A Guide to Research Methodology : An Overview of Research Problems, Tasks and Methods.

Cover -- Half Title -- Title Page -- Copyright Page -- Table of contents -- Preface -- Acknowledgements -- About the Author -- 1 Research - Objectives and Process -- 1.1 Introduction -- 1.2 Research Objectives -- 1.3 Types of Research -- 1.4 Research Process and Research Output -- 1.5 Phases in Research -- 1.6 Innovation and Research -- 1.7 Changing Nature and Expanding Scope of Research -- 1.8 Need for Research Methodology -- Concluding Remarks -- 2 Formulation of Research Problems -- 2.1 Nature of Research Problems -- 2.2 Choice of Problem Area -- 2.3 Formulation of Research Problems -- 2.4 Role of Counter-Examples and Paradoxes -- 2.5 Illustrations of Problems -- 2.6 Concretizing Problem Formulation -- 3 Research Design -- 3.1 Introduction -- 3.2 Choice of Variables -- 3.3 Choice of Proxy Variables -- 3.4 Design for Gathering Data -- 3.4.1 Need for Data -- 3.4.2 Mechanisms for Data Collection -- 3.4.3 Design for Data Collection -- 3.5 Measurement Design -- 3.6 Quality of Measurements -- 3.7 Design of Analysis -- 3.8 Credibility and Generalizability of Findings -- 3.9 Interpretation of Results -- 3.10 Testing Statistical Hypotheses -- 3.11 Value of Information -- 3.12 Grounded Theory Approach -- 3.13 Ethical Considerations -- 4 Collection of Data -- 4.1 Introduction -- 4.2 Collection of Primary Data -- 4.2.1 Sample Surveys and Designed Experiments -- 4.2.2 Design of Questionnaires -- 4.2.3 Scaling of Responses -- An Example -- 4.2.4 Survey Data Quality -- 4.3 Planning of Sample Surveys -- 4.3.1 Some General Remarks -- 4.3.2 Problems in Planning a Large-Scale Sample Survey -- Problems in Developing a Sampling Frame -- Problems in Use of Stratification -- Sample Size Determination -- 4.3.3 Abuse of Sampling -- 4.3.4 Panel Surveys -- 4.4 Use of Designed Experiments -- 4.4.1 Types and Objectives of Experiments -- 4.5 Collection of Secondary Data.

4.6 Data for Bio-Medical Research -- 4.7 Data for Special Purposes -- 4.8 Data Integration -- 5 Sample Surveys -- 5.1 Introduction -- 5.2 Non-Probability Sampling -- 5.3 Randomized Response Technique -- 5.4 Panel Surveys -- 5.5 Problems in Use of Stratified Sampling -- 5.5.1 Problem of Constructing Strata -- 5.5.2 Problem of Allocation of the Total Sample across Strata -- 5.6 Small-Area Estimation -- 5.7 Network Sampling -- 5.8 Estimation without Sampling -- 5.9 Combining Administrative Records with Survey Data -- 6 More about Experimental Designs -- 6.1 Introduction -- 6.2 Optimality of Designs -- 6.3 Fractional Factorial Experiments -- 6.4 Other Designs to Minimize the Number of Design Points -- 6.5 Mixture Experiments -- 6.6 Sequential Experiments: Alternatives to Factorial Experiments -- 6.7 Multi-Response Experiments -- 6.8 Design Augmentation -- 6.9 Designs for Clinical Trials -- 7 Models and Modelling -- 7.1 The Need for Models -- 7.2 Modelling Exercise -- 7.3 Types of Models -- 7.4 Probability Models -- 7.4.1 Generalities -- 7.4.3 Discretization of Continuous Distributions -- 7.4.4 Multivariate Distributions -- 7.4.5 Use of Copulas -- 7.4.6 Choosing a Probability Model -- 7.5 Models Based on Differential Equations -- 7.5.1 Motivation -- 7.5.2 Fatigue Failure Model -- 7.5.3 Growth Models -- 7.6 The ANOVA Model -- 7.7 Regression Models -- 7.7.1 General Remarks -- 7.7.2 Linear Multiple Regression -- 7.7.3 Non-Parametric Regression -- 7.7.4 Quantile Regression -- 7.7.5 Artificial Neural Network (ANN) Models -- 7.8 Structural Equation Modelling -- 7.9 Stochastic Process Models -- 7.10 Glimpses of Some Other Models -- 7.11 Optimization Models -- 7.12 Simulation - Models and Solutions -- 7.13 Model Uncertainty -- 8 Data Analysis -- 8.1 Introduction -- 8.2 Content Analysis of Mission Statements -- 8.3 Analysis of a Comparative Experiment.

8.4 Reliability Improvement through Designed Experiment -- 8.4.1 Exponential Failure Model -- 8.4.2 Weibull Failure Model -- 8.4.3 Lognormal Failure Model -- 8.5 Pooling Expert Opinions -- 8.5.1 Delphi Method -- 8.5.2 Analysis of Rankings -- 8.6 Selecting a Regression Model -- 8.7 Analysis of Incomplete Data -- 8.8 Estimating Process Capability -- 8.9 Estimation of EOQ -- 8.10 Comparison among Alternatives Using Multiple Criteria -- 8.10.1 Some Points of Concern -- 8.10.2 Analytic Hierarchy Process -- 8.10.3 Data Envelopment Analysis -- 8.10.4 TOPSIS -- 8.10.5 OCRA -- Principal-Component Analysis (PCA) -- 8.11 Conjoint Analysis -- 8.12 Comparison of Probability Distributions -- 8.13 Comparing Efficiencies of Alternative Estimation Procedures -- 8.14 Multiple Comparison Procedures -- 8.15 Impact of Emotional Intelligence on Organizational Performance -- 9 Multivariate Analysis -- 9.1 Introduction -- 9.2 MANOVA -- 9.3 Principal-Component Analysis -- 9.4 Factor Analysis -- 9.5 Cluster Analysis -- 9.5.1 Generalities -- 9.5.2 Hierarchical Clustering (Based on Linkage Model) -- 9.6 Discrimination and Classification -- 9.6.1 Bayes Discriminant Rule -- 9.6.2 Fisher's Discriminant Function Rule -- 9.6.3 Maximum Likelihood Discriminant Rule -- 9.6.4 Classification and Regression Trees -- 9.6.5 Support Vector Machines and Kernel Classifiers -- 9.7 Multi-Dimensional Scaling -- 9.7.1 Definition -- 9.7.2 Concept of Distance -- 9.7.3 Classic MDS (CMDS) -- 9.7.4 An Illustration -- 9.7.5 Goodness of Fit -- 9.7.6 Applications of MDS -- 9.7.7 Further Developments -- 10 Analysis of Dynamic Data -- 10.1 Introduction -- 10.2 Models in Time-Series Analysis -- 10.2.1 Criteria for Model Selection -- 10.3 Signal Extraction, Benchmarking, Interpolation and Extrapolation -- 10.4 Functional Data Analysis -- 10.5 Non-Parametric Methods -- 10.6 Volatility Modelling.

11 Validation and Communication of Research Findings -- 11.1 Introduction -- 11.2 Validity and Validation -- 11.3 Communication of Research Findings -- 11.4 Preparing a Research Paper/Report -- 11.5 Points to Remember in Paper Preparation -- References and Suggested Reading -- Index.

The present book attempts to provide readers with a broad framework for research in any field. Attention has been given to provide illustrations from different disciplines. Problems of data collection using survey sampling and design of experiments as well as methods of data analysis and the associated use of models have been discussed.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.