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Need

In any research problem, we aim to draw conclusion about the underlying population either from the population data or from the representative sample data obtained from the population in a scientific manner. It should be objective rather than subjective. For objective analysis and conclusion, we need qualitative as well as quantitative methods to be used.

Syllabus

Defining research problem, objectives, hypotheses, data base, types of data, methods of colleting primary data, scale of measurements, sampling, types of sampling- SRS, stratified, systematic, multistage, cluster, quota, convenience and judgement, summarizing univarite and bivariate data, central tendency, dispersion, skewness, kurtosis, sampling distribution, estimation, testing of hypothesis, Experimental designs- CRD, RBD, LSD, two-variable LRM, k- variable LRM- assumptions, estimation, inference, prediction, ANOVA, model selection, Violation of assumptions- multicollinearity, heteroscedasticity, autocorrelaton, dummy variable models, structural change model, interaction model, dummy dependent variable model, logit model, probit model, discriminant analysis, principal component analysis, factor analysis.

Objectives

1. Defining a research problem, objectives, hypotheses
2. Summarizing univariate and bivariate data using different quantitative tools.
3. Studying relationship between two or more variables
4. Using multivariate techniques to summarize multivariate data sets.
Learning Outcomes

1. Should be able to formulate a research problem
2. Should be able to identify types of analysis and tools to be used to draw meaningful conclusion for the research problem at hand.
3. Should be able to use the appropriate quantitative tools and analysis.
4. Should be able to interpret the results of the analysis in an objective manner.
Provisional Session Plan

Session No	Topic/Activity
1-2	Condensation and summarization of univariable data
3	Construction of empirical probability distributions.
4-5	Sampling- need, types
6-7	Estimation and hypothesis testing
8-9 10-11 12-14 15-16 17-18 19-20	Experimental designs Two-variable Linear regression Multiple Linear regression Multicollinearity, Heteroscedasticity and Auto-correlation Dummy variable models, Model selection Discriminant analysis, Factor analysis, Report writing and presentation

Evaluation

1. 3 quizzes- 30%
2. End term- 30%
3. Project- 30%
4. Class participation- 10%

Suggested Reading

1. Business Research Methods, by Mishra P., Oxford University Press.
2. Basic Econometrics, by Gujurati D.N, Porter D.C. and Gunasekar, McGraw Hill, Fifth Edition.
3. Applied Multivariate Statistical Analysis, by Johnson R.A and Wichern D.W., Prentice Hall, Third Edition.
4. Multivariate Data Analysis, by Joseph F. Hair, Jr; William C. Black; Barry J. Babin and Rolph E. Anderson, Prentice Hall, Seventh Edition.

Created By: Bijoy Kar on 09/14/2016 at 05:47 PM
Category: Course Outlines-HRM-I Doctype: Document