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Analyzing Data using SPSS

Analyzing Data using SPSS. Testing for difference . Parametric Test. t-test . Is used in a variety of situations involving interval and ratio variables. Independent – Samples Dependent - Samples. Independent-Samples T-Test .

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Analyzing Data using SPSS

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  1. Analyzing Data using SPSS

  2. Testing for difference

  3. Parametric Test

  4. t-test Is used in a variety of situations involving interval and ratio variables. Independent – Samples Dependent - Samples

  5. Independent-Samples T-Test • What it does: The Independent Samples T Test compares the mean scores of two groups on a given variable.

  6. Where to find it: Under the Analyze menu, choose Compare Means, the Independent Samples T Test. Move your dependent variable into the box marked "Test Variable." Move your independent variable into the box marked "Grouping Variable." Click on the box marked "Define Groups" and specify the value labels of the two groups you wish to compare.

  7. Assumptions:-The dependent variable is normally distributed. You can check for normal distribution with a Q-Q plot.-The two groups have approximately equal variance on the dependent variable. You can check this by looking at the Levene's Test. See below.-The two groups are independent of one another

  8. Hypotheses:Null: The means of the two groups are not significantly different.Alternate: The means of the two groups are significantly different.

  9. SPSS Output • Following is a sample output of an independent samples T test. We compared the mean blood pressure of patients who received a new drug treatment vs. those who received a placebo (a sugar pill).

  10. First, we see the descriptive statistics for the two groups. We see that the mean for the "New Drug" group is higher than that of the "Placebo" group. That is, people who received the new drug have, on average, higher blood pressure than those who took the placebo.

  11. Our • Finally, we see the results of the Independent Samples T Test. Read the TOP line if the variances are approximately equal. Read the BOTTOM line if the variances are not equal. Based on the results of our Levene's test, we know that we have approximately equal variance, so we will read the top line

  12. Our T value is 3.796. • We have 10 degrees of freedom. • There is a significant difference between the two groups (the significance is less than .05). • Therefore, we can say that there is a significant difference between the New Drug and Placebo groups. People who took the new drug had significantly higher blood pressure than those who took the placebo.

  13. Example Independent – samples t – test • A study to determine the effectiveness of an integrated statistics/experimental methods course as opposed to the traditional method of taking the two courses separately was conducted. • It was hypothesized that the students taking the integrated course would conduct better quality research projects than students in the traditional courses as a result of their integrated training. • Ho : there is no difference in students performance as a result of the integrated versus traditional courses. • H1 : students taking the integrated course would conduct better quality research projects than students in the traditional courses

  14. Output SPSS • Students taking the integrated course would conduct better • quality research projects than students in the traditional courses

  15. Exercise1 • The following data were obtained in an experiment designed to check whether there is a systematic difference in the weights (in grams) obtained with two different scales.

  16. Use the 0.01 level of significance to test whether the difference between the means of the weights obtained with the two scales is significant • Ho : there is no significant difference between the means of the weight obtained with the two scales. • H1 : there is significant difference between the means of the weight obtained with the two scales.

  17. Exercise 2 • The following are the scores for random samples of size ten which are taken from large group of trainees instructed by the two methods. • Method 1 : teaching machine as well as some personal attention by an instructor • Method 2 : straight teaching-machine instruction What we can conclude about the claim that the average amount by which the personal attention of an instructor will improve trainee’s score. Use =5%.

  18. Paired samples t-test

  19. Paired Samples T Test • What it does: The Paired Samples T Test compares the means of two variables. It computes the difference between the two variables for each case, and tests to see if the average difference is significantly different from zero.

  20. Paired Samples T Test • Where to find it: Under the Analyze menu, choose Compare Means, then choose Paired Samples T Test. Click on both variables you wish to compare, then move the pair of selected variables into the Paired Variables box.

  21. Paired Samples T Test • Assumption:-Both variables should be normally distributed. You can check for normal distribution with a Q-Q plot.

  22. Paired Samples T Test • Hypothesis:Null: There is no significant difference between the means of the two variables.Alternate: There is a significant difference between the means of the two variables

  23. SPSS Output • Following is sample output of a paired samples T test. We compared the mean test scores before (pre-test) and after (post-test) the subjects completed a test preparation course. We want to see if our test preparation course improved people's score on the test.

  24. First, we see the descriptive statistics for both variables. • The post-test mean scores are higher than pre-test scores

  25. Next, we see the correlation between the two variables • There is a strong positive correlation. People who did well on the pre-test also did well on the post-test.

  26. Finally, we see the results of the Paired Samples T Test. Remember, this test is based on the difference between the two variables. Under "Paired Differences" we see the descriptive statistics for the difference between the two variables

  27. To the right of the Paired Differences, we see the t, degrees of freedom, and significance. The t value = -2.171 We have 11 degrees of freedom Our significance is .053 If the significance value is less than .05, there is a significant difference.If the significance value is greater than. 05, there is no significant difference. Here, we see that the significance value is approaching significance, but it is not a significant difference. There is no difference between pre- and post-test scores. Our test preparation course did not help!

  28. Example • Twenty first-grade children and their parents were selected for a study to determine whether a seminar instructing on inductive parenting techniques improve social competency in children. The parents attended the seminar for one month. The children were tested for social competency before the course began and were retested six months after the completion of the course.

  29. Hypothesis • Ho : there is no significant difference between the means of pre and post seminar social competency scores • In other words, the parenting seminar has no effect on child social competency scores

  30. There is a strong positive correlation. children who did well on the pre-test also did well on the post-test. There is significant difference between pre- and post-test scores. the parenting seminar has effect on child social competency scores!

  31. Exercise 3 • The table below shows the number of words per minute readings of 20 student before and after following a particular method that can improve reading.

  32. Using a 0.05 level of significance, test the claim that the method is effective in improve reading.

  33. Exercise 4 • The table below shows the weight of seven subjects before and after following a particular diet for two months • Subject A B C D E F G • After 156 165 196 198 167 199 164 • Before 149 156 194 203 153 201 152 • Using a 0.01 level of significance, test the claim that the diet is effective in reducing weight.

  34. One-WayANOVA • Similar to a t-test, in that it is concerned with differences in means, but the test can be applied on two or more means. • The test is usually applied to interval and ratio data types. For example differences between two factors (1 and 2). • The test can be undertaken using the Analyze - Compare Means - One-Way ANOVA menu items, then select for appropriate variables. • You will observe the One-Way ANOVA for factor 1 and factor 2

  35. Procedure • 1. You will need one column of group codes labelling which group your data belongs to. The codes need to be numerical, but can be labelled with text. • 2. You will also need a column containing the data points or scores you wish to analyze. • 3. Select One-way ANOVA from the Analyze and Compare Means menus. • 4. Click on your dependent variables (data column) and click on the top arrow so that the selected column appears in the dependent list box. • 5. Click on your code column (your condition labels) and click on the bottom arrow so that the selected column appears in the factor box.

  36. 6. Click on Post Hoc if you wish to perform post-hoc tests.(optional). • 7. Choose the type of post-hoc test(s) you wish to perform by clicking in the small box next to your choice until a tick appears. Tukey's and Scheffe's tests are commonly used. • 8. Click on Dunnett to perform a Dunnett's test which allows you to compare experimental groups with a control group.Choose whether your control category is the first or last code entered in your code column.

  37. The main output table is labelled ANOVA. The F-ratio of the ANOVA, the degrees of freedom and the significance are all displayed. The top value of the df column is the df of the factor, the bottom value is the df of the error term. • Tukey's test will also try to find combinations of similar groups or conditions. • In the Score table there will be one column for each pair of conditions that are shown to be 'similar'. The mean of each condition within the pair are given in the appropriate column. The p-value for the difference between the means of each pair of groups is given at the bottom of the appropriate column.

  38. Example – one-way ANOVA • We would like to determine whether the scores on a test of aggression are different across 4 groups of children (each with 5 subjects) • Each child group has been exposes to differing amounts of time watching cartoons depicting ‘toon violence’

  39. At the 0.05 significance level, test the claim that the four groups have the same mean if the following sample results have been obtained.

  40. Output SPSS

  41. Exercise 5 • At the same time each day, a researcher records the temperature in each of three greenhouses. The table shows the temperatures in degree Fahrenheit recorded for one week. • Greenhouse #1 greenhouse #2 greenhouse #3 73 71 61 72 69 63 73 72 62 66 72 61 68 65 60 71 73 62 72 71 59 Use a 0.05 significance level to test the claim that the average temperature is the same in each greenhouse.

  42. Nonparametric Test

  43. Sign Test • A sign test compares the number of positive and negative differences between related conditions

  44. Procedure • 1. You should have data in two or more columns - one for each condition tested. • 2. Select 2 Related Samples from the Analyze - Nonparametric Tests menu. • 3. Click on the first variable in the pair and the second variable in the pair. • The names of the variables appear in the current selections section of the dialogue box. • 5. Click on the central selection arrow when you are happy with the variable pair selection. • The chosen pair appairs in the Test Pair(s) List. • Make sure the Sign box is ticked and remove the tick from the Wilcoxon box

  45. Example • The data in table on the next slide are matched pairs of heights obtained from a random sample of 12 male statistics students. Each student reported his height, then his weight was measured. Use a 0.05 significance level to test the claim that there is no difference between reported height and measured height.

  46. Reported and measured height of male statistics student Ho: there is no significant difference between reported heights and measured heights H1 : there is a difference

  47. Output Reject Ho. There is sufficient evidence to reject the claim that no significant difference between the reported and measured heights.

  48. Exercise 6 • Listed here are the right- and left-hand reaction times collected from 14 subject with right handed. Use 0.05 significance level to test the claim of no difference between the right hand- and left-hand reaction times.

  49. Right/left reaction times

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