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14 Cards in this Set

  • Front
  • Back
Measures of Association
Provide information about the strength and direction of relationships
Independent Variable
(X) the causal variable
Dependent Variable
(Y) the effect variable
Limits of Measures of Association
Measures of association are evidence for a causal relationship, but cannot prove that two variables are causally related.
Prediction using Measures of Association
If two variables are associated, the score of a case on one variable can predict the score of that case on another variable.
Bivariate Tables
Display the scores of cases on two different variables.

Independent (X) variables arranged in columns

Dependent (Y) variable arranged in rows.
Conditional Distributions of Y
Columns frequencies
Chi Square Statistic
Indicates weather or not the variables are associated

Nonzero values indicates association
Questions That Bivariate Associations try to adress
1) Is there a relationship?
2) How strong is the relationship?
3) In what direction is the relationship?
Column Percentages
When column totals are different, percentages can be computed to make changes easier to read.
Maximum difference
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
Direction of association
When both variables are at least ordinal, association can be described in terms of direction.
Positive association
Variables vary in the same direction.
Negative association
Variable vary in opposite directions.