Research Methods Exam 4

Front 1

factorial design

Back 1

have experiments with more than one IV; multiply variables to get possible number of conditions.

Front 2

main effects

Back 2

average of one variable across the other variables; use one variable and average as if its constant. effect of just one of the IV. Just A

Front 3

interaction

Back 3

Combining A and B; the effect of one IV changes in effect to another IV

Front 4

two way interaction

Back 4

the effect one IV changes across another IV

Front 5

three way interaction

Back 5

pattern between two way interaction changes across the level of a third IV

Front 6

within-subjects design(repeated measures)

Back 6

each person participates in all levels of the IV/condition. within a person each person goes through each variable manipulating IV within a group of individuals.

Front 7

between-subject design

Back 7

each person participated in only one condition. protect from order or carry over effects

adv: cost efficient and control over participants. each subject is his or her own control. no diff. in subjects

dis:participants have to do diff. conditons in specific order. participants can develop diff. attitudes as experiment continues. subjects can experience carry over effect or order effect

Front 8

carry over effect
disadvantage

Back 8

subjects result/effect of one condition is affected by the preceding condition

Front 9

order effect
disadvantage

Back 9

having participants go through conditions in a diff. order to control for interactions

Front 10

quasi-experimental method

Back 10

misisng something that makes them a true experiment; no comparison or control part or lacks randomization

Front 11

observation-treatment-observation

Back 11

pretest-post-test; one treatment given before one after; have small group of subjects who you want to apply a new treatment to but dont have a control group ; no background. cant tell if change is made by treatment.

Front 12

interrupted-time-series design

Back 12

lots of observations before treatment; have times series of measurment/observations, then interrupt wiht treatment; no control group; can see changes in DV

Front 13

non-equivalent control group design

Back 13

have a group of subjects but want a control group so you put a non randomized group together

Front 14

matched-groups design

Back 14

creates non equivalent control groups but matched with treatment group with similar variables(variables you can control)

Front 15

natural manipulations

Back 15

look at measures but dont manipulate anything; differences in sex, gender, handedness, pathology...

Front 16

handedness

Back 16

left handed people die younger. more right handed people than left

Front 17

cross sectional method

Back 17

diff. ppl at diff. ages

Front 18

longitudinal

Back 18

same ppl at diff. ages

Front 19

cross sequential

Back 19

diff. ages, get measures two times. mix of cross sectional and longitudinal

Front 20

history and threats to internal validity

Back 20

something happens around the time of the treatment; outdide of the subject in the world

Front 21

maturation and threats to internal validity

Back 21

changes in body; how infants change as they get older; change inside subject, effect from inside the subject(change in brain/body)

Front 22

regression to the mean and threats to internal validity

Back 22

when you look at a person initially and then look another time, the second measure is closer to the mean than the extreme first measurement( all hitters whether in a slump or doing well at the begining all end up around the same average)

Front 23

mortality/subject attrition and threats to internal validity

Back 23

subjects dont come back, lose subjects becasue they didnt eant to or couldnt find them

Front 24

individual diff./subject variables and threats to internal validity

Back 24

if treatment didnt work they arent likely to to return

Front 25

chi-square test for independence

Back 25

same logic as correlation; when a null hypothesis is true; used to compare observed data with data we would expect to obtain from a more specific hypothesis- null

Front 26

contingency table

Back 26

used to record and analyze relationships between 2 variables

Front 27

correlational methods

Back 27

correlation doesnt equal causation

Front 28

Pearson's product-moment correlation coefficient

Back 28

problem id directionality, confounding variables-intervening variables

Front 29

scatter diagrams

Back 29

one extreme value effects the mean and size of the correlation; assume the relationship is linear

Front 30

intervening variable

Back 30

when the unused variable affects the outcome

Front 31

directionality

Back 31

does A cause B or does B cause A?

Front 32

truncated range

Back 32

not looking at entire range the alters the conclusion

ex. SAT scores and college acceptance

Front 33

nonlinearity

Back 33

relationship between variable is not statistic or directly proprtional to the input, but dynamic and variable

Front 34

hypothesis testing

Back 34

approach to answering questions about results

Front 35

significance testing

Back 35

how likely it is that the results occured by chance

Front 36

null results

Back 36

you cant prove a negative, so the same is true of a null hypothesis. you can say we have no evidence to reject the null hypothesis.

Front 37

null hypothesis

Back 37

might be falsified by a specific statistical test

Front 38

null hypothesis significance testing

Back 38

through replicating variables, whats the probability of getting the same results through different conditions? if null hypothesis is true how likely are we to observe those same results. how likely is it that changes in the study may be due to error- random variation?

Front 39

random variation

Back 39

error

Front 40

type one error

Back 40

false positive, leads you to believe there is systematic variance but isnt

Front 41

type two error

Back 41

false negative

Front 42

eliminate null hypothesis

Back 42

cant prove the null hypothesis; how likely is it that i would observe the same results if the null is true? if probability is small it is unlikely to be true

Front 43

ceiling effect

Back 43

all stats near top limits of the DV; making test too easy so everyone gets an A. IV has no room to move up more

Front 44

floor effect

Back 44

all stats near bottom of DV; making test too hard os everyone fails. IV has no room to move down further

Front 45

null results

Back 45

can reject but never prove null hypothesis
easiest way to get null is small samples with lots of variance

Front 46

4 ways to increase power of an experiment

Back 46

1. increase effect size; increase diff. between mean distribution, when you increase diff. you gain power.
2. increase alpha to decrease beta you can increase power
3. decrease error and variability by increasing experimental control
4. increase number of participants/population