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Data analysis
- 1. Data Analysis AninditaC. Rao
- 2. Data Analysis D Data Analysis Results A interpretation T A C O L Getting data ready L for analysis Feel for data Goodness of Hypothesis E •Editing 1.Mean Data Testing •Handling Blank 2.S.D. •Reliability •Appropriate C Reponses 3.Correlation •Validity statistical tools T 4.Frequency •Coding I •Categorizing distribution O •Creating data file N •Programming
- 3. Data Preparation • Getting data ready for analysis(SPSS) • Editing • Handling Blank Reponses • Coding • Categorizing • Creating data file • Programming
- 4. Coding & Categorization •Usually a number to each response • Coding of questionnaires • If male=1 and if female=2 • Negatively worded questions Some compromises with ethics helps you in practical life Strongly disagree1-2-3-4-5-6-7Strongly agree • EXCEL we designate specific columns to specific questions and responses
- 5. Descriptive statistics • Frequency(tabulation) •Measure of central tendency(mean, median & mode) • Measure of Dispersion(range, variance, standard deviation & interquartile range)
- 6. Inferential Statistics • Correlation(Direction, Strength & Significance) -1 to +1 • Parametric: Pearson’s correlation • Non Parametric: Spearman’s Correlation & Kendall’s rank correlation(ordinal)
- 7. Data Analysis • Simple tabulation & Cross tabulation • ANOVA • Correlation & Regression • Discriminant Analysis • Factor Analysis • Conjoint Analysis • Multidimensional Scaling • Cluster Analysis
- 8. Tabulation Variables Male Female Total 25 35 60 Frequency Family Background Descriptive Frequen Valid Cumulative Statistics cy Percent Percent Percent Valid Nuclear 72 60.0 60.0 60.0 Joint 48 40.0 40.0 100.0 Total 120 100.0 100.0
- 9. Cross Tabulation Two variable interaction Variable 1(nominal) Variable 2(nominal) Qualification * Centrality Crosstabulation Count Centrality -2 0 1 2 3 4 Total Qualification Doctorate 4 6 0 18 6 0 34 Post Graduate 0 4 3 20 14 8 49 Graduate 0 0 5 28 4 0 37 Total 4 10 8 66 24 8 120
- 10. Chi square • Todetermine the systematic association between two variables • Null hypothesis: no association • Expected cell frequencies comparison with actual cell frequencies • Greater the discrepancies, greater will be chi square statistic
- 11. Chi square test •Two nominal variables • Cross tabulation • Non parametric • SPSS code 1. Analyze from SPSS bar 2.Analyze> Nonparametric test> Chi-square
- 13. Exercise • In this case study , we are observing association between educational background(independent variable) of the PGDBM students and their performance in the terms of grade(dependent variable) secured. We want to test at 90% and 95% confidence level, what is the level of significance of association.(refer to file in SPSS) Educational Background Code B.Com 1 B.E. 2 B.Sc. 3 BBA 4 B.A. 5 Grades as follows Grade Obtained Grade Code A 1 B 2 C 3
- 15. t test • Significantmean difference between two groups • Nominal variable on interval or ratio scale • (smokers & non smokers on extent of well being) • Sample size less than 30 • Df = N-1 • Mann Whitney U test
- 16. ANOVA • Significant meandifference among multiple groups • Multiple regression Variance caused by independent variable on dependent variables simultaneously
- 17. Hypothesis Testing • Errors • Normal population • Degrees of freedom • One tailed or two tailed • Single Population • . p value
- 18. Univariate Techniques Metric Data Nonmetric data (interval or ratio) (nominal or ordinal scale) One sample Two or More Sample •Frequency One sample Two or More •Chi square samples •T test •K-S •Z test •Runs Independent Related •Binomial •Two group t •Paired t test test Independent Related •Z test •Chi square •Sign •One way anova •Mann whitney •Wilcoxon •Median •Mc Nemar •K-S •Chi Square •K-W Anova
- 19. Multivariate Techniques Independence Dependence Techniques Techniques Interobject One dependent More than one Variable Similarity variable dependent Interdependence variable •Cluster •Cross tabs •Factor Analysis Analysis •Cannonical •Anova & correlation •MDS Covariance •Multiple •Multiple discriminant regression •Discriminant •Conjoint
- 20. Criterion(dependent) One Two or More Nomin Ordinal Interval No Ordinal Interval al min al One Nominal Chi •Sign test Analysis of Multiple Squar •Mann variance discriminant e whinney analysis •Krushal Wallis Anova Ordinal •Spearman rank Correlation •Kendall’s rank correlation Two Interval Anova Regression Ano Multiple or Analysis va Regression More Analysis Nominal Friedman Anova Anova two way Factorial analysis Design Ordinal