ACADEMIC RESEARCH

Dr Kishor BhanushaliDirector

Global Institute of ManagementGandhinagar

kishorkisu@gmail.com

UNITEDWORLD SCHOOL OF BUSINESS (17TH MAY 2012)

Research

Search and Research Scientific Investigation Systematic Investigation New knowledge Academic activity

Objective of Research

To discover answer to questions through the application of scientific procedure

To find out undiscovered truth Gaining familiarity with the phenomenon –

exploratory research Study the characteristics of variable –

descriptive research Study the relationship/association – causal

research Test the causal relationship between variable

– hypothesis testing

Research Problem…… defined General statement of the problem Understanding the nature of problem Survey of relevant literature Developing ideas through

discussions Rephrasing research problem Specific Statement of problem Scope of problem Assumptions

Types of Research

Descriptive & Analytical Research Applied & Fundamental Research Quantitative & Qualitative Research Conceptual & Empirical Research One Time & Longitudinal Research Field setting & Simulation Research &

Laboratory Research

Research Process

Define Research Problem Review of Literature : Review Concepts and

Theories , Review Previous Research Findings

Formulate Hypothesis Prepare research design Designing Research : including sampling Data Collection Data Analysis: Hypothesis Testing Interpret and report

Good Research

Clearly defined purpose Well defined research process Planned research procedure Frank reporting Adequate and relevant analysis Conclusions based on research

findings Ethical standards

Sampling

Probability and Non Probability Sampling Purposive sampling Simple random sampling Systematic sampling Stratified sampling Quota sampling Cluster sampling Multi stage sampling Snowball sampling

Good Sample

Representativeness Small sampling error Consistent with financial availability Controlling systematic biases Generalization of results

Sampling

Need for sampling Statistics and parameters Sampling error Confidence and significant level Sampling distribution CENTRAL LIMIT THEOREM Concept of Standard Error Estimation

Sample Size Determination

Nature of universe Number of classes proposed Nature of study Type of sampling Standard of accuracy and acceptable

confidence level Availability of Financial Resources Availability of human resource

Data Collection

By observation Through personal interview Through telephonic interview By mailing questionnaire In depth interview Case study Focus Group Discussion

Secondary Data

Reliability of data Suitability of data Adequacy of data

Data Processing

Editing Coding Classification Tabulation Percentages

Analysis

Univariate analysis: Measures of central tendency and measure of dispersion

Bivariate analysis : Measure of association and causality

Multivariate analysis : Simultaneous analysis of more than two variables

Index number Time series

Hypothesis

Research hypothesis is predictive statement , capable of being tested by scientific methods, that relates an independent variables to some dependent variable

Specific Precise Testable Consistent with known facts Explain the facts

Hypothesis Testing

Null and Alternate Hypothesis The level of significance Decision rule or test of hypothesis Type I and Type II error Tow tailed and one tailed tests

Procedure for Hypothesis Testing Making formal Statement Selecting a significant level Deciding the distribution to be used Selecting a random sample and

computing appropriate value Calculating the probability Comparing probability

Test of Hypothesis

Hypothesis testing helps to decide on the basis of sample data, whether the hypothesis about population is likely to be true of false

Test of hypothesis: (a) Parametric tests or standard test of hypothesis and (b) Non parametric tests or distribution free test of hypothesis

Parametric Test

Parametric test usually assume certain properties of the parent population from which we draw sample

Assumption like observations come from normal population, sample size is large, assumptions about population parameters like mean, variance etc. must hold good before parametric test can be used

Non-parametric tests

In certain situation when the researcher cannot of does not want to make such assumptions. In such situation we use statistical methods for testing hypothesis which are called non-parametric tests because such test do not depends on any assumptions about the parameter of the parent population

Most non-parametric tests assumes only nominal or ordinal data, where as parametric test require measurements equivalent to at least interval scale

Z-test Z-test is based on the normal probability distribution and

used for judging the significance of several statistical measures ,particularly the mean

Z-test is generally used for comparing the mean of sample to some hypothesized mean of population in case of large sample or when the population variance is known

Z-test is also used for judging the significance of difference between means of two independent samples in case of large samples or when population variances are known

Z-test is also used for comparing the sample proportion to a theoretical value of population proportion or judging the difference in proportion of tow independent sample when ‘n’ happens to be very large

Z-test is also used for judging the significance of median, mode, coefficient of correlation and several other measures

t-test T-test is considered an appropriate for judging

the significance of the sample mean or for judging the significance of difference between the means of two samples in case of small samples when population variance is not known

In the case two samples are related, we use paired t-test for judging the significance if the means of differences between the two related samples

It can also be used for judging the significance of the coefficient of simple and partial correlations

T-test is applied only in the case of small samples when population variance is not known

Chi-Square test

Chi-square test is based on chi-square distribution and as a parametric test is used for comparing a sample variance to a theoretical population variance

F-test

F-test is based on F distribution Used to compare the variance of the

two – independent samples Also used in the context of ANOVA

for judging the significance of more than two sample means at one and the same time

Also used for judging the significance of multiple correlation coefficients

Nonparametric Tests

Test of a hypothesis concerning some single value for the given data : One Sample Sign Test

Test of hypothesis concerning no difference among two or more set of data: Two Sample Sing Test, Fisher-Irwin test, Rank Sum Test

Test of hypothesis of a relationship between variables: Rank Correlation Kendall’s Coefficient of Concordance etc.

Cont… Test of a hypothesis concerning

variations in the given data: Kruskal-Wallis Test

Test of randomness of a sample based in the theory of runs: One Sample Run Test

Test of hypothesis to determine if categorical data shows dependence or if two classifications are independent: Chi square Test

To be continue…………..