Data analysis

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Data analysis

  1. 1. Data Analysis Anindita C. Rao
  2. 2. Data AnalysisD Data Analysis ResultsA interpretationTACOL Getting data readyL for analysis Feel for data Goodness of HypothesisE •Editing 1.Mean Data Testing •Handling Blank 2.S.D. •Reliability •AppropriateC Reponses 3.Correlation •Validity statistical toolsT 4.Frequency •CodingI •Categorizing distributionO •Creating data fileN •Programming
  3. 3. Data Preparation• Getting data ready for analysis(SPSS)• Editing• Handling Blank Reponses• Coding• Categorizing• Creating data file• Programming
  4. 4. Coding & Categorization• Usually a number to each response• Coding of questionnaires• If male=1 and if female=2• Negatively worded questionsSome 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. 5. Descriptive statistics• Frequency(tabulation)• Measure of central tendency(mean, median & mode)• Measure of Dispersion(range, variance, standard deviation & interquartile range)
  6. 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. 7. Data Analysis• Simple tabulation & Cross tabulation• ANOVA• Correlation & Regression• Discriminant Analysis• Factor Analysis• Conjoint Analysis• Multidimensional Scaling• Cluster Analysis
  8. 8. Tabulation Variables Male Female Total 25 35 60 Frequency Family BackgroundDescriptive Frequen Valid CumulativeStatistics 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. 9. Cross Tabulation Two variable interaction Variable 1(nominal)Variable2(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. 10. Chi square• To determine 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. 11. Chi square test• Two nominal variables• Cross tabulation• Non parametric• SPSS code1. Analyze from SPSS bar2.Analyze> Nonparametric test> Chi-square
  12. 12. 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 CodeB.Com 1B.E. 2B.Sc. 3BBA 4B.A. 5Grades as followsGrade Obtained Grade CodeA 1B 2C 3
  13. 13. t test• Significant mean 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
  14. 14. ANOVA• Significant mean difference among multiple groups• Multiple regressionVariance caused by independent variable on dependent variables simultaneously
  15. 15. Hypothesis Testing• Errors• Normal population• Degrees of freedom• One tailed or two tailed• Single Population• . p value
  16. 16. Univariate Techniques Metric Data Nonmetric data (interval or ratio) (nominal or ordinal scale) One sample Two or More Sample •FrequencyOne 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
  17. 17. Multivariate Techniques Independence Dependence Techniques Techniques InterobjectOne dependent More than one Variable Similarityvariable dependent Interdependence variable •Cluster•Cross tabs •Factor Analysis Analysis •Cannonical•Anova & correlation •MDSCovariance •Multiple•Multiple discriminantregression•Discriminant•Conjoint
  18. 18. Criterion(dependent) One Two or More Nomin Ordinal Interval No Ordinal Interval al min alOne Nominal Chi •Sign test Analysis of Multiple Squar •Mann variance discriminant e whinney analysis •Krushal Wallis Anova Ordinal •Spearman rank Correlation •Kendall’s rank correlationTwo Interval Anova Regression Ano Multipleor Analysis va RegressionMore Analysis Nominal Friedman Anova Anova two way Factorial analysis Design Ordinal

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