Statistical Analysis of Management Data is especially designed to provide doctoral students with a theoretical knowledge of the basic concepts underlying the most important multivariate techniques and with an overview of actual applications in various fields. The content herein addresses both the underlying mathematics and problems of application. As such, a reasonable level of competence in both statistics and mathematics is needed. This book is not intended as a first introduction to statistics and statistical analysis. Instead it assumes that the student is familiar with basic statistical techniques. The techniques are presented in a fundamental way but in a format accessible to students in a doctoral program, to practicing academicians, and to data analysts.

Inhalt

1: Introduction. 1.1. Overview. 1.2. Objectives. 1.3. Types of Scales. 1.4. Topics Covered. 1.5. Pedagogy. 2: Multivariate Normal Distribution. 2.1. Univariate Normal Distribution. 2.2. Bivariate Normal Distribution. 2.3. Generalization to Multivariate Case. 2.4. Tests about Means. >2.5. Examples. 2.6. Assignment. 2.7. References. 3: Measurement Theory: Reliability and Factor Analysis. 3.1. Notions of Measurement Theory. 3.2. Factor Analysis. 3.3. Conclusion - Procedure for Scale Construction. 3.4. Application Examples. 3.5. Assignment. 3.6. References.4: Multiple Regression with a Single Dependent Variable. 4.1. Statistical Inference: Least Squares and Maximum Likelihood. 4.2. Pooling Issues. 4.3. Examples of Linear Model Estimation with SAS. 4.4. Assignment. 4.5. References. 5: System of Equations. 5.1. Seemingly Unrelated Regression (SUR). 5.2. A System of Simultaneous Equations. 5.3. Simultaneity and Identification. 5.4. Summary. 5.5. Examples Using SAS. 5.6. Assignment. 5.7. References. 6: Categorial Dependent Variables. 6.1. Discriminant Analysis. 6.2. Quantal Choice Models. 6.3. Examples. 6.4. Assignment. 6.5. References. 7: Rank Ordered Data. 7.1. Conjoint Analysis - MONANOVA. 7.2. Ordered Probit. 7.3. Examples. 7.4. Assignment. 7.5. References. 8: Error in Variables - Analysis of Covariance Structure. 8.1. The Impact of Imperfect Measures. 8.2. Analysis of Covariance Structures. 8.3. Examples. 8.4. Assignment. 8.5. References. 9: Analyses of Similarity and Preference Data. 9.1.Proximity Matrices. 9.2. Problem Definition. 9.3. Individual Differences in Similarity Judgements. 9.4. Analysis of Preference Data. 9.5.Examples. 9.6. Assignment. 9.7. References. Appendices. A: Rules in Matrix Algebra. B: Statistical Tables. C: Description of Data Sets.