On stats Descriptive statistics reduce data sets to allow for easier interpretation. Statistics allow use to look at average scores. For instance, we could take the scores on the midterm exam and look at them. We would be looking at them as 22 individual scores. These numbers would not mean as much to us as would the average of these scores. Most people are very scared of the term statisitics. But if you will consider them differently this will help you. Fortunately, my stats teachers did not believe people should learn the formula’s but instead must know how to work the formula and when to apply a certain statistic to a certain situation. In our case we are going to work through a variety of statistical measures. We will look at the mean median and mode. In addition we will learn about chi-square, spearman rank order, t-test, ANOVA. By placing these numbers in a table we can begin to see a pattern and our interpretation of the data will help us.

Frequency We are interested in frequency distribution and the table let’s us look at that. How do we get the mean score of something. Frequency is the measure of the number of subjects or responses or cases. The mean is the arithmetic average of a set of scores. We can look at the distribution of scores. There are three locations of center. They are the mean-median and mode.

Median What this basically means is that your score value which divides distribution into halves. Half of scores are above and half are below. It is the mid point of distribution scores. You must order your scores. A ranking of scores is what you do. The median (Mdn) If the distribution has an odd number of scores, the median is the middle score; in the case of this set of numbers we would need to reorder them.

Remember the mean can be affected by extreme scores. The median can not be affected by extreme values. The problem is that it is an end of itself The mean can be affected by extreme values; the median cannot.

Proportion is the percentage of terms in specific observation. Measures of central tendencies refer to the typical score. Dispersion describes the way that scores are spread out about this central point. When you compare difference distributions this can be valuable. For instance, two classes where you have excellent students and a few not so hot. And a class where all students are above average. Most of the time you will report the mean and an index of dispersion. The three measures of dispersion are Range, variance and standard deviation. Range takes the highest and lowest scores and subtracts them. Range always increases with sample size and is not very descriptive. Range is seldom used in mass communication Variance is how much deviation from the mean score. A small variance means that there is little difference between the mean and the scores around it. A large variance means lots of widely scattered scores. Variance is tied to the degree of dispersion or difference among the group of scores. To compute variance the mean is subtracted from each score; these deviation scores are then squared, and the squares are summed and divided by n-1.. Variance is tied to analysis of variance or ABOVA. Widely used in inferential statistics. In this case there is a problem because it is expressed in terms of squared deviation from the mean and not the original measurement. So a better measure would be to employ the standard deviation. Standard deviation is the square root of the variance. The standard deviation is more useful than variance because it is expressed in the same unites as the measurement used to compute it.

By computing the mean and standard deviation of a set of scores or measurements, researchers can compute standard scores for any distribution of data. Z scores allow researchers to compare scores or measurements obtained from totally different methods. They allow for comparisons of apples and oranges. The z formula is z= x-x / s Each score represents how many standard deviation units a sorce, rating or entity is above or below the mean of the data set.