1 / 23

Objectives

Slides to accompany Weathington, Cunningham & Pittenger (2010), Chapter 12: Single Variable Between-Subjects Research. Objectives. Independent Variable Cause and Effect Gaining Control Over the Variables The General Linear Model Components of Variance The F -ratio ANOVA Summary Table

ania
Download Presentation

Objectives

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. Slides to accompany Weathington, Cunningham & Pittenger (2010), Chapter 12: Single Variable Between-Subjects Research

  2. Objectives • Independent Variable • Cause and Effect • Gaining Control Over the Variables • The General Linear Model • Components of Variance • The F-ratio • ANOVA Summary Table • Interpreting the F-ratio • Effect Size and Power • Multiple Comparisons of the Means

  3. Multi-level Independent Variable • More than 2 levels of the IV • Permits more detailed analysis • Can’t identify certain types of relationships with only two data points (Figure 12.1) • Can increase a study’s power by reducing variability within the multiple treatment condition groups

  4. Figure 12.1

  5. Searching for Cause and Effect • Identifying differences among multiple groups is a starting point for causal study • Control is the key: • Through research design • Through research procedure

  6. Control through Design • Most easily secured in a true experiment • You manipulate and control the IV • Control groups are possible  isolating effects of IV • You control random assignment of participants • Helps to reduce confounding effects

  7. Control through Procedure • Each participant needs to experience the same process (except the manipulation) • Systematic • Identifying and trying to limit as many confounding factors as possible • Pilot studies are a great way to test your process and your control strategies

  8. General Linear Model • Xij = µ + αj + εij • A person’s performance (score = Xij) will reflect: • Typical score in that group (µ) • Effect of the treatment/manipulation (αj) • Random error (εij) • Ho: all µi equal

  9. Figure 12.2

  10. GLM and Between-Subj. Research • Goal is to determine proportion of total variance due to IV and proportion due to random error • Size of between-groups variance is due to error (εij) and IV (αj) • If b-g variance > w-g variance  IV has some effect

  11. ANOVA • Compares different types of variance • Total variance = variability among all participants’ scores (groups do not matter) • Within-groups variance = average variability among scores within a group or condition (random) • Between-groups variance = variability among means of the different treatment groups • Reflects joint effects of IV and error

  12. F-ratio • Allows us to determine if b-g variance > w-g variance • F = Treatment Variance + Error Variance Error Variance • F = MSbetween/MSwithin

  13. F-ratio: No Effect • Treatment group M may not all be exactly equal, but if they do not differ substantially relative to the variability within each group  nonsignificant result • When b-g variance = w-g variance, F = 1.00, n.s.

  14. Figure 12.4

  15. F-ratio: Significant Effect • If IV influences DV, then b-g variance > w-g variance and F > 1.00 • Examining the M can highlight the difference(s)

  16. Figure 12.5

  17. F-ratio Distribution • Represents probability of various F-ratios when Ho is true • Shape is determined by two df • 1st = b-g = (# of groups) - 1 • 2nd = w-g = (# of participants in a group) – 1 • Positive skew, αon right extreme region

  18. Figure 12.6

  19. Summarizing ANOVA Results • Figure 12.7 • Using the critical value from appropriate table in Appendix B, if Fobs > Fcrit significant difference among the M • Rejecting Ho requires further interpretation • Follow-up contrasts

  20. Figure 12.7

  21. Interpreting F-ratio • Omega squared indicates degree of association between IV and DV • f is similar to d for the t-test • Typically requires further M comparisons • t-test time, but with reduced α to limit chances of committing a Type I error

  22. Multiple Means Comparisons • You could consider lowering α to .01, but this would increase your Type II probability • Instead use a post-hoc correction for α: • αe= 1 – (1 – αp)c • Tukey’s HSD = difference required to consider M statistically different from each other

  23. What is Next? • **instructor to provide details

More Related