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Why Research ?

Analysis of Results A lecture delivered to participants In Research Methodology And Use of Technologies In Research On 16 th Nov. (G.V.M. College of Edu.Sonepat) Prof. Rajbir Singh Dept. of Psychology M.D.U.Rohtak. Why Research ?.

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Why Research ?

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  1. Analysis of ResultsA lecture delivered to participantsIn Research Methodology And Use of Technologies In ResearchOn 16th Nov. (G.V.M. College of Edu.Sonepat)Prof. Rajbir SinghDept. of PsychologyM.D.U.Rohtak

  2. Why Research ? • In the behavioral sciences we conduct research in order to determine the acceptability of hypotheses which we derive from our theories of behavior. Having selected a certain hypothesis which seems important in a certain theory, we collect empirical data which should yield direct information on the acceptability of that hypothesis. Our decision about the meaning of the data may lead us to retain, revise, or reject the hypothesis and the theory which was its source.

  3. TheoryHypothesisDataAnalysisExhibit: Research as a process (Bidirectional)

  4. Frequently Asked Questions before analysis 1.When analysis? When having data/information and to draw inference 2.What type of data? Qualitative/Quantitative? 3.If qualitative what type? Narratives Verbal Product Non-Verbal Performance 4.If quantitative what is the Level of measurement ? Nominal, Ordinal, Interval and Ratio Scale

  5. 5. What Kind of Sample? Large/Small:n1, ....N=30,----N 6.How the sample has been drawn? Probability Non-probability 7. Estimation of population parameters? µ, σ 8. Verification of hypothesis, if any ? 9. Correlational/ Experimental ? 10.Parametric/Nonparametric ? 11.Univariate/bivariate/multivariate ?

  6. Results • Analysis of Data A. Descriptive Presentation n=1, Xs-listing, ordering, bunching, categorization X¯, Med., Percentile, Quartile etc. Mode: Uni/bimodal/Multimodal Distribution- frequencies, class intervals SD, Range,SEM Graphic presentation- Bar diagrams, pie-charts etc. Histogram/polygon Data transformation- monotonic , uni-directional,calculative destress: √X, 1/ X,, log transformation, Arcsine transformation, X+…, X-… etc. • Normalization T-scaling

  7. B. Pre-Verification Test • Test of deviation from normality-SK ,Ku • Test of homogeneity- Bartlett's test, Cochran's test • Test of homosedacity – Range restriction-Comparing distribution • Data scanning for assumptions C. Verification of Hypothesis/ Goodness of fit • Statistical test yields a value that has associated probability alpha, the level of significance ( the p of committing type I error, i.e.; rejecting Ho when it is true). Beta , the p of type II error, accepting Ho when it is infact false. Alpha is inversely related to beta, so to reduce these errors, we must increase N. Sample mean and SD should be equal to population parameters. Are they or are not? Sampling distribution of various statistic has p of x¯ and SD to approximate of µ and σ. It is the process of estimation.

  8. Applying a statistical test D.Post-hoc tests: individual comparisons, Range statistics, sample effects e.g.; Duncan’s test , Newman Keul’s test. E. Interpretation: Making a StatementType –IV errors

  9. Some common Tests for verification of Hypothesis Non-parametric Test Parametric univariate Multivariate • Sign test, Wilcoxen X2 t Manova • Median test r F Canonical • Mann-Whitney-U test R Cluster • Kruskal -Wallis-ANOVA β Discriminate Functions • Friedman’s ANOVA Factor Analysis • Kendall’s coefficient Structural Equation Modeling • Spearman’s rank correlation

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