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Class 20

Class 20. The Elaboration Model. Class Outline. The Elaboration Model—Analyses of Three Variables Three Types of Elaboration Models Intervening Variables Confounding Variables Interactions. Steps in the Elaboration Model. A relationship is observed to exist between two variables.

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Class 20

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  1. Class 20 The Elaboration Model

  2. Class Outline • The Elaboration Model—Analyses of Three Variables • Three Types of Elaboration Models • Intervening Variables • Confounding Variables • Interactions

  3. Steps in the Elaboration Model • A relationship is observed to exist between two variables. • A third variable is held constant in the sense that the cases under study are subdivided according to the attributes of that third variable. • The original two-variable relationship is recomputed within each of the subgroups. • The comparison of the original relationship with the relationships found within each subgroup provides a fuller understanding of the original relationship itself.

  4. X Y Z X Y Z X Y Z Three Scenarios of Elaboration Analysis 1. The 3rd variable is an intervening variable. 2. The 3rd variable is a confounding variable. 3. The 3rd variable interacts with the independent variable.

  5. Intervening Variables Three steps to show that Z explains the effect of independent variable X on dependent variable Y. • Argue that X causally precedes Z, and Z causally precedes Y. • Show that X and Z are correlated, Z and Y are correlated. • Show that the correlation between X and Y is weakened after we control for Z in the model.

  6. gender admission Intervening Variables: Berkeley Admission Example Gender affects admission rate.

  7. gender admission major Intervening Variables:Berkeley Admission Example After controlling for major, there is no NET effect of gender on admission rate.

  8. Intervening Variables:Euthanasia Attitude Example Religiosity - + Raceblack=1 + + Political viewslib=3, cons=1 Support for euthanasia - + Libertarianism Note: Even though “political views” is an intervening variable, it does not explain blacks’ less support for euthanasia.

  9. + Raceblack=1 Religiosity Intervening Variables:Euthanasia Attitude Example

  10. - Religiosity Support for euthanasia Intervening Variables:Euthanasia Attitude Example

  11. Confounding and Bias:Sex, Exercise, and Heart Attacks Exercise - - Sex(Female=1) - Heart attack Leaving out the confounding variable sex in the analysis would underestimate the effect of exercise on the risk of heart attacks.

  12. Confounding and Bias:Sex, Exercise, and Heart Attacks Note: Not real data If we don’t consider sex, weekly exercise reduces the risk of heart attach from 0.16 (48/300) to 0.1 (30/300). However, if we consider that men exercise more and are also more likely to get heart attacks, weekly exercise reduces the risk from 0.12 to 0.06 for women and from 0.24 to 0.12 for men.

  13. Confounding and Bias:Age, Exercise, and Heart Attacks Exercise - - + Age Heart attack Leaving out the confounding variable age in the analysis would overestimate the effect of exercise on the risk of heart attacks.

  14. Z Y X Z=1 Z=1 Y Y Z=0 Z=0 X Y Z X X Interactions • We say that X and Z have interactive effects on Y when • the effect of X on Y depends on the level of Z, • or equivalently, the effect of Z on Y depends on the level of X. • Graphically, Identical phenomenon No interaction between X and Z Interaction between X and Z

  15. Interactions Belief in individual rights Passive Active

  16. Interactions • Example: • X = Libertarian attitude • Y = Support for euthanasia • Z = Type of euthanasia (passive or active) • Read the graph on the previous slide and choose the correct word for each statement below: • Libertarian attitude has a (greater/smaller) effect on support for passive euthanasia than that for active euthanasia. • The type of euthanasia makes a (greater/smaller) difference for individuals with libertarian views.

  17. Babbie’s Classification of Elaboration Analysis • Replication – A set of partial relationships is essentially the same as the corresponding zero-order relationship. • Explanation - A set of partial relationships is reduced essentially to zero when an antecedent variable is held constant.

  18. Babbie’s Classification of Elaboration Analysis • Interpretation - A set of partial relationships is reduced essentially to zero when an intervening variable is held constant). • Specification - One partial relationship is reduced, ideally to zero, and the other remains about the same as the original relationship or is stronger.

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