LEADER 05388nam 2200673Ia 450 001 9910141423003321 005 20230725033902.0 010 $a1-118-49168-8 010 $a1-283-59290-8 010 $a9786613905352 010 $a1-118-49171-8 035 $a(CKB)2670000000236998 035 $a(EBL)1011369 035 $a(OCoLC)809041644 035 $a(SSID)ssj0000704430 035 $a(PQKBManifestationID)11406295 035 $a(PQKBTitleCode)TC0000704430 035 $a(PQKBWorkID)10705537 035 $a(PQKB)10931416 035 $a(MiAaPQ)EBC1011369 035 $a(Au-PeEL)EBL1011369 035 $a(CaPaEBR)ebr10595380 035 $a(CaONFJC)MIL390535 035 $a(EXLCZ)992670000000236998 100 $a20100514d2011 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aANOVA and ANCOVA$b[electronic resource] $ea GLM approach /$fAndrew Rutherford 205 $a2nd ed. 210 $aHoboken, NJ $cWiley$dc2011 215 $a1 online resource (360 p.) 300 $aDescription based upon print version of record. 311 $a0-470-38555-3 320 $aIncludes bibliographical references and index. 327 $aANOVA and ANCOVA A GLM Approach; Contents; Acknowledgments; 1 An Introduction to General Linear Models: Regression, Analysis of Variance, and Analysis of Covariance; 1.1 Regression, Analysis of Variance, and Analysis of Covariance; 1.2 A Pocket History of Regression, ANOVA, and ANCOVA; 1.3 An Outline of General Linear Models (GLMs); 1.3.1 Regression; 1.3.2 Analysis of Variance; 1.3.3 Analysis of Covariance; 1.4 The ""General"" in GLM; 1.5 The ""Linear"" in GLM; 1.6 Least Squares Estimates; 1.7 Fixed, Random, and Mixed Effects Analyses; 1.8 The Benefits of a GLM Approach to ANOVA and ANCOVA 327 $a1.9 The GLM Presentation 1.10 Statistical Packages for Computers; 2 Traditional and GLM Approaches to Independent Measures Single Factor ANOVA Designs; 2.1 Independent Measures Designs; 2.2 Balanced Data Designs; 2.3 Factors and Independent Variables; 2.4 An Outline of Traditional ANOVA for Single Factor Designs; 2.5 Variance; 2.6 Traditional ANOVA Calculations for Single Factor Designs; 2.7 Confidence Intervals; 2.8 GLM Approaches to Single Factor ANOVA; 2.8.1 Experimental Design GLMs; 2.8.2 Estimating Effects by Comparing Full and Reduced Experimental Design GLMs; 2.8.3 Regression GLMs 327 $a2.8.4 Schemes for Coding Experimental Conditions 2.8.4.1 Dummy Coding; 2.8.4.2 Why Only (p - 1) Variables Are Used to Represent All Experimental Conditions?; 2.8.4.3 Effect Coding; 2.8.5 Coding Scheme Solutions to the Overparameterization Problem; 2.8.6 Cell Mean GLMs; 2.8.7 Experimental Design Regression and Cell Mean GLMs; 3 Comparing Experimental Condition Means, Multiple Hypothesis Testing, Type 1 Error, and a Basic Data Analysis Strategy; 3.1 Introduction; 3.2 Comparisons Between Experimental Condition Means; 3.3 Linear Contrasts; 3.4 Comparison Sum of Squares; 3.5 Orthogonal Contrasts 327 $a3.6 Testing Multiple Hypotheses 3.6.1 Type 1 and Type 2 Errors; 3.6.2 Type 1 Error Rate Inflation with Multiple Hypothesis Testing; 3.6.3 Type 1 Error Rate Control and Analysis Power; 3.6.4 Different Conceptions of Type 1 Error Rate; 3.6.4.1 Test wise Type 1 Error Rate; 3.6.4.2 Family wise Type 1 Error Rate; 3.6.4.3 Experiment wise Type 1 Error Rate; 3.6.4.4 False Discovery Rate; 3.6.5 Identifying the ""Family"" in Family wise Type 1 Error Rate Control; 3.6.6 Logical and Empirical Relations; 3.6.6.1 Logical Relations; 3.6.6.2 Empirical Relations; 3.7 Planned and Unplanned Comparisons 327 $a3.7.1 Direct Assessment of Planned Comparisons 3.7.2 Contradictory Results with ANOVA Omnibus F-tests and Direct Planned Comparisons; 3.8 A Basic Data Analysis Strategy; 3.8.1 ANOVA First?; 3.8.2 Strong and Weak Type 1 Error Control; 3.8.3 Step wise Tests; 3.8.4 Test Power; 3.9 The Three Basic Stages of Data Analysis; 3.9.1 Stage 1; 3.9.2 Stage 2; 3.9.2.1 Rom's Test; 3.9.2.2 Shaffer's R Test; 3.9.2.3 Applying Shaffer's R Test After a Significant F-test; 3.9.3 Stage 3; 3.10 The Role of the Omnibus F-Test; 4 Measures of Effect Size and Strength of Association, Power, and Sample Size 327 $a4.1 Introduction 330 $aProvides an in-depth treatment of ANOVA and ANCOVA techniques from a linear model perspective ANOVA and ANCOVA: A GLM Approach provides a contemporary look at the general linear model (GLM) approach to the analysis of variance (ANOVA) of one- and two-factor psychological experiments. With its organized and comprehensive presentation, the book successfully guides readers through conventional statistical concepts and how to interpret them in GLM terms, treating the main single- and multi-factor designs as they relate to ANOVA and ANCOVA. The book begins with a brief history of 606 $aAnalysis of variance 606 $aAnalysis of covariance 606 $aLinear models (Statistics) 615 0$aAnalysis of variance. 615 0$aAnalysis of covariance. 615 0$aLinear models (Statistics) 676 $a519.538 700 $aRutherford$b Andrew$f1958-$0876108 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910141423003321 996 $aANOVA and ANCOVA$91956549 997 $aUNINA