The SAGE Dictionary of Quantitative Management Research

A must-have reference resource for quantitative management researchers, the Dictionary contains over 100 entries covering the fundamentals of quantitative methodologies; covering both analysis and implementation and examples of use, as well as detailed graphics to aid understanding. Every entry features: -An introduction to the topic, -Key relevant features, -A worked example, -A concise summary and a selection of further reading suggestions -Cross-references to associated concepts within the dictionary

Table of Contents:
Introduction A/B Split Testing Analytic Hierarchy Process Association and Causation in Quantitative Research Association Rule Mining Bass Model Canonical Correlation Chaos Theory Classification and Ranking Belief Simplex Clustering Algorithms Cluster Analysis Coding Conventions for Representing Information Conceptual Equivalence Confidence Intervals Conjoint Analysis Construct Operationalization Review (CORE) Correlation Analysis Correspondence Analysis Cross-National/Cultural Comparisons Cross-Sectional Designs Data Envelopment Analysis in Management Data Mining Data Set Structure Data Transformation Dempster-Shafer Theory Designing Experiments Discriminant Analysis Dominance Analysis DS/AHP Dummy Variable Coding Event Studies Methodology Evaluation Research Experimental Design Exploratory or Confirmatory Factor Analysis Factor Analysis Fuzzy Decision Trees Fuzzy Sets Fuzzy Time Series Models Generalized Linear Mixed Models Generalized Linear Models Geodemographics Geographical Information Systems Growth Models Intraclass Correlation Coefficient Internal and External Validity Item Response Theory Item Response Theory for Management Latent Segments Analysis Latent Variable Models Logical Discriminant Models Logistic Growth Model Logistic Regression Logistic Spline Model Measurement Reliability Measurement Invariance in Multigroup Research Measurement Scales Moderator-Mediator Variable Distinction Mixture Models Multi-Attribute Utility/Value Theory Multidimensional Scaling Multilevel Models Multi-Logistic Growth Model Multinomial Logistic Regression Multi-State Modelling Non-Parametric Measures of Association Optimal Control Models in Management Ordinary Least-Squares Regression Panel Design Paper Versus Electronic Surveys Parametric Tests Partial Correlation Principal Components Analysis Programme Evaluation PROMETHEE Method of Ranking Alternatives Proportional Odds Model Quantile Estimators Quantile Estimators - Bootstrap Confidence Intervals Rasch Model for Measurement Receiver Operating Characteristic Response Styles in Cross-National Research Retail Site Selection Sampling Equivalence in Cross-National Research Sample Size for Proportions Sample Sizes Versus Usable Observations Self-Organizing Maps Single-Case Research Designs Single-Case Single-Baseline Designs Sngle-Case Multiple-Baseline Designs Simulation - Discrete Event Simulation - Methodology Structural Equation Modelling in Business Management Structural Equation Modelling in Marketing - Part 1: Introduction and Basic Concepts Structural Equation Modelling in Marketing - Part 2: Model Calibration Structural Equation Modelling in Marketing - Part 3: An Example Study Design Survey Design Tabu Search Testing a Simple Hypothesis Validity Variable Precision Rough Sets Voronoi Diagrams Web Surveys Index

About the Author :
Luiz Moutinhois Foundation Chair of Marketing at the Department of Management, University of Glasgow Graeme's teaching and research interests are broadly in the area of methodology and data analysis. His PhD was a study into the acquisition of complex syntax in children and since then he has worked on a number of projects from a wide range of research areas including the conversational behaviour of children, interviewing techniques used on child witnesses, alcohol use in the workplace, the application of knowledge-based expert systems, the application of generalized linear models in predictive modelling, multi-level analysis of data related to Inclusion and Pupil achievement, an assessment of the Early Support Project (DfES) and a project investigating mathematically demanding F and HE programmes.