Richard H. Thaler has spent his career studying the radical notion that the central agents in the economy are humans—predictable, error-prone individuals. Misbehaving is his arresting, frequently hilarious account of the struggle to bring an academic discipline back down to earth—and change the way we think about economics, ourselves, and our world.
Traditional economics assumes rational actors. Early in his research, Thaler realized these Spock-like automatons were nothing like real people. Whether buying a clock radio, selling basketball tickets, or applying for a mortgage, we all succumb to biases and make decisions that deviate from the standards of rationality assumed by economists. In other words, we misbehave. More importantly, our misbehavior has serious consequences. Dismissed at first by economists as an amusing sideshow, the study of human miscalculations and their effects on markets now drives efforts to make better decisions in our lives, our businesses, and our governments.
Coupling recent discoveries in human psychology with a practical understanding of incentives and market behavior, Thaler enlightens readers about how to make smarter decisions in an increasingly mystifying world. He reveals how behavioral economic analysis opens up new ways to look at everything from household finance to assigning faculty offices in a new building, to TV game shows, the NFL draft, and businesses like Uber.
Laced with antic stories of Thaler’s spirited battles with the bastions of traditional economic thinking, Misbehaving is a singular look into profound human foibles. When economics meets psychology, the implications for individuals, managers, and policy makers are both profound and entertaining.
Shortlisted for the Financial Times & McKinsey Business Book of the Year Award
In recounting his patients' dilemmas, Yalom not only gives us a rare and enthralling glimpse into their personal desires and motivations but also tells us his own story as he struggles to reconcile his all-too human responses with his sensibility as a psychiatrist. Not since Freud has an author done so much to clarify what goes on between a psychotherapist and a patient.
Understanding statistics is a requirement for obtaining and making the most of a degree in psychology, a fact of life that often takes first year psychology students by surprise. Filled with jargon-free explanations and real-life examples, Psychology Statistics For Dummies makes the often-confusing world of statistics a lot less baffling, and provides you with the step-by-step instructions necessary for carrying out data analysis.
Psychology Statistics For Dummies:Serves as an easily accessible supplement to doorstop-sized psychology textbooks Provides psychology students with psychology-specific statistics instruction Includes clear explanations and instruction on performing statistical analysis Teaches students how to analyze their data with SPSS, the most widely used statistical packages among students
This book attempts to understand normal social motives in murder as products of the process of evolution by natural selection. They note that the implications for psychology are many and profound, touching on such matters as parental affection and rejection, sibling rivalry, sex differences in interests and inclinations, social comparison and achievement motives, our sense of justice, lifespan developmental changes in attitudes, and the phenomenology of the self.
This is the first volume of its kind to analyze homicides in the light of a theory of interpersonal conflict. Before this study, no one had compared an observed distribution of victim-killer relationships to "expected" distribution, nor asked about the patterns of killer-victim age disparities in familial killings. This evolutionary psychological approach affords a deeper view and understanding of homicidal violence.
The applied emphasis provides clear illustrations of the principles and provides worked examples of the types of applications that are possible. Researchers learn how to specify regression models that directly address their research questions. An overview of the fundamental ideas of multiple regression and a review of bivariate correlation and regression and other elementary statistical concepts provide a strong foundation for understanding the rest of the text. The third edition features an increased emphasis on graphics and the use of confidence intervals and effect size measures, and an accompanying website with data for most of the numerical examples along with the computer code for SPSS, SAS, and SYSTAT, at www.psypress.com/9780805822236 .
Applied Multiple Regression serves as both a textbook for graduate students and as a reference tool for researchers in psychology, education, health sciences, communications, business, sociology, political science, anthropology, and economics. An introductory knowledge of statistics is required. Self-standing chapters minimize the need for researchers to refer to previous chapters.
Features of the Fourth Edition include:New material on sample size calculations for chance-corrected agreement coefficients, as well as for intraclass correlation coefficients. The researcher will be able to determine the optimal number raters, subjects, and trials per subject.The chapter entitled “Benchmarking Inter-Rater Reliability Coefficients” has been entirely rewritten.The introductory chapter has been substantially expanded to explore possible definitions of the notion of inter-rater reliability.All chapters have been revised to a large extent to improve their readability.
". . . [this book] should be on the shelf of everyone interested in . . . longitudinal data analysis."
—Journal of the American Statistical Association
Features newly developed topics and applications of the analysis of longitudinal data
Applied Longitudinal Analysis, Second Edition presents modern methods for analyzing data from longitudinal studies and now features the latest state-of-the-art techniques. The book emphasizes practical, rather than theoretical, aspects of methods for the analysis of diverse types of longitudinal data that can be applied across various fields of study, from the health and medical sciences to the social and behavioral sciences.
The authors incorporate their extensive academic and research experience along with various updates that have been made in response to reader feedback. The Second Edition features six newly added chapters that explore topics currently evolving in the field, including:Fixed effects and mixed effects models Marginal models and generalized estimating equations Approximate methods for generalized linear mixed effects models Multiple imputation and inverse probability weighted methods Smoothing methods for longitudinal data Sample size and power
Each chapter presents methods in the setting of applications to data sets drawn from the health sciences. New problem sets have been added to many chapters, and a related website features sample programs and computer output using SAS, Stata, and R, as well as data sets and supplemental slides to facilitate a complete understanding of the material.
With its strong emphasis on multidisciplinary applications and the interpretation of results, Applied Longitudinal Analysis, Second Edition is an excellent book for courses on statistics in the health and medical sciences at the upper-undergraduate and graduate levels. The book also serves as a valuable reference for researchers and professionals in the medical, public health, and pharmaceutical fields as well as those in social and behavioral sciences who would like to learn more about analyzing longitudinal data.
· Downloadable data sets
· Library of computer programs in SAS, SPSS, Stata, HLM, MLwiN, and more
· Additional material for data analysis
Accompanying the book is the Exploratory Software for Confidence Intervals (ESCI) package, free software that runs under Excel and is accessible at www.thenewstatistics.com. The book’s exercises use ESCI's simulations, which are highly visual and interactive, to engage users and encourage exploration. Working with the simulations strengthens understanding of key statistical ideas. There are also many examples, and detailed guidance to show readers how to analyze their own data using the new statistics, and practical strategies for interpreting the results. A particular strength of the book is its explanation of meta-analysis, using simple diagrams and examples. Understanding meta-analysis is increasingly important, even at undergraduate levels, because medicine, psychology and many other disciplines now use meta-analysis to assemble the evidence needed for evidence-based practice.
The book’s pedagogical program, built on cognitive science principles, reinforces learning:
Boxes provide "evidence-based" advice on the most effective statistical techniques. Numerous examples reinforce learning, and show that many disciplines are using the new statistics. Graphs are tied in with ESCI to make important concepts vividly clear and memorable. Opening overviews and end of chapter take-home messages summarize key points. Exercises encourage exploration, deep understanding, and practical applications.
This highly accessible book is intended as the core text for any course that emphasizes the new statistics, or as a supplementary text for graduate and/or advanced undergraduate courses in statistics and research methods in departments of psychology, education, human development , nursing, and natural, social, and life sciences. Researchers and practitioners interested in understanding the new statistics, and future published research, will also appreciate this book. A basic familiarity with introductory statistics is assumed.
The culmination of master psychiatrist Dr. Irvin D. Yalom’s more than thirty-five years in clinical practice, The Gift of Therapy is a remarkable and essential guidebook that illustrates through real case studies how patients and therapists alike can get the most out of therapy. The bestselling author of Love’s Executioner shares his uniquely fresh approach and the valuable insights he has gained—presented as eighty-five personal and provocative “tips for beginner therapists,” including:
•Let the patient matter to you
•Acknowledge your errors
•Create a new therapy for each patient
•Do home visits
•(Almost) never make decisions for the patient
•Freud was not always wrong
A book aimed at enriching the therapeutic process for a new generation of patients and counselors, Yalom’s Gift of Therapy is an entertaining, informative, and insightful read for anyone with an interest in the subject.
“This book should be an essential part of the personal library of every practicing statistician.”—Technometrics
Thoroughly revised and updated, the new edition of Nonparametric Statistical Methods includes additional modern topics and procedures, more practical data sets, and new problems from real-life situations. The book continues to emphasize the importance of nonparametric methods as a significant branch of modern statistics and equips readers with the conceptual and technical skills necessary to select and apply the appropriate procedures for any given situation.
Written by leading statisticians, Nonparametric Statistical Methods, Third Edition provides readers with crucial nonparametric techniques in a variety of settings, emphasizing the assumptions underlying the methods. The book provides an extensive array of examples that clearly illustrate how to use nonparametric approaches for handling one- or two-sample location and dispersion problems, dichotomous data, and one-way and two-way layout problems. In addition, the Third Edition features:The use of the freely available R software to aid in computation and simulation, including many new R programs written explicitly for this new edition New chapters that address density estimation, wavelets, smoothing, ranked set sampling, and Bayesian nonparametrics Problems that illustrate examples from agricultural science, astronomy, biology, criminology, education, engineering, environmental science, geology, home economics, medicine, oceanography, physics, psychology, sociology, and space science Nonparametric Statistical Methods, Third Edition is an excellent reference for applied statisticians and practitioners who seek a review of nonparametric methods and their relevant applications. The book is also an ideal textbook for upper-undergraduate and first-year graduate courses in applied nonparametric statistics.
Looking for an easily accessible overview of research methods in psychology? This is the book for you! Whether you need to get ahead in class, you're pressed for time, or you just want a take on a topic that's not covered in your textbook, Research Methods in Psychology For Dummies has you covered.
Written in plain English and packed with easy-to-follow instruction, this friendly guide takes the intimidation out of the subject and tackles the fundamentals of psychology research in a way that makes it approachable and comprehensible, no matter your background. Inside, you'll find expert coverage of qualitative and quantitative research methods, including surveys, case studies, laboratory observations, tests and experiments—and much more.Serves as an excellent supplement to course textbooks Provides a clear introduction to the scientific method Presents the methodologies and techniques used in psychology research Written by the authors of Psychology Statistics For Dummies
If you're a first or second year psychology student and want to supplement your doorstop-sized psychology textbook—and boost your chances of scoring higher at exam time—this hands-on guide breaks down the subject into easily digestible bits and propels you towards success.
What constitutes “normal” behavior among happy couples? What steps you should take if that “normal” is one you want to strive for? To help answer those questions, wellness entrepreneur Chrisanna Northrup teamed with two of America’s top sociologists, Yale Ph.D. Pepper Schwartz and Harvard Ph.D. James Witte, to design a unique interactive survey that would draw feedback from around the world.
What has resulted is the clearest picture yet of how well couples are communicating, romancing each other, satisfying each other in the bedroom, sharing financial responsibilities, and staying faithful – or not. Since the Normal Bar survey methodology sorts for age and gender, racial and geographic differences and sexual preferences, the authors are able to reveal , for example, what happens to passion as we grow older, which gender wants what when it comes to sex, the factors that spur marital combat, how kids figure in, how being gay or bisexual turns out to be both different and the same, and –regardless of background -- the tiny habits that drive partners absolutely batty.
The book is dense with revelations, from the unexpected popularity of certain sexual positions, to the average number of times happy – and unhappy -- couples kiss, to the prevalence of lying, to the surprising loyalty most men and women feel for their partner (even when in a deteriorating relationship), to the vivid and idiosyncratic ways individuals of different ages, genders and nationalities describe their “ideal romantic evening.”
Much more than a peek behind the relationship curtain, The Normal Bar offers readers an array of prescriptive tools that will help them establish a “new normal.” Mindful of what keeps couples stuck in ruts, the book’s authors suggest practical and life-changing ways to break cycles of disappointment and frustration.
The focus of the book is that the purpose of statistics is to organize a useful argument from quantitative evidence, using a form of principled rhetoric. Five criteria, described by the acronym MAGIC (magnitude, articulation, generality, interestingness, and credibility) are proposed as crucial features of a persuasive, principled argument.
Particular statistical methods are discussed, with minimum use of formulas and heavy data sets. The ideas throughout the book revolve around elementary probability theory, t tests, and simple issues of research design. It is therefore assumed that the reader has already had some access to elementary statistics. Many examples are included to explain the connection of statistics to substantive claims about real phenomena.
New to This Edition
*Updated throughout to incorporate important developments in latent variable modeling.
*Chapter on Bayesian CFA and multilevel measurement models.
*Addresses new topics (with examples): exploratory structural equation modeling, bifactor analysis, measurement invariance evaluation with categorical indicators, and a new method for scaling latent variables.
*Utilizes the latest versions of major latent variable software packages.
*Winner of the 2014 Distinguished Publication Award (DPA) from the Association for Women in Psychology (AWP)*
Successful Qualitative Research: A Practical Guide for Beginners is an accessible, practical textbook. It sidesteps detailed theoretical discussion in favor of providing a comprehensive overview of strategic tips and skills for starting and completing successful qualitative research.
Uniquely, the authors provide a "patterns framework" to qualitative data analysis in this book, also known as "thematic analysis." The authors walk you through a basic thematic approach, and compare and contrast this with other approaches. This discussion of commonalities, explaining why and when each method should be used, and in the context of looking at patterns, will provide you with complete confidence for your qualitative research journey.
This textbook will be an essential textbook for undergraduates and postgraduates taking a course in qualitative research or using qualitative approaches in a research project.
Electronic Inspection Copy available for instructors here.
New in the fourth edition of Latent Variable Models:
*a data CD that features the correlation and covariance matrices used in the exercises;
*new sections on missing data, non-normality, mediation, factorial invariance, and automating the construction of path diagrams; and
*reorganization of chapters 3-7 to enhance the flow of the book and its flexibility for teaching.
Intended for advanced students and researchers in the areas of social, educational, clinical, industrial, consumer, personality, and developmental psychology, sociology, political science, and marketing, some prior familiarity with correlation and regression is helpful.
"This book is . . . an excellent source of examples for regression analysis. It has been and still is readily readable and understandable."
—Journal of the American Statistical Association Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. Regression Analysis by Example, Fifth Edition has been expanded and thoroughly updated to reflect recent advances in the field. The emphasis continues to be on exploratory data analysis rather than statistical theory. The book offers in-depth treatment of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression.
The book now includes a new chapter on the detection and correction of multicollinearity, while also showcasing the use of the discussed methods on newly added data sets from the fields of engineering, medicine, and business. The Fifth Edition also explores additional topics, including:Surrogate ridge regression Fitting nonlinear models Errors in variables ANOVA for designed experiments
Methods of regression analysis are clearly demonstrated, and examples containing the types of irregularities commonly encountered in the real world are provided. Each example isolates one or two techniques and features detailed discussions, the required assumptions, and the evaluated success of each technique. Additionally, methods described throughout the book can be carried out with most of the currently available statistical software packages, such as the software package R.
Regression Analysis by Example, Fifth Edition is suitable for anyone with an understanding of elementary statistics.
Following a logical progression from basic concepts to more advanced topics, the book first explains classical test theory, covering true score, measurement error, and reliability. It then presents generalizability theory, which provides a framework to deal with various aspects of test scores. In addition, the authors discuss the concept of validity in testing, offering a strategy for evidence-based validity. In the two chapters devoted to item response theory (IRT), the book explores item response models, such as the Rasch model, and applications, including computerized adaptive testing (CAT). The last chapter looks at some methods used to equate tests.
Equipped with the essential material found in this book, advanced undergraduate and graduate students in the behavioral sciences as well as researchers involved in measurement and testing will gain valuable insight into the research methodologies and statistical data analyses of behavioral testing.
Updated throughout, the second edition features three new chapters—growth modeling with ordered categorical variables, growth mixture modeling, and pooled interrupted time series LGM approaches. Following a new organization, the book now covers the development of the LGM, followed by chapters on multiple-group issues (analyzing growth in multiple populations, accelerated designs, and multi-level longitudinal approaches), and then special topics such as missing data models, LGM power and Monte Carlo estimation, and latent growth interaction models. The model specifications previously included in the appendices are now available on the CD so the reader can more easily adapt the models to their own research.
This practical guide is ideal for a wide range of social and behavioral researchers interested in the measurement of change over time, including social, developmental, organizational, educational, consumer, personality and clinical psychologists, sociologists, and quantitative methodologists, as well as for a text on latent variable growth curve modeling or as a supplement for a course on multivariate statistics. A prerequisite of graduate level statistics is recommended.
In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.
Topics include:Statistical inference, exploratory data analysis, and the data science processAlgorithmsSpam filters, Naive Bayes, and data wranglingLogistic regressionFinancial modelingRecommendation engines and causalityData visualizationSocial networks and data journalismData engineering, MapReduce, Pregel, and Hadoop
Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.
This book is designed to help the reader develop a way of thinking about multivariate statistics, as well as to understand in a broader and more intuitive sense what the procedures do and how their results are interpreted. Presenting important procedures of multivariate statistical theory geometrically, the author hopes that this emphasis on the geometry will give the reader a coherent picture into which all the multivariate techniques fit.
Here we witness Jung the clinician more vividly than ever before--and he is witty, impatient, sometimes authoritarian, always wise and intellectually daring, but also a teacher who, though brilliant, could be vulnerable, uncertain, and humbled by life's great mysteries. These seminars represent the most penetrating account of Jung's insights into children's dreams and the psychology of childhood. At the same time they offer the best example of group supervision by Jung, presenting his most detailed and thorough exposition of Jungian dream analysis and providing a picture of how he taught others to interpret dreams. Presented here in an inspired English translation commissioned by the Philemon Foundation, these seminars reveal Jung as an impassioned educator in dialogue with his students and developing the practice of analytical psychology.
An invaluable document of perhaps the most important psychologist of the twentieth century at work, this splendid volume is the fullest representation of Jung's views on the interpretation of children's dreams, and signals a new wave in the publication of Jung's collected works as well as a renaissance in contemporary Jung studies.
From the Trade Paperback edition.
In the age of Big Data we often believe that our predictions about the future are better than ever before. But as risk expert Gerd Gigerenzer shows, the surprising truth is that in the real world, we often get better results by using simple rules and considering less information.
In Risk Savvy, Gigerenzer reveals that most of us, including doctors, lawyers, financial advisers, and elected officials, misunderstand statistics much more often than we think, leaving us not only misinformed, but vulnerable to exploitation. Yet there is hope. Anyone can learn to make better decisions for their health, finances, family, and business without needing to consult an expert or a super computer, and Gigerenzer shows us how.
Risk Savvy is an insightful and easy-to-understand remedy to our collective information overload and an essential guide to making smart, confident decisions in the face of uncertainty.
Multivariate Time Series Analysis: With R and Financial Applications is the much anticipated sequel coming from one of the most influential and prominent experts on the topic of time series. Through a fundamental balance of theory and methodology, the book supplies readers with a comprehensible approach to financial econometric models and their applications to real-world empirical research.
Differing from the traditional approach to multivariate time series, the book focuses on reader comprehension by emphasizing structural specification, which results in simplified parsimonious VAR MA modeling. Multivariate Time Series Analysis: With R and Financial Applications utilizes the freely available R software package to explore complex data and illustrate related computation and analyses. Featuring the techniques and methodology of multivariate linear time series, stationary VAR models, VAR MA time series and models, unitroot process, factor models, and factor-augmented VAR models, the book includes:
• Over 300 examples and exercises to reinforce the presented content
• User-friendly R subroutines and research presented throughout to demonstrate modern applications
• Numerous datasets and subroutines to provide readers with a deeper understanding of the material
Multivariate Time Series Analysis is an ideal textbook for graduate-level courses on time series and quantitative finance and upper-undergraduate level statistics courses in time series. The book is also an indispensable reference for researchers and practitioners in business, finance, and econometrics.
This correspondence reveals Jung fielding keen theoretical challenges from one of his most sensitive and perceptive colleagues, and provides a useful historical grounding for all those who work with, or are interested in, Jungian psychology and psychological typology.
This perfectly observed and passionately imagined book takes us inside one of the supervised group homes that, in an age of shrinking state budgets and psychotropic drugs, have emerged as the backbone of America's mental health system. As it follows the progress and setbacks of residents, their families, and counselors and notes the embittered resistance their presence initially aroused in the neighborhood, 9 Highland Road succeeds in opening the locked world of mental illness. It does so with an empathy and insight that will change forever the way we understand and act in relation to that world.
An Introduction to Applied Multivariate Analysis with R explores the correct application of these methods so as to extract as much information as possible from the data at hand, particularly as some type of graphical representation, via the R software. Throughout the book, the authors give many examples of R code used to apply the multivariate techniques to multivariate data.
Twenty-three characteristics shared by jailed violent criminals are analyzed and considered in terms of neuropsychology and developmental psychology. The book also probes psychopathy in its various degrees, in children, adolescents, and adults, and explains a controversial but increasingly accepted theory that psychopathy is a "natural" outgrowth of evolution, describing how this "natural" psychopathy can become a condition typified by violent, sadistic, and irreversible personality disorder.
"A must-have book for anyone expecting to do research and/or applications in categorical data analysis."
—Statistics in Medicine
"It is a total delight reading this book."
"If you do any analysis of categorical data, this is an essential desktop reference."
The use of statistical methods for analyzing categorical data has increased dramatically, particularly in the biomedical, social sciences, and financial industries. Responding to new developments, this book offers a comprehensive treatment of the most important methods for categorical data analysis.
Categorical Data Analysis, Third Edition summarizes the latest methods for univariate and correlated multivariate categorical responses. Readers will find a unified generalized linear models approach that connects logistic regression and Poisson and negative binomial loglinear models for discrete data with normal regression for continuous data. This edition also features:An emphasis on logistic and probit regression methods for binary, ordinal, and nominal responses for independent observations and for clustered data with marginal models and random effects models Two new chapters on alternative methods for binary response data, including smoothing and regularization methods, classification methods such as linear discriminant analysis and classification trees, and cluster analysis New sections introducing the Bayesian approach for methods in that chapter More than 100 analyses of data sets and over 600 exercises Notes at the end of each chapter that provide references to recent research and topics not covered in the text, linked to a bibliography of more than 1,200 sources A supplementary website showing how to use R and SAS; for all examples in the text, with information also about SPSS and Stata and with exercise solutions
Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and methodologists, such as biostatisticians and researchers in the social and behavioral sciences, medicine and public health, marketing, education, finance, biological and agricultural sciences, and industrial quality control.
This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables.
Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-the-art techniques for building, interpreting, and assessing the performance of LR models. New and updated features include:A chapter on the analysis of correlated outcome data A wealth of additional material for topics ranging from Bayesian methods to assessing model fit Rich data sets from real-world studies that demonstrate each method under discussion Detailed examples and interpretation of the presented results as well as exercises throughout
Applied Logistic Regression, Third Edition is a must-have guide for professionals and researchers who need to model nominal or ordinal scaled outcome variables in public health, medicine, and the social sciences as well as a wide range of other fields and disciplines.
-Illustrative examples using Mplus 7.4 include conceptual figures, Mplus program syntax, and an interpretation of results to show readers how to carry out the analyses with actual data.
-Exercises with an answer key allow readers to practice the skills they learn.
-Applications to a variety of disciplines appeal to those in the behavioral, social, political, educational, occupational, business, and health sciences.
-Data files for all the illustrative examples and exercises at www.routledge.com/9781138925151 allow readers to test their understanding of the concepts.
-Point to Rememberboxes aid in reader comprehension or provide in-depth discussions of key statistical or theoretical concepts.
Part 1 introduces basic structural equation modeling (SEM) as well as first- and second-order growth curve modeling. The book opens with the basic concepts from SEM, possible extensions of conventional growth curve models, and the data and measures used throughout the book. The subsequent chapters in part 1 explain the extensions. Chapter 2 introduces conventional modeling of multidimensional panel data, including confirmatory factor analysis (CFA) and growth curve modeling, and its limitations. The logical and theoretical extension of a CFA to a second-order growth curve, known as curve-of-factors model (CFM), are explained in Chapter 3. Chapter 4 illustrates the estimation and interpretation of unconditional and conditional CFMs. Chapter 5 presents the logical and theoretical extension of a parallel process model to a second-order growth curve, known as factor-of-curves model (FCM). Chapter 6 illustrates the estimation and interpretation of unconditional and conditional FCMs. Part 2 reviews growth mixture modeling including unconditional growth mixture modeling (Ch. 7) and conditional growth mixture models (Ch. 8). How to extend second-order growth curves (curve-of-factors and factor-of-curves models) to growth mixture models is highlighted in Chapter 9.
Ideal as a supplement for use in graduate courses on (advanced) structural equation, multilevel, longitudinal, or latent variable modeling, latent growth curve and mixture modeling, factor analysis, multivariate statistics, or advanced quantitative techniques (methods) taught in psychology, human development and family studies, business, education, health, and social sciences, this book’s practical approach also appeals to researchers. Prerequisites include a basic knowledge of intermediate statistics and structural equation modeling.
MATLAB for Psychologists expertly guides readers through the component steps, skills, and operations of the software, with plentiful graphics and examples to match the reader’s comfort level. Using an extended illustration, this concise volume explains the program’s usefulness at any point in an experiment, without the limits imposed by other types of software. And the authors demonstrate the responsiveness of MATLAB to the individual’s research needs, whether the task is programming experiments, creating sensory stimuli, running simulations, or calculating statistics for data analysis.
Key features of the coverage:
Thinking in a matrix way.Handling and plotting data.Guidelines for improved programming, sound, and imaging.Statistical analysis and signal detection theory indexes.The Graphical User Interface.The Psychophysics Toolbox.
MATLAB for Psychologists serves a wide audience of advanced undergraduate and graduate level psychology students, professors, and researchers as well as lab technicians involved in programming psychology experiments.
A Jason Aronson Book
Data Analysisalso describes how the model comparison approach and uniform framework can be applied to models that include product predictors (i.e., interactions and nonlinear effects) and to observations that are nonindependent. Indeed, the analysis of nonindependent observations is treated in some detail, including models of nonindependent data with continuously varying predictors as well as standard repeated measures analysis of variance. This approach also provides an integrated introduction to multilevel or hierarchical linear models and logistic regression. Finally, Data Analysis provides guidance for the treatment of outliers and other problematic aspects of data analysis. It is intended for advanced undergraduate and graduate level courses in data analysis and offers an integrated approach that is very accessible and easy to teach. ?
Highlights of the third edition include:
a new chapter on logistic regression;
expanded treatment of mixed models for data with multiple random factors;
an enhanced website with PowerPoint presentations and other tools that demonstrate the concepts in the book; exercises for each chapter that highlight research findings from the literature; data sets, R code, and SAS output for all analyses; additional examples and problem sets; and test questions.
The new edition features:
Each chapter begins with an outline, a list of key concepts, and a research vignette related to the concepts. Realistic examples from education and the behavioral sciences illustrate those concepts. Each example examines the procedures and assumptions and provides tips for how to run SPSS and develop an APA style write-up. Tables of assumptions and the effects of their violation are included, along with how to test assumptions in SPSS. Each chapter includes computational, conceptual, and interpretive problems. Answers to the odd-numbered problems are provided. The SPSS data sets that correspond to the book’s examples and problems are available on the web.?
The book covers basic and advanced analysis of variance models and topics not dealt with in other texts such as robust methods, multiple comparison and non-parametric procedures, and multiple and logistic regression models. Intended for courses in intermediate statistics and/or statistics II taught in education and/or the behavioral sciences, predominantly at the master's or doctoral level. Knowledge of introductory statistics is assumed.
This volume contributes deeply to both to the science of learning through in-depth video studies of human interaction in learning environments—whether classrooms or other contexts—and to the uses of video for creating descriptive, explanatory, or expository accounts of learning and teaching. It is designed around four themes—each with a cornerstone chapter that introduces and synthesizes the cluster of chapters related to it:Theoretical frameworks for video research; Video research on peer, family, and informal learning; Video research on classroom and teacher learning; and Video collaboratories and technological futures.
Video Research in the Learning Sciences is intended for researchers, university faculty, teacher educators, and graduate students in education, and for anyone interested in how knowledge is expanded using video-based technologies for inquiries about learning and teaching.
Visit the Web site affiliated with this book: www.videoresearch.org
The book provides clear coverage of statistical procedures, and includes everything needed from nominal level tests to multi-factorial ANOVA designs, multiple regression and log linear analysis. It features detailed and illustrated SPSS instructions for all these procedures eliminating the need for an extra SPSS textbook.
New features in the sixth edition include:
"Tricky bits" - in-depth notes on the things that students typically have problems with, including common misunderstandings and likely mistakes.
Improved coverage of qualitative methods and analysis, plus updates to Grounded Theory, Interpretive Phenomenological Analysis and Discourse Analysis.
A full and recently published journal article using Thematic Analysis, illustrating how articles appear in print.
Discussion of contemporary issues and debates, including recent coverage of journals’ reluctance to publish replication of studies.
Fully updated online links, offering even more information and useful resources, especially for statistics.
Each chapter contains a glossary, key terms and newly integrated exercises, ensuring that key concepts are understood. A companion website (www.routledge.com/cw/coolican) provides additional exercises, revision flash cards, links to further reading and data for use with SPSS.
The book is divided into two sections. Section One, "Issues and Approaches in Teaching Introductory Psychology," contains 52 articles on critical issues, such as: how to approach the course; understanding students' interests, perceptions, and motives; students' existing knowledge of psychology (including their misconceptions); a comparison of introductory textbooks and tips on how to evaluate them; test questions and student factors affecting exam performance; an overview of different forms of feedback; giving extra credit; and how to deal with academic dishonesty.
Section Two consists of 37 articles that present demonstrations, class and laboratory projects, and other techniques to enhance teaching and learning in both the introductory, as well as advanced courses in the discipline. This section is organized so as to parallel the order of topics found in most introductory psychology textbooks.
Intended for academicians who teach the introductory psychology course and/or oversee grad assistants who teach the course, all royalties of the book go directly to the Society for the Teaching of Psychology to promote its activities to further improve the teaching of psychology.
Students will learn a wide range of quantitative data analysis techniques and become familiar with how these techniques can be implemented through the latest version of Minitab. Techniques covered include univariate analysis (with frequency table, dispersion and histograms), bivariate (with contingency tables correlation, analysis of varience and non-parametric tests) and multivariate analysis (with multiple regression, path analysis, covarience and factor analysis). In addition the book covers issues such as sampling, statistical significance, conceptualisation and measurement and the selection of appropriate tests. Each chapter concludes with a set of exercises.
Social science students will welcome this integrated, non mathematical introduction to quantitative data anlysis and the minitab package.
*short, independent, chapters that do not have to be read in order;
*a guide to understanding why a particular statistic was selected;
*an emphasis on effects sizes including measures of risk potency;
*numerous cross-disciplinary examples to illustrate the material; and
*methods to help determine practical and clinical significance and their relation to meta-analysis and evidence-based practice.
This book is intended for practitioners and students in psychology, education, counseling, mental and allied health, nursing, and medicine, and as a text for courses on understanding research methods and statistics.
'I am happy to recommend this to my students as it covers jargon without using jargon and explains all those simple things that many academics take for granted. It also gives good examples of how to get the best from your time studying psychology from how to write good essays to the rules of writing lab reports' - Dr Jay Coogan University of East London
'I am happy to recommend this to my students as it covers jargon without using jargon and explains all those simple things that many academics take for granted. It also gives good examples of how to get the best from your time studying psychology from how to write good essays to the rules of writing lab reports.'
Dr Joy Coogan, University of East London
This book provides students with a wide range of research and study skills necessary for achieving a successful classification on a psychology degree course. It replaces the stress and fear experienced when encountering essays, reports, statistics and exams with a sense of confidence, enthusiasm and even fun.
Sieglinde McGee presents indispensable instruction, advice and tips on note making and note taking, evaluating academic literature, writing critical essays, preparing for and doing essay and MCQ exams, understanding research methods and issues associated with conducting research, writing and presenting reports and research and also some important computer skills. Examples provided will show how to score well on assignments and exams and also the sort of approach, layout, errors, omissions or answer-style that would achieve a lower grade. Practical exercises and interactive tasks are integrated throughout to clarify key points and give the students a chance to practise on their own.
This is a useful resource for students taking modules in study and research skills in psychology and an essential guide for all other students studying on psychology programmes.
Dr Sieglinde McGee is an Associate of the School of Psychology at Trinity College, Dublin, where she taught for several years.