1 / 27

Chapter 16

Chapter 16. Elaborating Bivariate Tables. Chapter Outline. Introduction Controlling for a Third Variable Interpreting Partial Tables Partial Gamma (Gp ). Introduction. Social science research projects are multivariate, virtually by definition.

kadeem
Download Presentation

Chapter 16

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Chapter 16 Elaborating Bivariate Tables

  2. Chapter Outline • Introduction • Controlling for a Third Variable • Interpreting Partial Tables • Partial Gamma (Gp )

  3. Introduction • Social science research projects are multivariate, virtually by definition. • One way to conduct multivariate analysis is to observe the effect of 3rd variables, one at a time, on a bivariate relationship. • The elaboration technique extends the analysis of bivariate tables presented in Chapters 12-14.

  4. 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 will be used to illustrate these procedures.

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

  6. The column %s and G show a strong, positive relationship: fluency increases with length of residence. Problem 16.1: Bivariate Table

  7. 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 (Z1)and the other shows the relationship for women (Z2).

  8. Problem 16.1: Partial Tables • Partial table for males. • G = .78

  9. Problem 16.1: Partial Tables • Partial table for females. • G = .65

  10. Problem 16.1: A Direct Relationship • The percentage patterns and G’s for all three tables are essentially the same. • Sex (Z) has little effect on the relationship between fluency (Y) and length of residence (X).

  11. Problem 16.1:A Direct Relationship • For both sexes, Y increases with X in about the same way. • There seems to be a direct relationship between X and Y.

  12. 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

  13. Other Possible Relationships Between X, Y, and Z: • Spurious relationships: • X and Y are not related, both are caused by Z. • Intervening relationships: • X and Y are not directly related but are linked by Z.

  14. Other Possible Relationships Between X, Y, and Z: • 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.

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

  16. Spurious Relationships • Immigrants with relatives who are Americanized (Z) are more fluent (Y) and more likely to stay (X). Length of Res. Relatives Fluency

  17. With Relatives G = 0.00 Spurious Relationships

  18. No relatives G = 0.00 Spurious Relationships

  19. 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.

  20. 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

  21. 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.

  22. Interaction • Interaction occurs when the relationship between X and Y changes across the categories of Z.

  23. 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. Z1 X Y Z2 0 Z1 + X Y Z2 -

  24. Interaction • Perhaps the relationship between fluency and residence is affected by the level of education residents bring with them.

  25. Interaction • Well educated immigrants are more fluent regardless of residence. • Less educated immigrants’ fluency depends on length of residence.

  26. Summary: Table 16.5

  27. Summary: Table 16.5

More Related