Central Tendency
Central Tendency is commonly referred to as the "measure of central tendency". A measure of central tendency is used to describe a data set by identifying the central position within that set of data. In statistics, the three most commonly used measures of central tendency are mean, median, and mode.
Mean: The mean is the average of all the numbers within a data set. To find the mean value, one would add up all the values in a data set and divide that sum by the total number of data points within the data set. Example - 3+4+5+6+7 = 25. There are 5 values in this data set. 25/5 = 5. In this scenario the mean, or average, is 5.
Median: The median is the middle point in a sorted set of data. The median is identified by organizing the data set into order of magnitude (starting with the smallest number). Once sorted the median is identified as the number directly in the middle of that sorted data. Example - 6, 9, 23, 15, 2. If we put this data set in order by magnitude it is displayed as: 2, 6, 9, 15, 23. In this data set 9 is the median.
Mode: The mode is the number that occurs most frequently in a data set. Example - 2, 3, 15, 3, 5, 7, 8, 3, 2, 1, 10, 9. In this data set, 3, is the number that occurs most frequently and would be identified as the mode.
contributed by Scott Trungadi
Having finally mastered the skills necessary to report my data, and thanks to the help from fellow students in my doctoral program, I was ready to write up a description of what all the numbers meant. I was excited to have reached this point in my central tendency assignment, as there is one thing I love doing, and that is write. Finally, something I might be good at! However, this also meant that I needed to understand and be able to explain what all the numbers meant.
In October of 2007 during the kindergarten year, the Letter Naming Fluency ( LNF) portion of the Dynamic Indicators of Basic Early Literacy Skills (Dibels) was administered to a class of 18 kindergarten students, 10 males and 8 females. The SPSS helped to process the following data regarding the testing administration. The mean score for the eighteen students for the Beginning LNF portion of Dibels was 32.33 with the median or midpoint being 35.0(Table 1). By gender, the boys in the class scored slightly lower with the mean score of 32.30 and the midpoint or median score of 34.50 as compared to the girls’ mean score of 32.38 and a median of 37.00(Table 5).
The standard deviation is based on all the scores in the group and is determined by how much each score deviates from the mean, or in other words, it is an estimate of what the range of scores probably was. The standard deviation tells me that in the Beginning and Ending LNF administration, all students tested, in a similar range-16.01 and 16.87(Tables 1 and 2). However, in looking at the Beginning LNF scores analyzed by gender, there is a large discrepancy between how well the boys did when compared to the girls. There was a higher standard of deviation for the boys than the girls, respectively 16.34 for the boys and 6.71 for the girls. In trying to understand the possible reasons for this, one must consider birthdates. Although birthdates were not considered in this collection of data, it is important to note that there was a higher incidence of younger birth dates for the boys than the girls which might account for this wide range in the boys’ scores.
The Z scores in this statistical analysis refer to how many standard deviations a particular raw score lies above or below the group means. Table 6 indicates the range of Z scores for the students who had taken the Ending LNF portion of the Dibels test. The score range from 1.44, or 1.44 standard deviations above the group mean of 64.67 to -1.82, or 1.82 below the group mean of 64.67.
I actually felt that I began to really understand what everything meant as I was scripting my report. It was very helpful. Hope this helps someone!
contributed by Debbie Mumford
Skewness and Central Tendency
Identifying where the mean, median, and mode are in relation to each other can help to determine the skew of a curve.
In a normal distribution, the mean, median, and mode will all be very close (mean = median = mode)
In a distribution that is skewed left, otherwise known as a negatively skewed distribution, it is outliers on the lower end of the number line that is impacting the shape of the curve. In a curve that is skewed left the mean (which is the mathematical average) will be furthest to the left on the number line, the median remains at the mid point of the distribution on the number line, and the mode will be the farthest point to the right.
In a distribution that is skewed right, otherwise known as a positively skewed distribution, it is outliers on the higher end of the number line that is impacting the shape of the curve. In a curve that is skewed right the mean (which is the mathematical average) will be furthest to the right on the number line, the median remains at the mid point of the distribution on the number line, and the mode will be the farthest point to the left.
contributed by Ashley Brooksbank
Image citation: Statistics: Basic Statistics II. (n.d.). Retrieved from https://guides.douglascollege.ca/c.php?g=408742&p=2970198.'
Descriptive Statistics
Descriptive statistics are very important because if we simply presented our raw data it would be hard to visualize what the data was showing, especially if there was a lot of it. Descriptive statistics therefore enables us to present the data in a more meaningful way, which allows simpler interpretation of the data.
Measures of central tendency: these are ways of describing the central position of a frequency distribution for a group of data. In this case, the frequency distribution is simply the distribution and pattern of marks scored by the 100 students from the lowest to the highest.
Measures of spread: these are ways of summarizing a group of data by describing how spread out the scores are. For example, the mean score of our 100 students may be 65 out of 100. However, not all students will have scored 65 marks. Rather, their scores will be spread out. Some will be lower and others higher. Measures of spread help us to summarize how spread out these scores are.
Both of these measures are important to calculate through SPSS prior to conducting other analyses as they can speak to you about what your data is saying and inform you of the next steps you should take.
contributed by Sheri Prendergast
