TY  - BOOK
AU  - Swain, A.K.P.C.
TI  - A text book of research methodology
SN  - 9788127241728
U1  - 001.42 
PY  - 2007///
CY  - Ludhiana
PB  - Kalyani
N1  - 1. INTRODUCTION
Science and Scientific Method
1.2 What is Research ?
1.2.1 Characteristics of Research
1.2.2 Research and Scientific Method
1.2.3 Purpose of Research
1.2.4 Rationale of Research
1.2.5 Significance of Research
1.2.6 Desiderata of a Good Research
1.3 Kinds of Research
1.4 Types of Research
1.5 Research Methods and Research Methodology
1.6 Research Design
1.7 Ethics in Research
1.8 Different Approaches to Research
1.8.1 Scientific and Non-sclentlflc Approaches.
1.8.2 Quantitative and Qualitative Approaches
1.9 Management Research
1.9.1 Meaning and Significance of Management Research
1.9.2 Nature and scope of Business Research
1.9.3 Types of Business Research Studies
1.9.4 Limitations of Business Research.
1.9.5 The Manager-Researcher Relationship.
1.10 Foundations-Basic Concepts and Connections
1.10.1 Concepts
1.10.2 Constructs
1.10.3 Definitions
1.10.4 Variables
1.10.5 Measurement
1.10.6 Levels of Measurement
1.10.7 Validity and Reliability
1.10.8 Types of data
1.10.9 Deduction and Induction
1.10.10 Unit of Analysis
1.10.11 Concept Mapping
EXERCISES
2. THE RESEARCH PROCESS
2.1 Introduction
2.2 Components of Research Study
2.3 Research Planning
2.3.1 Basic Steps In a Research Plan
2.4 The Sponsor/manager Research Question Hierarchy
2.5 Review of Literature
2.5.1 Need for the Literature Review
2.6 Research Problem
2.6.1. What Is a Reaearch Problem ?
2.6.2 Identification of a Problem
2.6.3 Formulation of Research Problems
• Qualitative Research Problems
• Quantitative Research Problem
2.6.4 Origin of Research Problem
2.6.5 Statement of the Problem
2.6.6 Some Common Mistakes In problem-formulation
2.6.7 Evaluation of a proposed Research Problem
2.6.8 Check List for testing the feasibility of the Research problem
2.7 The Research Question
2.7.1 Types of Questions
2.8 The Hypothesis
2.8.1 Definition Hypothesis
2.8.2 The Role of Hypothesis
2.8.3 Nature of H}rpothesls
2.8.4 Types of Hypothesis
2.8.5 liieory and Hypothesis
2.8.6 Deductive and Inductive Hypothesis
2.8.7 Difference between Hypothesis and Problem
2.8.8 What Is a Good HypoUiesls ?
2.8.9 What Is a Research hypothesis ?
2.8.10 Scientific Hypothesis versus Statistical Hypothels
2.8.11 Research Hypothesis and null Hypothesis
2.8.12 Formulation of Hypothesis
2.8.13 Generation of Research Hypothesis.
2.8.14 Testing the Hypothesis
2.8.15 Theory and Model
2.9 Research Objectives
2.10 Research Methods
2.10.1 Qualitative Research
2.10.2 Quantitative Research
2.10.3 Basic difference between Quantitative and Qualitative Research
Methods
2.10.4 Some selected Research Methods
Descriptive Research
Exploratory Research
Action Research
Historical Research
Case Studies
Ethnographic Research
Survey Research
Observation Research
Field Research
Correlational Research
Ex. post-facto Research
Experimental Research
Evaluation Research.
2.11 The Research Proposal
2.11.1 Types of Research Proposals
2.11.2 Structure of a Research Proposal
2.11.3 Common Mistakes in Proposal Writing.
2.11.4 Ethics in Business Research
2.11.5 Steps in Conducting Research
EXERCISES
3. RESEARCH DESIGN
3.1 What is a Research Design ?
3.2 , Need for Research Design
3.3 Structure of a Research Design
3.4 Classification of Research Designs.
3.5 Characteristics of a good Research Design
3.6 Formulation of a Research Design
3.7 Research Design Versus Research Method
3.8 Quantitative and Qualitative Research (Are they designs or methods ?)
3.9 Basic Research Designs
3.9.1 Exploratory Research
• Secondary data Analysis
• Experience Surveys
The Focus Group
• Case Studies , ,
3.9.2 Conclusive Research
• Descriptive Research
• Causal Research ,
3.9.3 Research Design : a sceptical Approch to Research
3.9.4 Research Design : Plausible Rlvsil (Hypothesis.
EXERCISES
4. MEASUREMENT PROBLEMS AND SCALING
TECHNIQUES
4.1 What Is measurement ?
4.2 Data types and measurement scales.
4.3 Sources of measurement error
4.4 Characteristics of good measurement
• Reliability and validity
• Practicality
4.5 Selection of Measurement Scales
4.5.1 Response Methods
• Rating Scale
• Ranking Scale
4.5.2 Measurement Scale Construction
• Arbitrary Scaling
• Consensus Scaling
• Item Analysis Scaling
• Cumulative Scaling
• Factor Scaling
4.6 A Comparison of Scaling Techniques .and their construction
4.6.1 Comparative Scales
4.6.2 Non-Comparative Scales
4.7 Non-Comparative Itemized Rating Scale Decisions
4.8 Choosing an Approporlate Scale
4.9 Advanced Scaling Techniques
EXERCISES
5. SOURCES AND METHODS OF COLLECTION
OF DATA
5.1 Sources of Data
5.1.1 Prlmaiy Sources
5.1.2 Secondary Sources
5.1.3 Tertiary Sources
5.2 Secondary Data
• Advantages
• Disadvantages
• Internal data sources
• External data sources
5.2.1 Exploring Secondary Data
5.2.2 Searching a Bibliographic Database
5.2.3 Searching the World Wide Web
5.2.4 Search Engine
• Challenges faced by search engines
• How search engines work ?
• Categories of search engines
• Vertical search engines
Data Mining
• Data warehouse architecture
• Data storage methods
• Advantages of using data warehouses
• Concerns in using data warehouses
• Data Mining
• Data Mining Techniques
5.2.6 Government Publications
Primary Data
5.3.1 Qualitative Research Techniques
The Experience Survey
The Case Study
The Pilot Study
In-Depth Interview
The Focus Group
Brain Storming
Panels
Consumer Panels
The Nominal Group Technique
The Delphi Method
Projectlve Techniques
5.3.2 Quantitative Research Techniques
A. Observation technique
The Observer—^Participant Relationship
How to conduct an observational study ?
Classification of observation methods
Content specification of observations
Who should be an observer ?
Data Collection
Advantages of the observational Methods
Limitations of the observational Methods
B. Survey Techniques
Telephone —
Personal Interviewing
Nature of Interview
The Interviewer's task
Instrument for participant communication
Objectives of a Questionnaire
Design process for a questionnaire
Question Structure
Types of Questions
Question Content
Question Wording
Response Strategy
Logical Order and Sequencing
Selecting an optimal method
Outsourcing
C. Experimentation
What are experiments ?
What Is causality ?
What Is an experimental design ?
Advantages of experiment^
Disadvantages
Steps for conducting an experiment
Validity In experimentation
Extraneous variables
Control of Extraneous variables
Experimental Research Designs
Quasi-experimental Designs
Time Series Design
Multiple Time Series Design
Statistical Designs
Basic Principles of Design of Experiments
Selection of an Experimental Design
Completely Randomised Design
Randomised Block Design
Latin Square Design
Factorial Experiments
Laboratory experiments Vrs Field experiments
Limitations of experimentation
EXERCISES
6. PROCESSING OF DATA
(DATA PREPARATION AND PRESENTATION)
6.1 Introduction
6.2 Editing
6.3 Codl^ig
6.4 Data Entry Formats
6.5 Condensation/Summarization of Data
6.5.1
6.5.2
6.5.3
6.5.4
Classification of data
• Advantages of Classification
• Kinds of Classification
Seriation of Data
Tabulation
• Salient features of a Table
• Types of Table
Graphical Representation of Data
• Techniques of Construction
• Types of Graphs :
• Time series graph
• Two variables graph
• Frequency graph
—Histogram
—Frequency polygon
6.5.5
EXERCISES
—Frequency curve
—Cumulative frequency graph
• Classiflcation of graphs according to scales
Diagrammatic Representation of Data
• Types of Diagrams
• Bar Diagrams
• Area and Volume Diagrams
• Angular or Pie Diagram
• Pictorial Diagrams
• Statistical Maps
• Choosing a Diagram
7. BASIC DESCRIPTIVE STATISTICS
7.4
7.1 Introduction
7.2 Rate, Ratio and Proportion
7.3 Measures of central tendency
Arithmetic Mean
Median
Mode
Geometric Mean and Harmonic Mean
Measures of Dispersion
Variance
Standard Deviation
Mean Absolute Deviation
Range
Mean difference
Quartlle Deviation
Relative Measures of Dispersion
7.5 Computation of statistical derivatives from frequency distribution
7.5.1 Measures of Central tendency/location
Arithmetic Mean
Weighted Arithmetic Mean
Median
Mode
Comparison of Arithmetic Mean, Median and Mode
Geometric mean
Harmonic Mean
7.5.2 Measures of dispersion
Variance
Standard Deviation
Mean Absolute Deviation
Quartlle Deviation
Glnl's Coefllclent of concentration
Lorenz Curve
7.5.3 Measures of Shape (Skewness and Kurtosls)
7.6 Exploratory data analysis
7.6.1 The Stem and Leaf Plot
7.6.2 The Box Plot
EXERCISES
8. ELEMENTS OF PROBABILITY THEORY AND
PROBABILITY DISTRIBUTIONS
8.1 Introduction
• Sample space
• Random E^xperlment
• An event
8.2 Definition of Probability
8.2.1 Classical definition
8.2.2 Axiomatic definition
8.2.3 Empirical/statistical definition
8.2.4 Determination of weights attached to the sample points in the
sample space.
8.3 Probability Laws
8.3.1 Additive Law of Probability
8.3.2 Conditional Probability
8.3.3 Multiplicative Law of Probabilily
8.3.4 Statistical Independence
8.4 Bayes Rule
8.5 Random Variable
8.5.1 Discrete random variable and probability distribution
8.5.2 Continuous random variable and probability distribution.
8.5.3 Expected value and variance
8.6 Important Discrete Probability Distributions
8.6.1 Binomial distribution
8.6.2 Poisson distribution
8.7 Normal distribution
8.7.1 Application of normal distribution
8.7.2 Normal approximation to Binomial distribution
8.7.3 Normal approximation to Poisson distribution
8.8 Independence of random variables
8.9 Expectation of sum and product of two independent random variables
8.10 The weak law of large numbers
8.11 Central limit theorem
EXERCISES
9. SAMPLING TECHNIQUES
9.1 What is a survey ?
9.2 Population and sample
9.2.1 When is a census appropriate ?
9.2.2 When is a smaple study appropriate ?
9.2.3 Sampling unit
9.2.4 Sampling frame
9.2.5 Target population and survey population
9.2.6 Purpose of sampling
9.2.7 Prolaabillty (Random) sampling and Non-probability sampling
9.2.8 Steps in sampling
9.2.9 Major steps in a Sample Survey.
9.3 Errors in sample surveys
9.3.1 Sampling Error
• Accuracy and Precision
9.3.2 Non-Sampling Errors
• Observation Errors
• Errors due to Interviewers
• Errors due to respondents
• Detection of Response Errors
• Control and Measurement of Response Errors
• Non-response Errors
• Magnitude of Non^response and its Control
• Errors in Coverage
• Processing Errors
• Reporting Errors
• Systematic Errors Vrs. Variable Errors
9.3.3 Effect of Sample Size on Errors in Sampling
9.3.4 Determination of sample size.
9.4 Types of Probability Sampling Designs
• Simple Random Sampling
• Stratified Random Sampling
• Systematic Sampling
• Cluster Sampling
• Multistage Sampling
• Double Sampling
• Multiphase Sampling
• Inverse Sampling
• Unequal probability sampling
9.5 Types of Non-probability/Non-random sampling
• Purposive Sampling
• Judgement Sampling
• Convenience Sampling
• Quota Sampling
• Modal Instance Sampling
• Expert Sampling
• Snowball Sampling
9.6 Basic Sampling Designs
9.6.1 Simple Random Sampling
• Method of drawing a simple random sample
• Estimation of Population Means and Total
• Estimation of sample size
• Estimation of proportion
9.6.2. Stratified Sampling
• Estimation of Population Mean/Total
• Allocation of sample to different strata
9.6.3 Systematic Sampling
9.6.4 Cluster Sampling
9.6.5 Two stage sampling
EXERCISES
10. PARAMETRIC STATISTICAL INFERENCE
(Problems of Estimation and H3npothesis
Testing) 355—411
10.1 Introduction
10.2 Problems of Estimation
"• Point estimation
• Interval estimation
10.2.1 Estimation of Population Mean
• Infinite Population
• Finite Population
• Normal Population (Known variance)
• Normal Population (Unknown variance)
• Determination of sample size
• Non-normal population
10.2.2 Estimation of difference between two population means
10.2.3 Estimation of variance of a Normal population
10.2.4 Estimation of variance ratio from two normal populations
10.2.5 Estimation of proportion
10.2.6 Estimation of the difference between two proportions (Large samples)
10.2.7 Determination of sample size in estimating proportion
10.3 Hypothesis Testing
10.3.1 Steps for testing of hypothesis
10.3.2 Test of Significance
10.3.3 One-tailed and two-tailed test
10.3.4 Test of mean of a normal population (one sample test)
10.3.5 Test of difference between means of two normal populations
(Two sample test)
10.3.6 Test of population proportion
10.3.7 Test of equality of two population proportions
10.3.8 The Chi-square tests of hypothesis
• Test of variance
• The Goodness of fit test
• Test of Independence
• Test of Homogeneity
10.3.9 Testing of equality of two population variances
10.3.10 Test of equality of several means of Normal Populations
10.3.11 Test for equality of variances from several populations.
EXERCISES
11. NONPARAMETRIC TESTS 412—434
11.1 Introduction
11.2 One sample tests
11.2.1 Sign test
11.2.2 One-sample Wllcoxon signed rank test
11.2.3 Kolmogorov—Smimov test
11.2.4 Run test fTest of randomness)
11.3 Two sample Tests :
11.3.1 Two sample sign test
11.3.2 TWo sample Wllcoxon signed rank test (matched samples)
11.3.3 Median test
11.3.4 The Wllcoxon Rank sum W-test/Mann Whitney U-test
11.3.5 Two sample Kolmogorov—Smimov test.
11.4 K-sampIe tests
11.4.1 The H-test/The Kruskal—Wallls test
11.4.2 K—Samples Median Test.
EXERCISES
12. ANALYSIS OF VARIANCE
12.1 Introduction
12.2 Analysis of variance Model
12.3 One way Classlfled Data (One way analysis of variance)
12.4 Two way Classified Data (Two way analysis of variance)
12.5 Analysis of Latin square Design
12.6 Analysis of Factorial Experiments
EXERCISES
13. CORRELATION AND REGRESSION
ANALYSIS
13.1 Introduction
13.2 Measurement of Linear Correlation
13.2.1 Computation of Correlation Coefficient
13.2.2 Coefficient of determination
13.2.3 Rank Correlation Coefficient
13.2.4 Non-linear Association
13.2.5 Calculation of r from grouped data
13.2.6 Testing the significance of the correlation coefficient
13.3 Linear Regression Lines
13.3.1 Fitting of Regression Line
13.3.2 Relation between regression coefficients and correlation coefficient
13.3.3 Can there be always two regression lines ?
13.3.4 How good Is the Regression ?
13.3.5 Examination and Interpretation of Regression line
13.3.6 Test of hypothesis about the slope (regression coefficient)
13.3.7 Test of hypothesis about the Intercept
13.3.8 Test of significance of regression
13.4 Polynomial Regression
13.5 Time Series Regression
13.6 Exponential Regression
13.7 Non-parametric measures of Association
13.7.1 Categorized data—Association In 2 x 2 Tables
13.7.2 Measures of Association In 2 x 2 tables (Dlchotomous Association)
13.7.3 The Relative Risk Coefllclent (RR)
13.7.4 Measurement of Association (Ordinal data)
• Gamma (7)
• Kendall's tau-b (xt)
• Kendall's tau-c (xj
• Somers'd
13.7.5 Measurement of Association In r x c contingency table
(chl-square based)
• Phi Coefficient
• The Contingency Coefficient (Pearson's C)
• Tshuprow's T
• Cramer' V
• Likelihood Ratio
13.7.6 PRE Measures
• Lambda (A,)
• The Uncertainty coefficient or Thlel's U
• Goodman and Kruskal tau (x)
13.7.7 Eta (t|) coefficient
13.7.8 Point—Blseiial Correlation coefficient (vph)
13.7.9 Blserlal Correlation Coefficient (rb)
13.7.10 Rank-Blserlal Coefficient (rrfc)
13.7.11 Rank based measures of association.
(cQ Spearman's Rank Correlation coefficient
(b) Kendall's tau rank correlation coefficient
13.7.12 The Choice of Measures
EXERCISES
14. MULTIPLE REGRESSION ANALYSIS
14.1 Introduction
14.2 The Multiple Regression Model
14.2.1 Fitting of Regression Model
14.2.2 Test of regression coefficient
14.2.3 Test of equality of two regression coefficients
14.2.4 Test of regression function
14.3 Multiple correlation coefficient R^
• Adjusted R^
• Test of Multiple correlation coefficient
14.4 Beta Regression Coefficients
14.5 Coefficient of Partial correlation
• Test of significance of observed Partial correlation
14.6 The main questions, which the multiple regression answers :
• Selecting the Independent variables (regressors)
• Backward Elimination
• Forward selection
• Stepwlse Regression
14.8 Checking Model Assumptions
14.9 Multlcolllnearlty
14.10 Autocorrelation
14.11 Multiple regression equation In Matrix notation
14.12 Regression with Qualitative Explanatory (Predictor) Variables
EXERCISES
15. MULTIVARIATE APPLICATIONS-AN
OVERVIEW
15.1 Introduction
15.2 Sampling from a multlvarlate normal population
15.2.1 Test of Hypothesis on mean vector (T^ - TesQ
15.2.2 Test of Hypothesis on equality of two mean vectors
15.2.3 Paired T^ - Test.
15.3 Testing of equality of several multlvarlate population mean vectors
15.3.1 Multlvarlate Analysis of Variance (MANOVA)
15.4 Discriminant Analysis
15.4.1 Classification Rule
15.4.2 Classification for several groups
15.4.3 Optimal Classification
15.5 Cluster Analysis
15.5.1 Distance measures
15.5.2 Steps for cluster Analysis
15.5.3 Clust^erlng Procedures
» Hierarchical Clustering Method
• Non-hlerarchlcal Clustering Methods
15.6 Principal Component Analysis—
15.6.1 Principal Component Theory
15.6.2 Sample Principal Components
15.6.3 Standardizing the sample principal components
15.7 Factor Analysis
15.7.1 Introduction
15.7.2 Some commonly used terms In factor analysis
15.7.3 The mathematical model for factor structure
15.7.4 Covarlance structure for the orthogonal factor model
15.7.5 Method of estimation
15.7.6 Rotation Methods
15.8 Multidimensional Scaling
15.8.1 Multidimensional scaling procedure
15.8.2 Some Applications of multidimensional scaling
15.8.3 Analysis of multidimensional scaling using SPSS packages
15.9 Conjoint Analysis
15.9.1 Steps in developing a conjoint Analysis
15.9.2 Conducting Conjoint Analysis
15.10 Correspondence Analysis
15.11 LISREL
EXERCISES
16. ANALYSIS AND INTERPRETATION OF DATA
16.1 Introduction
16.2 Outline
16.3 Data preparation—an overview
16.3.1 Data Editing
16.3.2 Data Cleaning
16.3.3 Data Coding
16.3.4 Data reduction
16.3.5 Exploring the data
16.3.6 Cross Tabulation
16.3.7 Missing Values
16.4 Descriptive Analysis
16.5 Analytical Statistics and Inference
16.6 Content analysis
16.6.1 Content Analysis—an Introduction
16.6.2 What Is a content ?
16.6.3 Media content and Audience content
16.6.4 Uses of content analysis
16.6.5 Stages of content analysis
16.6.6 Advantages of content analysis
16.6.7 Disadvantages of content analysis
16.7 SWOT Analysis
16.8 Participatory Research.
16.9 Data Analysis by software packages
EXERCISES
17. REPORT WRITING AND ORAL
PRESENTATION
17.1 What Is a report ?
17.1.1 Objectives
17.1.2 Purpose of Reports
17.2 Types of Reports
17.2.1 Formal and Informal Reports
17.2.2 Types of Formal Reports
17.2.3 Some common Informal Reports
17.2.4 Research Report Formal
17.3 Basic components of a Research Report
17.4 Report Writing Process
17.5 Guidelines for effective Report Writing
17.6 Data analysis and Writing the Report
17.7 What Is a Technical Report ?
17.7.1 Purpose of Technical Report
17.7.2 Audience and Audience analysis
17.7.3 Structure of Report
17.7.4 Presentation
17.7.5 The Report Layout
17.7.6 Language and Style
17.7.7 Planning a Report and Preparation
17.8 Technical Writing
17.9 Instruction Manual
17.10 Technical Proposal
17.11 Oral Prsentatlon
17.11.1 Basic strategies for effective oral presentation
17.11.2 Audlovlsuals
17.11.3 Benefits for using visual aids
17.11.4 How to use visual aids effectively ?
17.12 Precis writing and reporting committee findings
17.12.1 Introduction
17.12.2 Precis writing
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