Second Test

Question 1

Categorical Data

Answer 1

Observations fall in one, and only one, of a set of non-overlapping categories

Question 2

Continuous Data

Answer 2

observations can take on any value within a range of possible values
some research questions are better address with continuous predictor designs
for instance, when the predictor variable cannot or should not be directly manipulated

Question 3

Correlation Data

Answer 3

Captures the degree to which two variables “vary together” - covariance

Question 4

Effect (covariance)

Answer 4

Degree to which they co-vary

Question 5

Error (covariance)

Answer 5

Degree to which they vary

Question 6

Regression

Answer 6

A model (i.e., equation) where one variable predicts another (outcome) variable

Question 7

“Line of best fit” is called a ___________

Answer 7

Regression line

Question 8

The error between the prediction and the observed data is called ___________

Answer 8

least squares regression

Question 9

Standardized regression coefficient (β):

Answer 9

When the data have been “standardized” (Z-scored),

than the slope of the equation is equal to the correlation coefficient (r) – one predictor

Question 10

Standard error of the estimate:

Answer 10

like “standard error” in t-tests, this can be interpreted as a
standard deviation of data points around the regression line (i.e., residual or error variability)

Question 11

r-squared: (in regression)

Answer 11

captures the degree of variability

accounted for by the regression equation (“percentage of variability explained”)

Question 12

if p < α: Results are ___________

Answer 12

“significant” – relationship will hold in the population

Question 13

“Effect” term (ANOVA)

Answer 13

“significant” – relationship will hold in the population

Question 14

“Error” term (ANOVA)

Answer 14

differences within groups – individual differences/error

Question 15

Within-subject designs:

Answer 15

Same group experience different conditions

Question 16

Between-subject designs:

Answer 16

Different groups experience different conditions

Question 17

Cross-sectional design:

Answer 17

measure attention from a group of kids at different ages

Question 18

Longitudinal design:

Answer 18

measure attention from the same group of kids as they get older

Question 19

Advantages of within-subject design (1-4)

Answer 19

1: More “direct”…applies to highly idiosyncratic behaviors

2: Avoids potential confounds inherent in between-subject designs
- differences on DV between groups prior to the experiment confound the results
- avoided in within-subjects designs / same people in every condition

3: More efficient use of research resources
- maximal data collection from each participant
- can be a big advantage when participants are costly/difficult to come by

4: Allows for more powerful statistical tests
- each participant serves as their own “baseline”
- allows statistical test to “account for more variability” (less error variance)

Question 20

Repeated-measures ANOVA:

Answer 20

Testing difference for multiple means from within-subject design

Question 21

Between-subjects test (i.e., one-way ANOVA)

Answer 21

includes individual differences in the “error term”