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Interaction Effects in Factorial Analysis of Variance

Interaction Effects in Factorial Analysis of Variance
Author: James Jaccard
Publisher: SAGE
Total Pages: 116
Release: 1998
Genre: Mathematics
ISBN: 9780761912217

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Although factorial analysis is widely used in the social sciences, there is some confusion as to how to use the techniqueÆs most powerful featureùthe evaluation of interaction effects. Written to remedy this situation, author James Jaccard clearly describes the issues underlying the effective analysis of interaction in factorial designs. The book begins by describing different ways of characterizing interactions in ANOVA, elucidating both moderator conceptualizations of interactions as well as that of residualized means. After discussing interaction effects using traditional hypothesis testing approaches, he then covers alternative analytic frameworks that focus on effect size methodology and interval estimation. Jaccard summarizes criticisms of classical null hypothesis testing and offers practical guidelines for pursuing magnitude estimation and interval estimation approaches. In addition, Jaccard shows applications of all three approaches to the analysis of interactions using a complete numerical example; discusses strategies for effectively exploring interactions in higher order designs and designs with more than two levels per factor; highlights the central role of single degree of freedom contrasts and provides numerous illustrations for formulating such contrasts; considers simplified approaches to statistical power analysis; describes approaches to consider when statistical assumptions are not met; explicates the case of unequal sample sizes; considers the impact of measurement error; and demonstrates computer applications. Readers who have wanted a book that fully discusses different conceptualizations of interactions as well as one that provides practical guidelines for analyzing complex interactions will find this volume the one that they have been seeking.


Learning Statistics with R

Learning Statistics with R
Author: Daniel Navarro
Publisher: Lulu.com
Total Pages: 617
Release: 2013-01-13
Genre: Computers
ISBN: 1326189727

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"Learning Statistics with R" covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software and adopting a light, conversational style throughout. The book discusses how to get started in R, and gives an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book. For more information (and the opportunity to check the book out before you buy!) visit http://ua.edu.au/ccs/teaching/lsr or http://learningstatisticswithr.com


Interaction Effects in Multiple Regression

Interaction Effects in Multiple Regression
Author: James Jaccard
Publisher: SAGE Publications
Total Pages: 108
Release: 2003-03-05
Genre: Social Science
ISBN: 1544332572

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Interaction Effects in Multiple Regression has provided students and researchers with a readable and practical introduction to conducting analyses of interaction effects in the context of multiple regression. The new addition will expand the coverage on the analysis of three way interactions in multiple regression analysis.


Multiple Comparison Procedures

Multiple Comparison Procedures
Author: Larry E. Toothaker
Publisher: SAGE
Total Pages: 108
Release: 1993
Genre: Mathematics
ISBN: 9780803941779

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If you conduct research with more than two groups and want to find out if they are significantly different when compared two at a time, then you need Multiple Comparison Procedures. Using examples to illustrate major concepts, this concise volume is your guide to multiple comparisons. Toothaker thoroughly explains such essential issues as planned vs. post-hoc comparisons, stepwise vs. simultaneous test procedures, types of error rate, unequal sample sizes and variances, and interaction tests vs. cell mean tests.


Analysis of Variance and Interaction

Analysis of Variance and Interaction
Author: Glenda Francis
Publisher:
Total Pages: 340
Release: 2012
Genre: Analysis of variance
ISBN: 9781486007097

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CONTENTS; INTRODUCTION 1. SECTION 1 EXTENDING BEYOND THE BASICS OF HYPOTHESIS TESTING 1. Topic 1.1 Review of Concepts 1; Populations and Samples 1; Hypothesis Testing - The Mechanics 2; The 95% Confidence Interval 5; Observational versus Experimental Studies - Confounding and Causation 6; Experimental Design 7; Choosing the Appropriate Analysis 10. Topic 1.2 Entering Your Own Data in SPSS 14; Defining the Variables 15; Entering the Data 16; Saving the data file 17; Deleting cases and variables. 17. Topic 1.3 Correlation and Regression Revisited 19; Scatterplots 19; Pearson's r and the regression line 21; Significance Testing and the Report 23; Checking the Direction of the Relationship 27. Topic 1.4 Limitations of Hypothesis Testing 30; Significance versus Importance 30; Not Significant versus No Relationship 33; Power Analysis 34. Topic 1.5 The concept of Interaction 39. Topic 1.6 t-tests Revisited 45; Revision 45; Checking the Direction of the Difference 47; Assumptions underlying the independent samples t-test 50; Non-significant t-tests - Power 58; Some terminology 58; Type I and Type II Errors 59; Interaction revisited 60. SECTION 2 THE ANALYSIS OF VARIANCE 64. Topic 2.1 Introduction to the Analysis of Variance - The Single Factor Independent Groups Design 65; Understanding the Basis of the Analysis of Variance 65; Calculating the F-Ratio 69; The Sampling Distribution of F 73; Special Case - Two Treatment Conditions 74; Effect Size 74; Review Exercises for Topic 2.1 75. Topic 2.2 Producing and Reporting a One-Way Analysis of Variance Using SPSS for Windows 77; Using SPSS to Produce ANOVA Tables 77; Power Analysis for Oneway ANOVA 81; Presenting Results 83; Assumptions 89; A Complete Worked Example 90; Review Exercises for Topic 2.2 95. Topic 2.3 Analytical Comparisons in the Single Factor Independent Groups Design 97; Introduction 97; Specifying Comparisons in SPSS 99; Experimentwise Versus Comparisonwise Errors 101; Planned Versus Unplanned Comparisons 102; Performing Post Hoc Tests Using SPSS 105; Presentation of Results 107; A Review Example 110; Review Exercises for Topic 2.3 114. Topic 2.4 The Completely Randomised Factorial Design 117; Advantages of Factorial Designs 118; Main Effects and Interaction 118; Using SPSS to Produce the ANOVA Table for Factorial Designs 122; Interpreting the SPSS Output 127; Reporting Results from a Factorial ANOVA (No Significant Interaction) 128; Reporting Results from a Factorial ANOVA (Significant Interaction) 130; Analytical Comparisons in Factorial Designs 136; Assumptions 144; A Review Example - Significant Interaction 145; A Review Example - No Significant Interaction 149; Review Exercises for Topic 2.4 154. Topic 2.5 Analysis of Variance for the Single Factor Within Subjects Design 156; Techniques for Controlling Nuisance Variables 156; Practice Effects in the Repeated Measures Design 157; Theory Behind the Analysis of Variance for Related Samples Designs 159; Calculating the F-ratio by Hand - A Worked Example 161; Effect Size and Power Analysis 163; The Assumptions for a Within-Subjects Analysis of Variance 164; Using SPSS To Obtain The ANOVA Table 164; Analytical Comparisons for the Single Factor Within Subjects Design 172; A Review Example 176; Review Exercises 183. Topic 2.6 The Mixed Factorial Design 186; Using SPSS to Produce the ANOVA Table for a Mixed Design 186; Interpreting the SPSS Output 190; Report on a Mixed Design ANOVA 191; Further Analyses for Mixed Design ANOVA 192; A Review Example 200; Longitudinal studies involving a Control Group 215; Review Exercises 228. REVISION EXERCISES 232.


Experimental Design and the Analysis of Variance

Experimental Design and the Analysis of Variance
Author: Robert K. Leik
Publisher: SAGE Publications
Total Pages: 209
Release: 1997-04-19
Genre: Social Science
ISBN: 1452250359

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Why is this Book a Useful Supplement for Your Statistics Course? Most core statistics texts cover subjects like analysis of variance and regression, but not in much detail. This book, as part of our Series in Research Methods and Statistics, provides you with the flexibility to cover ANOVA more thoroughly, but without financially overburdening your students.


Statistical Methods for Communication Science

Statistical Methods for Communication Science
Author: Andrew F. Hayes
Publisher: Routledge
Total Pages: 682
Release: 2020-10-14
Genre: Language Arts & Disciplines
ISBN: 1135250898

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Statistical Methods for Communication Science is the only statistical methods volume currently available that focuses exclusively on statistics in communication research. Writing in a straightforward, personal style, author Andrew F. Hayes offers this accessible and thorough introduction to statistical methods, starting with the fundamentals of measurement and moving on to discuss such key topics as sampling procedures, probability, reliability, hypothesis testing, simple correlation and regression, and analyses of variance and covariance. Hayes takes readers through each topic with clear explanations and illustrations. He provides a multitude of examples, all set in the context of communication research, thus engaging readers directly and helping them to see the relevance and importance of statistics to the field of communication. Highlights of this text include: *thorough and balanced coverage of topics; *integration of classical methods with modern "resampling" approaches to inference; *consideration of practical, "real world" issues; *numerous examples and applications, all drawn from communication research; *up-to-date information, with examples justifying use of various techniques; and *downloadable resources with macros, data sets, figures, and additional materials. This unique book can be used as a stand-alone classroom text, a supplement to traditional research methods texts, or a useful reference manual. It will be invaluable to students, faculty, researchers, and practitioners in communication, and it will serve to advance the understanding and use of statistical methods throughout the discipline.


Applied Univariate, Bivariate, and Multivariate Statistics

Applied Univariate, Bivariate, and Multivariate Statistics
Author: Daniel J. Denis
Publisher: John Wiley & Sons
Total Pages: 576
Release: 2021-03-19
Genre: Mathematics
ISBN: 1119583020

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AN UPDATED GUIDE TO STATISTICAL MODELING TECHNIQUES USED IN THE SOCIAL AND BEHAVIORAL SCIENCES The revised and updated second edition of Applied Univariate, Bivariate, and Multivariate Statistics: Understanding Statistics for Social and Natural Scientists, with Applications in SPSS and R contains an accessible introduction to statistical modeling techniques commonly used in the social and behavioral sciences. The text offers a blend of statistical theory and methodology and reviews both the technical and theoretical aspects of good data analysis. Featuring applied resources at various levels, the book includes statistical techniques using software packages such as R and SPSS®. To promote a more in-depth interpretation of statistical techniques across the sciences, the book surveys some of the technical arguments underlying formulas and equations. The thoroughly updated edition includes new chapters on nonparametric statistics and multidimensional scaling, and expanded coverage of time series models. The second edition has been designed to be more approachable by minimizing theoretical or technical jargon and maximizing conceptual understanding with easy-to-apply software examples. This important text: Offers demonstrations of statistical techniques using software packages such as R and SPSS® Contains examples of hypothetical and real data with statistical analyses Provides historical and philosophical insights into many of the techniques used in modern social science Includes a companion website that includes further instructional details, additional data sets, solutions to selected exercises, and multiple programming options Written for students of social and applied sciences, Applied Univariate, Bivariate, and Multivariate Statistics, Second Edition offers a text to statistical modeling techniques used in social and behavioral sciences.


Study Guide to Accompany Neil J. Salkind's Statistics for People Who (Think They) Hate Statistics

Study Guide to Accompany Neil J. Salkind's Statistics for People Who (Think They) Hate Statistics
Author: Neil J. Salkind
Publisher: SAGE Publications
Total Pages: 187
Release: 2016-11-25
Genre: Social Science
ISBN: 1506377920

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The Study Guide to Accompany Neil J. Salkind's Statistics for People Who (Think They) Hate Statistics, Sixth Edition includes chapter outlines; chapter summaries; learning objectives; key terms; exercises; true/false, multiple choice, and essay questions; as well as answers to all questions. The guide has been updated to match the organization of Salkind’s text and includes activities for the book's new Chapter 19: Data Mining: An Introduction to Getting the Most Out of Your BIG Data.


Study Guide for Education to Accompany Neil J. Salkind's Statistics for People Who (Think They) Hate Statistics

Study Guide for Education to Accompany Neil J. Salkind's Statistics for People Who (Think They) Hate Statistics
Author: Neil J. Salkind
Publisher: SAGE Publications
Total Pages: 212
Release: 2017-11-17
Genre: Social Science
ISBN: 1544320035

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This Study Guide for introductory statistics courses in education departments is designed to accompany Neil J. Salkind’s best-selling Statistics for People Who (Think They) Hate Statistics, Sixth Edition. Extra exercises; activities; and true/false, multiple choice, and essay questions (with answers to all questions) feature education-specific content to help further student mastery of text concepts. A dataset is provided for use with the book at edge.sagepub.com/salkind6e. The dataset contains simulated data to represent a random elementary school in the US. This fictitious elementary school consists of grade K-5 and has traditional classes taught in English as well as Spanish immersion classes. The simulated data represents a set of 70 teachers in this school. The dataset allows students to run various practice exercises in SPSS.