## Applied Multivariate Research: Design and InterpretationThis book provides full coverage of the wide range of multivariate topics that graduate students across the social and behavioral sciences encounter, using a conceptual, non-mathematical, approach. Addressing correlation, multiple regression, exploratory factor analysis, MANOVA, path analysis, and structural equation modeling, it is geared toward the needs, level of sophistication, and interest in multivariate methodology that serves students in applied programs in the social and behavioral sciences. Readers are encouraged to focus on design and interpretation rather than the intricacies of specific computations. |

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### Contents

Chapter 1 An Introduction to Multivariate Design | 2 |

Chapter 2 Some Fundamental Research Design Concepts | 11 |

Chapter 3A Data Screening | 37 |

Chapter 3B Data Screening Using IBM SPSS | 75 |

Part II Comparisons of Means | 139 |

Chapter 4A Univariate Comparison of Means | 140 |

Chapter 4B Univariate Comparison of Means Using IBM SPSS | 165 |

Chapter 5A Multivariate Analysis of Variance | 224 |

Chapter 12B Principal Components Analysis and Exploratory Factor Analysis Using IBM SPSS | 688 |

Chapter 13A Canonical Correlation Analysis | 750 |

Chapter 13B Canonical Correlation Analysis Using IBM SPSS | 759 |

Chapter 14A Multidimensional Scaling | 770 |

Chapter 14B Multidimensional Scaling Using IBM SPSS | 790 |

Chapter 15A Cluster Analysis | 818 |

Chapter 15B Cluster Analysis Using IBM SPSS | 833 |

Part V Fitting Models to Data | 849 |

Chapter 5B Multivariate Analysis of Variance Using IBM SPSS | 247 |

Part III Predicting the Value of a Single Variable | 283 |

Chapter 6A Bivariate Correlation and Simple Linear Regression | 284 |

Chapter 6B Bivariate Correlation and Simple Linear Regression Using IBM SPSS | 315 |

Statistical Methods | 324 |

Statistical Methods Using IBM SPSS | 366 |

Beyond Statistical Regression | 382 |

Beyond Statistical Regression Using IBM SPSS | 413 |

Chapter 9A Multilevel Modeling | 466 |

Chapter 9B Multilevel Modeling Using IBM SPSS | 484 |

Chapter 10A Binary and Multinomial Logistic Regression and ROC Analysis | 522 |

Chapter 10B Binary and Multinomial Logistic Regression and ROC Analysis Using IBM SPSS | 557 |

Part IV Analysis of Structure | 585 |

Chapter 11A Discriminant Function Analysis | 586 |

Chapter 11B Discriminant Function Analysis Using IBM SPSS | 609 |

Chapter 12A Principal Components Analysis and Exploratory Factor Analysis | 640 |

Chapter 16A Confirmatory Factor Analysis | 850 |

Chapter 16B Confirmatory Factor Analysis Using Amos | 880 |

Multiple Regression | 903 |

Multiple Regression Using IBM SPSS | 921 |

Structural Modeling | 937 |

Structural Modeling Using Amos | 951 |

Chapter 19A Structural Equation Modeling | 974 |

Chapter 19B Structural Equation Modeling Using Amos | 982 |

Applying a Model to Different Groups | 1001 |

Chapter 20B Assessing Model Invariance Using Amos | 1007 |

1032 | |

Statistics Tables | 1056 |

Selected IBM SPSS Amos Menus and Commands | 1058 |

1063 | |

1072 | |

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alpha level ANOVA assess associated beta coefficients between-subjects canonical correlation chapter chi-square Click Continue cluster coded computed covariance criterion data file data set dealership degrees of freedom dependent variable Descriptive Statistics dimensions discriminant function analysis distance eigenvalue equation error estimated evaluate example extracted factor analysis hypothesized IBM SPSS imputation independent indicate interaction interpret labeled latent variable likelihood Linear Model linear regression logistic regression main dialog window main effects matrix mean measured variables mediation method missing data missing values model fit multilevel modeling multiple regression multivariate null hypothesis obtained ordinary least squares outliers output panel parameters path analysis path coefficients perform the analysis plot Positive Affect predicted predictor variables principal components analysis procedure pushbutton regression analysis relationship represent researchers rotation sample scale scatterplot Section self-esteem shown in Figure solution specific standard statistically significant strategy structure coefficients subscales tion total variance univariate variate zero