psych209 LO9

Moderator

variable depending on its level, changes the relationship between two other variables

Spurious association

in a bivariate relationship, appears in overall sample but actually is caused by systematic differences between subgroups. When the data are examined within those subgroups, the original association disappears

third-variable problem

in a correlational study, the existence of an alternative explanation for the association between two variables

directionality problem

in a correlational study, the occurrence of both variables being measured around the same time, making it unclear which variable in the association came first

3 requirements for causality

covariance, temporal precedence, internal validity

cons of correlations research

can’t make causal claims from correlational research

pros of correlational research

before conducting experimental research (correlation requirement for internal validity)

valid form of research, as long as only make association claims 

Strong in across types of validities

good if you can’t manipulate variables, or it’s not ethical to do so

covariance

correlation/association

Does correlational research have (strong) internal validity?

no

Key phrase for moderators

it depends

bivariate correlation

association that involves exactly two variables

mean

average; measure of central tendency computed from the sum of all the scores in a data set, divided by the total number of scores

effect size

magnitude, or strength, of a relationship between two or more variables

statistically significant

In NHST, conclusion assigned when p < .05; that is, when it is unlikely the result came from the null hypothesis population

replication

conducting a study again to test whether the result is consistent

outlier

score that stands out as either much higher or much lower than most of the other scores in a sample

restriction of range

In a bivariate correlation, the absence of a full range of possible scores on one of the variables, so the relationship from the sample underestimates the true correlation

curvilinear association

association between two variables that’s not a straight line; as one variable increases, the level of the other variable increases and then decreases (or vice versa)

reverse causation

In a study that finds a relationship between variables A and B, the inference that A could cause B or B could cause A

temporal precedence

variable causing the changes comes first

internal validity

Ensure that only the manipulated variable is causing changes in the measured variable→ no confounds or alternative explanations