- By
**kass** - Follow User

- 116 Views
- Uploaded on

Download Presentation
## PowerPoint Slideshow about ' Basic Data Analysis ' - kass

**An Image/Link below is provided (as is) to download presentation**

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript

Tabulation

- Frequency table
- Percentages

CROSS-TABULATION

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

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

Degrees of Significance

- Mathematical differences
- Statistically significant differences
- Managerially significant differences

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

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.

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 hypothesis.

Download Presentation

Connecting to Server..