ch8 Flashcards
Bivariate Correlations (bivariate associations)
- associations that examine exactly two variables at a time
- When there are multiple variables, the authors of the study can present the bivariate correlations between different pairs of variables separately
types of bivariate correlations
- Describing associations between 2 quantitative variables
- Describing associations with categorical data
how do you describe associations between 2 quantitative variables
Describe the relationship between variables using scatterplots and correlation coefficient r
qualities of r:
- Direction
- Strength- how close r is to (-)1
Cohens guide to evaluating strength of association
r… would be considered to have an effect size of
(-).10 = small/weak
(-) .30 = medium/moderate
(-) .50 = large/strong
how to describe associations with categorical data
- More common to plot the results of an association with a categorical variable as a bar graph: examines difference between the group means
- t-test statistic
mean
arithmetic avg
t-test
a statistic to test the difference between two group averages (means)
What makes a study correlational? (What is it supported by?)
- Having two measured variables
- An association claim is supported by a study design- correlational research- in which all variables are measured, not supported by statistics or graphs
Which validities are most important for association claims?
construct and statistical
how to interrogate construct validity in association claims
- How well are each of the variables measured?
- Does the measure have good reliability?
- Is it measuring what it’s supposed to?
what kinds of validity are important in the construct validity of association claims
- Face validity
- Concurrent validity (are these results consistent with previous established measures of this construct?)
- Discriminant and convergent validity
how to interrogate statistical validity in association claims
look at:
- effect size
- statistically significance
-outliers
- restriction of range
- curvilinear association
effect size (+ how to compute)
Effect size: describes the strength of an association
compute by taking value for r and square it
how does effect size help us get accurate predictions?
- Stronger effect sizes mean more accurate predictions
- a larger effect size is usually more important, but in life/death situations even a small effect size can have dramatic effects
statistical significance
Statistical significance: how likely it is to get a correlation of that size by chance if there isn’t one in the population
**depends on effect size and sample size
uses p value
When researchers obtain a correlation coefficient (r ) they establish
- Direction of slope
- Strength of relationship
- Statistical significance
Logic of statistical inference
if there is a correlation in a sample there probably is one in the population
Probability estimate (p value or sig)
provides information about stat. Significance by evaluating the probability that the sample’s association came from a zero-association population.
- Less than .05 is statistically significant, considered rare
- more than .05 = not rare not statisitically significant/nonsignificant/ not rare
how does effect size effect statistical significance?
Usually the stronger a correlation is ( the larger its effect size) the more likely it will be statistically significant
- Because, the stronger an association is, the more rare it would be in a zero-association population
- Need to examine the p value to make sure, because it is dependent on both effect size and sample size
when can a small effect size be statistically significant
A small effect size might be significant if the sample is large, but not in a small sample
how is statistically significant info indicated in journal articles
indicated with an asterisk (*), “sig,” or p<.05 or p<.01
Outlier (+ how to find)
an extreme score
can have effect on correlation coefficient
- look at scatterplot
when are outliers most problematic
- Most problematic when extreme scores on both variables
- Most influential and problematic when sample size is small
