the landscape of mental health treatment in the United States. Often underserved in outpatient community support programs, probationers with mental health illness (PMIs) face stigma and obstacles in seeking mental health treatment and rehabilitation. Examining the lived experiences of both PMIs and their probation officers, this book offers insights into the study of stigma as it relates to probationers and the work of probation officers in furthering treatment and rehabilitation options for PMIs.

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.

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.

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.

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.

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.

Drawing on the authors' varied experiences working and teaching in the field, Analysis of Multivariate Social Science Data, "Second Edition"enables a basic understanding of how to use key multivariate methods in the social sciences. With updates in every chapter, this edition expands its topics to include regression analysis, confirmatory factor analysis, structural equation models, and multilevel models.

After emphasizing the summarization of data in the first several chapters, the authors focus on regression analysis. This chapter provides a link between the two halves of the book, signaling the move from descriptive to inferential methods and from interdependence to dependence. The remainder of the text deals with model-based methods that primarily make inferences about processes that generate data.

Relying heavily on numerical examples, the authors provide insight into the purpose and working of the methods as well as the interpretation of data. 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 exercises, encouraging readers to explore new ground in social science research.

Requiring minimal mathematical and statistical knowledge, this book shows how various multivariate methods reveal different aspects of data and thus help answer substantive research questions.