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Three primary pieces of information are typically used to provide indicators of subjects' performances in data. These three pieces of information are: the shape of the distribution of scores (symmetrical, positively or negatively skewed), its "average" or typical score (e.g., mean, median, or mode), and the spread or variability of the scores in the
distribution (e.g., range, variance, and standard deviation). The shape of the distribution of scores is reflected in the relationship among the "average" or typical scores in that distribution. Although each measure of central tendency attempts to identify the most typical score in that distribution of scores, each measure has its own interpretation of the most typical score. The mean defines
central tendency as the mathematical average of all the scores (a measure that you are very familiar with). The median defines central tendency as the point where half the scores fall above that value and half the scores fall below it. Finally, the mode defines central tendency as the most frequently occurring score in that distribution of scores. If the data being analyzed is qualitative, then the only measure of central tendency that can be reported is the mode. However, if the data is quantitative in nature (ordinal or interval/ratio) then the mode, median, or mean can be used to describe the data. With quantitative data, the shape of the
distribution of scores (symmetrical, negatively or positively skewed) plays an important role in determining the appropriateness of the specific measure of central tendency to accurately describe the data. If the distribution of scores is symmetrical or nearly so, the median and mean (as well as the mode) will be real close to each other in value. In this case, the mean is the value of central tendency that is usually reported. However, if the distribution of scores is
positively or negatively skewed, the mean will tend to either overestimate (in positively skewed distributions) or underestimate (in negatively skewed distributions) the true central tendency of the distribution. In extreme cases of skewed data, the mean can lie at a considerable distance from most of the scores. Therefore, in skewed distributions, the median will tend to be the more accurate measure to represent the data than the mean because the median can never have more
than one half the scores above or below it. As with measures of central tendency, different measures of dispersion are appropriate for different problems. The most common measures of dispersion are the range, variance, and standard deviation. The appropriateness of each would depend, in part, on the type of data that you have and which measure of central tendency you are using. If the data is qualitative, then there is no measure of variability to report. For data that is quantitative (ordinal and interval/ratio) all three measures are possible. However, the shape of the distribution of scores and the measure of central tendency reported will determine which measure of variability to use. If the distribution of scores is symmetrical in nature, then the measures of variability usually reported are the variance and standard deviation, although the standard deviation would be more interpretable. However, if the data is skewed, then the measure of variability that would be appropriate for that data would be the range. In summary, with qualitative data, the only additional measure to be
concerned with to further describe that data would be the mode. With quantitative data, the mean, variance, and standard deviation would be appropriate with symmetrical distributions while the median and range would be appropriate when the distribution is skewed (either positively or negatively). Which measures of central tendency is when the score in the middle of the set of items cuts or divides the set into two groups?Median. The median of a data set is the value that is at the middle of a data set arranged from smallest to largest. In the data set 1, 2, 3, 4, 5, the median is 3. In a data set with an even number of observations, the median is calculated by dividing the sum of the two middle values by two.
What measure of central tendency is represented by the middle number when the data is sorted from lowest to highest?Median. The median is the middle value in a distribution. It is the point at which half of the scores are above, and half of the scores are below. It is not affected by outliers, so the median is preferred as a measure of central tendency when a distribution has extreme scores.
Which measure of central tendency is obtained using the middle score if the scores are arranged according to magnitude?The median is the middle value. It is the value that splits the dataset in half, making it a natural measure of central tendency. To find the median, order your data from smallest to largest, and then find the data point that has an equal number of values above it and below it.
Which measure of central tendency is obtained using the middle score when all scores are arranged in increasing or decreasing order?The median is the middle value of the ordered data. The most important step in finding the median is to first order the data from smallest to largest. Steps to finding the median for a set of data: Arrange the data in increasing order, i.e. smallest to largest.
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