#Median stata how to#

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#Median stata how to#

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Interpolated medians are another way of overcoming the gross nature of the median on ordinal data with few values.Our services are extremely affordable It isn't nearly making money for us.

Ordinal variables that are ranks do not suffer from this problem of 'grossness'. When it comes to summarising the central tendency of Likert items, I find the mean to be much more useful and sensitive to meaningful differences. When you are describing the central tendency of data on an ordinal variable with only a small number of response options (i.e., perhaps less than 20 or 50 or 100), the median can be quite gross (e.g., 1,1,3,3,3 and 1,3,3,5,5 both have a median of 3, but the second example would have a higher mean). This partially depends on what you want to get out of your measure of central tendency.

In practice, the median is often not the most useful summary of central tendency with ordinal variables.

In theory the median can be used on data from any variable where the values can be ordered.

When the sample size is even, then the median is the mean of the values either side of middle most point after rank ordering all values ( see wikipedia description). The median is the middle value of the data it is not by definition the middle value of the scale.