Applied Multivariate Research : Design and Interpretation (Record no. 1239)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | nam a22 7a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20180113103848.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 180113b xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 978-1-5063-2976-5 |
| 028 ## - PUBLISHER NUMBER | |
| Source | Allied Informatics, Jaipur |
| Bill Number | 4440 |
| Bill Date | 04/01/2018 |
| Purchase Year | 2017-18 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | BSDU |
| Language of cataloging | English |
| Transcribing agency | BSDU |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 300.1519535 |
| Item number | MEY |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Meyers, Lawrence S |
| 245 ## - TITLE STATEMENT | |
| Title | Applied Multivariate Research : Design and Interpretation |
| 250 ## - EDITION STATEMENT | |
| Remainder of edition statement | 3rd |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | New Delhi |
| Name of publisher, distributor, etc. | Sage Publications India Pvt. Ltd. |
| Date of publication, distribution, etc. | 2017 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 978 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc. note | Part I. The Basics of Multivariate Design<br/> Chapter 1. An Introduction to Multivariate Design Chapter 2. Some Fundamental Research Design Concepts Chapter 3A. Data Screening Chapter 3B. Data Screening Using IBM SPSS <br/>Part II. Comparisons of Means<br/> Chapter 4A. Univariate Comparison of Means Chapter 4B. Univariate Comparison of Means Using IBM SPSS Chapter 5A. Multivariate Analysis of Variance (MANOVA) Chapter 5B. Multivariate Analysis of Variance (MANOVA) Using IBM SPSS <br/>Part III. Predicting the Value of a Single Variable<br/> Chapter 6A. Bivariate Correlation and Simple Linear Regression Chapter 6B. Bivariate Correlation and Simple Linear Regression Using IBM SPSS Chapter 7A. Multiple Regression: Statistical Methods Chapter 7B. Multiple Regression: Statistical Methods Using IBM SPSS Chapter 8A. Multiple Regression: Beyond Statistical Regression Chapter 8B. Multiple Regression: Beyong Statistical Regression Using IBM SPSS Chapter 9A. Multilevel Modeling Chapter 9B. Multilevel Modeling Using IBM SPSS Chapter 10A. Binary and Multinomial Logistic Regression and ROC Analysis Chapter 10B. Binary and Multinomial Logistic Regression and ROC Analysis Using IBM SPSS <br/>Part IV. Analysis of Structure<br/> Chapter 11A. Discriminant Function Analysis Chapter 11B. Discriminant Function Analysis Using IBM SPSS Chapter 12A. Principal Components and Exploratory Factor Analysis Chapter 12B. Principal Components and Exploratory Factor Analysis Using IBM SPSS Chapter 13A. Canonical Correlation Analysis Chapter 13B. Canonical Correlation Analysis Using IBM SPSS Chapter 14A. Multidimensional Scaling Chapter 14B. Multidimensional Scaling Using IBM SPSS Chapter 15A. Cluster Analysis Chapter 15B. Cluster Analysis Using IBM SPSS <br/>Part V. Fitting Models to Data<br/> Chapter 16A. Confirmatory Factor Analysis Chapter 16B. Confirmatory Factor Analysis Using Amos Chapter 17A. Path Analysis: Multiple Regression Chapter 17B. Path Analysis: Multiple Regression Using IBM SPSS Chapter 18A. Path Analysis: Structural Modeling Chapter 18B. Path Analysis: Structural Modeling Using Amos Chapter 19A. Structural Equation Modeling Chapter 19B. Structural Equation Modeling Using Amos Chapter 20A. Model Invariance: Applying a Model to Different Groups Chapter 20B. Assessing Model Invariance Using Amos |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | PART I: THE BASICS OF<br/>MULTIVARIATE DESIGN 1<br/>Chapter 1: An Introduction to<br/>Multivariate Design 2<br/>1.1: The Use of Multivariate<br/>Designs 2<br/>1.2: The Definition of the Multivariate<br/>Domain 2<br/>1.3: The Importance of Multivariate<br/>Designs 3<br/>1.4: The General Form of a Variate 4<br/>1.5: The Type of Variables Combined<br/>to Form a Variate 5<br/>1.6: The General Organization of the<br/>Book 6<br/>1.7: Recommended Readings 10<br/>Chapter 2: Some Fundamental Research<br/>Design Concepts 11<br/>2.1: Populations and Samples 11<br/>2.2: Scales of Measurement 12<br/>2.3: Independent Variables, Dependent<br/>Variables, and Covariates 18<br/>2.4: Between-Subjects and<br/>Within-Subjects Independent<br/>Variables 21<br/>2.5: Latent Variables or Variates and<br/>Measured Variables 22<br/>2.6: Endogenous and Exogenous<br/>Variables 24<br/>2.7: Statistical Significance 24<br/>2.8: Statistical Power 32<br/>2.9: Recommended Readings 36<br/>Chapter 3A: Data Screening 37<br/>3A.1: Overview 37<br/>3A.2: Value Cleaning 38<br/>3A.3: Patterns of Missing<br/>Values 44<br/>3A.4: Overview of Methods of<br/>Handling Missing Data 48<br/>3A.5: Deletion Methods of Handling<br/>Missing Data 49<br/>3A.6: Single Imputation<br/>Methods of Handling Missing<br/>Data 51<br/>3A.7: Modern Imputation Methods of<br/>Handling Missing Data 54<br/>3A.8: Recommendations for Handling<br/>Missing Data 57<br/>3A.9: Outliers 57<br/>3A.10: Using Descriptive Statistics in<br/>Data Screening 63<br/>3A.11: Using Pictorial Representations<br/>in Data Screening 64<br/>3A.12: Multivariate Statistical<br/>Assumptions Underlying the<br/>General Linear Model 67<br/>3A.13: Data Transformations 72<br/>3A.14: Recommended Readings 73<br/>Chapter 3B: Data Screening Using IBM<br/>SPSS 75<br/>3B.1: The Look of IBM SPSS 75<br/>3B.2: Data Cleaning: All<br/>Variables 76<br/>3B.3: Screening Quantitative<br/>Variables 80<br/>3B.4: Missing Values: Overview 81<br/>3B.5: Missing Value Analysis 81<br/>3B.6: Multiple Imputation 89<br/>3B.7: Mean Substitution as a Single<br/>Imputation Approach 102<br/>3B.8: Univariate Outliers 106<br/>3B.9: Normality 109<br/>3B.10: Linearity 120<br/>3B.11: Multivariate Outliers 122<br/>3B.12: Screening Within Levels of<br/>Categorical Variables 128<br/>3B.13: Reporting the Results 136<br/>PART II: COMPARISONS OF<br/>MEANS 139<br/>Chapter 4A: Univariate Comparison of<br/>Means 140<br/>4A.1: Overview 140<br/>4A.2: Means are Compared With<br/>Respect to Their Associated<br/>Variability 141<br/>4A.3: The t and F Tests 143<br/>4A.4: One-Way Between-Subjects<br/>Designs 144<br/>4A.5: Two-Way (Factorial) BetweenSubjects<br/>Design 148<br/>4A.6: One-Way Within-Subjects<br/>Design 152<br/>4A.7: Two-Way Simple Mixed<br/>Design 153<br/>4A.8: One-Way Between-Subjects<br/>ANCOVA 156<br/>4A.9: The General Linear Model 162<br/>4A.10: Recommended Readings 164<br/>Chapter 4B: Univariate<br/>Comparison of Means<br/>Using IBM SPSS 165<br/>4B.1: One-Way Between-Subjects<br/>Design 165<br/>4B.2: Two-Way Between-Subjects<br/>Design 172<br/>4B.3: One-Way Within-Subjects<br/>Design 181<br/>4B.4: Simple Mixed Design 187<br/>4B.5: Trend Analysis 196<br/>4B.6: Analysis of Covariance 201<br/>4B.7: One-Way Between-Subjects<br/>Design Using Generalized Linear<br/>Models 211<br/>4B.8: Simple Mixed Design<br/>Using Generalized Linear<br/>Models 215<br/>Chapter 5A: Multivariate Analysis of<br/>Variance 224<br/>5A.1: Overview 224<br/>5A.2: Working With Multiple<br/>Dependent Variables 224<br/>5A.3: Benefits of and Drawbacks to<br/>Using MANOVA 227<br/>5A.4: Hotelling’s T2 229<br/>5A.5: Multivariate Significance<br/>Testing With More Than Two<br/>Groups 232<br/>5A.6: What to Do After a Significant<br/>Multivariate Effect 235<br/>5A.7: Advantages of Multivariate<br/>Factorial Designs 237<br/>5A.8: A Strategy For Examining TwoWay<br/>Between-Subjects MANOVA<br/>Results 238<br/>5A.9: The Time Dimension<br/>in Multivariate Data<br/>Analysis 242<br/>5A.10: Recommended Readings 245<br/>Chapter 5B: Multivariate Analysis of<br/>Variance Using IBM SPSS 247<br/>5B.1: Numerical Example 247<br/>5B.2: Alternatives to Performing a<br/>MANOVA Analysis 248<br/>5B.3: Two-Group<br/>MANOVA 248<br/>5B.4: k-Group MANOVA 257<br/>5B.5: Two-Way Between-Subjects<br/>Factorial MANOVA 269<br/>PART III: PREDICTING THE VALUE<br/>OF A SINGLE VARIABLE 283<br/>Chapter 6A: Bivariate Correlation and<br/>Simple Linear Regression 284<br/>6A.1: The Concept of<br/>Relationship 284<br/>6A.2: Different Types of<br/>Relationships 285<br/>6A.3: Statistical Significance<br/>of the Correlation<br/>Coefficient 292<br/>6A.4: Strength of<br/>Relationship 294<br/>6A.5: Pearson Correlation Using a<br/>Quantitative Variable and a<br/>Dichotomous Nominal<br/>Variable 298<br/>6A.6: Simple Linear<br/>Regression 302<br/>6A.7: Statistical Error in<br/>Prediction: Why Bother With<br/>Regression? 309<br/>6A.8: How Simple Linear Regression Is<br/>Used 311<br/>6A.9: Factors Affecting the Computed<br/>Pearson r and Regression<br/>Coefficients 311<br/>6A.10: Recommended<br/>Readings 314<br/>Chapter 6B: Bivariate Correlation and<br/>Simple Linear Regression Using IBM<br/>SPSS 315<br/>6B.1: Bivariate Correlation: Analysis<br/>Setup 315<br/>6B.2: Simple Linear Regression 319<br/>6B.3: Reporting Results 323<br/>Chapter 7A: Multiple Regression:<br/>Statistical Methods 324<br/>7A.1: General Considerations 324<br/>7A.2: A Range of Regression<br/>Methods 325<br/>7A.3: The Variables in a Multiple<br/>Regression Analysis 325<br/>7A.4: Multiple Regression<br/>Research 327<br/>7A.5: The Regression Equations 329<br/>7A.6: The Variate in Multiple<br/>Regression 332<br/>7A.7: The Standard (Simultaneous)<br/>Regression Method 333<br/>7A.8: Partial Correlation 337<br/>7A.9: The Squared Multiple<br/>Correlation 338<br/>7A.10: The Squared Semipartial<br/>Correlation 339<br/>7A.11: Structure Coefficients 344<br/>7A.12: Statistical Summary of the<br/>Regression Solution 345<br/>7A.13: Evaluating the Overall<br/>Model 346<br/>7A.14: Evaluating the Individual<br/>Predictor Results 351<br/>7A.15: Step Methods of Building the<br/>Model 357<br/>7A.16: The Forward Method 357<br/>7A.17: The Backward Method 358<br/>7A.18: The Backward Versus Forward<br/>Solutions 358<br/>7A.19: The Stepwise Method 359<br/>7A.20: Evaluation of the Statistical<br/>Methods 361<br/>7A.21: Collinearity and<br/>Multicollinearity 363<br/>7A.22: Recommended Readings 365<br/>Chapter 7B: Multiple Regression:<br/>Statistical Methods Using IBM<br/>SPSS 366<br/>7B.1: Standard Multiple<br/>Regression 366<br/>7B.2: Stepwise Multiple<br/>Regression 372<br/>Chapter 8A: Multiple Regression: Beyond<br/>Statistical Regression 382<br/>8A.1: A Larger World of<br/>Regression 382<br/>8A.2: Hierarchical Linear<br/>Regression 382<br/>8A.3: Suppressor Variables 386<br/>8A.4: Linear and Nonlinear<br/>Regression 388<br/>8A.5: Dummy and Effect<br/>Coding 391<br/>8A.6: Moderator Variables and<br/>Interactions 396<br/>8A.7: Simple Mediation 399<br/>8A.8: Recommended Readings 411<br/>Chapter 8B: Multiple Regression: Beyond<br/>Statistical Regression Using IBM<br/>SPSS 413<br/>8B.1: Hierarchical Linear<br/>Regression 413<br/>8B.2: Polynomial Regression 419<br/>8B.3: Dummy and Effect Coding 428<br/>8B.4: Interaction Effects of<br/>Quantitative Variables in<br/>Regression 439<br/>8B.5: Mediation 457<br/>Chapter 9A: Multilevel Modeling 466<br/>9A.1: The Name of the<br/>Procedure 466<br/>9A.2: The Rise of Multilevel<br/>Modeling 466<br/>9A.3: The Defining Feature of<br/>Multilevel Modeling: Hierarchically<br/>Structured Data 467<br/>9A.4: Nesting and the Independence<br/>Assumption 468<br/>9A.5: The Intraclass Correlation as an<br/>Index of Clustering 469<br/>9A.6: Consequences of Violating<br/>the Independence<br/>Assumption 470<br/>9A.7: Some Ways in Which Level 2<br/>Groups Can Differ 472<br/>9A.8: The Random Coefficient<br/>Regression Model 474<br/>9A.9: Centering the Variables 476<br/>9A.10: The Process of Building the<br/>Multilevel Model 479<br/>9A.11: Recommended<br/>Readings 483<br/>Chapter 9B: Multilevel Modeling Using<br/>IBM SPSS 484<br/>9B.1: Numerical Example 484<br/>9B.2: Assessing the Unconditional<br/>Model 484<br/>9B.3: Centering the Variables 490<br/>9B.4: Building the Multilevel Models:<br/>Overview 493<br/>9B.5: Building the First Model 496<br/>9B.6: Building the Second<br/>Model 504<br/>9B.7: Building the Third Model 509<br/>9B.8: Building the Fourth Model 515<br/>9B.9: Reporting Multilevel Modeling<br/>Results 519<br/>Chapter 10A: Binary and Multinomial<br/>Logistic Regression and ROC<br/>Analysis 522<br/>10A.1: Overview 522<br/>10A.2: The Variables in Logistic<br/>Regression Analysis 523<br/>10A.3: Assumptions of Logistic<br/>Regression 524<br/>10A.4: Coding of the Binary Variables<br/>in Logistic Regression 524<br/>10A.5: The Logistic Regression<br/>Model 528<br/>10A.6: Logistic Regression and Odds 530<br/>10A.7: The Logistic Regression<br/>Model 532<br/>10A.8: Calculating the Changes of<br/>Cases Belonging to the Target<br/>Group 534<br/>10A.9: Binary Logistic Regression With<br/>a Single Binary Predictor 534<br/>10A.10: Binary Logistic Regression<br/>With a Single Quantitative<br/>Predictor 536<br/>10A.11: Binary Logistic Regression<br/>With a Categorical and a<br/>Quantitative Predictor 540<br/>10A.12: Evaluating the Logistic<br/>Model 541<br/>10A.13: Strategies For Building the<br/>Logistic Regression Model 544<br/>10A.14: ROC Analysis 545<br/>10A.15: Recommended Readings 556<br/>Chapter 10B: Binary and Multinomial<br/>Logistic Regression and ROC<br/>Analysis Using IBM SPSS 557<br/>10B.1: Binary Logistic Regression 557<br/>10B.2: ROC Analysis 565<br/>10B.3: Multinomial Logistic<br/>Regression 575<br/>PART IV: ANALYSIS OF<br/>STRUCTURE 585<br/>Chapter 11A: Discriminant Function<br/>Analysis 586<br/>11A.1: Overview 586<br/>11A.2: Discriminant Function<br/>Analysis and Logistic Analysis<br/>Compared 588<br/>11A.3: Discriminant Function<br/>Analysis and<br/>MANOVA 588<br/>11A.4: Assumptions Underlying<br/>Discriminant Function<br/>Analysis 589<br/>11A.5: Sample Size for Discriminant<br/>Analysis 590<br/>11A.6: The Discriminant<br/>Function 590<br/>11A.7: The Number of Discriminant<br/>Functions That Can Be<br/>Extracted 592<br/>11A.8: Dynamics of Extracting<br/>Discriminant Functions 593<br/>11A.9: Testing Statistical<br/>Significance 594<br/>11A.10: Evaluating the Quality of the<br/>Solution 596<br/>11A.11: Coefficients Associated With<br/>the Interpretation of Discriminant<br/>Functions 601<br/>11A.12: Different Discriminant<br/>Function Methods 606<br/>11A.13: Recommended Readings 608<br/>Chapter 11B: Discriminant Function<br/>Analysis Using IBM SPSS 609<br/>11B.1: Two-Group Disciminant<br/>Function Analysis Setup 609<br/>11B.2: Two-Group Discriminant<br/>Function Analysis Output 613<br/>11B.3: Reporting the Results of a TwoGroup<br/>Discriminant Function<br/>Analysis 620<br/>11B.4: Three-Group Discriminant<br/>Function Analysis Setup 622<br/>11B.5: Three-Group Discriminant<br/>Function Analysis Output 625<br/>11B.6: Reporting the Results of a<br/>Three-Group Discriminant<br/>Function Analysis 637<br/>Chapter 12A: Principal Components<br/>Analysis and Exploratory Factor<br/>Analysis 640<br/>12A.1: Orientation and<br/>Terminology 640<br/>12A.2: How Factor Analysis Is Used in<br/>Psychological Research 641<br/>12A.3: Origins of Factor<br/>Analysis 641<br/>12A.4: The General Organization of<br/>This Chapter 642<br/>12A.5: Where the Analysis Begins: The<br/>Correlation Matrix 642<br/>12A.6: Acquiring Perspective on Factor<br/>Analysis 648<br/>12A.7: Distinctions Within Factor<br/>Analysis 651<br/>12A.8: The First Phase: Component<br/>Extraction 652<br/>12A.9: Distances of Variables From a<br/>Component 658<br/>12A.10: Principal Components Analysis<br/>Versus Factor Analysis 662<br/>12A.11: Different Extraction<br/>Methods 664<br/>12A.12: Recommendations<br/>Concerning Extraction 666<br/>12A.13: The Rotation Process 667<br/>12A.14: Orthogonal Factor<br/>Rotation 672<br/>12A.15: Oblique Factor Rotation 673<br/>12A.16: Choosing Between Orthogonal<br/>and Oblique Rotation<br/>Strategies 674<br/>12A.17: The Factor Analysis<br/>Printout 676<br/>12A.18: Interpreting Factors 680<br/>12A.19: Selecting the Factor<br/>Solution 683<br/>12A.20: Sample Size Issues 686<br/>12A.21: Recommended Readings 687<br/>Chapter 12B: Principal Components<br/>Analysis and Exploratory Factor<br/>Analysis Using IBM SPSS 688<br/>12B.1: Numerical Example 688<br/>12B.2: Preliminary Principal<br/>Components Analysis 690<br/>12B.3: Principal Components<br/>Analysis With a Promax Rotation:<br/>Two-Component Solution 700<br/>12B.4: ULS Analysis With<br/>a Promax Rotation: Two-Factor<br/>Solution 704<br/>12B.5: Wrap-Up of the Two-Factor<br/>Solution 708<br/>12B.6: Looking For Six<br/>Dimensions 708<br/>12B.7: Principal Components Analysis<br/>With a Promax Rotation: SixComponent<br/>Solution 708<br/>12B.8: ULS Analysis With a Promax<br/>Rotation: Six-Component<br/>Solution 713<br/>12B.9: Principal Axis Factor Analysis<br/>With a Promax Rotation: SixComponent<br/>Solution 717<br/>12B.10: Wrap-Up of the Six-Factor<br/>Solution 720<br/>12B.11: Assessing Reliability: General<br/>Principles 721<br/>12B.12: Assessing Reliability: The<br/>Global Domains 724<br/>12B.13: Assessing Reliability: The Six<br/>Item Sets Based on the ULS/Promax<br/>Structure 729<br/>12B.14: Computing Scales<br/>Based on the ULS Promax<br/>Structure 729<br/>12B.15: Using the Computed Variables<br/>in Further Analyses 736<br/>12B.16: Reporting the Results 745<br/>Chapter 13A: Canonical Correlation<br/>Analysis 750<br/>13A.1: Overview 750<br/>13A.2: Canonical Functions or<br/>Roots 751<br/>13A.3: The Index of Shared<br/>Variance 752<br/>13A.4: The Dynamics of Extracting<br/>Canonical Functions 753<br/>13A.5: Testing Statistical<br/>Significance 754<br/>13A.6: The Multivariate<br/>Tests 755<br/>13A.7: Redundancy Index 756<br/>13A.8: Coefficients Associated With<br/>the Canonical Functions 757<br/>13A.9: Interpreting the Canonical<br/>Functions 758<br/>13A.10: Recommended Readings 758<br/>Chapter 13B: Canonical Correlation<br/>Analysis Using IBM SPSS 759<br/>13B.1: Canonical Correlation: Analysis<br/>Setup 759<br/>13B.2: Canonical Correlation:<br/>Overview of Output 760<br/>13B.3: Canonical Correlation:<br/>Multivariate Tests of<br/>Significance 761<br/>13B.4: Canonical Correlation:<br/>Eigenvalues and Canonical<br/>Correlations 761<br/>13B.5: Canonical Correlation:<br/>Dimension Reduction<br/>Analysis 763<br/>13B.6: Canonical Correlation: How<br/>Many Functions Should Be<br/>Interpreted? 764<br/>13B.7: Canonical Correlation:<br/>The Coefficients in the<br/>Output 764<br/>13B.8: Canonical Correlation:<br/>Interpreting the Dependent<br/>Variates 765<br/>13B.9: Canonical Correlation:<br/>Interpreting the Predictor<br/>Variates 766<br/>13B.10: Canonical Correlation:<br/>Interpreting the Canonical<br/>Functions 767<br/>13B.11: Reporting Canonical<br/>Correlation Analysis<br/>Results 768<br/>Chapter 14A: Multidimensional<br/>Scaling 770<br/>14A.1: Overview 770<br/>14A.2: The Paired Comparison<br/>Method 771<br/>14A.3: Dissimilarity Data in<br/>MDS 772<br/>14A.4: Similarity/Dissimilarity<br/>Conceived as an Index of<br/>Distance 773<br/>14A.5: Dimensionality in MDS 774<br/>14A.6: Data Collection Methods 775<br/>14A.7: Similarity Versus<br/>Dissimilarity 777<br/>14A.8: Distance Models 778<br/>14A.9: A Classification Schema for<br/>MDS Techniques 780<br/>14A.10: Types of MDS Models 782<br/>14A.11: Assessing Model Fit 784<br/>14A.12: Recommended Readings 788<br/>Chapter 14B: Multidimensional Scaling<br/>Using IBM SPSS 790<br/>14B.1: The Structure of This<br/>Chapter 790<br/>14B.2: Metric CMDS 790<br/>14B.3: Nonmetric CMDS 799<br/>14B.4: Metric WMDS 807<br/>Chapter 15A: Cluster Analysis 818<br/>15A.1: Introduction 818<br/>15A.2: Two Types of Clustering 818<br/>15A.3: Hierarchical Clustering 819<br/>15A.4: k-Means Clustering 829<br/>15A.5: Recommended Readings 832<br/>Chapter 15B: Cluster Analysis Using IBM<br/>SPSS 833<br/>15B.1: Hierarchical Cluster<br/>Analysis 833<br/>15B.2: k-Means Cluster Analysis 841<br/>PART V: FITTING MODELS TO<br/>DATA 849<br/>Chapter 16A: Confirmatory Factor<br/>Analysis 850<br/>16A.1: Overview 850<br/>16A.2: The General Form of a<br/>Confirmatory Model 851<br/>16A.3: The Difference Between Latent<br/>and Indicator Variables 852<br/>16A.4: Contrasting Principal<br/>Components Analysis, Exploratory<br/>Factor Analysis, and Confirmatory<br/>Factor Analysis 853<br/>16A.5: Confirmatory Factor Analysis<br/>Is Theory Based 860<br/>16A.6: The Logic of Performing a<br/>Confirmatory Factor Analysis 861<br/>16A.7: Model Specification 861<br/>16A.8: Model Identification 862<br/>16A.9: Model Estimation 866<br/>16A.10: Model Evaluation<br/>Overview 867<br/>16A.11: Assessing Fit of Hypothesized<br/>Models 868<br/>16A.12: Model Estimation: Assessing<br/>Pattern/Structure<br/>Coefficients 873<br/>16A.13: Model Respecification 874<br/>16A.14: General<br/>Considerations 878<br/>16A.15: Recommended Readings 879<br/>Chapter 16B: Confirmatory Factor<br/>Analysis Using Amos 880<br/>16B.1: Using Amos 880<br/>16B.2: Numerical Example 880<br/>16B.3: Model Specification 881<br/>16B.4: Model Identification 885<br/>16B.5: Performing the Analysis 888<br/>16B.6: Working With the Analysis<br/>Output 890<br/>16B.7: Considering the Respecification<br/>of the Model 894<br/>16B.8: Respecifying the Model 898<br/>16B.9: Output From the<br/>Respecification 898<br/>16B.10: Reporting Confirmatory<br/>Factor Analysis Results 901<br/>Chapter 17A: Path Analysis: Multiple<br/>Regression 903<br/>17A.1: Overview 903<br/>17A.2: Principles of Path Analysis 904<br/>17A.3: Causality and Path<br/>Analysis 905<br/>17A.4: The Concept of a Path<br/>Model 907<br/>17A.5: The Roles Played by Variables<br/>in a Path Structure 907<br/>17A.6: The Assumptions of Path<br/>Analysis 909<br/>17A.7: Missing Values in Path<br/>Analysis 910<br/>17A.8: Analyzing the Path<br/>Structure 911<br/>17A.9: The Multiple Regression<br/>Approach to Path Analysis 911<br/>17A.10: Indirect and Total Effects 913<br/>17A.11: Comparing Multiple<br/>Regression and Model-Fitting<br/>Approaches 914<br/>17A.12: A Path Analysis<br/>Example 914<br/>17A.13: The Multiple Regression<br/>Strategy to Perform a Path<br/>Analysis 916<br/>17A.14: Examining Mediation<br/>Effects 917<br/>17A.15: Respecifying the Model 919<br/>17A.16: Recommended Readings 920<br/>Chapter 17B: Path Analysis: Multiple<br/>Regression Using IBM SPSS 921<br/>17B.1: The Data Set and Model Used<br/>in Our Example 921<br/>17B.2: Specifying the Variables in Each<br/>Analysis 921<br/>17B.3: Predicting Exercise 923<br/>17B.4: Predicting Diet 925<br/>17B.5: Predicting Social Desirability 926<br/>17B.6: Predicting Acceptance 927<br/>17B.7: Mediation Effects in the Larger<br/>Model 929<br/>17B.8: Reporting Path Analysis<br/>Results 934<br/>Chapter 18A: Path Analysis: Structural<br/>Modeling 937<br/>18A.1: The Model-Fitting Approach to<br/>Path Analysis 937<br/>18A.2: Comparing Multiple Regression<br/>and Model-Fitting Approaches 938<br/>18A.3: The Model-Fitting Strategy to<br/>Perform a Path Analysis With Only<br/>Measured Variables 940<br/>18A.4: Differences Between Regression<br/>and Structural Equations 940<br/>18A.5: The Analysis of a Structural<br/>Model 941<br/>18A.6: Configuring the Structural<br/>Model 942<br/>18A.7: Identifying the Structural<br/>Model 942<br/>18A.8: The Model Results 944<br/>18A.9: Respecifying the Model 946<br/>18A.10: Respecified Model<br/>Results 948<br/>18A.11: Recommended Readings 949<br/>Chapter 18B: Path Analysis: Structural<br/>Modeling Using Amos 951<br/>18B.1: Overview 951<br/>18B.2: The Data Set and Model Used<br/>in Our Example 951<br/>18B.3: Drawing the Model 952<br/>18B.4: Model Identification 954<br/>18B.5: Performing the Analysis 955<br/>18B.6: The Analysis Output 956<br/>18B.7: The Structural Model 961<br/>18B.8: Specification Search to Delete<br/>Paths 961<br/>18B.9: Reporting Path Analysis<br/>Results 972<br/>Chapter 19A: Structural Equation<br/>Modeling 974<br/>19A.1: Overview 974<br/>19A.2: The Measurement and<br/>Structural Models 974<br/>19A.3: From Path Analysis to<br/>SEM 975<br/>19A.4: Building a Structural Model<br/>From Our Path Model 977<br/>19A.5: Results for our Structural<br/>Model 979<br/>19A.6: Recommended Readings 981<br/>Chapter 19B: Structural Equation<br/>Modeling Using Amos 982<br/>19B.1: Overview 982<br/>19B.2: The Example<br/>We Use 983<br/>19B.3: The Variables in Our Example<br/>Model 984<br/>19B.4: The Measurement<br/>Model 984<br/>19B.5: The Variables Configured<br/>in the Full Structural<br/>Model 988<br/>19B.6: Performing the<br/>Analysis 988<br/>19B.7: Output for the Full Structural<br/>Model 990<br/>19B.8: Respecification of the<br/>Model 994<br/>19B.9: Output for the Full Respecified<br/>Structural Model 995<br/>19B.10: Reporting SEM Analysis<br/>Results 998<br/>Chapter 20A: Model Invariance:<br/>Applying a Model to Different<br/>Groups 1001<br/>20A.1: Overview 1001<br/>20A.2: The General Strategy Used to<br/>Compare Groups 1002<br/>20A.3: The Omnibus Model<br/>Comparison Phase 1002<br/>20A.4: The Coefficient Comparison<br/>Phase 1005<br/>20A.5: Recommended Readings 1005<br/>Chapter 20B: Assessing Model Invariance<br/>Using Amos 1007<br/>20B.1: Overview 1007<br/>20B.2: Confirmatory Factor<br/>Analysis 1007<br/>20B.3: Path Analysis 1018 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Research |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Social Science research |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Gamst, Glenn |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Guarino, A J |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Books |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Home library | Current library | Shelving location | Date acquired | Cost, normal purchase price | Total Checkouts | Full call number | Barcode | Date last seen | Date last checked out | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | Reference | BSDU Knowledge Resource Center, Jaipur | BSDU Knowledge Resource Center, Jaipur | Reference | 01/13/2018 | 9997.00 | 4 | 300.1519535 MEY | 014400 | 09/10/2022 | 09/10/2022 | 01/13/2018 | Books |