Lectures (Midterm II)

Question 1

Factorial designs

Answer 1

More than one IV measured at a time. All combinations of IVs represented, so 2x2, 2x3, etc. Use factorial instead of looking at variables one by one because there may be interactions between the IVs - effect of one independent variable may depend on level of another variable

Question 2

Main effect

Answer 2

In a factorial design, not an interaction. The effect of a change in one IV independent of other variables

Question 3

Identifying Interactions in Line and Bar graphs

Answer 3

Line graphs - lines not parallel. Bar graphs - difference between bars is unequal

Question 4

3 things to look at in a 2x2

Answer 4

(1) Main Effect I
(2) Main Effect II
(3) Interaction

Question 5

Correlation

Answer 5

Measures strength of relationship between variables

Question 6

Pearson’s r

Answer 6

Measures linear correlation

Question 7

Ways of comparing means

Answer 7

t-tests for two groups. Can be independent samples t-test (between subjects) or paired samples t-test (within subjects).

ANOVA - more than 2 groups or if there are interactions of more than 1 IV

Question 8

Type I vs. Type II Error

Answer 8

Type 1 = False Positive, incorrectly rejecting the null hypothesis.

Question 9

Conditions that make it easier to find differences between groups/find significant results

Answer 9

Smaller variability ->smaller overlap in distributions ->easier to find differences. Do this by eliminating noise and increasing controls
Big effect size - difference between means
Larger sample size - better representation of true distribution

Question 10

Possible challenges to internal validity

Answer 10

Confounding variables, history, maturation, instrumentation (problems with consistent methodology and observer objectivity), statistical regression, biased selection of subjects

Question 11

Why is a pilot study important?

Answer 11

Manipulation check - whether manipulation of independent variable really accomplished anything. Feedback. An idea of what results might be like.

Question 12

Correlational studies

Answer 12

Examines relations among DVs w/o experimental manipulation. If there are no groups, use correlation. Often difficult to establish causality

Question 13

Two kinds of surveys

Answer 13

Open-ended or categorical

Question 14

Scale biases

Answer 14

Central tendency - not rating extreme
Acquiescence bias - always agreeing with item
Social desirability bias - say things that have good social meaning

Question 15

Non-experimental designs

Answer 15

Case studies, single-variable (does behavior differ from chance for ex.), multiple-variable

Question 16

Experimental designs

Answer 16

Between subjects (randomized or matched-groups), within subjects, mixed design

Question 17

Error Variance

Answer 17

Variability may not be caused by the IV if there are individual differences between subjects, inconstant environmental conditions, fluctuations in the physical or mental state of participants.

Question 18

Within-subject advantages and disadvantages

Answer 18

Less error variance from individual differences, making it easier to detect differences. However, fatigue or carryover effects

Question 19

Mixed design

Answer 19

Partially within subjects and partially between subjects