1 / 11

S519: Evaluation of Information Systems

Learn about main effects and interaction effects in factor analysis, including how to evaluate mean differences and hypotheses using two-factor ANOVA. Discover the comprehensive design that factors in real-world behavior.

Download Presentation

S519: Evaluation of Information Systems

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. S519: Evaluation of Information Systems Social Statistics Inferential Statistics Chapter 12: Factor analysis

  2. This week • When to analysis of variance with more than one factor • Main and interaction effects • ToolPak

  3. Factorial Designs • Factorial Design: More than one factor (IV) is manipulated in the same experiment • This can produce main effects of either factor, and an interaction effect between the factors • This is the most comprehensive design, since factors interact with one another to produce behavior in the real world • The downside…you need far more subjects, time, and effort

  4. Main Effects and Interactions • Main effect: Mean differences along the levels of one factor (one-way F-ratio) • In addition to the two factors alone, we can evaluate mean differences that result from unique combinations of the two factors. • An interaction between two factors occurs whenever mean differences between individual treatment conditions (combinations of two factors) are different from the overall mean effects of the factors • “The effects of one factor vary as a function of the other”

  5. Hypotheses and F-ratios • Two-factor ANOVA will do three things: • Examine differences in sample means for humidity (factor A) • Examine differences in sample means for temperature (factor B) • Examine differences in sample means for combinations of humidity and temperature (factor A and B). • Three sets of hypotheses and three F-ratios.

  6. Example • Two factors: gender (male or female) and treatment (high or low impact) • The same people experience both the high and low impact conditions

  7. Example • Three questions: • Is there a difference between the levels of impact (main effect)? • Is there a difference between the two levels of gender (main effect)? • What is the effect of difference levels of impact for males or females (interaction effects)

  8. Data Two-way ANOVA or factorial ANOVA

  9. Hypotheses • Null hypothesis • Research hypothesis

  10. Excel Toolpak

  11. Excel Toolpak • There is no main effect for treatment or gender (p=0.127, 0.176) • There is interaction effect (p=0.004) • It does not matter if you are in the high or low impact treatment group, or if you are male or female • It does matter if you are in both conditions simultaneously  the treatment does have an impact differentially on the weight loss of males than on females

More Related