## Guide to Advanced Data Analysis using IDAMS SoftwareP.S. NAGPAUL, New Delhi (India)

 Cover/Preface 1.Chapter 1 - General Principles of Data Analysis 1.1.Characteristics of the Analysis Question 1.2.Characteristics of the Data 1.3.Types of Variables 1.4.Statistical Techniques available in IDAMS 1.5.Choice of Statistical Techniques 2.Chapter 2 - Data Exploration and Pre-Analysis 2.1.Exploratory Data Analysis 2.1.1.Histogram 2.1.2.Box Plots 2.1.3.Scatter Plot 2.2.Local Regression 2.2.1.Construction of Indices 2.2.2.Summated Ratings 2.2.3.Notation 2.2.4.Calculation of scores 2.3.Examples 2 3.Chapter 3 - Univariate Analysis 3.1.Descriptive Statistics 3.1.1.Measures of Central Tendency 3.1.2.Measures of Dispersion 3.1.3.Measures of Shape 3.1.4.Measures of Inequality 3.2.Analysis of Preference Data 3.3.Modeling of Preferences 3.4.Constructing a Fuzzy Relation from Preference Data 3.5.Techniques of Rank - ordering 3.5.1.Ranking by Classical Logic (ELECTRE) 3.5.2.Fuzzy method - 1 (Non-dominated layers) 3.5.3.Fuzzy method - 2 (Ranks) 3.5.4.Concluding Remarks 3.6.Examples 3 4.Chapter 4 - Bivariate Analysis 4.1.Correlation 4.2.Non-parametric Measures of Bivariate Relationships 4.3.Analysis of Variance 4.4.Regression Analysis 4.5.Examples 4 5.Chapter 5 - Multiple Regression and Multiple Classification Analysis 5.1.Key concepts and definitions 5.2.Multiple Regression Model 5.3.Multiple Classification Analysis 5.4.Examples 5 6.Chapter 6 - Principal Components and Correspondence Analysis 6.1.Module Factor 6.2.Principal Components Analysis 6.3.Factor Analysis 6.4.Descriptive Principal Components Analysis 6.4.1.Supplementary elements 6.4.2.Interpretation of Principal Components Analysis 6.4.3.Factor Graphics 6.5.Correspondence Analysis 6.5.1.Basic Concepts and Definitions 6.5.2.Reduction of Dimensionality 6.5.3.Interpretation of correspondence analysis 6.5.4.Quality of representation 6.5.5.Supplementary elements 6.5.6.Outlier points 6.5.7.Graphics 6.5.8.Mathematics of Correspondence Analysis 6.6.Examples 6 7.Chapter 7 - Cluster Analysis 7.1.Module Clusfind 7.1.1.Partitioning Around Medoids (Pam) 7.1.2.Clustering Large Applications (Clara) 7.1.3.Fuzzy Analysis (Fanny) 7.1.4.Agglomerative Nesting (Agnes) 7.1.5.Divisive Analysis (Diana) 7.1.6.Monothetic Analysis (Mona) 7.2.Iterative Typology and Ascending Classification (TYPOL) 7.2.1.Notation 7.2.2.Building of an initial typology 7.2.3.Description of resulting typology 7.3.Examples 7 8.Chapter 8 - Multidimensional Scaling 8.1.Non-metric Multidimensional Scaling 8.1.1.Goodness of fit 8.1.2.Configuration Analysis 8.1.3.Rotation of configuration 8.2.Examples 8 9.Chapter 9 - Discriminant Function Analysis 9.1.Key terms and concepts 9.2.Discriminant Functions 9.3.Two-group Discriminant Analysis 9.4.Multiple Discriminant Analysis 9.5.Examples 9 10.Chapter 10 - Classification and Regression Trees 10.1.Trees 10.1.1.Regression Trees 10.1.2.Classification Trees 10.2.Examples 10 Appendix Dataset "PRORITIES OF ACADEMIC INSTITUTIONS IN INDIA" Dataset "INVOLVEMENT OF ACADEMIC SCIENTISTS IN TEACHING" Dataset "SPECIALIZATION PATTERNS IN CHEMISTRY" Dataset "TRANSATIONAL LINKAGES OF INDIAN SCIENCE" "Example of Stepwise Multiple Regression Analysis" Dataset "INTERNATIONAL COMPARATIVES STUDY ON ORGANIZATION AND PREFERENCE OF RESEARCH UNITS: SECOND ROUND" Dataset "Data used by Longley to predict total employment using values of six independent variables" Dataset "RESEARCH PRIORITIES IN PHYSICS" Dataset "INVOLVEMENT OF ACADEMIC SCIENTISTS IN TEACHING" Dataset "TIME BUDGET DATA"