Ps 225 lecture 21
This presentation is the property of its rightful owner.
Sponsored Links
1 / 13

PS 225 Lecture 21 PowerPoint PPT Presentation


  • 96 Views
  • Uploaded on
  • Presentation posted in: General

PS 225 Lecture 21. Relationships between 3 or More Variables. Relationships Between Multiple Variables. Three or more variables can be interrelated Confounding variables Example: Individuals given the medication Lipitor are more likely to die of a heart attack. Partial Correlation.

Download Presentation

PS 225 Lecture 21

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.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Ps 225 lecture 21

PS 225Lecture 21

Relationships between 3 or More Variables


Relationships between multiple variables

Relationships Between Multiple Variables

  • Three or more variables can be interrelated

  • Confounding variables

  • Example: Individuals given the medication Lipitor are more likely to die of a heart attack


Partial correlation

Partial Correlation

  • Changes in a bivariate relationship when a third variable is introduced

  • Third variable (z) is a control variable


Variable types

Variable Types

  • X

    • Interval-ratio

    • Independent

  • Y

    • Interval-ratio

    • Dependent

  • Z

    • Any level of measurement

    • Control


Correlation coefficient

Correlation Coefficient

  • Rxy

  • Rxz

  • Rzy

  • Detailed notation for R

  • Relationship between 2 variables without incorporating third variable

  • Zero-order correlation


Partial correlation coefficient

Partial Correlation Coefficient

  • Rxy,z

  • Detailed notation for R

  • Relationship between x and y controlling for z

  • First-order partials


Types of relationships

Types of Relationships

  • Direct

  • Spurious

  • Intervening

  • Example: Possible relationship between geographic location, school performance and poverty


Direct relationship

Direct Relationship

X causes changes in Y. Rxy and Rxy,z are similar.

Y

X


Spurious relationship

Spurious Relationship

Z has a relationship with both the independent and dependent variable. Rxy and Rxy,z are different

X

Z

Y


Intervening relationship

Intervening Relationship

Z has a relationship with both the independent and dependent variable. Rxy and Rxy,z are different.

Z

X

Y


Determining relationship

Determining Relationship

  • Establish existence of a relationship between independent (x) and Dependent (y) variables

  • Explore relationship between x, y and any associated confounding variables (z)

  • Calculate partial correlation coefficient and identify relationship type


Multiple regression

Multiple Regression

  • Include any number of variable

  • Coefficients are partial slopes

  • Remove non-significant coefficients from the equation


Spss assignment

SPSS Assignment

Last class we answered the following questions:

  • Does the number of years of education an individual has affect the hours of television a person watches?

  • Does age affect the hours of television a person watches?

    This class: Use SPSS to find the regression equation that best represents the relationship between age and hours of television a person watches. Treat years of education as a confounding variable.

  • Give the relationship between each pair of variables.

  • Calculate the partial correlation coefficient. What is the most probable relationship type between variables?

  • Give the multiple regression equation and predict the number of hours of television you watch. Compare the prediction to the actual number of hours.


  • Login