PSY 202 Chapter 5 Notes
PSY 202 Chapter 5 Notes Psy 202
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This 2 page Class Notes was uploaded by Stephanie on Thursday September 15, 2016. The Class Notes belongs to Psy 202 at University of Mississippi taught by Matthew Mervin in Fall 2016. Since its upload, it has received 9 views. For similar materials see Elementary Statistics in Psychology at University of Mississippi.
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Date Created: 09/15/16
PSY 202: Elementary Statistics Chapter 5: Describing the Position of a Case within a Set of Scores I. Expressing the Ordinal Position of a Score a. We are trying to figure out where a specific score in a distribution falls b. 1 Percentile and Percentile Rank i. Definition: The percentile rank tells you the percentage of scores that are at or below the level of the specific score that you are looking at ii. It does not give distance, only rank information iii. With percentile rank you start with the raw scores and then turn them into cumulative relative frequency iv. With first percentile you start with the cumulative relative frequency and then turn it into the original raw score v. We use percentile rank the most 1. It is an easier conversion vi. Always write cumulative relative frequency in decimal form and use it to find the percentile rank vii. How to write out percentile 1. Ex: 63 Percentile Rank 2. When writing it out convert the cumulative relative frequency into a percentage and then round down a. Ex: 63.7 = 63 Percentile not 64 th viii. Interpretation 1. It only tells us where a score is but not how far away it is from other scores (above or below) ix. Misinterpretations of Percentile Rank 1. It is not a percentage 2. It cannot tell us anything else about the raw scores II. The Position of a Score Relative to the Mean a. This gives both rank and distance information b. Setting the Standard i. Compare deviation from raw score to standard deviation 1. We use standard deviation as a unit of measurement 2. These are called standardized scores c. ztransformation i. Same as standardized scores ii. They rename the scores but do not change the distribution iii. Subtract the raw score from the mean and then divide by the standard deviation iv. zscores always sum to 0 v. Their average is 0 so the mean is always 0 vi. Variance for zscores will always be equal to 1 vii. A positive zscore means above the mean while a negative zscore means below the mean 1. The absolute value tells how many standard deviations the zscore is away from the mean III. Converting Raw Scores to Standard Scores a. zscores make it easier to compare different raw scores i. Ex: Say the in one distribution the raw score is 230 and the mean is 200 and in another distribution the raw score is 117 and the mean is 100. To see which one is bigger you subtract the mean from the raw score and then divide by the standard deviation. 1. 230 – 200 = 30 ÷ 40 = 0.75 2. 117 – 100 = 17 ÷ 20 = 0.85
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