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Researchers are usually interested in examining group mean differences after exhibiting significant MANOVA to adequately answer the research questions. When considering which post hoc method to apply, univariate post hoc is more dominant in the literature indicating a discrepancy between theory and practice. That is, the theory underpinning MANOVA, as with any other statistical analyses, highlights the relationships among dependent variables (Huberty & Morris, 1989); following up with univariate post hoc approaches totally relinquishes this theoretical basis. Although, the majority of scholars and experts recommend DFA for its theoretical superiority, multiple ANOVAs with some alpha correction is more applied and approved.
Discriminant Analysis and Applications comprises the proceedings of the NATO Advanced Study Institute on Discriminant Analysis and Applications held in Kifissia, Athens, Greece in June 1972. The book presents the theory and applications of Discriminant analysis, one of the most important areas of multivariate statistical analysis. This volume contains chapters that cover the historical development of discriminant analysis methods; logistic and quasi-linear discrimination; and distance functions. Medical and biological applications, and computer graphical analysis and graphical techniques for multidimensional data are likewise discussed. Statisticians, mathematicians, and biomathematicians will find the book very interesting.
A complete introduction to discriminant analysis--extensively revised, expanded, and updated This Second Edition of the classic book, Applied Discriminant Analysis, reflects and references current usage with its new title, Applied MANOVA and Discriminant Analysis. Thoroughly updated and revised, this book continues to be essential for any researcher or student needing to learn to speak, read, and write about discriminant analysis as well as develop a philosophy of empirical research and data analysis. Its thorough introduction to the application of discriminant analysis is unparalleled. Offering the most up-to-date computer applications, references, terms, and real-life research examples, the Second Edition also includes new discussions of MANOVA, descriptive discriminant analysis, and predictive discriminant analysis. Newer SAS macros are included, and graphical software with data sets and programs are provided on the book's related Web site. The book features: Detailed discussions of multivariate analysis of variance and covariance An increased number of chapter exercises along with selected answers Analyses of data obtained via a repeated measures design A new chapter on analyses related to predictive discriminant analysis Basic SPSS(r) and SAS(r) computer syntax and output integrated throughout the book Applied MANOVA and Discriminant Analysis enables the reader to become aware of various types of research questions using MANOVA and discriminant analysis; to learn the meaning of this field's concepts and terms; and to be able to design a study that uses discriminant analysis through topics such as one-factor MANOVA/DDA, assessing and describing MANOVA effects, and deleting and ordering variables.
Become a successful evidence-based practitioner. How do you evaluate the evidence? Is the information accurate, relevant and meaningful for clinical decision making? Did the design fit the research questions and was the analysis and interpretation of data appropriate? Here are all the materials you need to take your first steps as evidence-based practitioners…how to use the design, data and analysis of research as the foundation for effective clinical decision making. You’ll find support every step of the way as you progress from the foundations of clinical research and concepts of measurement through the processes of designing studies and analyzing data to writing their own research proposal.
Multivariate procedures allow social workers and other human services researchers to analyze complex, multidimensional social problems and interventions in ways that minimize oversimplification. This pocket guide provides a concise, practical, and economical introduction to four procedures for the analysis of multiple dependent variables: multivariate analysis of variance (MANOVA), multivariate analysis of covariance (MANCOVA), multivariate multiple regression (MMR), and structural equation modeling (SEM). Each procedure will be presented in a way that allows readers to compare and contrast them in terms of (1) appropriate research context; (2) required statistical assumptions, including levels of measurement of variables to be modeled; (3) analytical steps; (4) sample size; and (5) strengths and weaknesses. This invaluable guide facilitates course extensibility in scope and depth by allowing instructors to supplement course content with rigorous statistical procedures. Detailed annotated examples using Stata, SPSS (PASW), SAS, and Amos, together with additional resources, discussion of key terms, and a companion website, make this an unintimidating guide for producers and consumers of social work research knowledge.
A concise, straightforward overview of research design and analysis, helping readers form a general basis for designing and conducting research The practice of designing and analyzing research continues to evolve with advances in technology that enable greater technical analysis of data—strengthening the ability of researchers to study the interventions and relationships of factors and assisting consumers of research to understand and evaluate research reports. Research Design and Analysis is an accessible, wide-ranging overview of how to design, conduct, analyze, interpret, and present research. This book helps those in the sciences conduct their own research without requiring expertise in statistics and related fields and enables informed reading of published research. Requiring no background in statistics, this book reviews the purpose, ethics, and rules of research, explains the fundamentals of research design and validity, and describes how to select and employ appropriate statistical techniques and reporting methods. Readers gain knowledge central to various research scenarios, from sifting through reports of meta-analyses and preparing a research paper for submission to a peer-reviewed journal to discussing, evaluating, and communicating research results. This book: Provides end-to-end guidance on the entire research design and analysis process Teaches readers how to both conduct their own research and evaluate the research of others Offers a clear, concise introduction to fundamental topics ideal for both reference and general education functions Presents information derived from the author’s experience teaching the subject in real-world classroom settings Includes a full array of learning tools including tables, examples, additional resource suggestions, complete references, and appendices that cover statistical analysis software and data sets Research Design and Analysis: A Primer for the Non-Statistician is a valuable source of information for students and trainees in medical and allied health professions, journalism, education, and those interested in reading and comprehending research literature.
Drawing upon more than 30 years of experience in working with statistics, Dr. Richard J. Harris has updated A Primer of Multivariate Statistics to provide a model of balance between how-to and why. This classic text covers multivariate techniques with a taste of latent variable approaches. Throughout the book there is a focus on the importance of describing and testing one's interpretations of the emergent variables that are produced by multivariate analysis. This edition retains its conversational writing style while focusing on classical techniques. The book gives the reader a feel for why one should consider diving into more detailed treatments of computer-modeling and latent-variable techniques, such as non-recursive path analysis, confirmatory factor analysis, and hierarchical linear modeling. Throughout the book there is a focus on the importance of describing and testing one's interpretations of the emergent variables that are produced by multivariate analysis.
Richard A. Swanson and Elwood F. Holton, leading scholars in the field, bring together contributions from more than twenty distinguished researchers from multiple disciplines to provide a comprehensive introductory textbook on organizational research. Designed for use by professors and students in graduate-level programs in business, management, organizational leadership, and human resource development, Research in Organizations teaches how to apply a range of methodolgies to the study of organizations. This comprehensive guide covers the theoretical foundations of various research methods, shows how to apply those methods in organizational settings, and examines the ethical conduct of research. It provides a holistic perspective, embracing quantitative, qualitative, and mixed-methodology approaches and illuminating them through numerous illustrative examples.
Enables readers to start doing actual data analysis fast for a truly hands-on learning experience This concise and very easy-to-use primer introduces readers to a host of computational tools useful for making sense out of data, whether that data come from the social, behavioral, or natural sciences. The book places great emphasis on both data analysis and drawing conclusions from empirical observations. It also provides formulas where needed in many places, while always remaining focused on concepts rather than mathematical abstraction. SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics offers a variety of popular statistical analyses and data management tasks using SPSS that readers can immediately apply as needed for their own research, and emphasizes many helpful computational tools used in the discovery of empirical patterns. The book begins with a review of essential statistical principles before introducing readers to SPSS. The book then goes on to offer chapters on: Exploratory Data Analysis, Basic Statistics, and Visual Displays; Data Management in SPSS; Inferential Tests on Correlations, Counts, and Means; Power Analysis and Estimating Sample Size; Analysis of Variance – Fixed and Random Effects; Repeated Measures ANOVA; Simple and Multiple Linear Regression; Logistic Regression; Multivariate Analysis of Variance (MANOVA) and Discriminant Analysis; Principal Components Analysis; Exploratory Factor Analysis; and Non-Parametric Tests. This helpful resource allows readers to: Understand data analysis in practice rather than delving too deeply into abstract mathematical concepts Make use of computational tools used by data analysis professionals. Focus on real-world application to apply concepts from the book to actual research Assuming only minimal, prior knowledge of statistics, SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics is an excellent “how-to” book for undergraduate and graduate students alike. This book is also a welcome resource for researchers and professionals who require a quick, go-to source for performing essential statistical analyses and data management tasks.
Modern computer-intensive statistical methods play a key role in solving many problems across a wide range of scientific disciplines. Like its bestselling predecessors, the fourth edition of Randomization, Bootstrap and Monte Carlo Methods in Biology illustrates a large number of statistical methods with an emphasis on biological applications. The focus is now on the use of randomization, bootstrapping, and Monte Carlo methods in constructing confidence intervals and doing tests of significance. The text provides comprehensive coverage of computer-intensive applications, with data sets available online. Features Presents an overview of computer-intensive statistical methods and applications in biology Covers a wide range of methods including bootstrap, Monte Carlo, ANOVA, regression, and Bayesian methods Makes it easy for biologists, researchers, and students to understand the methods used Provides information about computer programs and packages to implement calculations, particularly using R code Includes a large number of real examples from a range of biological disciplines Written in an accessible style, with minimal coverage of theoretical details, this book provides an excellent introduction to computer-intensive statistical methods for biological researchers. It can be used as a course text for graduate students, as well as a reference for researchers from a range of disciplines. The detailed, worked examples of real applications will enable practitioners to apply the methods to their own biological data.