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INF397C
Introduction to Research in Information
Studies
Spring, 2009
Day 13
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
1
Correlation
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• With correlation, we return to
DESCRIPTIVE statistics. (This is
counterintuitive. To me.) (Well, it’s
BOTH descriptive and inferential.)
• We are describing the strength and
direction of the relationship between two
variables.
• And how much one variable predicts the
other.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
2
Correlation
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• Formula –
– Hinton, p. 259, or
– S, Z, & Z, p. 393
• Two key points:
– How much predictability does one variable
provide, for another.
– NOT causation.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
3
Correlation (cont’d.)
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• Go to the McGraw-Hill statistics primer
http://highered.mcgrawhill.com/sites/0072494468/student_view0
/statistics_primer.html
and click on “Correlational Statistics.”
Read the three sub-sections. I will NOT
ask you to calculate a correlation, but I
want you to understand the concepts
surrounding it.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
4
Chi-square
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• Hinton, p. 247
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
5
Chi square test
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• Let’s work an example.
• Just know that you use the chi square
test when you have FREQUENCY data.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Let’s talk about the final
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• Here’s what you’ve read:
– Huff (How to lie with statistics)
– Dethier (To know a fly)
– Hinton: Ch. 1 – 15, 20
– S, Z, & Z: Ch. 1-8, 10-13
– Several other articles
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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For the final, EMPHASIZE…
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• Descriptive stat
–
–
–
–
–
Measures of central tendency, dispersion
Z scores
Probability
Frequency distributions, tables, graphs
Correlation (interpret, not calculate)
• Inferential stat
–
–
–
–
–
–
–
Hypothesis testing
Standard error of the mean
t test (calculate one, for one sample; interpret others)
Confidence intervals (interpret, not calculate)
Chi square (interpret, not calculate)
ANOVA – interpret summary table
Type I and II errors
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Emphasize . . .
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• Experimental design
–
–
–
–
–
–
–
IV, DV, controls, confounds, counterbalancing
Repeated measures, Independent groups
Sampling
Operational definitions
Individual differences variable
Ethics of human study
Possible sources of bias and error variance and how to
minimize/eliminate
• Qualitative methods
– Per Rice Lively, Gracy
– Survey generation (from SZZ, Ch. 5)
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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De-emphasize
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•
•
•
•
•
Complicated probability calculations
APA ethical standard (S,Z, & Z, Ch. 3)
Content analysis (SZZ, Ch. 6)
Calculating an ANOVA.
Nonequivalent control group design
(SZZ, Ch. 11) (Indeed, de-emphasize all
Ch. 11)
• Hinton, Ch. 12
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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