<ul><li>Spearman's Rank Correlation Coefficient </li></ul><ul><li>Chi-Square </li></ul><ul><li>Both tests look at the relationship between two </li></ul><ul><li>variables </li></ul><ul><li>They tell us whether the relationship is significant, </li></ul><ul><li>i.e. if one is related to the other </li></ul>Statistical Tests
Spearman's Rank Best used to look at either: Distance and Environmental Quality OR Distance and Counts Either look at total environmental quality score for a survey point or just one element. Choose a point on your map to say the distance it is from your survey point. Try and choose a point that will get results! Doesn't matter where your point is as long as you can explain why
1. Create a Null Hypothesis There is no significant relationship between x and y. The Null Hypothesis tells us that there is no significant relationship between two factors. That means we cannot say with any degree of certainty that one affects the other. You state the null hypothesis as shown below before any piece of statistical analysis. The purpose of your statistical analysis is to disprove the null hypothesis. Therefore you can say that there IS a significant relationship between x and y.
2. Create Table Site No. Dist from x Rank Env Q. Rank Diff Diff Sq don't need real distance - use cm on map (1) (2) (1)-(2) Sum of 1 2 3 4 5 20 15 7 35 25 3 2 1 5 4 2.5 2.5 1 5 4 14 14 12 25 24 0.5 -0.5 0 0 0 0.25 0.25 0 0 0 0.5
Answer is between +1 and -1 +1 - perfect positive relationship 0 - no relationship -1 - perfect negative relationship 3. Apply formula n = your number of sites
4. Can we reject null hypothesis Degrees of Freedom = number of sample sites. If larger than critical value then with 0.05 you are 95% certain that you can reject null hypothesis. 0.01 you are 99% certain you can reject the null
5. Writing it up Introduce: It was thought that litter would be worse closer to the cinema entrance. In order to investigate this relationship a Spearman's Rank test was carried out (figure 2). Null Hypothesis and All your calculations go in figure 2. Then explain what you have found out Environmental survey KQ1 - Counts KQ2
Chi Square Test Best place to use - look at the relationship between people's opinions against their age or gender. For example: Do men feel safer in Cardinal Park than women?
1. Create null hypothesis There is no significant relationship between .... What would it be if we were looking at gender and feelings of safety in Cardinal Park?
2. Create results table If you have a not sure column - you will need to merge these with agree or disagree See worksheet I have given you for an example!
3. Check over 5 Check all cells are over 5. If you have lower you will need to merge categories Again - there is an example of this on the sheet.
4. Calculate Expected You need to know: Total number of questionnaires Total in rows Total in columns Need to do 4 calculations for expected values - see sheet.
6. Degrees of Freedom - Does it fit the Null Slightly different calculation for degrees of freedom this time. It is the number of rows minus one x number of columns minus one. Again look at the significance level. Can you be 95% or even 99% certain?
7. Write up in same way as you did with Spearman's Introduce: It was thought that women would be more scared in Cardinal Park that men. In order to investigate this relationship a Chi Squared test was carried out (figure 3). Null Hypothesis and All your calculations go in figure 3. Then explain what you have found out
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