1. SPSS Part 2 Kin 260 Jackie Kiwata

2. Overview Review Comparing Sets of Data Correlation T-Tests

3. 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

4. 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

5. 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 -.9moderately strong.7 -.8 moderate.5 -.7 low <.5no relationship Correlation Review

6. Correlation: SPSS example Research question: Can a person’s height predict their vertical jump height? Height (in)Vertical Jump Height (in) 11.564 17.566 19.569 1168 23.567 2368

7. Correlation in SPSS Analyze > Correlate > Bivariate …

8. 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

9. Sample Correlation Output R value Significance

10. 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

11. 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

12. 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

13. 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. YesY Is p < 0.05 ? No Report as p<.10 Y Is p < 0.01 ? No Report as p<.05 Report as p<.01

14. 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

15. 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

16. 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.

17. 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: MalesFemales 19.120.4 17.225.0 20.126.9 18.227.1 16.528.3 16.922.2 21.223.8 19.724.8 15.925.4 16.019.0

18. 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?

19. T-test: SPSS 1. 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

20. T-test: SPSS con’t.

21. T test: SPSS

22. 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

23. 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

24. Ex – Paired T test Pre (min)Post (min) 9.59.1 12.212.0 12.812.6 10.2 10.810.9 9.59.4 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?

25. 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

26. Correlation vs. T-test When and how to use each? CorrelationT 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.

27. More Information SPSS: http://www.calstatela.edu/its/docs/pdf/SPSS1 4Part2.pdf T-tests: http://en.wikipedia.org/wiki/Student's_t-test