As a seasonal example, just because people in the UK tend to spend more in the shops when it's cold and less when it's hot doesn't mean cold weather causes frenzied high-street spending. A more plausible explanation would be that cold weather tends to coincide with Christmas and the new year sales.ĭespite embodying an important truth, the phrase has not caught on in the wider world. Our preconceptions and suspicions about the way things work tempt us to make the leap from correlation to causation without any hard evidence.Ĭorrelations between two things can be caused by a third factor that affects both of them. This sneaky, hidden third wheel is called a confounder.Īrguably the most well known and important example of a correlation being clear but caustion being in doubt concerned smoking and lung cancer in the 1950s. There had been a sixfold increase in the rate of lung cancer in the preceding two decades. Nobody disputed that there was a correlation between lung cancer and smoking, but to prove that one caused the other would be no mean feat. There might be a confounder that was responsible for the correlation between smoking and lung cancer. Perhaps people who were more genetically predisposed to want to smoke were also more susceptible to getting cancer? The increased rate could have been the result of better diagnosis, more industrial pollution or more cars on the roads belching noxious fumes. It took a study involving more than 40,000 doctors in the UK to show conclusively that smoking really does cause cancer.Looking at the six statements in Table 1 below, it is evident that one statement has been negatively worded. However, the scoring system does not reflect it.
This is because all variables, except Variable 2, are positively worded. We must therefore recode this variable to ensure that high score indicates a positive response. Overall, how satisfied are you with your physical health? Overall, how anxious did you feel yesterday? Overall, how happy did you feel yesterday? Table 1 : List of Items for Factor Analysis. Overall, how satisfied are you with the balance between time spent on paid job and the time spent on other aspects of life? Overall, how satisfied are you with your work situation? Overall, how satisfied are you with your mental health? The measures of central tendency displayed in Figure 1 indicate the sample is quite positive in terms of happiness with a mean score of 7.6 in a scale of 0–10. This is further reflected with a mean anxiety score of 2.8 on a scale of 0–10. We must now look at the KMO measure of sampling adequacy and Bartlett’s Test of Sphericity ( Table 2). 74 and, therefore, suggests that the sample is adequate for Factor Analysis. Bartlett’s test of sphericity was significant (.32. We can therefore maintain the use of the Direct Oblimin Rotation method. If all coefficients are <.32, we must use Varimax Rotation, as this method of Rotation assumes factors are not correlated. We can now examine the Factor Loadings in the pattern matrix below in Table 4. Table 4: Pattern Matrix and Factor Loadings.ġ.
Overall, how happy did you feel yesterday?Ģ. Overall, how anxious did you feel yesterday?ģ. Overall, how satisfied are you with your physical health?Ĥ. Overall, how satisfied are you with your mental health?ĥ. Overall, how satisfied are you with your work situation?Ħ.
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