Measures of Central Tendency are ways of describing the central position of a frequency distribution for a group of data. You can describe this central position using the mean, median, or mode. Which you use will depend on how much and the type of data you collected.
Type of Data for Responding Variable | Best Measure of Central Tendency |
Qualitative Nominal (no ranking or order) | Mode |
Qualitative Ordinal (has ranking or order) | Median |
Quantitative With majority of skew values less than |1.0| | Mean |
Quantitative With majority of skew values more than |1.0| | Median |
The arithmetic mean is the most commonly used measure of central tendency. The mean is essentially a model of a data set for normally distributed (non-skewed) data. The mean has one main disadvantage: it is particularly susceptible to the influence of outliers. These are values that are unusual compared to the rest of the data set by being especially small or large in numerical value. That’s why we determined the skew.
The mean is equal to the sum of all the values in the data set divided by the number of values in the data set. So, if we have N values in a data set and they have values x1,x2, … ,xn, the sample mean, usually denoted by x̄ (pronounced "x bar"), is:
Calculating the Mean in Sheets
| Calculating the Mean in ExcelOpen Excel and enter your data in columns. You can label the columns if you prefer. To calculate mean:
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The median is the middle score for a set of data that has been arranged in order of magnitude. The median should be used if there are outliers or skewed data (meaning the data does not fit a “normal distribution”).
The mode is the most frequent score in a data set. Calculate the mode by tallying the result that is repeated more often than any other.