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SPSS Part 2
Kin 260
Jackie Kiwata
Overview
Review Comparing Sets of Data
Correlation T-Tests
Overview in depth
Once data is organized, we generally analyze the data by Evaluating raw scores
-OR- Comparing sets of data
Last time we evaluated raw scores For instance, we calculated percentiles
Today, we’ll compare sets of data using SPSS
Statistical Significance Tests
Correlation Basic idea: Use to determine if a relationship
exists between variables T-tests
Basic idea: Use to determine if the means of 2 samples are statistically the same or different
Indicates the extent to which two variables are related. The technique used to measure this is Pearson’s correlation
coefficient, r The closer r is to 1 or -1, the stronger the relationship But Pearson’s correlation coefficient does not indicate
causation Not correct to say X causes Y
The strength of the relationship is classified using:
.9 or greater strong
.8 - .9 moderately strong
.7 - .8 moderate
.5 - .7 low
< .5 no relationship
Correlation Review
Correlation: SPSS example Research question: Can a person’s height predict
their vertical jump height?
Height (in) Vertical Jump Height (in)
11.5 64
17.5 66
19.5 69
11 68
23.5 67
23 68
Correlation in SPSS Analyze > Correlate > Bivariate …
A word about Tails…
Two-tailed test Use if prior research or logical reasoning does not
clearly indicate a significant difference between the mean values should be expected
Will use most of the time One-tailed test
Use if the direction (+ or -) of the difference between the means is well established before data collection
Sample Correlation Output
Descriptive Statistics
17.667 5.4467 6
67.00 1.789 6
VJ
Height
Mean Std. Deviation N
Correlations
1 .431
.393
6 6
.431 1
.393
6 6
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
VJ
Height
VJ Height
R value
Significance
Significance
The probability that: a result is not likely to be due to chance alone a result is correct
Expressed as a p value (probability value) In research, significance is set before
collecting data
Levels of Confidence & Probability of Error
In general, statistical significance is reported at one of three levels: If p=0.01, 99% confident the results are correct
and 1% incorrect If p=0.05, 95% confident the results are correct
and 5% incorrect If p=0.10, 90% confident the results are correct
and 10% incorrect If p>0.10, don’t report at all, because not
statistically significant
Significance Example
Suppose SPSS gives p = 0.02 At which level is this value significant? To answer this question, compare p value to
each level of confidence If p is less than a given level, try the next
P < 0.10 P < 0.05 P < 0.01
Significance example, con’t.
For p=0.02, We say p is significant at p<0.05 or at the 95% LOC
Is p < 0.10 ?
No
Report as NS.
Yes
Y
Is p < 0.05 ?
No
Report as p<.10
Y
Is p < 0.01 ?NoReport as p<.05
Report as p<.01
Correlation Significance Example
R=0.75 but p=0.35 Obtained a moderate correlation, but we are
only 65% certain results are correct and due to chance
P=0.35 is not statistically significant, so we can conclude results not due to chance
Possible reasons Sample is not representative of population Data has been tampered with in some way
T-test
Use to compare one sample mean to the population mean
-OR- Use to compare two independent, unrelated
samples drawn from same population
Tells us if the two means are statistically different
Like correlation, the T test does not indicate causation
Types of T tests Independent Samples
Run this test if just need to compare the means between two groups
e.g. Obtained VO2max scores from Kin 260 Sec 1 and Sec 3. Want to compare the means between the two sections
Paired Samples Run this test if research design required a pre and
post test and same subjects were tested twice e.g. All Kin 260 students took a VO2max test
before beginning a conditioning program, then took another VO2max test 6 weeks later.
Ex – Independent Sample T Test A biomechanics student gave a sit-and-reach test to 10 male
and 10 female undergrads. The following measurements in cm were obtained:
Males Females
19.1 20.4
17.2 25.0
20.1 26.9
18.2 27.1
16.5 28.3
16.9 22.2
21.2 23.8
19.7 24.8
15.9 25.4
16.0 19.0
T-Test Example con’t.
Male mean = 18.1 Female mean = 24.3 Appears females are more flexible at the hip
than males But is this difference statistically significant?
T-test: SPSS1. Define variables in Variable View
- ONE BIG DIFFERENCE: Need to create a “grouping” variable using Value Labels
2. Enter data in Data View
3. Analyze > Compare Means > Independent Samples T test…
4. Add variables
5. Define Groups
T-test: SPSS con’t.
T test: SPSS
T-test: SPSS Output•SPSS reports highly significant values as 0.00
- take this to be p<0.01
• So t = 5.573 and p<0.01
T test - Results
If p<0.10, get to say difference between the means is statistically significant report t value and level of confidence But remember t value does not tell us why the
means are different If p>0.10, report as NS (not significant)
One or both of these samples were not randomly drawn, OR
Some factor has affected these samples, causing them to be different from the original population
Ex – Paired T test
Pre (min) Post (min)
9.5 9.1
12.2 12.0
12.8 12.6
10.2 10.2
10.8 10.9
9.5 9.4
•A running coach gave a 1.5 mile running test to her athletes before and after a 3 wk cardiovascular training program
•Did the training program improve running time?
Paired t test steps similar to independent t test
1. Define variables in Variable View Do not need to use Value Labels Set up variables in same manner as
correlation
2. Enter data in Data View
3. Analyze > Compare Means > Paired Samples T test…
4. Add variables
Correlation vs. T-test
When and how to use each?
Correlation T test
Does x data predict y data?
Compare the means between the two groups.
Can y data be predicted from x data?
Compare the pre and post tests.
Graph in Excel, analyze data in SPSS.
Analyze data in SPSS.
More Information
SPSS: http://www.calstatela.edu/its/docs/pdf/SPSS14Part2.pdf
T-tests: http://en.wikipedia.org/wiki/Student's_t-test