Academic Research

Dr Kishor Bhanushalikishorkisu@gmail.com

M-9898422620

GUJARAT UNIVERSITY – PHD COURSE WORK

25TH DECEMBER 2013

ResearchSearch and ResearchScientific InvestigationSystematic InvestigationNew knowledgeAcademic activity

Objective of ResearchTo discover answer to questions through

the application of scientific procedureTo find out undiscovered truth Gaining familiarity with the phenomenon

– exploratory researchStudy the characteristics of variable –

descriptive research Study the relationship/association –

causal researchTest the causal relationship between

variable – hypothesis testing

Research Problem…… definedGeneral statement of the

problemUnderstanding the nature of

problemSurvey of relevant literature Developing ideas through

discussionsRephrasing research problemSpecific Statement of problemScope of problemAssumptions

Types of ResearchDescriptive & Analytical ResearchApplied & Fundamental ResearchQuantitative & Qualitative

ResearchConceptual & Empirical ResearchOne Time & Longitudinal

ResearchField setting & Simulation

Research & Laboratory Research

Research ProcessDefine Research ProblemReview of Literature : Review Concepts

and Theories , Review Previous Research Findings

Formulate HypothesisPrepare research designDesigning Research : including sampling Data CollectionData Analysis: Hypothesis TestingInterpret and report

Good ResearchClearly defined purposeWell defined research processPlanned research procedureFrank reportingAdequate and relevant analysisConclusions based on research

findingsEthical standards

SamplingProbability and Non Probability

SamplingPurposive samplingSimple random samplingSystematic samplingStratified samplingQuota samplingCluster samplingMulti stage samplingSnowball sampling

Good SampleRepresentativeness Small sampling errorConsistent with financial

availabilityControlling systematic biases Generalization of results

SamplingNeed for samplingStatistics and parametersSampling error Confidence and significant levelSampling distributionCentral Limit TheoremConcept of Standard ErrorEstimation

Sample Size Determination Nature of universeNumber of classes proposedNature of study Type of samplingStandard of accuracy and

acceptable confidence levelAvailability of Financial ResourcesAvailability of human resource

Data CollectionBy observationThrough personal interviewThrough telephonic interviewBy mailing questionnaireIn depth interviewCase studyFocus Group Discussion

Secondary DataReliability of dataSuitability of dataAdequacy of data

Data ProcessingEditingCodingClassificationTabulationPercentages

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 numberTime series

HypothesisResearch hypothesis is predictive

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

SpecificPreciseTestableConsistent with known factsExplain the facts

Hypothesis TestingNull and Alternate HypothesisThe level of significanceDecision rule or test of

hypothesisType I and Type II errorTow tailed and one tailed tests

Procedure for Hypothesis Testing

Making formal Statement Selecting a significant levelDeciding the distribution to be

usedSelecting a random sample and

computing appropriate valueCalculating the probability Comparing probability

Test of HypothesisHypothesis 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 testsIn 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 testChi-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-testF-test is based on F distributionUsed to compare the variance of

the two – independent samplesAlso 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 TestsTest 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…………..