1. 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 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

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. 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. Theory Hypothesis Data Analysis Exhibit: 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 Nominal, Ordinal, Interval and Ratio Scale

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

7. B. Pre-Verification Test Test of deviation from normality-SK,Ku Test of deviation from normality-SK,Ku Test of homogeneity- Bartlett's test, Cochran's test Test of homogeneity- Bartlett's test, Cochran's test Test of homosedacity – Test of homosedacity – Range restriction-Comparing distribution Data scanning for assumptions 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. 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 Applying a statistical test D.Post-hoc tests: individual comparisons, Range statistics, sample effects e.g.; Duncan’s test, Newman Keul’s test. D.Post-hoc tests: individual comparisons, Range statistics, sample effects e.g.; Duncan’s test, Newman Keul’s test. E. Interpretation: Making a Statement Type –IV errors

9. Some common Tests for verification of Hypothesis Non-parametric Test Parametric univariate Multivariate Sign test, Wilcoxen X 2 t Manova Sign test, Wilcoxen X 2 t Manova Median test r F Canonical Median test r F Canonical Mann-Whitney-U test R Cluster Mann-Whitney-U test R Cluster Kruskal -Wallis-ANOVA β Discriminate Functions Kruskal -Wallis-ANOVA β Discriminate Functions Friedman’s ANOVA Factor Analysis Friedman’s ANOVA Factor Analysis Kendall’s coefficient Structural Equation Kendall’s coefficient Structural Equation Modeling Modeling  Spearman’s rank correlation