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Chapter 12 Bivariate Association: Introduction and Basic Concepts

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Introduction Two variables are said to be associated when they vary together, when one changes as the other changes. Association can be important evidence for causal relationships, particularly if the association is strong.

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Introduction If variables are associated, score on one variable can be predicted from the score of the other variable. The stronger the association, the more accurate the predictions.

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Association and Bivariate Tables Bivariate association can be investigated by finding answers to three questions: Does an association exist? How strong is the association? What is the pattern or direction of the association?

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Association and Bivariate Tables: Problem 12.1 The table shows the relationship between authoritarianism of bosses (X) and the efficiency of workers (Y) for 44 workplaces. Low Author.High Author. Low Efficiency101222 High Efficiency17522 271744

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Is There an Association? An association exists if the conditional distributions of one variable change across the values of the other variable. With bivariate tables, column percentages are the conditional distributions of Y for each value of X. If the column % change, the variables are associated.

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Association and Bivariate Tables The column % is (cell frequency / column total) * 100. Problem 12.1: (10/27)*100 = 37.04% (12/17)* 100 = 70.59% (17/27)*100 = 62.96% (5/17)*100 = 29.41% Low Author.High Author. Low Effic 10 (37.04%)12 (70.59%)22 High Effic 17 (62.96%)5 (29.41%)22 27 (100.00%)17 (100.00%)44

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Is There an Association? The column %s show efficiency of workers (Y) by authoritarianism of supervisor (X). The column %s change, so these variables are associated. Low Auth High Auth Low Effic 37.04%70.59% High Effic 62.96%29.41% 100%

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How Strong is the Association? The stronger the relationship, the greater the change in column %s (or conditional distributions). In weak relationships, there is little or no change in column %s. In strong relationships, there is marked change in column %s.

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How Strong is the Association? One way to measure strength is to find the maximum difference, the biggest difference in column %s for any row of the table. DifferenceStrength Between 0 and 10% Weak Between 10 and 30% Moderate Greater than 30% Strong

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How Strong is the Association? The Maximum Difference in Problem 12.1 is 70.59 – 37.04 = 33.55. This is a strong relationship. Low Auth. High Auth. Low Effic 37.04%70.59% High Effic 62.96%29.41% 100%

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What is the Pattern of the Relationship? Pattern = which scores of the variables go together? To detect, find the cell in each column which has the highest column %.

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What is the Pattern of the Relationship? Low on Authoritarianism goes with High on efficiency. High on Authoritarianism goes with Low in efficiency. LowHigh Low37.04 %70.59 % High62.96 %29.41 % 100%

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What is the Direction of the Relationship? If both variables are ordinal, we can discuss direction as well as pattern. In positive relationships, the variables vary in the same direction. As one increases, the other increases. In negative relationships, the variables vary in opposite directions. As one increases, the other decreases.

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What is the Direction of the Relationship? Relationship in Problem 12.1 is negative. As authoritarianism increases, efficiency decreases. Workplaces high in authoritarianism are low on efficiency. LowHigh Low37.04 %70.59 % High62.96 %29.41 % 100%

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What is the Direction of the Relationship? This relationship is positive. Low on X is associated with low on Y. High on X is associated with high on Y. As X increase, Y increases. LowHigh Low60%30% High40%70% 100%

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Summary: Problem 12.1 There is a strong, negative relationship between authoritarianism and efficiency. These results would be consistent with the idea that authoritarian bosses cause inefficient workers (mean bosses make lazy workers). What else besides association do you need to show causation? Low Auth High Auth Low Effic 37.04%70.59% High Effic 62.96%29.41% 100%

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Correlation vs. Causation Correlation and causation are not the same things. Strong associations may be used as evidence of causal relationships but they do not prove variables are causally related. What else would we need to know to be sure there is a causal relationship between authoritarianism and efficiency?

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Criteria for bivariate causation 1. Association between variables 2. Time order 3. Lack of spuriousness

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Sometimes time order is easy; sometimes its not. Which comes first, inefficient workers or authoritarian bosses Also possible that inefficient workers produce authorit. bosses Low Effic High Effic Low Auth 12 50% 17 77.2% High Auth 12 50% 5 22.8% 24 100% 22 100%

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