SPSS Workbook 3 Chi-squared & Correlation

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1 TEESSIDE UNIVERSITY SCHOOL OF HEALTH & SOCIAL CARE SPSS Workbook 3 Chi-squared & Correlation Research, Audit and data RMH 2023-N Module Leader:Sylvia Storey Phone:

2 SPSS Workbook 3 Inferential Statistics 1 (Chi Square & Correlation) Last week you created a database and entered data into SPSS. This is something you need to become familiar with (especially as it will form part of your SPSS exam). This week you will again be entering data into SPSS but the data files will be much simpler. 1.Chi-square (X 2 ) Chi-square is a test of association between 2 categorical (nominal) variables (remember last week when we looked to see if there was any relationship between gender and smoking status?). The data below looks at the results of playing a Wii tennis game. All participants are paired male/female and we are looking to see if there is a relationship between the variables: gender and winner. Please set up your database accordingly and enter the data below (the data tables will not always be presented in the same format as they need to be entered into SPSS so you need to think about this): Part Sex m f m f m f m f m f m f m f m f m f m f Win? y n y n n y y n n y y n y n y n n y y n To carry out the chi-square test select: Analyse- Descriptive statistics Crosstabs: then move gender into the Row(s) box and winner into the Column(s) box. Selected Display clustered bar charts and then click on Statistics. 2

3 Click on Chi-square and Continue. Click on Cells Now select Expected and Row as detailed bellowed and the Continue and OK to finish. : 3

4 The output from this test should appear as: This table details the number of cases and would identify any missing values This is the same as the table produced last week but we have added additional information. We can now see the % of males and females who won. This is particularly helpful when the numbers are uneven but for our data we had equal numbers of males and females. This table reports the chi-squared and p- values for the test, ie it tells us whether there is any significant difference between the groups. What do the results tell you? 4

5 2.Correlation Correlation looks at the relationship between 2 variables eg weight & height. The type of correlation we are going to look at is Pearson s correlation (a non-parametric correlation called Spearman s correlation is also possible). We need to determine if the data we have meets certain parametric assumptions. These are : The data is at least interval level The data are drawn from normally distributed populations The relationship is linear and points are evenly distributed along the straight line (we will look at this later). Now open the data set POMS data you will need to download this from BB and save it. A copy of the questionnaire can be found in the appendices and scores have been calculated for the following subscales of the questionnaire: The Profile of Mood State (POMS) Questionnaire This questionnaire looks at six aspects of mood state, 5 having a negative effect on performance and one, vigour/activity having a positive effect (McNair et al, 1971) Tension-Anxiety(T): Relates to increased body tension, some which may not be readily observable (feeling tense, anxious, on edge) and some which are observable (feeling shaky, being restless). Related to the ability of the body to behave in a controlled and co-ordinated manner. Depression-Dejection(D): Represents a mood of depression accompanied by feelings of personal inadequacy, difficulty in adjusting to requirements of the situation. Anger-Hostility(A): Characterised by feelings of intense, overt anger (bad-tempered, rebellious), sulleness and milder feelings of hostility. Vigour-Activity(V): Represents vigorousness and high energy. Negatively related to the other mood state measures in that a high V score is likely to be associated with lower scores on the other measures. Fatigue-Inertia(F): Represents a mood of weariness, inertia and low energy level. May be related to physical and psychological stress. Confusion-Bewilderment(C): MAY represent some cognitive (thinking) inefficiency as well as a mood state. Often the result of negative forms of arousal (anxiety, worry). From the information identify 2 sets of variables where you would expect to see the following: A negative correlation A positive correlation 5

6 You will need to test the variables to see if they are normally distributed. This can be done in a number of ways and we will look at this in a later session. Then carry out a correlation by selecting: Analyse Correlate Bivariate And move the variables you wish to compare into the Variables box and click on OK to finish. 6

7 The output from the test will appear as: Correlations vigour fatigue vigour Pearson Correlation ** Sig. (2-tailed).002 fatigue Pearson Correlation ** 1 Sig. (2-tailed).002 The correlation coefficient (r) for this test is The p-value is 0.002, which is significant at the 0.01 level. **. Correlation is significant at the 0.01 level (2-tailed). What does this mean? Now repeat this with the other set of variables. Correlations tension fatigue tension Pearson Correlation ** Sig. (2-tailed).000 fatigue Pearson Correlation.561 ** 1 Sig. (2-tailed).000 **. Correlation is significant at the 0.01 level (2-tailed). The correlation coefficient (r) for this test is The p-value is (ie you need to round up the value as you cannot quote it as being 0.000) Now produce a graph for these by selecting: Graphs Legacy Dialogs Scatter/dot Select Simple and click on Ok to continue. 7

8 Move the 2 variables from your fist correlation into the boxes marked Y-axis and X-axis. Click on OK to continue. Now repeat for the 2 nd set of variables and compare the 2 graphs. 8

10 Appendices. Data set 1: Output from Chi-squared: gender * winner Crosstabulation yes winner no Total gender male Count Expected Count % within gender 70.0% 30.0% 100.0% female Count Expected Count % within gender 30.0% 70.0% 100.0% Total Count Expected Count % within gender 50.0% 50.0% 100.0% 10

11 From this table we can see that 70% males (compared to 30% females) were winners. But remember this is not cause and effect ie being male does not make you a winner. The table below tells us that the relationship is not significant ie p=>0.05 Chi-Square Tests Asymp. Sig. (2- Exact Sig. (2- Exact Sig. (1- Value df sided) sided) sided) Pearson Chi-Square a Continuity Correction b Likelihood Ratio Fisher's Exact Test Linear-by-Linear Association N of Valid Cases 20 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is b. Computed only for a 2x2 table In a report this would be expressed as ( X 2 = 3.2, df = 1, p = 0.074). The p value is however for a 2-tailed test and if we had a directional hypothesis this value would be halved and at p = the results would be significant. The clustered bar chart below shows the results in visual format 11

12 2.Correlation. Correlations vigour fatigue vigour Pearson Correlation ** Sig. (2-tailed).002 fatigue Pearson Correlation ** 1 Sig. (2-tailed).002 **. Correlation is significant at the 0.01 level (2-tailed). The table shows that there is a relationship between the fatigue and vigour scores and that this relationship is a negative one (ie the r value is negative). If you look back at the explanation of the scales it says that of the 6 sub-scales, 5 represent negative aspects of mood and that the vigour scale represents a positive aspect of mood. We would therefore expect to see this scale negatively correlated with the other mood subscales. Compare this to the results below which show a positive correlation ie the r value is Correlations 12

13 tension fatigue tension Pearson Correlation ** Sig. (2-tailed).000 fatigue Pearson Correlation.561 ** 1 Sig. (2-tailed).000 The p value identifies that the relationship is significant and the r value indicates the strength of the relationship (range from -1 to +1). The classification of these ranges vary between text books. You should have a table for the correlation values from your own search. Scattergrams 13

14 14

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