inf397c introduction to research in information studies fall, 2009 week 12

PowerPoint PresentationR. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | [email protected]
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INF397C
Fall, 2009
Week 12
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | [email protected]
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From Blink, by Malcolm Gladwell
P. 32. The author is talking about negative emotions that tend to predict subsequent break-ups of marriages:
“. . . there is one emotion that [Dr. Gottman] considers the most important of all: contempt. If Gottman observes one or both partners in a marriage showing contempt for one another, he considers it the single most important sign that the marriage is in trouble.”
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p. 33
“Gottman has found, in fact, that the presence of contempt in a marriage can even predict such things as how many colds a husband or wife gets; in other words, having someone you love express contempt toward you is so stressful that it begins to affect the functioning of your immune system.”
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More resources – Assorted things
http://www.stat.berkeley.edu/~stark/Java/Html/NormHiLite.htm Use the slider bars! Man, don’t you wish you had had access to this tool for the last question on the midterm?!
http://www.stat.berkeley.edu/~stark/Java/Html/StandardNormal.htm Play with the standard deviation slider bar!
http://www-stat.stanford.edu/~naras/jsm/NormalDensity/NormalDensity.html
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Dr. Phil Doty’s 26-minute online tutorial on standard error of the mean. Very helpful: http://cobra.gslis.utexas.edu:8080/ramgen/Content2/faculty/doty/research/dsmbb.rm
Note, I had not talked of “expected value.” When you hear that “M is the expected value of µ,” you can substitute “M is used to estimate µ.”
Note also we have not talked about “CV,” though last week I did say to expect that S is kinda of the same general order of magnitude, but smaller than, M. Same idea.
Don’t forget Dr. Doty’s page of tutorials, http://www.gslis.utexas.edu/~lis397pd/fa2002/tutorials.html
where you will find also an eight-minute introduction to inferential statistics, two tutorials on confidence intervals, and one on Chi squared.
I think one thing you’ll find interesting in these tutorials is that here is a second professor, using a different text book (Spatz), who studied at a different school, who’s never heard me lecture (nor I him) . . . and we use much the same language to describe things. The point is, this stuff (descriptive and inferential statistics) is universal.
Two pages of explanation of standard error of the mean: http://davidmlane.com/hyperstat/A103735.html
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More resources - Probability
A visual demonstration of probability and outliers (or some such). Here’s a good one: http://www.ms.uky.edu/~mai/java/stat/GaltonMachine.html
Here’s another: http://www.stattucino.com/berrie/dsl/Galton.html
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t tests
Go to the McGraw-Hill statistics primer and read the subsections on Inferential Statistics. Not a lot of meat, there, but it will help you to hear it stated in a slightly different way.
For some examples of the use of t tests . . .
http://www.yogapoint.com/info/research.htm for an example of some t tests.
Go find more examples, just for yourself.
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We calculate a confidence interval for a population parameter.
The mean of a random sample from a population is a point estimate of the population mean.
But there’s variability! (SE tells us how much.)
What is the range of scores between which we’re 95% confident that the population mean falls?
Think about it – the larger the interval we select, the larger the likelihood it will “capture” the true (population) mean.
CI = M +/- (t.05)(SE)
See Box 12.2 on “margin of error.” NOTE: In the box they arrive at a 95% confidence that the poll has a margin of error of 5%. It is just coincidence that these two numbers add up to 100%.
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CI = M +/- (t.05)(SE)
Establish the level of α (two-tailed) for the CI. (.05)
M=15.0 s=5.0 N=25
Use Table A.2 to find the critical value associated with the df.
t.05(24) = 2.064
= 15.0 +/- 2.064
= 12.935 – 17.064
“The odds are 95 out of 100 that the population mean falls between 12.935 and 17.064.”
(NOTE: This is NOT the same as “95% of the scores fall within this range!!!)
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What if we did this via confidence intervals?
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World Our decision
Type II error (β)
Correct decision (1-β)
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Only one IV at a time (with two levels)
But you say, “Why don’t I just run a bunch of t tests”?
It’s a pain.
You multiply your chances of making a Type I error.
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ANOVA
Analysis of variance, or ANOVA, or F tests, were designed to overcome these shortcomings of the t test.
An ANOVA with ONE IV with only two levels is the same as a t test.
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ANOVA (cont’d.)
Remember back to when we first busted out some scary formulas, and we calculated the standard deviation.
We subtracted the mean from each score, to get a feel for how spread out a distribution was – how DEVIANT each score was from the mean. How VARIABLE the distribution was.
Then we realized if we added up all these deviation scores, they necessarily added up to zero.
So we had two choices: we coulda taken the absolute value, or we coulda squared ‘em. And we squared ‘em.
Σ(X – M)2
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Σ(X – M)2
This is called the Sum of the Squares (SS). And when we add ‘em all up and average them (well – divide by N-1), we get S2 (the “variance”).
We take the square root of that and we have S (the “standard deviation”).
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Let’s work through the Hinton example on p. 119.
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M =
10.unknown
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X
M
X-M
(X-M)2
X
M
X-M
(X-M)2
X
M
X-M
(X-M)2
5
10
-5
25
11
10
1
1
14
10
4
16
6
10
-4
16
10
10
0
0
15
10
5
25
7
10
-3
9
9
10
-1
1
17
10
7
49
5
10
0
25
11
10
1
1
13
10
3
9
3
10
-7
49
9
10
-1
1
17
10
7
49
4
10
-6
36
10
10
0
0
14
10
4
16
160
4
164
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X
M
X-M
(X-M)2
X
M
X-M
(X-M)2
X
M
X-M
(X-M)2
5
5
0
0
11
10
1
1
14
15
-1
1
6
5
1
1
10
10
0
0
15
15
0
0
7
5
2
4
9
10
-1
1
17
15
2
4
5
5
0
0
11
10
1
1
13
15
-2
4
3
5
-2
4
9
10
-1
1
17
15
2
4
4
5
-1
1
10
10
0
0
14
15
-1
1
10
4
14
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X
M
X-M
(X-M)2
5
10
5
25
150
10
10
0
0
0
15
10
5
25
150
50
300
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So . . .
Within conditions SS = 28
Between conditions SS = 300
Within conditions df = 15
Between conditions df = 2
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Error variance
Think about it – why is the “within condition variance” called “error variance”?
Note, what happens where there are no systematic differences?
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Anova summary table, p. 128
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15
28
1.87
Total
17
328
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Reading the F table
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Check out . . .
ANOVA summary table on p. 128. This is for a ONE FACTOR anova (i.e., one IV). (Maybe MANY levels.)
Sample ANOVA summary table on p. 132.
The only thing you need to realize in Chapter 13 is that for repeated measures ANOVA, we also tease out the between subjects variation from the error variance. (See p. 154 and 158.)
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Check out, also . . .
Note, in Chapter 15, that as factors (IVs) increase, the comparisons (the number of F ratios) multiply. See p. 172, 179.
What happens when you have 3 levels of an IV, and you get a significant F? (As we did in our worked example.)
Memorize the table on p. 182. (No, I’m only kidding.)
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Interaction effects
Here’s what I want you to understand about interaction effects:
They’re WHY we run studies with multiple IVs.
A significant interaction effect means different levels of one IV have different influences on the other IV.
You can have significant main effects and insignificant interactions, or vice versa (or both sig., or both not sig.) (See p. 164, 166.)
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An Experiment
First, let’s divide into two groups – who has an ODD SSN (last digit is odd).
Group 1 – You’ll do List A first, then List B. (Group 2 will keep their eyes closed during List A.) (I’m just sure of it.) Then we’ll all do List B. Then Group B will go back and do List A.
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Presented visually.
After the 10th I’ll say “go” and you’ll write down as many as you can.
Don’t have to remember them in order.
Pencils down.
Ready?
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balloon
doorknob
minivan
meatloaf
teacher
zebra
pillow
barn
sidewalk
coffin
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Same task -- recall them.
After the 10th one I’ll say “Go,” write down as many of the 10 words as you can.
Again, don’t have to remember them in order.
Pencils down.
Ready?
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forget
interest
anger
imagine
fortitude
smart
peace
effort
hunt
focus
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List A again
Now, for Group 2. (Group 1 can keep their eyes open – just don’t participate.)
I’ll present 10 words, one at a time.
Presented visually.
After the 10th I’ll say “go” and you’ll write down as many as you can.
Don’t have to remember them in order.
Pencils down.
Ready?
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balloon
doorknob
minivan
meatloaf
teacher
zebra
pillow
barn
sidewalk
coffin
i
balloon
doorknob
minivan
meatloaf
teacher
zebra
pillow
barn
sidewalk
coffin
forget
interest
anger
imagine
fortitude
smart
peace
effort
hunt
focus
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I’ll collect the data via a show of hands.
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inf397c introduction to research in information studies fall, 2009 week 12

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