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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.


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


A Student's Guide to Analysis of Variance

A Student's Guide to Analysis of Variance
Author: Maxwell J. Roberts
Publisher: Psychology Press
Total Pages: 290
Release: 1999
Genre: Mathematics
ISBN: 9780415165648

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Roberts and Russo cover a range of techniques associated with ANOVA, including single and multiple factor designs, post hoc tests and how to make sense of interactions, and provide guidelines for writing reports.


Understanding Statistics and Experimental Design

Understanding Statistics and Experimental Design
Author: Michael H. Herzog
Publisher: Springer
Total Pages: 146
Release: 2019-08-13
Genre: Science
ISBN: 3030034992

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This open access textbook provides the background needed to correctly use, interpret and understand statistics and statistical data in diverse settings. Part I makes key concepts in statistics readily clear. Parts I and II give an overview of the most common tests (t-test, ANOVA, correlations) and work out their statistical principles. Part III provides insight into meta-statistics (statistics of statistics) and demonstrates why experiments often do not replicate. Finally, the textbook shows how complex statistics can be avoided by using clever experimental design. Both non-scientists and students in Biology, Biomedicine and Engineering will benefit from the book by learning the statistical basis of scientific claims and by discovering ways to evaluate the quality of scientific reports in academic journals and news outlets.


The Analysis of Variance

The Analysis of Variance
Author: Henry Scheffé
Publisher: John Wiley & Sons
Total Pages: 500
Release: 1999-03-05
Genre: Mathematics
ISBN: 9780471345053

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Originally published in 1959, this classic volume has had a major impact on generations of statisticians. Newly issued in the Wiley Classics Series, the book examines the basic theory of analysis of variance by considering several different mathematical models. Part I looks at the theory of fixed-effects models with independent observations of equal variance, while Part II begins to explore the analysis of variance in the case of other models.


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.


Two-Way Analysis of Variance

Two-Way Analysis of Variance
Author: Thomas W. MacFarland
Publisher: Springer Science & Business Media
Total Pages: 146
Release: 2011-12-09
Genre: Social Science
ISBN: 1461421330

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​In statistics, analysis of variance (ANOVA) is a collection of statistical models used to distinguish between an observed variance in a particular variable and its component parts. In its simplest form, ANOVA provides a statistical test of whether or not the means of several groups are all equal, and therefore generalizes a test between these groups. One test often used by statisticians and researchers in their work is the Two-Way ANOVA, which determines the differences--and possible interactions--when variables are presented from the perspective of two or more categories. When a Two-Way ANOVA is implemented, it enables one to compare and contrast variables resulting from independent or joint actions. This brief provides guidance on how R can be used to facilitate Two-Way ANOVA for data analysis and graphical presentation. Along with instruction on the use of R and R syntax associated with Two-Way ANOVA, this brief will also reinforce the use of descriptive statistics and graphical figures to complement outcomes from parametric Two-Way ANOVA.


Two-Way Analysis of Variance

Two-Way Analysis of Variance
Author: Thomas W. MacFarland
Publisher: Springer Science & Business Media
Total Pages: 145
Release: 2011-12-10
Genre: Social Science
ISBN: 1461421349

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​In statistics, analysis of variance (ANOVA) is a collection of statistical models used to distinguish between an observed variance in a particular variable and its component parts. In its simplest form, ANOVA provides a statistical test of whether or not the means of several groups are all equal, and therefore generalizes a test between these groups. One test often used by statisticians and researchers in their work is the Two-Way ANOVA, which determines the differences--and possible interactions--when variables are presented from the perspective of two or more categories. When a Two-Way ANOVA is implemented, it enables one to compare and contrast variables resulting from independent or joint actions. This brief provides guidance on how R can be used to facilitate Two-Way ANOVA for data analysis and graphical presentation. Along with instruction on the use of R and R syntax associated with Two-Way ANOVA, this brief will also reinforce the use of descriptive statistics and graphical figures to complement outcomes from parametric Two-Way ANOVA.