PSY 202 Chapter 6 - Day 2
PSY 202 Chapter 6 - Day 2 Psy 202
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This 2 page Class Notes was uploaded by Stephanie on Thursday September 22, 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: 09/22/16
PSY 202: Elementary Statistics Chapter 6: Describing the Relationship between Two Quantitative Variables: Correlation – Day 2 I. Assessing the Strength of Linear Association a. Restriction in Range i. Issues in interpretation 1. There are a bunch of values for X & Y but we are restricted to a certain few 2. This causes us to underestimate the strength of the relationship b. The Presence of Outliers i. Outliers make the relationship look stronger/ weaker than it actually is ii. The regression line has to fit all of the points and that includes outliers iii. The flatter the line the weaker the relationship c. The Presence of Subgroups i. This strengthens the correlation coefficient ii. This makes the relationship look stronger than it actually is iii. This happens when you look at different groups and then group them together, but they are supposed to be looked at separately d. Causality i. Correlation does not equal causation II. Other ProductMoment Correlation Coefficient a. Use when two sets of scores have a continuous, linear relationship b. Spearman Rank Correlation i. Use when you have 2 sets of ordinal scores 1. It gives you rank information but not distance c. PointBiserial Correlation i. You get the difference between sample means ii. You use this when you have 2 different categories and you compare the means of both iii. One score is dichotomous and one is continuous d. The Phi Coefficient i. You have two sets of categorical scores 1. Arranged to have two possible outcomes in each of them ii. Dichotomous: two scores that are categorical iii. This deals with two sets of categorical scores III. NonProduct Moment Correlation Coefficient a. Use this with quantitative and dichotomous scores i. You work with quantitative scores and treat them as if they are categorical b. Biserial Correlation Coefficient i. Both are continuous but you treat one as dichotomous c. Tetrachoric Correlation Coefficient i. Both sets are continuous but treat both as dichotomous ii. Both have arbitrary cut off points IV. Categorical Variables a. Contingency Coefficient i. Chisquare: A squared zscore ii. Use this when you have two sets of categorical scores when one score has more than one category iii. 1 – 1 rule for correlations do not apply and make it hard to interpret b. Cramer’s V i. This is the most preferred because it is bound between 1 1 ii. Use this when you have two sets of categorical scores where one has more than one category
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