computing our example n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Computing our example PowerPoint Presentation
Download Presentation
Computing our example

Loading in 2 Seconds...

play fullscreen
1 / 21

Computing our example - PowerPoint PPT Presentation


  • 104 Views
  • Uploaded on

Computing our example. Step 1: compute sums of squares Recall our data…. 1. 2. N = 15. Computing our example. Step 1: compute sums of squares SS total = [10 2 + 13 2 + 5 2 + 9 2 + 8 2 + 6 2 + 8 2 + 10 2 + 4 2 +12 2 + 1 2 + 3 2 + 4 2 + 5 2 + 2 2 ] -

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Computing our example' - ferris-cooke


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
computing our example
Computing our example
  • Step 1: compute sums of squares
    • Recall our data…

1

2

N = 15

computing our example1
Computing our example
  • Step 1: compute sums of squares
    • SStotal

= [102 + 132 + 52 + 92 + 82 + 62 + 82 + 102 + 42 +122 + 12 + 32 + 42 + 52 + 22] -

= 854 – 666.67 = 187.33

1

computing our example2
Computing our example
  • Step 1: compute sums of squares
    • SSgroup

= 27.14 + 8.84 + 67.34= 103.32

1

2

computing our example3
Computing our example
  • Step 1: compute sums of squares
    • SSerror
      • =SStotal-SSgroup

= 187.33 – 103.32 = 84.01

  • So…
    • SSgroup = 103.32
    • SSerror = 84.01
    • Sstotal = 187.33

1

computing our example4
Computing our example
  • Step 2: Compute df
    • df group = k – 1 = 3 – 1 = 2
    • dferror = N – k = 15 – 3 = 12
    • df total = N – 1 = 15 – 1 = 14

1

computing our example5
Computing our example
  • Step 3: Compute Mean Squares (MS)

1

computing our example6
Computing our example
  • Step 4: Put all the info in the ANOVA table:

1

computing our example7
Computing our example
  • Step 5: Compare Fobs to Fcritical:
    • Fobs = 7.38
    • Fcritical = …need to obtain Fcrit from tables for F
      • df will be (numerator, denominator) in F-ratio
      • df = 2, 12
    • F (2,12, α = .05) = 3.89
  • Reject H0 (Fobs > Fcritical)

1

2

1 way anova in spss
1-way ANOVA in SPSS

Data: One column for the grouping variable (IV: group in this case), one for the measure (DV: fitness in this case)

Data: Note grouping variable has 3 levels (goes from 1 to 3)

1

1 way anova in spss1
1-way ANOVA in SPSS

Procedure: Choose the appropriate procedure, and…

1

1 way anova in spss2
1-way ANOVA in SPSS

Dialog box: slide the variables…

1

…into the appropriate places

1 way anova in spss3

n – k = 15 - 3 = 12

k-1 = 3-1 = 2

n-1 = 15-1 = 14

Here we see the between and within sources of variance

Here are the SD’s (here expressed as the “mean square” – that’s the average sum of squares, which is after all a ‘standardized’ deviation)

1-way ANOVA in SPSS

1

Result!

significant result now what
Significant result…now what?

1

  • Follow-up tests
    • ONLY compute after a significant ANOVA
    • Like a collection of little t-tests
    • But they control overall type 1 error comparatively well
    • They do not have as much power as the omnibus test (the ANOVA) – so you might get a significant ANOVA & no sig. Follow-up
    • Purpose is to identify the locus of the effect (what means are different, exactly?)

2

significant result now what1
Significant result…now what?
  • Follow-up tests – most common…
    • Tukey’s HSD (honestly sig. diff.)
    • Formula:
    • But it’s easier to use SPSS…

1

follow ups to anova in spss
Follow-ups to ANOVA in SPSS

1

2

Choose “post-hoc” test (meaning ‘after this’)

Check the appropriate box for the HSD (Tukey, not Tukey’s b)

follow ups to anova in spss1

Sig. levels between pairs of groups

Groups that do not differ

Follow-ups to ANOVA in SPSS

2

And one that does (from the other 2)

1

3

follow ups to anova in spss2
Follow-ups to ANOVA in SPSS

So “TV Movie” differs from both “Soap Opera” and “infomercial” , significantly

1

“Soap Operas” and “infomercials” do not differ significantly

assumptions to test in one way
Assumptions to test in One-Way
  • Samples should be independent (as with independent t-test – does not mean perfectly uncorrelated)
  • Each of the k populations should be normal (important only when samples are small…if there’s a problem, can use Kruskal-Wallis test)
  • The k samples should have equal variances (this is the homogeneity of variance assumption, and we’ll look at it shortly…violations are important mostly with small samples and unequal n’s)

1

homogeneity of variance spss
Homogeneity of variance - SPSS

1. Click on the ‘options’ button

2. Choose homogeneity of variance

3. Click continue

homogeneity of variance spss1
Homogeneity of variance - SPSS

Homogeneity test output

As you can see, no problems here. The test has to be significant for there to be a violation

interpret output
Interpret output
  • “The amount of aggression arising from watching TV changed according to the type of program watched, F(2,12) = 7.38, p .05. Tukey’s HSD follow-up tests showed that those watching violent movies (M = 3) experienced less aggression than those watching soap operas (M = 8) or infomercials (M = 9). There was no difference in aggression level between those who watched soap operas and those who watched infomercials.”

1