Acknowledgments | p. vii |
Dedication | p. viii |
Using This Book | p. ix |
Principal Component Analysis | p. 1 |
Introduction: The Basics of Principal Component Analysis | p. 2 |
Example: Analysis of the Prosocial Orientation Inventory | p. 10 |
SAS Program and Output | p. 13 |
Steps in Conducting Principal Component Analysis | p. 21 |
An Example with Three Retained Components | p. 41 |
Conclusion | p. 55 |
Assumptions Underlying Principal Component Analysis | p. 55 |
References | p. 56 |
Exploratory Factor Analysis | p. 57 |
Introduction: When Is Exploratory Factor Analysis Appropriate? | p. 59 |
Introduction to the Common Factor Model | p. 60 |
Exploratory Factor Analysis versus Principal Component Analysis | p. 68 |
Preparing and Administering the Investment Model Questionnaire | p. 72 |
SAS Program and Analysis Results | p. 73 |
Steps in Conducting Exploratory Factor Analysis | p. 79 |
A More Complex Example: The Job Search Skills Questionnaire | p. 107 |
Conclusion | p. 125 |
Assumptions Underlying Exploratory Factor Analysis | p. 126 |
References | p. 127 |
Assessing Scale Reliability with Coefficient Alpha | p. 129 |
Introduction: The Basics of Scale Reliability | p. 130 |
Coefficient Alpha | p. 132 |
Assessing Coefficient Alpha with PROC CORR | p. 133 |
Summarizing the Results | p. 138 |
Conclusion | p. 140 |
References | p. 140 |
Path Analysis with Manifest Variables | p. 141 |
Introduction: The Basics of Path Analysis | p. 143 |
A Path-Analytic Investigation of the Investment Model | p. 150 |
Overview of the Rules for Performing Path Analysis | p. 151 |
Preparing the Program Figure | p. 153 |
Preparing the SAS Program | p. 164 |
Interpreting the Results of the Analysis | p. 181 |
Modifying the Model | p. 198 |
Preparing a Formal Description of the Analysis and Results for a Paper | p. 225 |
Path Analysis of a Model Predicting Victim Reactions to Sexual Harassment | p. 234 |
Conclusion: Learning More about Path Analysis | p. 245 |
References | p. 245 |
Developing Measurement Models with Confirmatory Factor Analysis | p. 249 |
Introduction: A Two-Step Approach to Path Analysis with Latent Variables | p. 250 |
A Model of the Determinants of Work Performance | p. 251 |
Basic Concepts in Latent-Variable Analyses | p. 254 |
Advantages of Path Analysis with Latent Variables | p. 257 |
Necessary Conditions for Confirmatory Factor Analysis and Path Analysis with Latent Variables | p. 259 |
Example: The Investment Model | p. 261 |
Testing the Fit of the Measurement Model from the Investment Model Study | p. 264 |
Conclusion: On to Path Analysis with Latent Variables? | p. 340 |
References | p. 340 |
Path Analysis with Latent Variables | p. 343 |
Recapitulation: Basic Concepts in Path Analysis with Latent Variables | p. 344 |
Testing the Fit of the Theoretical Model from the Investment Model Study | p. 347 |
Preparing a Formal Description of Results for a Paper | p. 410 |
Additional Examples | p. 422 |
Conclusion: Learning More about Latent Variable Models | p. 436 |
References | p. 436 |
Introduction to SAS Program, SAS Logs, and SAS Output | p. 439 |
Introduction: What is the SAS System? | p. 439 |
Three Types of SAS System Files | p. 440 |
Conclusion | p. 447 |
Data Input | p. 449 |
Introduction: Inputting Questionnaire Data versus Other Types of Data | p. 450 |
Keying Data: An Illustrative Example | p. 450 |
Inputting Data Using the CARDS Statement | p. 455 |
Additional Guidelines | p. 460 |
Inputting a Correlation or Covariance Matrix | p. 469 |
Inputting Data Using the INFILE Statement Rather than the CARDS Statement | p. 474 |
Controlling the Size of the Output and Log Pages with the OPTIONS Statement | p. 476 |
Conclusion | p. 477 |
References | p. 477 |
Working with Variables and Observations in SAS Data Sets | p. 479 |
Introduction: Manipulating, Subsetting, Concatenating, and Merging Data | p. 480 |
Placement of Data Manipulation and Data Subsetting Statements | p. 481 |
Data Manipulation | p. 486 |
Data Subsetting | p. 498 |
A More Comprehensive Example | p. 504 |
Concatenating and Merging Data Sets | p. 505 |
Conclusion | p. 514 |
References | p. 514 |
Exploring Data with PROC MEANS, PROC FREQ, PROC PRINT, and PROC UNIVARIATE | p. 515 |
Introduction: Why Perform Simple Descriptive Analyses? | p. 516 |
Example: A Revised Volunteerism Survey | p. 517 |
Computing Descriptive Statistics with PROC MEANS | p. 519 |
Creating Frequency Tables with PROC FREQ | p. 523 |
Printing Raw Data with PROC PRINT | p. 526 |
Testing for Normality with PROC UNIVARIATE | p. 527 |
Conclusion | p. 546 |
References | p. 546 |
Preparing Scattergrams and Computing Correlations | p. 547 |
Introduction: When Are Pearson Correlations Appropriate? | p. 548 |
Interpreting the Coefficient | p. 548 |
Linear versus Nonlinear Relationships | p. 550 |
Producing Scattergrams with PROC PLOT | p. 552 |
Computing Pearson Correlations with PROC CORR | p. 557 |
Assumptions Underlying the Pearson Correlation Coefficient | p. 563 |
Reference | p. 564 |
Data Sets | p. 565 |
Critical Values of the Chi-Square Distribution | p. 569 |
Index | p. 571 |
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