academic research academic research dr kishor bhanushali [email protected] m-9898422620
TRANSCRIPT
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Academic Research
Dr Kishor [email protected]
M-9898422620
GUJARAT UNIVERSITY – PHD COURSE WORK
25TH DECEMBER 2013
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ResearchSearch and ResearchScientific InvestigationSystematic InvestigationNew knowledgeAcademic activity
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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
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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
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Types of ResearchDescriptive & Analytical ResearchApplied & Fundamental ResearchQuantitative & Qualitative
ResearchConceptual & Empirical ResearchOne Time & Longitudinal
ResearchField setting & Simulation
Research & Laboratory Research
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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
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Good ResearchClearly defined purposeWell defined research processPlanned research procedureFrank reportingAdequate and relevant analysisConclusions based on research
findingsEthical standards
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SamplingProbability and Non Probability
SamplingPurposive samplingSimple random samplingSystematic samplingStratified samplingQuota samplingCluster samplingMulti stage samplingSnowball sampling
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Good SampleRepresentativeness Small sampling errorConsistent with financial
availabilityControlling systematic biases Generalization of results
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SamplingNeed for samplingStatistics and parametersSampling error Confidence and significant levelSampling distributionCentral Limit TheoremConcept of Standard ErrorEstimation
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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
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Data CollectionBy observationThrough personal interviewThrough telephonic interviewBy mailing questionnaireIn depth interviewCase studyFocus Group Discussion
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Secondary DataReliability of dataSuitability of dataAdequacy of data
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Data ProcessingEditingCodingClassificationTabulationPercentages
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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
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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
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Hypothesis TestingNull and Alternate HypothesisThe level of significanceDecision rule or test of
hypothesisType I and Type II errorTow tailed and one tailed tests
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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
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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
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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
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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
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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
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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
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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
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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
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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.
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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 categorical data shows dependence or if two classifications are independent: Chi square Test
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To be continue…………..