Behavioral Measurements Virginia Tech Exam 3

Front 1

why do we conduct statistical analyses?

Back 1

to assess if the IV had any effect on the DV (assess systematic variance)

Front 2

even when confounds are controlled for the means of 2 groups can be significantly different due to..

Back 2

error variance

Front 3

we can conclude that the IV has an effect when

Back 3

the differences between the means of the experimental condition is LARGER than what we expect it to be solely due to error variance

Front 4

the key word with inferential statistics is...

Back 4

probability: that the difference we observe b/w the means is due to error variance

Front 5

rejecting the null hypothesis means...

Back 5

the IV did indeed affect the DV

Front 6

failing to reject the null hypothesis means...

Back 6

the IV had NO effect on the DV

Front 7

4 possible outcomes for deciding if IV had an effect

Back 7

1) correct decision:
a. reject null
b. fail to reject null
2) incorrect decision:
a. Type I error
b. Type II error

Front 8

type I error

Back 8

researcher erroneously concludes null hypothesis is false and rejects it (incorrectly reject null)
--> you say IV had affect on DV when it did not

Front 9

type II error

Back 9

researcher mistakenly fail to reject null when it is false (incorrectly accept null)
-->say IV had no affect on DV when really it did

Front 10

when do you use a t-test and an f-test?

Back 10

t-test: with 2 groups
f-test: with more groups than 2

Front 11

what does a t-test do?

Back 11

error variance in the data is calculated to determine how much the means are expected to differ solely due to random chance

Front 12

if absolute value of the calculated t-value exceeds the critical value of t obtained from the table, that means...

Back 12

statistically significant (reject null)

Front 13

directional vs nondirectional hypotheses: define and what type of test is appropriate

Back 13

-directional: states which of the two condition means is expected to be larger... uses one-tailed t-test
-nondirectional: only states that the 2 means are expected to be different... uses two-tailed t-test

Front 14

paired t-test: what does it take into account? what does it due overall?

Back 14

-takes into account the fact that the participants in 2 conditions are similar (/identical) on an attribute related to the DV
-reduces the estimate of error variance used to calculate t, which leads to a more powerful test of the null hypothesis

Front 15

computer analysis- 2 good and 2 bad things

Back 15

-greatly increased speed & flexibility, allow to conduct preliminary analysis to examine data quality
-problem of data entry and problem of improper use of statistical tests

Front 16

alpha level- define, what is it set at, and aka

Back 16

the probability of making a type I error
-set at .05
-aka the p-value

Front 17

beta level

Back 17

the probability of making a type II error

Front 18

ways Beta can be increased

Back 18

-improper measurement of DV
-unreliable measurement technique
-mistakes in collecting, coding, analyzing data
-too few participants to detect effect of IV
-very heterogeneous samples
-poor experimental control

Front 19

you reduce the likelihood of type II errors by designing experiments that have...

Back 19

high power (power= probability study will correctly reject null when it is false)

Front 20

power can be thought of as

Back 20

the opposite of beta (1-beta)

Front 21

power is related to..

Back 21

the number of participants in a study

Front 22

a power analysis can be conducted prior to research in order to determine..

Back 22

how many participants are needed to detect the effects of the IV

Front 23

power of .8 means...

Back 23

you have an 80% chance of detecting an effect that is there, and 20% chance of a type II error

Front 24

effect size- definition & why done

Back 24

-the proportion of variability in the DV that is due to the IV
-done b/c when we reject the null and conclued the IV had an effect on the DV we'd like to know how strong it is

Front 25

the more t-tests you do...

Back 25

the more chance one or more is wrong (more likely to have type 1 error)

Front 26

for every t-test, what is that chance of being wrong?

Back 26

5%

Front 27

type I error increases as

Back 27

we perform more t-tests

Front 28

formula for the probability of making a type I error

Back 28

1-(1-alpha)^c

c= number of tests
alpha= usually .05

Front 29

you can prevent type I error by using.. (2 options)

Back 29

-bonferoni adjustment (not used often)
-analysis of variance (preferred)

Front 30

bonferoni adjustment: definition and problem w/ it

Back 30

-divides desired alpha level by # of tests (to prevent type I error)
-more stringent alpha increases risk of type II error

Front 31

when the number of t-tests is large, to prevent type I errors we prefer to use

Back 31

analysis of variance (ANOVA)

Front 32

ANOVA- when used? why? what does it do?

Back 32

-used to analyze data from designs involving more than TWO conditions
-we do this b/c it analyzes differences b/w all condition means in an experiment simulataneously
-determines whether any set of means differs from another using a single test that holds the alpha at .05 (rather than doing each t-test separately)

Front 33

variance between groups (__) and within groups (__)

Back 33

-variance b/w groups= IV
-variance w/in groups= error variance

Front 34

if variance between groups experimental conditions is markedly greater than within groups

Back 34

we conclude the IV is having an effect (IV greater than error variance)

Front 35

in a study which IV has NO EFFECT on the DV, we can estimate error variance in data in 2 ways

Back 35

1) look @ variability among participants w/in each condition
2) look @ differences b/w condition means

Front 36

when IV has no effect, variability among condition means and variability within groups are reflections of

Back 36

error variance

Front 37

f-test

Back 37

ratio of the variance between groups to the variance w/in groups (F= between/within)

Front 38

if the IV has no effect, in an f-test....

Back 38

the F should be around 1.00 (numerator and denominator should be estimates of same thing)

Front 39

if the IV has an effect, in an f-test...
-what does it depend on?

Back 39

we expect the numerator to be larger (b/c it contains systematic variance in addition to error variance)
-how much larger depends on critical value

Front 40

sum of squares between-groups... if IV has no effect, then

Back 40

we expect group means to be equal, aside from what differences are due to error variance and any group mean should be equal to mean of all group means (grand mean)

Front 41

in ANOVA we know that... BUT...

Back 41

-one group significantly differs from another, but not which groups if we have more than 2 IV's
-to identify which means differ we conduct post-hoc tests

Front 42

in addition to main effects, an ANOVA can tell us about... (& how would we interpret that)

Back 42

interactions (look @ simple main effects in order to interpret interactions)

Front 43

what does a MANOVA test? what 2 reasons do we use it for?

Back 43

-tests differences between the means of 2 or more conditions on 2 OR MORE dependent variables simultaneously
-used for:
1) conceptually related dependent variables (id 10 diff depression test scores)
2) reduction of type 1 error (more tests=> more likely)

Front 44

a MANOVA works by creating.... (called:)

Back 44

a composite variable that is the weighted sum of the original DVs called the CANONICAL variable

Front 45

quasi-experimental design

Back 45

-when we lack control over assignment of participants to condition and/or do not manipulate causal variable of the interest (in many cases used to indicate variable is not a true IV manipulated by researcher but an event that occured for other reasons)

Front 46

quasi-independent variable

Back 46

used to indicate that the variable is not a true IV because it has not been manipulated by the researcher

Front 47

benefit and 2 drawbacks of quasi-experimental designs

Back 47

(+): allow us to examine real-world phenomena
(-): no initial equivalence, cannot randomly assign participants to conditions and control for extraneous variables so there's a lack of internal validity (degree to which researcher draws accurate conclusions about effect of IV)

Front 48

4 types of quasi-experimental designs

Back 48

-pretest-posttest designs
-time series designs
-longitudinal designs
-program evaluations

Front 49

how does a one-group pretest-posttest design work? what is wrong with it?

Back 49

pretest-treatment-posttest
-very weak b/c lack of control, threats to internal validity

Front 50

what are threats to internal validity in pretest-posttest designs?

Back 50

-maturation, history, testing effects, extraneous factors, REGRESSION TOWARDS MEAN (low scores go up, high scores go down)

Front 51

what can we do to obtain more internal validity in a pretest-posttest design?

Back 51

a control group is added--> nonequivalent control group design

Front 52

2 types of nonequivalent control group designs

Back 52

1) posttest-only design
2) pretest-posttest design

Front 53

problems with nonequivalent control group design

Back 53

-no initial equivalence b/c of lack of random assignment-> true control impossible
-selection bias (don't know if groups were similar to begin w/)

Front 54

nonequivalent pretest-posttest design allows for

Back 54

comparison b/w groups before treatment condition (to get baseline)

Front 55

time series designs

Back 55

-several pretests, several posttests (done to eliminate some weakness of a nonequivalent control group)

Front 56

3 types of time series designs

Back 56

1) simple interrupted: observing participants behavior several times before quasi-IV is introduced and several times after
2)interrupted time series w/ reversal: taking several pretest measures before the quasi-IV and then before/after it is removed
2)control groups interrupted: measuring more than 1 group on several occasions, only 1 of which receives the quasi-IV

Front 57

3 weaknesses of interrupted time series with reversal

Back 57

1) researchers may not have power to remove quasi-IV
2)effects of quasi-IV remain after it's removed
3) removal of quasi-IV may produce unintended changes

Front 58

Longitudinal design- what is the quasi-IV? when is it used most often? what does it avoid?

Back 58

-the quasi-IV is TIME ITSELF
-used frequently by developmental psych researchers to study age-related changes
-avoids generational effects

Front 59

the goal of longitudinal design is to

Back 59

uncover developmental changes that occur w/ age

Front 60

3 most common limitations of longitudinal quasi-experimental designs

Back 60

1) difficult to find willing subject
2)difficult to accurately track participants
3)requires great deal of time and money

Front 61

program evaluation

Back 61

uses behavioral research methods to assess the effects of interventions (programs) designed to influence behavior

Front 62

to infer causality we must meet 3 criteria

Back 62

1) presumed causal variable preceded effect of time
2)cause and effect covary
3) all other explanations eliminated due to randomization/experimental control

Front 63

4 problems with designs that study 1 group before and after quasi-IV

Back 63

1)history
2)maturation
3)regression to the mean
4)pretest sensitization

Front 64

2 problems with designs that compare 2 or more nonequivalent groups

Back 64

1)selection bias- groups differed even before occurrence of quasi-IV
2) local history- extraneous event occurs in 1 group but not the other