Interval data

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Interval data

  1. 1.  Exploring data<br />Dr Janelle Yorke<br />University of Salford<br /> & <br />Professor Carol Haigh<br />Manchester Metropolitan University<br />
  2. 2. Background & Aims<br />Based upon our joint reviewing experience<br />Differing degrees of irritation regarding application of statistical tests.<br />Seemed to be much confusion about test to be used<br />Was it just us?<br />
  3. 3. Making the terms clear…<br />
  4. 4. Nominal Data<br />A set of data is said to be nominal if the values / observations belonging to it can be assigned a code in the form of a number where the numbers are simply labels. You can count but not order or measure nominal data. For example -<br />In this example yes could be coded as 1, No as 2<br />
  5. 5. Categorical data<br />A categorical variable is for mutual exclusive, but not ordered, categories. For example, A Likert scale;<br />You can code the five categories with numbers if you want, but the order is arbitrary and any calculations (for example, computing an average) would be meaningless.<br />
  6. 6. Ordinal data<br />A ordinal variable, is one where the order matters but not the difference between values. For example, Pain Scales<br />Patients are asked to express the amount of pain they are feeling on a scale of 1 to 10. A score of 7 means more pain that a score of 5, and that is more than a score of 3. But the difference between the 7 and the 5 may not be the same as that between 5 and 3. The values simply express an order<br />
  7. 7. Interval data<br />A interval variable is a measurement where the difference between two values is meaningful. The difference between a temperature of 100 degrees and 90 degrees is the same difference as between 90 degrees and 80 degrees<br />
  8. 8. Ratio Data<br />A ratio variable, has all the properties of an interval variable, and also has a clear definition of 0.0. When the variable equals 0.0, there is none of that variable<br />
  9. 9.  <br />In summary….<br />
  10. 10. Know your tests<br />
  11. 11. Non-parametric tests<br />Nonparametric tests are often when certain assumptions about the underlying population are questionable.<br />For example, when comparing two independent sample non-parametric tests do not assume that the difference between the samples is normally distributed whereas parametric tests do<br />Nonparametric tests may be more powerful in detecting population differences when certain assumptions are not satisfied.<br />All tests involving ranked data, i.e. data that can be put in order, are nonparametric.<br />
  12. 12. Parametric tests<br />Parametric statistics allow you to assume the data come from a type of probability distribution and make inferences about the parameters of the distribution.<br />Generally speaking parametric methods make more assumptions than non-parametric methods.<br /> If those extra assumptions are correct, parametric methods can produce more accurate and precise estimates.<br /> They are said to have more statistical power.<br />
  13. 13. Which test for what sort of data?<br />
  14. 14. Thank you for your attention<br />

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