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Statistics in Psychology In descriptive, correlational and experimental research, statistics are tools that help us see & interpret what the unaided eye might miss… The type of statistics computed for a set of data depends on what type of data is collected. Nominal Data Gender is an example of nominal data, which is data that simply identifies categories. Yes/No answers on surveys & class level in school (senior, junior, etc.). Ordinal Data Identifies the order in which data falls in a set. Any ranking of items, from one-hit wonders to class rank to home-run sluggers, is done as ordinal data. Interval Data Includes data that falls within a number line that has a zero point. Weight has a real zero point in that a weight of zero means no weight (of course, zero weight means something does not exist!) Height is another example of interval data. Ratio Data Includes data that falls in a number line where zero is just another number on the line. For instance, a temperature of zero degrees does not mean there is no temperature. Test scores can also be ratio data in that a score of zero usually doesn’t mean “absence of knowledge.” Measures of Central Tendency ~A score that represents a whole set of scores. *Central tendency refers to how the data measure the center of a set of data. The mode, median & mean all point to where the middle of the data should be. *If the mode, median & mean are all the same number, the graph of the data will look like a normal curve. If they are different, the graph will be skewed, or off center, in some way. Positive & Negative Skew Positive skew occurs when scores pull the mean toward the higher end of the scores. Hence the mean is more “positive” or greater than the rest of the scores. Negative skew is the opposite, with the mean being pulled down toward the lower end of the scores. Measures of Variation The single number offered in measures of central tendency omits quite a bit of information. It helps to know something about the variation in the data – how similar or diverse the scores are. Averages derived from scores with low variability are more reliable than averages based on scores with high variability. Thus, if a group of scores has a smaller standard deviation, then you can draw more stable conclusions from that data set. Example: If a set of test scores has a standard deviation of 5, then everyone who took the test scored similarly. If the scores have a standard deviation of 50, then the test-takers’ scores were not very similar at all! The Normal Curve KNOWING THE PERCENTAGES OF SCORES THAT FALL ONE, TWO & THREE STANDARD DEVIATIONS FROM THE MEAN IS IMPORTANT FOR THE AP NATIONAL EXAM! KNOW THESE PERCENTAGES WELL!!!!!!!!!!!!!~ Z-Score A Z-Score is a statistical measurement of a score's relationship to the mean in a group of scores. A Z-score of 0 means the score is the same as the mean. A Z-score can also be positive or negative, indicating whether it is above or below the mean and by how many standard deviations.