1. PART 2 SPSS (the Statistical Package for the Social Sciences)

2. Lesson objectives  Recap SPSS Data entry  Data view  Variable view Descriptive analysis  Determining reliability  Inferential Statistics with SPSS

3. Inferential Statistics  Based on the assumption that the sample is random  Types of tests Chi Squared Correlation T test

4. Example research Purpose : To determine if a certain method of teaching will lead to higher achievement among visual learners

5. Design  Population: EDU 540 students (154)  Sample ( chosen at random, 3 lessons taught by the same person using the ‘method’) Class 1 (30) Class 2 (45) Class 3 (38) Dependent Variable Independent Variable Achievement Satisfaction Learning styles INTERVENTION Teaching method

6. Instruments  Learning style inventory Scores will determine learning styles Can categorize as visual, tactile or auditory  Questionnaire Satisfaction regarding the teaching method higher score – higher lesson satisfaction  Test Scores will determine achievement

7. What to describe?  Descriptive stats Age Gender Program Learning styles  Cross tabulate? Gender and learning styles

8. Significance  If significant, unlikely to have occurred by chance  there is statistical evidence that there is a difference, a correlation, an association between etc….

9. Significance level  Significance levels show you how likely a result is due to chance.  The most common level, used to mean something is good enough to be believed, is.95.  The finding has a 95% chance of being true.  No statistical package will show you "95%" or ".95" to indicate this level. Instead it will show you ".05," meaning that the finding has a five percent (.05) chance of not being true, which is the converse of a 95% chance of being true.  To find the significance level, subtract the number shown from one. For example, a value of ".01" means that there is a 99% (1-.01=.99) chance of it being true

10. Hypothesis testing  The Null hypothesis states there is no true difference/no relationship between parameters in the population We reject or accept the null hypothesis  It is rejected only when it becomes evidently false, that is, when the researcher has a certain degree of confidence, usually 95% to 99%, that the data do not support the null hypothesis  Example There is no significant difference between the mean test scores of visual and tactile learners

11. Hypothesis testing  YOU ALWAYS TEST THE NULL HYPOTHESIS!

12. Significance  Test of significance To decide whether to accept or reject the null hypothesis Select probability  5 out of 100 times the difference did not occur by chance ( Significance level: 0.05)  1 out of 100 times the difference did not occur by chance ( Significance level: 0.01) Confidence level?  95% or 99%

13. Example  Null hypothesis There is no relationship between variables..  Significance level : 0.05  Test statistic Probability value 0.009 or Sig. 0.009 (smaller than 0.05)  What does that mean? very unlikely that there’s no relationship between the variables Variables not independent of each other REJECT Null hypothesis

14. Example  Null hypothesis There is no relationship between variables..  Significance level : 0.01  Test statistic Probability value 0.12 or Sig. 0.12(greater than 0.01)  What does this mean? Higher likelihood that there’s no relationship between the variables Variables are independent of each other ACCEPT Null hypothesis

15. Let’s get on with inferential statistics

16. Now.. What to infer?  Independence/ Association  Correlation  Differences

17. Independence test –Chi squared  Chi squared test is used in situations where you have two categorical variables Gender and employment sector Gender and learning styles  Chi-square test of independence tests the null hypothesis that there is no association between the two variables

18. Example: Test for independence  Gender Female Male  Learning styles Visual Tactile Auditory  Null Hypothesis: No association between gender and learning styles

19. Using SPSS for chi squared  Click Analyze  Descriptive Crosstabs  Statistics

20. Using SPSS for chi squared  Low chi squared statistic  Sig.961  Accept null hypothesis  There is no association…  Variables independent of each other

21. Correlation  Measure of the linear relationship between two variables.  A correlation coefficient has a value ranging from -1 to 1.  Values that are closer to the absolute value of 1 indicate that there is a strong relationship between the variables being correlated whereas values closer to 0 indicate that there is little or no linear relationship.  The sign of a correlation coefficient describes the type of relationship between the variables being correlated. A positive correlation coefficient indicates that there is a positive linear relationship between the variables: as one variable increases in value, so does the other. A negative value indicates a negative linear relationship between variables: as one variable increases in value, the other variable decreases in value.

22. Example: Correlation  Correlation between learning styles and test scores  Correlation between learning styles and satisfaction

23. Correlation in SPSS  Start at the Analyze menu.  Select the Correlate option from this menu. You will see three options for correlating variables: Bivariate Partial Distances.  The bivariate correlation is for situations where you are interested only in the relationship between two variables

25. Correlation in SPSS  Then, consider is the type of correlation coefficient. Pearson's is appropriate for continuous data Kendall's tau-b and Spearman's, are designed for ranked data.  The choice between a one and two-tailed significance test in the Test of Significance box should be determined by the hypothesis you are testing if you are making a prediction that there is a negative or positive relationship between the variables, then the one- tailed test is appropriate if you are not making a directional prediction, you should use the two-tailed test (there is not a specific prediction about the direction of the relationship between the variables)

26. Output

27.  Correlation is not statistically significant

28. Let’s check for significant difference Differences between test scores of the groups of learners

29. Differences: Using t test  The t test is a useful technique for comparing mean values of two sets of numbers. Statistic for evaluating whether the difference between two means is statistically significant.  t tests can be used either to compare two independent groups (independent-samples t test) to compare observations from two measurement occasions for the same group (paired-samples t test).

30. Remember t test - tests the null hypothesis / that there is no difference …

31. t test  If you are using the t test to compare two groups, the groups should be randomly drawn from normally distributed and independent populations.  Using SPSS Analyze  Compare Means One-Sample T test... Independent-Samples T test... Paired-Samples T test...

32. Types of t-test  The one-sample t test is used compare a single sample with a population value. Example, a test could be conducted to compare the average test scores of U5C with a value that was known to represent the whole EDU 540 population.  The independent-sample t test is used to compare two groups' scores on the same variable. Example : Compare the test scores of U5C and PKPG to evaluate whether there is a difference in their scores.  The paired-sample t test is used to compare the means of two variables within a single group. Example, it could be used to see if there is a statistically significant difference between test 1 and test 2 among the members of U5C

33. Using SPSS : t test

34. Output  Notice the two parts of the output Equal variances assumed Equal variance not assumed  Which to use? Look at Levene’s test for equality of variance If small Sig. - groups have unequal variances

35. Output  t-statistics is -6.024  Sig. level :.000  The significance level tells us that the probability that (there is no difference between visual and tactile learners) – the “NULL” is very small  Hence, there is a significant difference in the test scores between visual and tactile learners

36. Have fun with SPSS! Proceed to Qualitative Analysis and Ethics in Research