correlation,regression, and SEM

Question 1

multiple regression

Answer 1

1- extenssion of correlation
(measures relationships among variables)

Question 2

what is the difference between regression

Answer 2

use languague of prediction

can use 1 or more variables to predict changes in another variable

Question 3

Regression models predictive relationships

Answer 3

PREDICTOR- score in one variable to predict changes in another variable CRITERION

When we have a good reason that one of the
variables could change the other

Question 4

when is it used ?

Answer 4

when u cant manipulate variables,can only measure them
ex:pratical problems

Common in personality, health, &
longitudinal developmental research

Question 5

correlation vs regression

Answer 5

temporal pressidence (regression has one variable come first)

ex : Correlation Question:
- Is there a relationship between social
support received from one’s spouse in
the morning and arthritis pain in the
afternoon?
- Regression Question:
Does the amount of social support
received in the morning predict how
much pain is felt in the afternoon?

Question 6

equation

Answer 6

Y=a+bx

Y=criterion
X= predictor/1

b=slope(rise over run)

a=intercept=0

Question 7

benefits of regression framework

Answer 7

-models predictive relationships

-can investigate the effect of multiple predictors on the criterion at the same time(in correletion u can only have to variable)

Question 8

Multiple Regression enables investigation
of how well many predictors
simultaneously predict the criterion

Answer 8

Multiple Regression Equation
-What is the unique contribution of each
predictor to the prediction of criterion?

y=a+ b1X1+ b2X2+ b3X3

change in y for a one unit change in X1 (predictor 1)
b2 = Change in y for a one unit change in X2 (predicto
b3 = Change in y for a one unit change

Question 9

features of multiple regression

Answer 9

-can have any number of predictors

-need to collect data on each predictor

-Calculate the contributions of each
predictor individually on predicting
criterion (b’s)

Calculate the contribution of all predictors
combined for predicting criterion
- Called the Multiple Correlation (R) but
discussed as R2

R2 is the proportion of variance in the criterion
that can be explained by all predictors combined

Question 10

R2

Answer 10

proportion
of variance in
the criterion that
can be explained
by all predictors
combined

Question 10

Partial Correlation & the 3rd Variable
Problem

Answer 10

3rd variable problem
-another variable driving relationship between x and y

-Statistically “control for” 3rd variable

Trying to remove the effects
of a variable we know likely
influences both variables of
interest
 Measure all three variables

Question 11

Structural Equation Modeling

Answer 11

What are the relationships amongst the
variables?
₋ Model how multiple variables relate to
(correlate with) each other and/or
“predict” others

regression in asteroids
but often data is cross-sectional