Chapter 8: Multivariate Correlational Research Flashcards
What are three techniques that researchers use to get closer to establishing a casual relationship?
longitudinal designs, multiple regression designs and pattern & parsimony approach.
Longitudinal designs allow us to?
evaluate temporal precedence in data
multiple regression designs help us to rule out what?
help us to rule out certain third variable explanations
pattern & parsimony approach?
results of a variety of correlational studies all support a single causal theory
Multivariate designs??
involve more than two measured variables
A longitudinal design can provide evidence for temporal precedence by?
measuring the same variables in the same people at different points in time.
Cross-sectional correlations?
test to see whether two variables, measured at the same point in time are correlated
Autocorrelations?
evaluate the associations of each variable with itself across time….they determine the correlation of one variable with itself, measured on two different occasions.
Cross-lag correlations?
show whether the earlier measure of one variable is associated with the later measure of the other variable….they address the directionality problem and help establish temporal precedence.
What are three ways to interpret longitudinal designs?
cross-sectional correlations, autocorrelations and cross-lag correlations.
By inspecting via cross-lag correlations we can investigate what?
change over time and therefore establish temporal precedence
Do longitudinal designs help rule out third variables?
NO
Significant relationships in longitudinal designs can help establish what?
covariance
A longitudinal design can help researchers make inferences about temporal precedence by comparing what?
the relative strength of the two cross lath correlations, the researchers can sees which path is stronger.
Why do we not just do an experiment?(2)
-commonly people cannot be randomly assigned to a variable
- could be unethical to assign some people to a condition
L> when an experiment is not practical or ethical a longitudinal correlational design is a good option
Multiple regression can help rule out _____. They can address questions of_____.
- third variables
- internal validity
Researchers are able to evaluate whether a relationship between two key variables still holds when they do what?
control for another variable.
When researchers use regression they are testing what?
whether some key relationship holds true even when a suspected third variable is statistically controlled for.
When researchers are using multiple regression they are studying how many variables?
3 or more
What is the first step in multiple regression?
- choose a dependent variable (criterion variable)
L> this is the variable one is most interested in understanding or predicting
In multiple regression the dependent variable is almost always where in the regression table?
specified in the top row or in the title of a regression table
Aside from the dependent variable what are the other measured variables called in regression analysis?
predictor variables aka independent variables
What does beta (B …standardized) show in regression analysis?
it reveals more than r does
L> positive: indicates a positive relationship between the predictor variable and the DV..when the other predictor variables are statistically controlled for.
L> negative: indicates a negative relationship between two variables when the other predictor variables are controlled for.
L> zero: not sig different from zero means that there is no relationship when other predictor variables are controlled for
Betas in regression analysis denote ___ and ____ of some relationship. The higher beta is, the ___ the relationship is between that predictor variable and the DV.
direction and strength
stronger
Betas change depending on?
other predictor variables are being used-bent controlled for-in the regression
What does the b unstandardized coefficient show?
- sign +/- denotes a positive or negative association
- cannot compare two b coefficients within the same table …you can compare betas.
Why can you not compare two b coefficients within the same regression table?
they are computed from the original measurements of the predictor variables
Why can you compare two beta coefficients within the same regression table?
- they are computed rom predictor variables that have been changed to standardized units.
If a predictor variable shows a large b does it actually denote a stronger relationship to the dependent variable than a predictor variable with a smaller b ?
no
Are there beta values for each predicator variable in a multiple regression table?
YES one for each
Each beta value is given a ___ value
p
P values indicate what about beta?
if it is statistically sig different from zero
If p is less than 0.05 what does this mean to beta?
the relationship between the predictor and the DV is stat sig
If betas are found to be statistically sig what does this mean about the relationship found in regression analysis?
that it is a replicable relationship
Adding several predictors to a regression analysis can help with what?
answering two kinds of questions
L> it helps control for several third variables at the same time
L> by looking at the betas for all of the other predictor variables we can get a sense of which factors most strongly affecting the DV
Popular press sources rarely discuss __,___ or ___ variables. However if you read carefully you can detect what?
betas, p values or predictor variables
- the tell tale signs that a multiple regression has been used.
The term controlled for is a clue that the study used what ?
multiple regression
The phrase taking into account means that researchers conducted what?
regression analysis
The phrase after correcting for indicates that the researchers used ?
multiple regression
The fact that researchers cannot control for variables they have not measured is why what kind of design is better than correlational designs?
- experimental designs
L> random assignment would make the two groups likely to be equal on any possible third variable even the third variables the researchers did not happen to measure.
Pattern and parsimony approach?
- a variety of correlational studies that all point in a single causal direction
Parsimony?
the simplest explanation of a pattern of data the best explanation that requires making the fewest exceptions or qualifications
If all predictions are tied back to one central principle this is a strong case for what?
parsimony?
Mediating variable?(mediator)
- researchers propose a mediating step between two of the variables….mediating analyses often rely on multivariate tools such as regression analyses
How does testing for a mediating variable work?
- test for relationship c…associationg between the IV and DV
- test for relationship a….IV and mediator
- test for relationship b….DV and mediator
- run a regression test using both the mediator and proposed IV to predict the DV to seed if relationship c goes away…..if the proposed mediator is truly the mediator in the test than when it is controlled for the relationship between the proposed IV and the DV will drop.
Mediation tests often involve testing what?
the opposite pattern as well and comparing how well each pattern fits the data.
Using statistic testings such as structural equation modelling with mediation models what can be compared?
two meditational models …the reverse and the preferred one …to see which fits the data best
Whats the main difference between third variables and mediators?
- third variables are external to the two variables in the original bivariate relationship
L> mediatiors: researchers propose this when they are interested in isolating which aspect of the causal variable is responsible for that relationship
L> it is internal to the causal variable and often not considered a nuisance like a third variable
Aside from internal validity what other validities should be interrogated in multivariate studies?
external, construct and statistical
