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14 Cards in this Set
- Front
- Back
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Measures of Association
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Provide information about the strength and direction of relationships
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Independent Variable
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(X) the causal variable
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Dependent Variable
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(Y) the effect variable
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Limits of Measures of Association
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Measures of association are evidence for a causal relationship, but cannot prove that two variables are causally related.
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Prediction using Measures of Association
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If two variables are associated, the score of a case on one variable can predict the score of that case on another variable.
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Bivariate Tables
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Display the scores of cases on two different variables.
Independent (X) variables arranged in columns Dependent (Y) variable arranged in rows. |
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Conditional Distributions of Y
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Columns frequencies
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Chi Square Statistic
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Indicates weather or not the variables are associated
Nonzero values indicates association |
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Questions That Bivariate Associations try to adress
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1) Is there a relationship?
2) How strong is the relationship? 3) In what direction is the relationship? |
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Column Percentages
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When column totals are different, percentages can be computed to make changes easier to read.
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Maximum difference
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Quick and easy, but not so accurate.
1)Subtract the highest column frequency value from the lowest value column frequency in each row. 2)Use the highest value to measure the strength of the relationship. 3)0-10 %: weak 10-30 %: moderate 30-100%: strong |
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Direction of association
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When both variables are at least ordinal, association can be described in terms of direction.
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Positive association
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Variables vary in the same direction.
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Negative association
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Variable vary in opposite directions.
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