Slide 1TabulationA Typical TableSlide 4Slide 5Slide 6CROSS-TABULATIONA Typical Cross-Tab TableData TransformationDegrees of SignificanceTesting the HypothesesDifferences Between GroupsExampleSlide 14Testing for Significant CausalitySlide 16Basic Data AnalysisTabulation•Frequency table•PercentagesA Typical TableGender Frequency Percentage Valid %Female 100 = 100/150 = 100/145Male 45 = 45/150 = 45/145Missing 5 = 5/150Total 150 = (100+45+5)/150= (100+45) / 145Type ofMeasurementType of descriptive analysisNominalCross TabsModeType ofMeasurementType of descriptive analysisOrdinalRank orderMedianType ofMeasurementType of descriptive analysisIntervalArithmetic meanCROSS-TABULATION•Analyze data by groups or categories•Compare differences•Percentage cross-tabulationsA Typical Cross-Tab TableGender XE-Commerce CustomerCustomer Non-CustomerTotalsFemale 100 50 150Male 75 60 135Totals 175 110 285Data Transformation•A.K.A data conversion•Changing the original form of the data to a new format•More appropriate data analysis•New variables–Summated–StandardizedDegrees of Significance•Mathematical differences•Statistically significant differences•Managerially significant differencesTesting the Hypotheses•The key question is whether we reject or fail to reject the hypothesis.•Depends on the results of the hypothesis test–If testing differences between groups, was the difference statistically significant–If testing impact of independent variable on dependent variable, was the impact statistically significant•How the hypothesis was wordedDifferences Between Groups•Primary tests used are ANOVA and MANOVA•ANOVA = Analysis of Variance•MANOVA = Multiple Analysis of Variance•Significance Standard:–Churchill (1978) Alpha or Sig. less than or equal to 0.05•If Sig. is less than or equal to 0.05, then a statistically significant difference exists between the groups.Example•Hypothesis: No difference exists between females and males on technophobia.•If a statistically significant difference exists, we reject the hypothesis.•If no s.s. difference exists, we fail to reject.Example•Hypothesis: Males are more technophobic then females (i.e., a difference does exist)•If a statistically significant difference exists, and it is in the direction predicted, we fail to reject the hypothesis.•If no s.s. difference exists, or if females are statistically more likely to be technophobic, we reject the hypothesis.Testing for Significant Causality•Simple regression or Multiple regression•Same standard of significance (Churchill 1978)•Adj. R2 = percentage of the variance in the dependent variable explained by the regression model.•If Sig. is less than or equal to 0.05, then the independent variable IS having a statistically significant impact on the dependent variable.•Note: must take into account whether the impact is positive or negative.Example•Hypothesis: Technophobia positively influences mental intangibility.•If a technophobia is shown to statistically impact mental intangibility (Sig. is less than or equal to 0.05), AND.•The impact is positive, we fail to reject the hypothesis.•Otherwise, we reject the