Smoking Data The investigation was based on examining the effectiveness of smoking cessation programs among heavy smokers who are also recovering alcoholics.

Smoking Data The investigation was based on examining the effectiveness of smoking cessation programs among heavy smokers who are also recovering alcoholics. This work is loosely based on the page. Mike Cox, Newcastle University, me fecit 30/11/2015 Tuesday, 25 April :20 PM

Smoking Data ALA – American Lung Association

Smoking Data Note that gender has been coded

Which Test? Do rates of smoking decrease from pre-intervention to post-intervention? Two-sample Correlated t-test

Two-sample Correlated t-test
Analyze > Compare Means > Paired-Samples t Test

Highlight pre, move across. You will see that pre now appears as Variable 1 in the Paired Variables box.

Highlight post, move across. You will see that post now appears as Variable 2 in the Paired Variables box.

Click on OK to run the analysis or Paste to preserve the syntax.

Syntax GET FILE='\\Client\f$\spss\1\1.sav'. T-TEST PAIRS = pre WITH post (PAIRED) /CRITERIA = CI(.95) /MISSING = ANALYSIS. Note, you can even include an option to load the data file.

The first table of the printout contains descriptive statistics while the second table contains inferential statistics. Study the printout you can identify n, Mean, and Std for each group.

The second table contains inferential statistics. You can identify t, df, and p (p is in the column labelled "Sig. (2-tailed)"). In this case, the mean number of cigarettes smoked prior to the intervention programs was significantly higher than the number of cigarettes smoked after the intervention programs, t29 = 18.57, p < Note that the confidence interval excludes zero.

Which Test? Do rates of smoking decrease across the four data collection periods. That is, does smoking not only decrease from pre-intervention to post-intervention but also does the rate continue to decrease during a 6-month and 12-month follow up? Repeated Measures Analysis of Variance (ANOVA)

Repeated Measures Analysis of Variance (ANOVA)
Analyze > General Linear Model > Repeated Measures

In the Within Subject Factor Name box designate a name for the repeated measure factor, let’s call it rate.

In the Number of Levels window type in the number of time periods measured. In this case it is 4.

Click on Add. To generate rate(4) Click on Define

Click on Define. A Repeated Measures box will appear.

Highlight your first time variable, pre, from the list of variables on the left, and click on the upper arrow button to move it into the Within Subject Variables window.

Add the remaining three time variables, post, follow6, and follow12, in the same fashion. Finally Click on the Options button.

Click on the square next to the word Descriptive Highlight rate in the Factor(s) and Factor Interaction box. Click on the arrow button and click on the square next to the word Compare main effects. Finally click on Continue.

Finally click on OK to run the analysis or Paste to preserve the syntax.

Syntax GLM pre post follow6 follow12 /WSFACTOR = rate 4 Polynomial /METHOD = SSTYPE(3) /EMMEANS = TABLES(rate) COMPARE ADJ(LSD) /PRINT = DESCRIPTIVE /CRITERIA = ALPHA(.05) /WSDESIGN = rate. Note, it is formatted as a General Linear Model.

You can identify n, Mean, and Std for each of the four time periods in the descriptive statistics output.

Repeated Measures Analysis of Variance (ANOVA) - Sphericity
ANOVAs with repeated measures (within-subject factors) are particularly susceptible to the violation of the assumption of sphericity. Sphericity is the condition where the variances of the differences between all combinations of related groups (levels) are equal. Violation of sphericity is when the variances of the differences between all combinations of related groups are not equal. Sphericity can be likened to homogeneity of variances in a between-subjects ANOVA.

Repeated Measures Analysis of Variance (ANOVA) - Sphericity
When the probability of Mauchly's test statistic is greater than or equal to .05 (i.e., p > .05), we fail to reject the null hypothesis that the variances are equal. Therefore we could conclude that the assumption has not been violated. However, when the probability of Mauchly's test statistic is less than or equal to .05 (i.e., p < .05), sphericity cannot be assumed and we would therefore conclude that there are significance differences between the variances. It should be noted that sphericity is always met for two levels of a repeated measure factor, and it is therefore unnecessary to evaluate. 25

Examine Mauchly's test of Sphericity to determine if the homogeniety of variance assumption is met. 26

For Mauchly's test if the p-value is significant (look under Sig.) then the assumption has been violated. This will determine which values you interpret on the ANOVA table (Tests of Within-Subject Effects). Which is the case here, so use values associated with Huynh-Feldt test.

Examine the Tests of Within-Subject Effects table (ANOVA table) to determine the significance of your omnibus test.

When the Sphericity assumption is not violated, you can interpret the top set of values (i.e., Sum of Squares, df, Mean Sum of Squares, F, and p (Sig.)).

When the Sphericity assumption is violated, you can interpret the values associated with Huynh-Feldt test. In this case, there is a significant difference in smoking rates across the time periods, F(1.31,38.09) = , p < (Huynh-Feldt). Since the results of the repeated measures ANOVA are significant, you will want to examine the post-hoc tests to determine between which time periods significant differences are occurring by using the Multiple Comparison table.

The Pairwise Comparisons table provides detailed information concerning the post-hoc results.

The table shows all possible comparisons between the four time periods. In the first row, the pre-intervention smoking rate is compared to the post-intervention smoking rates. The mean difference for this comparison is (i.e., the average smoking rate for pre-intervention, , is subtracted from the average post-intervention smoking rate, ). To determine whether this mean difference is statistically significant examine the "Sig. Column" which represents the p-value. The p-value is (p <) suggesting that the groups are significantly different from one another.

This is also supported by the 95% confidence interval which indicates that zero is outside the bounds. Following this comparison, a comparison is made between the pre-intervention smoking rates and smoking rates at a six month follow-up which shows a significant difference between the two groups, p < You will notice that SPSS places a star next to mean difference scores that differ significantly. The remaining rows provide the results for the other comparisons.

Which Test? Do the smoking rates differ across the three types of smoking cessation program over time? That is, does one program lead to greater reductions in smoking rates among smokers? Mixed Factorial ANOVA

Mixed Factorial ANOVA Analyze > General Linear Model > Repeated Measures

Mixed Factorial ANOVA Note the reset button used to remove redundant factors from old analysis. In the Within Subject Factor Name box designate a name for the repeated measure factor. In this case, let's call it time.

Mixed Factorial ANOVA In the Number of Levels window type in the number of time periods measured. In this case it is 2.

Mixed Factorial ANOVA Click on Add. Click on Define

Mixed Factorial ANOVA Having clicked on Define.

Mixed Factorial ANOVA Highlight your first time variable, pre, from the list of variables on the left, and click on the upper arrow button to move it into the Within Subject Variables window.

Mixed Factorial ANOVA Add the remaining time variable, post, in the same fashion.

Mixed Factorial ANOVA Highlight program and click on the arrow button in front of the Between-Subjects Factor(s) box. Click on the Options button.

Mixed Factorial ANOVA Click on the square next to the word Descriptive. Highlight time in the Factor(s) and Factor Interaction box. Click on the arrow button and click on the square next to the word Compare main effects. Click on Continue to return.

Mixed Factorial ANOVA Click on the Post Hoc... button

Mixed Factorial ANOVA Highlight program in the Factor(s) box. Click on the arrow button to move program to the “Post Hoc tests for:” box. Select a Tukey test by clicking on the box. Click on Continue to return to the Repeated Measures window. Then click on OK to run the analysis.

Mixed Factorial ANOVA Syntax GLM pre post BY program
/WSFACTOR = time 2 Polynomial /METHOD = SSTYPE(3) /POSTHOC = program ( TUKEY ) /EMMEANS = TABLES(time) COMPARE ADJ(LSD) /PRINT = DESCRIPTIVE /CRITERIA = ALPHA(.05) /WSDESIGN = time /DESIGN = program.

Mixed Factorial ANOVA You can identify n, Mean, and Std for each of the two time periods across the three interventions using the descriptive statistics output.

Mixed Factorial ANOVA Examine the Tests of Within-Subject Effects table (ANOVA table) to determine the significance of your omnibus test.

Mixed Factorial ANOVA The interaction effect should be examined first to determine if it is significant. In this case, the interaction effect is significant, F(1, 27) = 3.397, p = 0.05 (It should be noted that sphericity is always met for two levels of a repeated measure factor and it is, therefore, unnecessary to evaluate.) This suggests that there is a significant difference in the interventions programs across time. Since the interaction is significant, the main effect for time should not be interpreted. That completes the relevant ANOVA analysis.

Mixed Factorial ANOVA It should be noted that sphericity is always met for two levels of a repeated measure factor and it is, therefore, unnecessary to evaluate.

SPSS Tips Now you should go and try for yourself.
Each week our cluster (5.05) is booked for 2 hours after this session. This will enable you to come and go as you please. Obviously other timetabled sessions for this module take precedence.

Smoking Data The investigation was based on examining the effectiveness of smoking cessation programs among heavy smokers who are also recovering alcoholics.

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Smoking Data The investigation was based on examining the effectiveness of smoking cessation programs among heavy smokers who are also recovering alcoholics.

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Presentation on theme: "Smoking Data The investigation was based on examining the effectiveness of smoking cessation programs among heavy smokers who are also recovering alcoholics."— Presentation transcript:

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