Measures of central tendency and dispersion

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Created by
Hanas

Cards (18)

  • Descriptive Statistics

    The finings of a study are described using measures of central tendency and dispersion
  • Inferential Statitics
    Go beyond description and use statistics to infer that there results gained are significant and not a chance fining
  • Central Tendency

    Refers to averages, including mean median and mode
  • Dispersion refers to the spread of scores , includes the range
  • The mode is calculated by putting the data into a frequency table and seeing which scores have the highest frequency
  • The Mode Advantage - most suitable when using category data in the absense of normally scores
  • The Mode Limitation - not useful when data contains lots of common scores, also doesn’t use all of the scores
  • Calculating the median involves placing data into order from lowest to highest and chosing the middle score
  • Advantage of Median - most suitable measure if data can be ranked (ordinal) and if anomalous scores exist
  • Limitation of the Median - not useful if the sample is small and there are few scores to analyse
  • To find the mean add up all the scores in the set of data and divide by the number of scores in the set
  • The mean advantage - Most suitable for Interval data >
    Considers all of the scores in the set of data; Very analytical
  • The Mean limitation - Not useful if the data contains anomalous scores > skews calculation.
    Not useful on category data
  • Calculating the range involves listing the data into order order form lowest to highest and then subtracting the smallest score from the largest
  • The range advantages - most suitable with ordinal data(ranked) so used in conjunction with the median. Quick calculation
  • The range limitations - Not Useful When Data Contains an anomalous score A large range doesn’t tell us how the scores are dispersed
  • Advantages of standard deviation - Most Suitable measure of spread if data is Interval Very analytical > Considers all scores
  • Standard deviation limitation - Not useful when data contains an anomalous score – skews the SD