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Published bySean Wiley Modified over 2 years ago

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Mean, Mode, and Median Mean: A measure of central tendency which is more commonly known as an "average." The average or mean is calculated by adding all scores and then dividing by the number of scores. For example, the mean of 3, 5, and 1 is 3. Mode: A measure of central tendency which is defined by the most common number in an array. For example, the following string of numbers: 1, 3, 3, 3, 56, 89, 89; the mode in this case would be 3 since it is the most frequent number observed in the sample. Median: A measure of central tendency that is defined as the midpoint in an array of numbers. The median for 1, 6, 102, 1000 and 1,323 would be 102. If the array has an uneven number of scores, the midpoint is the average of the two numbers closest to the middle. For example, for the array 1, 2, 3, 4, the median would be 2.5

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Correlation Correlation: A correlation is a statistical index used to represent the strength of a relationship between two factors, how much and in what way those factors vary, and how well one factor can predict the other.

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Correlation does not equal causation! Using correlations does NOT (I repeat, does not) provide you with cause and effect information; it will not tell you if one factor causes or is caused by the other. This fact was an important component in the court cases against the tobacco companies that occurred in the late 1990's. The studies conducted previously on the effects of smoking indicated a positive correlation between smoking and cancer. This means that the studies found that as the rate of smoking increased, so did the occurrence of cancer; smoking goes up, presence of cancer goes up. BUT, this does not demonstrate that smoking causes cancer (does anyone disagree that it does?), only that there is a relationship between the two factors.

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Positive and Negative Correlations Scores with a positive correlation coefficient go up and down together (as with smoking and cancer). A negative correlation coefficient indicates that as one score increases, the other score decreases (as in the relationship between self-esteem and depression; as self-esteem increases, the rate of depression decreases).

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Positive, Negative, and Random Correlation- Which ones which? A B C

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Variables In an experiment there are two variables; the Independent Variable (IV) and the Dependent Variable (DV). In the most basic sense, you need two variables because as a researcher, you want to be able to examine if something (a drug, a therapy, a teaching technique, whatever) has an effect on some participant (person, people, animals, etc.). To accomplish this, you need to have something to examine (and manipulate -- this is the IV); some variable of interest, as well as something to measure the effect the IV has (this is the DV).

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Variables Therefore, we can define the independent variable as the experimental variable or variable that is manipulated by the research and has some effect on the DV. If there is a change or effect, we may conclude that the IV affected the DV. The ultimate here is to establish that the IV caused the change in the DV (this is the magical "cause- effect"). As a quick example, if you want to study the effect of drinking 12 ounces of beer before an exam on exam performance, the beer would be the IV (we may have one treatment group whose participants drink the beer and one control group who does not drink the beer); the performance on the exam would be the DV.

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