1 / 16

Basic Data Analysis

This guide explores basic data analysis techniques, including tabulation, frequency tables, and descriptive measures for nominal and ordinal data. You will learn about cross-tabulation methods for group comparison and significance testing using ANOVA and MANOVA. The significance standard is highlighted, including how to interpret p-values, and the implications of hypothesis testing. We examine the impact of independent variables on dependent variables through regression analysis, emphasizing the importance of statistical significance and the interpretation of results.

colleen
Télécharger la présentation

Basic Data Analysis

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. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Basic Data Analysis

  2. Tabulation • Frequency table • Percentages

  3. A Typical Table

  4. Type of Measurement Type of descriptive analysis Nominal Cross Tabs Mode

  5. Type of Measurement Type of descriptive analysis Ordinal Rank order Median

  6. Type of Measurement Type of descriptive analysis Interval Arithmetic mean

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

  8. A Typical Cross-Tab Table

  9. 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

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

  11. 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

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

More Related