Chapter 11: Complex Experimental Designs & Inferential Stats*

Question 1

Curvilinear relationship

Answer 1

requires at least 3 levels of the independent variable

Question 2

Complex (factorial) design

Answer 2

research with 2 or more independent variable (i.e. factor) and with each IV also having more than one level; Number of levels of first IV x Number of levels of second IV

Question 3

Factor

Answer 3

any outcome that you expect to be related to some outcome variable e.g. IV that affects DV

Question 4

What 2 distinct kinds of information do factorial designs yield?

Answer 4

main effect and interaction

Question 5

Main effect

Answer 5

the direct effect of an IV on a DV, ignoring any interaction with other IVs

Question 6

Interaction

Answer 6

When the effect of one IV on the DV depends on the level of another IV; parallel=no interaction; not parallel=interaction

Question 7

Marginal mean

Answer 7

average score of all participants in one condition of one independent variable, collapsing across the levels of the other IV

Question 8

Moderator variable

Answer 8

third variable that influences the relationship between an IV and DV; effect is revealed as interactions

Question 9

Simple main effect

Answer 9

effect of one IV on the DV at one particular level of another IV; mean difference at each level of one IV

Question 10

IV x PV design

Answer 10

factorial design that includes both an experimental IV and a non-experimental participant/person variable; allows researchers to investigate how different types of people respond to the same manipulated variable

Question 11

Mixed factorial design

Answer 11

a factorial experimental design that includes both between-subjects and within-subjects variables (IV x PV)

Question 12

Inferential statistics

Answer 12

statistics that estimate whether the results observed based on sample data are generalizable to the population from which that sample was drawn

Question 13

Statistically significant

Answer 13

observing that an outcome has a low probability of occurrence (p-value < .05), assuming that H0 is correct; difference between groups reflects a real difference in population

Question 14

Significance or alpha level

Answer 14

0.05; how willing you are to be wrong if you conclude there is an effect in the population; threshold probability which a test statistic is deemed to be unlikely to have come from sampling distribution

Question 15

Steps in null hypothesis significance testing (NHST)

Answer 15

(1) Formulate the null hypothesis and assume H0 (2) Collect data (3) Calculate p-value of getting such data or even more extreme data (4) Decide whether to reject or retain H0

Question 16

Null “dull” hypothesis

Answer 16

baseline conservative assumption that IV does not affect DV; No relationship between 2 variables

Question 17

Research hypothesis

Answer 17

IV does affect DV; there is a relationship between 2 variables (correlational) or there is a difference in means between experimental groups

Question 18

What are the 2 characteristics of null and research hypothesis?

Answer 18

mutually exclusive (cannot have both be true at the same time) and exhaustive (must account for all possible outcomes)

Question 19

High p-value

Answer 19

> 0.05; fairly likely that outcome was gotten from chance alone; retain H0

Question 20

Low p-value

Answer 20

< 0.05; not likely that outcome was gotten from chance alone; reject H0

Question 21

Sampling distribution

Answer 21

frequency distribution of values obtained if a study was repeated an infinite number of times, using the exact same parameters; done to evaluate likelihood of given result based on chance alone

Question 22

t-test

Answer 22

NHST statistic used to compare two means; t= group difference (difference between obtained means)/within-group variability

Question 23

F-test

Answer 23

NHST test for whether 2 or more means differ in the population

Question 24

True state of affairs

Answer 24

truth about the null hypothesis if you could sample the whole population

Question 25

Type I error

Answer 25

false positive; falsely reject the null hypothesis when it is actually true; error rate= alpha

Question 26

Type II error

Answer 26

false negative; retaining the null hypothesis when it is actually false; error rate= beta

Question 27

Power

Answer 27

probability of correctly rejecting the null hypothesis using a particular statistical test; directly related to type II error

Question 28

What 3 factors do power and type II error rate depend on?

Answer 28

sample size, magnitude of effect (effect size), and alpha level

Question 29

Sample size and power

Answer 29

greater sample size, greater power, less error in data to detect effect

Question 30

Effect size and power

Answer 30

larger the difference is in population, easier to detect, thus greater power

Question 31

Alpha level and power

Answer 31

larger alpha level, easier it is to find data consistent with research hypothesis (to reject null), thus greater power

Question 32

P-hacking

Answer 32

a set of ethically questionable practices researcher uses to get statistically significant results

Question 33

Obtained value

Answer 33

in a significance test, a statistic that captures the effect observed in your study; t=difference between means of conditions/(square root of sum of variances/sample sizes)

Question 34

How do you reduce noise to get larger t-values?

Answer 34

consider poorly worded questions, effect of uncontrolled variables, small sample sizes, between vs. within subjects designs

Question 35

Universe of possible values of t

Answer 35

sampling distribution of t or t distribution

Question 36

Relationship between p-values and t-obt; alpha levels and critical values

Answer 36

p-value is the amount of area in the t-distribution that corresponds to a particular value of t-obt; alpha levels correspond to critical values

Question 37

When do your reject or retain H0 in terms of t-obt and t-crit?

Answer 37

Reject H0: t-obt > t-crit; Retain H0: t-obt < t-crit