Basic Data Analysis

1. Basic Data Analysis

2. Levels of Scale Measurement & Suggested Descriptive Statistics

3. Creating & Interpreting Tabulation • Tabulation • Orderly arrangement of data in a table or other summary format showing the number of responses to each response category. • Called “Tallying” when the process is done by hand. • Frequency Table • Table showing the different ways respondents answered a question. • Sometimes called a marginal tabulation.

4. A Typical Table

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

6. Different Ways of Depicting the Cross-Tabulation of Biological Sex and Target Patronage

7. Another Typical Cross-Tab Table

8. 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

9. Degrees of Significance • Mathematical differences • Statistically significant differences • Managerially significant differences

10. Hypothesis Testing Procedure • The specifically stated hypothesis is derived from the research objectives. • Sample is obtained & relevant variable measured. • Measured sample value is compared to value either stated explicitly or implied in the hypothesis. • If the value is consistent with the hypothesis, the hypothesis is supported, or not rejected. • If the value is not consistent with the hypothesis, the hypothesis is not supported, or is rejected.

11. Type I & Type II Errors • Type I Error • An error caused by rejecting the null hypothesis when it is true. • Has a probability of alpha (α). • Practically, a Type I error occurs when the researcher concludes that a relationship or difference exists in the population when in reality it does not exist. • Type II Error • An error caused by failing to reject the null hypothesis when the alternative hypothesis is true. • Has a probability of beta (β). • Practically, a Type II error occurs when a researcher concludes that no relationship or difference exists when in fact one does exist.

12. The Law and Type I & Type II Errors • Our legal system is based on the concept that a person is innocent until proven guilty (null hypothesis) • If we make a Type I error, we will send an innocent person to prison, so our legal system takes precautions to avoid Type I errors. • A Type II error would set a guilty person free.

13. 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.

14. 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.

15. 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.

16. 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.

17. 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.