1 / 23

t-Static

t-Static. 1. Single Sample or One Sample t-Test AKA student t-test. 2. Two Independent sample t-Test, AKA B etween S ubject D esigns or Matched subjects Experiment. 3. Related Samples t-test or Repeated Measures Experiment AKA Within Subject Designs or Paired Sample T-Test. CHAPTER 11.

kennedy-larsen
Download Presentation

t-Static

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. t-Static 1. Single Sample or One Sample t-Test AKA student t-test. 2. Two Independent sample t-Test, AKA BetweenSubject Designs or Matched subjects Experiment. 3. Related Samples t-test or Repeated Measures Experiment AKA Within Subject Designs or Paired Sample T-Test .

  2. CHAPTER 11 • Repeated Measure Experiment, Related/Paired Sample t-test or,Within Subject Experiment Design

  3. Repeated Measure Experiment, or Related/Paired Sample t-test Within Subject Experiment Design • A single sample of individuals is measured more than once on the same dependent variable. The same subjects are used in all of the treatment conditions.

  4. Null Hypothesis • t-Statistics: • If the Population mean or µ is unknown the statistic of choice will be t-Statistic • Repeated Measure Experiment, or Related/Paired Sample t-test • If non-directional or two tailed test, then • Step. 1 • H0 : µD = 0 • H1: µD≠ 0

  5. Null Hypothesis • t-Statistics: • If directional or one tailed tests • Step. 1 • H0 : µD ≤ 0 • H1: µD> 0 or, Step. 1 • H0 : µD≥ 0 • H1 : µD < 0

  6. None-directional Hypothesis Test

  7. Calculations for t-testStep 3: Computations/ Calculations or Collect Data and Compute Sample Statistics t= MD-μD SMD SMD= S/√n or SMD=MD-μD t MD=t.SMD+μD μD=MD- SMD.t SMD= estimated standard error of the mean difference

  8. Calculations for t-testStep 3: Computations/ Calculations or Collect Data and Compute Sample Statistics df=n-1 Difference Score D= X2-X1 MD =ΣD n

  9. FYI VariabilitySS,Standard Deviations and Variances • X σ² = ss/N Pop 1 σ = √ss/N 2 4 s = √ss/df 5 s² = ss/n-1 or ss/dfSample SS=Σx²-(Σx)²/n new  SS=ΣD²-(ΣD)²/n SS=Σ(x-μ)² Sum of SquaredDeviationfrom Mean

  10. Cohn’s d=Effect Size for tUse S instead of σ for t-test • d = MD/s • S= MD/d • MD= d . s

  11. Percentage of Variance Accounted for by the Treatment (similar to Cohen’s d) Also known as ω² Omega Squared

  12. Problems Research indicates that the color red increases men’s attraction to women(Elliot & Niesta, 2008). In the original study, men were shown women’s photographs presented on either whiteorredbackground. Photographs presented onredwere rated significantly more attractive than the same photographs mounted on white.

  13. Problems In a similar study, a researcher prepares a set of 30 women’s photographs, with 15 mounted on a white background and 15 mounted on red. One picture is identified as the test photograph, and appears twice in the set, once on white and once on red.

  14. Problems • Each male participant looks through the entire set of photographs and rated the attractiveness of each woman on a 12-point scale. The data in the next slide summarizes the responses for a sample of n=9men. • Set the level of significance at α=.01 for two tails Do the data indicate the color red increases men’s attraction to women?

  15. Problems Participants White background X1 Red Background X2 D=X2-X1 D² A 6 9 +3 9 B 8 9 +1 1 C 7 10 +3 9 D 7 11 +4 16 E 8 11 +3 9 F 6 9 +3 9 G 5 11 +6 36 H 10 11 +1 1 I 8 11 +3 9 ΣD =27 ΣD²=99 MD =

  16. Null Hypothesis • For Non-Directional or two tailed tests • Step. 1 • H0 : µD = 0 • H1 : µD ≠ 0

  17. Problems One technique to help people deal with phobia is to have them counteract the feared objects by using imaginationto move themselves to a place of safety. In an experiment test of this technique, patients sit in front of a screen and are instructed to relax. Then they are shown a slide of the feared object for example, a picture of a spider, (arachnophobia). The patient signals the researcher as soon as feelings of anxiety begin to arise, and the researcher records the amount of time that the patient was able to endure looking at the slide.

  18. Problems • The patient then spends two minutes imagining a “safe scene” such as a tropical beach (next slide) beforethe slide is presented again. If patients can tolerate the feared object longer afterthe imagination exercise, it is viewed as a reduction in the phobia. The data in next slide summarize the items recorded from a sample of n=7patients. Do the data indicate the imagination technique effectively alters phobia? . Set the level of significance at α=.05 for one tailed test.

  19. Problems Participant Before imagination X1 After Imagination X2 D=X2-X1 D² A 15 24 +9 81 B 10 23 +13 169 C 7 11 +4 16 D 18 25 +7 49 E 5 14 +9 81 F 9 14 +5 25 G 12 21 +9 81 ΣD =56 ΣD²=502 MD =

  20. Null Hypothesis • For Directional or one tailed tests • Step. 1 • H0 : µD ≤ 0 (The amount of time is not increased.) • H1: µD> 0 (The amount of time is increased.)

More Related