Chapter 9- Multivariate correlational research Flashcards
Multivariate designs definition
Involve more than 2 measured variables. These techniques can be used to get closer to a causal claim without setting up an experiment.
Multivariate designs (3)
longitudinal designs, multiple-regression designs, and the pattern and parsimony approach.
3 criteria for establishing causation
covariance, temporal precedence, and internal validity.
Narcissism
A personality trait in which people feel superior to others, believe they deserve special treatment, and respond strongly when others put them down. Childhood narcissism is different from high self esteem.
Longitudinal designs
Measure the same variables in the same people at several points in time. It is used to study changes in a trait or ability as a person grows older.
How can results from longitudinal studies be interpreted? (3)
- Cross lag correlations
- Cross sectional correlations
- Autocorrelations
Cross-sectional correlations
Test to see whether 2 variables that were measured at the same point in time are correlated. Example- the correlation between mothers’ overvaluation and children’s narcissism at time 4 was r= .099. This is consistent with the hypothesis. However, this result can’t establish temporal precedence since the variables were measured at the same time.
Autocorrelations
Autocorrelations determine the correlation of one variable with itself, measured on 2 different occasions. For example, researchers asking whether mothers’ overvaluation at time 1 was associated with mothers’ overvaluation at time 2, 3, and 4.
Cross-lag correlations
Show whether the earlier measure of one variable is associated with the later measure of the other variable. They are the primary area of interest because they address the directionality problem and help to establish temporal precedence. Example- investigating whether mothers’ overvaluation at time 1 is correlated with child narcissism later on (time 2, etc).
A correlation is statistically significant if
The 95% confidence interval does not include zero.
What does it mean when a cross lag correlation is not significant?
The cross-lag correlations from mothers to children (time 1 to time 2, time 2 to time 3, etc) were significant. The correlations from children to mothers were not. This means that the mothers who overvalued their children at one time had children who were higher in narcissism 6 months later. However, children who were higher in narcissism at a particular time didn’t have mothers who overvalued them 6 months later. These correlations together suggest that the overvaluation, not the narcissism, came first. Establishes temporal precedence
3 possible patterns from a cross-lag study
- The pattern observed above- overvaluation came before narcissism
- Narcissism at earlier times was correlated with overpraise later- this would mean that childhood narcissistic tendency came first, leading parents to change their praise later.
- Overpraise predicted narcissism later, and narcissism predicted overpraise later- this means the two variables are mutually reinforcing. There is a cycle in which overpraise leads to narcissism, which leads parents to overpraise, and so on.
How do longitudinal designs establish covariance?
Statistical relationships in longitudinal designs help establish covariance. When two variables are correlated and their 95% CIs do not contain zero (as in the cross-lag correlations) there is covariance.
How do longitudinal designs establish temporal precedence?
Longitudinal designs can help researchers make inferences about temporal precedence. Each variable is measured at different points in time, so they know which one came first. By comparing the relative strength of the two cross-lag correlations, the researchers can see which path is stronger. If only one is statistically significant, the researchers are closer to establishing causation.
Can longitudinal studies establish internal validity?
If longitudinal studies only measure two key variables, they might not help rule out third variables. For example, high income parents might be more likely to overpraise their children and these children might also think they’re better than other kids. Gender is also a possible third variable, but the researchers were also able to study the longitudinal patterns of boys and girls separately and rule it out.
Why not just do an experiment?
For the Brummelman study, it’s too difficult to manipulate the variables. You can’t manipulate personality traits, and it’s difficult to assign parents to daily parenting styles. It can also be unethical to manipulate certain variables. If we suspect certain types of praise can cause narcissism, it would be unethical to assign children to receive this praise.
Multiple regression
A statistical technique that can help rule out some third variables, addressing some internal validity concerns.
Chandra et al. 2008
Participants reported how often they watched 23 programs popular with teens. Coders watched 14 episodes of each show, and counted how many scenes involved sex. Girls were asked if they had ever gotten pregnant, and boys were asked if they had ever gotten a girl pregnant. Researchers also measured the total amount of time teenage participants spent watching any kind of TV, their age, their academic grades, and whether they lived with both parents. This makes the study a multivariate correlational study.
How can multivariate designs control for third variables?
For example, age is a third variable correlated with both of the other variables, so researchers want to know what happens when they control for age. To control for this, researchers can hold age constant and see if the correlation still holds by analyzing each subgroup. Are watching sex on TV and pregnancy still correlated in 16 year old participants, in 18 year old participants, and 20 year old participants?
2 possible outcomes of subgroup analysis
- By graphing the data, we see that the relationship holds constant even within age subgroups.
- The relationship does not hold when only looking at 16 year olds or only 20 year olds, even though the relationship is positive overall. This would mean that the association goes away when we control for age, and that the third variable (age) was responsible for the relationship.
When researchers use regression, they are testing
Whether some key relationship holds true even when a suspected third variable is statistically controlled for.
Criterion/dependent variable
Researchers choose the variable they are most interested in understanding or predicting. In the Chandra study, this was pregnancy.
Predictor/independent variables
The rest of the variables (sexual content of the TV shows, age of each teen).
How is the beta value similar to r?
There is one beta value for each predictor variable. Like r, a positive/negative beta indicates a positive/negative relationship between the predictor and criterion variable. A zero value represents no relationship. The higher/lower the beta, the stronger/weaker the relationship