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ACADEMIC RESEARCH
Dr Kishor BhanushaliDirector
Global Institute of ManagementGandhinagar
UNITEDWORLD SCHOOL OF BUSINESS(17TH MAY 2012)
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Research
Search and Research
Scientific Investigation
Systematic Investigation
New knowledge
Academic activity
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Objective of Research
To discover answer to questions through theapplication of scientific procedure
To find out undiscovered truth
Gaining familiarity with the phenomenon exploratory research
Study the characteristics of variable descriptiveresearch
Study the relationship/association causalresearch
Test the causal relationship between variable hypothesis testing
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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
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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
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Research Process
Define Research Problem
Review of Literature : Review Concepts andTheories , Review Previous Research Findings
Formulate Hypothesis
Prepare research design
Designing Research : including sampling
Data Collection Data Analysis: Hypothesis Testing
Interpret and report
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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
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Sampling
Probability and Non Probability Sampling
Purposive sampling
Simple random sampling Systematic sampling
Stratified sampling
Quota sampling
Cluster sampling
Multi stage sampling
Snowball sampling
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Good Sample
Representativeness
Small sampling error
Consistent with financial availability
Controlling systematic biases
Generalization of results
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Sampling
Need for sampling
Statistics and parameters
Sampling error
Confidence and significant level
Sampling distribution
CENTRAL LIMIT THEOREM Concept of Standard Error
Estimation
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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
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Data Collection
By observation
Through personal interview
Through telephonic interview
By mailing questionnaire
In depth interview
Case study Focus Group Discussion
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Secondary Data
Reliability of data
Suitability of data
Adequacy of data
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Data Processing
Editing
Coding
Classification Tabulation
Percentages
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Analysis
Univariate analysis: Measures of centraltendency and measure of dispersion
Bivariate analysis : Measure of associationand causality
Multivariate analysis : Simultaneous analysisof more than two variables
Index number
Time series
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Hypothesis
Research hypothesis is predictive statement ,capable of being tested by scientificmethods, that relates an independent
variables to some dependent variable Specific
Precise
Testable Consistent with known facts
Explain the facts
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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
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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
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Test of Hypothesis
Hypothesis testing helps to decide on thebasis of sample data, whether the hypothesisabout population is likely to be true of false
Test of hypothesis: (a) Parametric tests orstandard test of hypothesis and (b) Nonparametric tests or distribution free test of
hypothesis
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Parametric Test
Parametric test usually assume certainproperties of the parent population fromwhich we draw sample
Assumption like observations come fromnormal population, sample size is large,assumptions about population parameters
like mean, variance etc. must hold goodbefore parametric test can be used
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Non-parametric tests
In certain situation when the researcher cannotof does not want to make such assumptions. Insuch situation we use statistical methods for
testing hypothesis which are called non-parametric tests because such test do notdepends on any assumptions about theparameter of the parent population
Most non-parametric tests assumes onlynominal or ordinal data, where as parametrictest require measurements equivalent to at leastinterval scale
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Z-test
Z-test is based on the normal probability distribution andused for judging the significance of several statisticalmeasures ,particularly the mean
Z-test is generally used for comparing the mean of sampleto some hypothesized mean of population in case of largesample or when the population variance is known
Z-test is also used for judging the significance of differencebetween means of two independent samples in case oflarge samples or when population variances are known
Z-test is also used for comparing the sample proportion toa theoretical value of population proportion or judging the
difference in proportion of tow independent sample whenn happens to be very large
Z-test is also used for judging the significance of median,mode, coefficient of correlation and several other measures
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t-test T-test is considered an appropriate for judging
the significance of the sample mean or forjudging the significance of difference betweenthe means of two samples in case of smallsamples when population variance is not known
In the case two samples are related, we usepaired t-test for judging the significance if themeans of differences between the two relatedsamples
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
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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 twosample means at one and the same time
Also used for judging the significance ofmultiple correlation coefficients
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Nonparametric Tests
Test of a hypothesis concerning some singlevalue for the given data : One Sample SignTest
Test of hypothesis concerning no differenceamong two or more set of data: Two SampleSing Test, Fisher-Irwin test, Rank Sum Test
Test of hypothesis of a relationship betweenvariables: Rank Correlation Kendalls
Coefficient of Concordance etc.
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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 categoricaldata shows dependence or if twoclassifications are independent: Chi squareTest
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To be continue..