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Basic Data Analysis

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Tabulation Frequency table Percentages

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A Typical Table GenderFrequencyPercentageValid % Female100= 100/150= 100/145 Male45= 45/150= 45/145 Missing5= 5/150 Total150= ( ) /150 = (100+45) / 145

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Type of Measurement Type of descriptive analysis Nominal Cross Tabs Mode

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Type of Measurement Type of descriptive analysis Ordinal Rank order Median

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Type of Measurement Type of descriptive analysis Interval Arithmetic mean

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CROSS-TABULATION Analyze data by groups or categories Compare differences Percentage cross-tabulations

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A Typical Cross-Tab Table Gender X E- Commerce Customer CustomerNon- Customer Totals Female Male Totals

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Data Transformation A.K.A data conversion Changing the original form of the data to a new format More appropriate data analysis New variables –Summated –Standardized

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Degrees of Significance Mathematical differences Statistically significant differences Managerially significant differences

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Testing 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 worded

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Differences 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.

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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.

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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.

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Testing for Significant Causality Simple regression or Multiple regression Same standard of significance (Churchill 1978) Adj. R 2 = 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.

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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 hypothesis.

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