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Week 10: Chapter 16 Controlling for a Third Variable Multivariate Analyses

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Introduction Social science research projects are multivariate, virtually by definition. One way to conduct multivariate analysis is to observe the effect of 3 rd variables, one at a time, on a bivariate relationship. The elaboration technique extends the analysis of bivariate tables presented in Chapters 12-15 and 17.

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Elaboration To elaborate, we observe how a control variable (Z) affects the relationship between X and Y. To control for a third variable, the bivariate relationship is reconstructed for each value of the control variable. Problem 16.1 (see Healey p. 452) will be used to illustrate these procedures.

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Problem 16.1: Bivariate Table Sample - 50 immigrants X = length of residence Y = Fluency in English G =.71

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Problem 16.1: Bivariate Table < 55+ Lo 80%40% Hi 20%60% 100% (N=25) 100% (N=25) The column %s and G show a strong, positive relationship: fluency increases with length of residence.

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Problem 16.1 Will the relationship between fluency (Y) and length of residence (X) be affected by gender (Z)? To investigate, the bivariate relationship is reconstructed for each value of Z. One partial table shows the relationship between X and Y for men (Z 1 ) and the other shows the relationship for women (Z 2 ).

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Problem 16.1: Partial Tables Partial table for males. G =.78 < 55 + Lo83%39% Hi17%61%

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Problem 16.1: Partial Tables Partial table for females. G =.65 < 55 + Lo77%42% Hi23%58%

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Problem 16.1: A Direct Relationship The percentage patterns and Gs for all three tables are essentially the same. Sex (Z) has little effect on the relationship between fluency (Y) and length of residence (X). For both sexes, Y increases with X in about the same way. There seems to be a direct relationship between X and Y.

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A. Direct Relationships In a direct relationship, the control variable has little effect on the relationship between X and Y. The column %s and gammas in the partial tables are about the same as the bivariate table. This outcome supports the argument that X causes Y. X Y

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Other Possible Relationships Between X, Y, and Z: B. Spurious relationships: X and Y are not related, both are caused by Z. C. Intervening relationships: X and Y are not directly related but are linked by Z. D. Interaction The relationship between X and Y changes for each value of Z. We will extend problem 16.1 beyond the text to illustrate these outcomes.

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B. Spurious Relationships X and Y are not related, both are caused by Z. X Z Y

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B. Spurious Relationships Immigrants with relatives who feel at home in the UK (Z) are more fluent (Y) and more likely to stay (X). Length of Stay Relatives Fluency

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B. Spurious Relationships With Relatives G = 0.00 < 55+ Low30% High70%

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B. Spurious Relationships No relatives G = 0.00 < 55 + Low65% High35%

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B. Spurious Relationships In a spurious relationship, the gammas in the partial tables are dramatically lower than the gamma in the bivariate table, perhaps even falling to zero.

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C. Intervening Relationships X and Y and not directly related but are linked by Z. Longer term residents may be more likely to find jobs that require English and be motivated to become fluent. Z X Y Jobs Length Fluency

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C. Intervening Relationships Intervening and spurious relationships look the same in the partial tables. Intervening and spurious relationships must be distinguished on logical or theoretical grounds. < 55+ Low30% High70% < 55 + Low65% High35%

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D. Interaction X and Y could only be related for some categories of Z. X and Y could have a positive relationship for one category of Z and a negative one for others. Z 1 XY Z 2 0 Z 1 + X Y Z 2 -

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D. Interaction Interaction occurs when the relationship between X and Y changes across the categories of Z. Perhaps the relationship between fluency and residence is affected by the level of education residents bring with them.

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D. Interaction Well educated immigrants are more fluent regardless of residence. Less educated immigrants are less fluent regardless of residence. < 55+ Low20% High80% < 55 + Low60% High40%

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Summary: Table 16.5 (see Healey, p. 441) Partials compared with bivariate PatternImplicationNext Step Theory that X Y is SameA. DirectDisregard Z Select another Z Supported Weaker B. Spurious Incorporate Z Focus on relationship between Z and Y Not supported

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Summary: Table 16.5 (see Healey, p. 441) Partials compared with bivariate PatternImplicationNext Step Theory that X Y is Weaker C. Intervening Incorporate Z Focus on relationship between X, Y, and Z Partially supported Mixed D. Interaction Incorporate Z Analyze categories of Z Partially supported

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