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This fully updated fourth edition of Research Design and Statistical Analysis provides comprehensive coverage of the design principles and statistical concepts necessary to make sense of real data. The guiding philosophy is to provide a strong conceptual foundation so that readers can generalize to new situations they encounter in their research, including new developments in data analysis.Key features include: Emphasis on basic concepts such as sampling distributions, design efficiency, and expected mean squares, relating the research designs and data analyses to the statistical models that underlie the analyses. Detailed instructions on performing analysis using both R and SPSS. Pedagogical exercises mapped to key topic areas to support students as they review their understanding and strive to reach their higher learning goals. Incorporating the analyses of both experimental and observational data, and with coverage that is broad and deep enough to serve a two-semester sequence, this textbook is suitable for researchers, graduate students and advanced undergraduates in psychology, education, and other behavioral, social, and health sciences.The book is supported by a robust set of digital resources, including data files and exercises from the book in an Excel format for easy import into R or SPSS; R scripts for running example analysis and generating figures; and a solutions manual.

This text is about doing science and the active process of reading, learning, thinking, generating ideas, designing experiments, and the logistics surrounding each step of the research process. In easy-to-read, conversational language, Kim MacLin teaches students experimental design principles and techniques using a tutorial approach in which students read, critique, and analyze over 75 actual experiments from every major area of psychology. She provides them with real-world information about how science in psychology is conducted and how they can participate.Recognizing that students come to an experimental design course with their own interests and perspectives, MacLin covers many subdisciplines of psychology throughout the text, including IO psychology, child psychology, social psychology, behavioral psychology, cognitive psychology, clinical psychology, health psychology, educational/school psychology, legal psychology, and personality psychology, among others. Part I of the text is content oriented and provides an overview of the principles of experimental design. Part II contains annotated research articles for students to read and analyze.New sections on how to critically evaluate media reports of scientific findings (in other words, how to identify ‘fake news'), authorship guidelines and decisions, survey research methods and AI tools have been included. Further, expanded information on the Open Science movement, and on ethics in research, and methods to achieve clarity and precision in thinking and writing are included.This edition is up to date with the latest APA Publication Manual (7th edition) and includes an overview of the bias-free language guidelines, the use of singular'they,'and an ethical compliance checklist.. This text is essential reading for students and researchers interested in and studying experimental design in psychology.

This text is about doing science and the active process of reading, learning, thinking, generating ideas, designing experiments, and the logistics surrounding each step of the research process. In easy-to-read, conversational language, Kim MacLin teaches students experimental design principles and techniques using a tutorial approach in which students read, critique, and analyze over 75 actual experiments from every major area of psychology. She provides them with real-world information about how science in psychology is conducted and how they can participate.Recognizing that students come to an experimental design course with their own interests and perspectives, MacLin covers many subdisciplines of psychology throughout the text, including IO psychology, child psychology, social psychology, behavioral psychology, cognitive psychology, clinical psychology, health psychology, educational/school psychology, legal psychology, and personality psychology, among others. Part I of the text is content oriented and provides an overview of the principles of experimental design. Part II contains annotated research articles for students to read and analyze. Classic articles have been retained and 11 new ones have been added, featuring contemporary case studies, information on the Open Science movement, expanded coverage on ethics in research, and a greater focus on becoming a better writer, clarity and precision in writing, and reducing bias in language.This edition is up to date with the latest APA Publication Manual (7th edition) and includes an overview of the updated bias-free language guidelines, the use of singular'they,'the new ethical compliance checklist, and other key changes in APA style. This text is essential reading for students and researchers interested in and studying experimental design in psychology.

E-Prime® is the leading software suite by Psychology Software Tools for designing and running Psychology lab experiments. The E-Primer is the perfect accompanying guide: It provides all the necessary knowledge to make E-Prime accessible to everyone. You can learn the tools of Psychological science by following the E-Primer through a series of entertaining, step-by-step recipes that recreate classic experiments. The updated E-Primer expands its proven combination of simple explanations, interesting tutorials and fun exercises, and makes even the novice student quickly confident to create their dream experiment. Featuring: • Learn the basic and advanced features of E-Studio's flexible user interface • 15 step-by-step tutorials let you replicate classic experiments from all Psychology fields • Learn to write custom code in E-Basic without having any previous experience in programming • Second edition completely revised for E-Prime 3 • Based on 10+ years of teaching E-Prime to undergraduates, postgraduates, and colleagues • Used by Psychology Software Tools to train their own staff!

Designing Experiments and Analyzing Data: A Model Comparison Perspective (3rd edition) offers an integrative conceptual framework for understanding experimental design and data analysis. Maxwell, Delaney, and Kelley first apply fundamental principles to simple experimental designs followed by an application of the same principles to more complicated designs. Their integrative conceptual framework better prepares readers to understand the logic behind a general strategy of data analysis that is appropriate for a wide variety of designs, which allows for the introduction of more complex topics that are generally omitted from other books. Numerous pedagogical features further facilitate understanding: examples of published research demonstrate the applicability of each chapter's content; flowcharts assist in choosing the most appropriate procedure; end-of-chapter lists of important formulas highlight key ideas and assist readers in locating the initial presentation of equations; useful programming code and tips are provided throughout the book and in associated resources available online, and extensive sets of exercises help develop a deeper understanding of the subject. Detailed solutions for some of the exercises and realistic data sets are included on the website (DesigningExperiments.com). The pedagogical approach used throughout the book enables readers to gain an overview of experimental design, from conceptualization of the research question to analysis of the data. The book and its companion website with web apps, tutorials, and detailed code are ideal for students and researchers seeking the optimal way to design their studies and analyze the resulting data.

Experimental design is important enough to merit a book on its own, without statistics, that instead links methodology to a discussion of how psychologists can advance and reject theories about human behaviour. The objective of this book is to fulfil this role. The first four chapters lay the foundations of design in experimental psychology. The first chapter justifies the prominent role given to methodology within the discipline, whilst chapters two and three describe between-subject and within-subject designs. Chapter four compares and contrasts the traditional experimental approach with that of the quasi-experimental, or correlational approach, concluding that the consequences of not recognizing the value of the latter approach can be far-reaching. The following three chapters discuss practical issues involved in running experiments. The first of these offers a comprehensive guide to the student researcher who wants to construct a good questionnaire, including a discussion of reliability and validity issues. The next chapter considers the basic tools of psychological research, whilst both discussing the theoretical problem of how a sample from a population is chosen and offering useful hints on the practical issue of finding adequate populations from which to select participants. The next chapter considers ethical practice within psychological research, written in large part so that psychology students will be better able to anticipate ethical problems in their studies before they occur. The final two chapters consider reporting and reading psychological papers. Chapter eight details what should and should not be included in a laboratory report. The contributors use their collective experience of marking numerous lab reports to highlight common errors and provide solutions. Finally, chapter nine describes the various elements of a journal article, including tips on how to get the best out of your journal reading.

Wissenschaftliche Forschungsberichte Abteilung C:Psychologie Band 81

This practical guide explains the use of randomization tests and provides example designs and macros for implementation in IBM SPSS and Excel. It reviews the theory and practice of single-case and small-n designs so readers can draw valid causal inferences from small-scale clinical studies. The macros and example data are provided on the book's website so that users can run analyses of the text data as well as data from their own studies.The new edition features: More explanation as to why randomization tests are useful and how to apply them. More varied and expanded examples that demonstrate the use of these tests in education, clinical work and psychology. A website with the macros and datasets for all of the text examples in IBM SPSS and Excel. Exercises at the end of most chapters that help readers test their understanding of the material. A new glossary that defines the key words that appear in italics when they are first introduced. A new appendix that reviews the basic skills needed to do randomization tests. New appendices that provide annotated SPSS and Excel macros to help readers write their own or tinker with the ones provided in the book. The book opens with an overview of single case and small n designs -- why they are needed and how they differ from descriptive case studies. Chapter 2 focuses on the basic concepts of randoization tests. Next how to choose and implement a randomization design is reviewed including material on how to perform the randomizations, how to select the number of observations, and how to record the data. Chapter 5 focuses on how to analyze the data including how to use the macros and understand the results. Chapter 6 shows how randomization tests fit into the body of statistical inference. Chapter 7 discusses size and power. The book concludes with a demonstration of how to edit or modify the macros or use parts of them to write your own.Ideal as a text for courses on single-case, small n design, and/or randomization tests taught at the graduate level in psychology (especially clinical, counseling, educational, and school), education, human development, nursing, and other social and health sciences, this inexpensive book also serves as a supplement in statistics or research methods courses. Practitioners and researchers with an applied clinical focus also appreciate this book's accessible approach. An introduction to basic statistics, SPSS, and Excel is assumed.

Noted for its comprehensive coverage, this greatly expanded new edition now covers the use of univariate and multivariate effect sizes. Many measures and estimators are reviewed along with their application, interpretation, and limitations. Noted for its practical approach, the book features numerous examples using real data for a variety of variables and designs, to help readers apply the material to their own data. Tips on the use of SPSS, SAS, R, and S-Plus are provided. The book's broad disciplinary appeal results from its inclusion of a variety of examples from psychology, medicine, education, and other social sciences. Special attention is paid to confidence intervals, the statistical assumptions of the methods, and robust estimators of effect sizes. The extensive reference section is appreciated by all. With more than 40% new material, highlights of the new editon include: three new multivariate chapters covering effect sizes for analysis of covariance, multiple regression/correlation, and multivariate analysis of variance more learning tools in each chapter including introductions, summaries,'Tips and Pitfalls'and more conceptual and computational questions more coverage of univariate effect sizes, confidence intervals, and effect sizes for repeated measures to reflect their increased use in research more software references for calculating effect sizes and their confidence intervals including SPSS, SAS, R, and S-Plus the data used in the book are now provided on the web along with new data and suggested calculations with IBM SPSS syntax for computational practice. Effect Sizes for Research covers standardized and unstandardized differences between means, correlational measures, strength of association, and parametric and nonparametric measures for between- and within-groups data. Intended as a resource for professionals, researchers, and advanced students in a variety of fields, this book is also an excellent supplement for advanced statistics courses in psychology, education, the social sciences, business, and medicine. A prerequisite of introductory statistics through factorial analysis of variance and chi-square is recommended.

Through this book's unique model comparison approach, students and researchers are introduced to a set of fundamental principles for analyzing data. After seeing how these principles can be applied in simple designs, students are shown how these same principles also apply in more complicated designs. Drs. Maxwell and Delaney believe that the model comparison approach better prepares students to understand the logic behind a general strategy of data analysis appropriate for various designs; and builds a stronger foundation, which allows for the introduction of more complex topics omitted from other books. Several learning tools further strengthen the reader's understanding: •flowcharts assist in choosing the most appropriate technique; •an equation cross-referencing system aids in locating the initial, detailed definition and numerous summary equation tables assist readers in understanding differences between different methods for analyzing their data; •examples based on actual research in a variety of behavioral sciences help students see the applications of the material; •numerous exercises help develop a deeper understanding of the subject. Detailed solutions are provided for some of the exercises and •realistic data sets allow the reader to see an analysis of data from each design in its entirety. Updated throughout, the second edition features: •significantly increased attention to measures of effects, including confidence intervals, strength of association, and effect size estimation for complex and simple designs; •an increased use of statistical packages and the graphical presentation of data; •new chapters (15 & 16) on multilevel models; •the current controversies regarding statistical reasoning, such as the latest debates on hypothesis testing (ch. 2); •a new preview of the experimental designs covered in the book (ch. 2); •a CD with SPSS and SAS data sets for many of the text exercises, as well as tutorials reviewing basic statistics and regression; and •a Web site containing examples of SPSS and SAS syntax for analyzing many of the text exercises. Appropriate for advanced courses on experimental design or analysis, applied statistics, or analysis of variance taught in departments of psychology, education, statistics, business, and other social sciences, the book is also ideal for practicing researchers in these disciplines. A prerequisite of undergraduate statistics is assumed. An Instructor's Solutions Manual is available to those who adopt the book for classroom use.