Academic Research UW

7/31/2019 Academic Research UW

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ACADEMIC RESEARCH

Dr Kishor BhanushaliDirector

Global Institute of ManagementGandhinagar

[email protected]

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..

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