Critical Inquiry Final Flash Exam

Front 1

Coefficent of Determinations

Back 1

R2 - how well the line of agression approximates your data

Front 2

So if you reject your null cuz X2 is bigger then X critical.....

Back 2

then you know your data isnt normally distributed.
So do ICC - reliability amoung raters

Front 3

what tests reliability amoung raters

Back 3

ICC

Front 4

what type of error effects reliability

Back 4

random

Front 5

is chi squared reliable?

what if you want to do a chi square but you dont have nutually exclussive data?

Back 5

no.
becuase it is just a comparison

then do McNemar Test

Front 6

You did a nonparamentric matched test but think there is a problem with variance.

Back 6

do a wilcoxcen T test

Front 7

Power =

Back 7

the probability to correctly reject the null

Front 8

4 ways to discribe variability

Back 8

variance
variation coefccient (%)
standard deviation (units)
Range (no outliers)

Front 9

R =

Back 9

strength of correlation

Front 10

What if you want to Do an ICC but only have ordinal data?

Back 10

do Kappa

Front 11

How do you check the internal consistancy of either a ICC or Kappa?

Back 11

chronbachs alpha

Front 12

How do you interpret a statistical ratio?

Back 12

1 = different due to sampling error

+1 = treatment effect

Front 13

If t > t critical

if u < ucritical

Back 13

reject null

reject null

Front 14

if p < alpha...

Back 14

reject null

Front 15

How do you interpret correlation?

Back 15

1.0 = perfect
0 = no correlation
if p<alpha = significant correlation

Front 16

what if X2 > X critical

Back 16

reject the null

Front 17

effect size =

Back 17

meaure of the strentgh of the relationships between 2 variables

Front 18

paramtertic data
1 ID
2 levels IV

Nonparametric data?

Back 18

unpaired t test

U test

Front 19

2 levles, 1 IV, paired test

parametric data =

nonparametric data =

ordinal data =

Back 19

paired t

sign test

u test

Front 20

You want to do multiple t tests (1IV, 3 levels)

and you reject the null so you

what if you realize uyou have unequal variance?

Back 20

ANOVA

run SR, then a pot hoc

correct with epsilon ghg or hf

Front 21

Nonparametric data
correlated paired samples
binomial info

nonparametric data
correlated paired samples
ordinal data

Back 21

sign test

wilcoxen signed ranks

Front 22

non parametric data
2 groups
correlated

nonparametric data
2 group
ID measures

Back 22

friedman 2 way

Kruskal wallis - then do man whit for post hoc and then a bonferroni correction

Front 23

with Sign test
x =
n =

Back 23

x = # fewer signs
n = # differences

Front 24

T < T crit

T =

Back 24

reject null (wilcox)

sum of least frequent direction

Front 25

You did a nonparamentric matched test but think there is a problem with variance.

Back 25

do a wilcoxcen T test

Front 26

Power =

Back 26

the probability to correctly reject the null

Front 27

4 ways to discribe variability

Back 27

variance
variation coefccient (%)
standard deviation (units)
Range (no outliers)

Front 28

R =

Back 28

strength of correlation

Front 29

What if you want to Do an ICC but only have ordinal data?

Back 29

do Kappa

Front 30

4 Strength of ICC

Back 30

1. can asses reliability amoung two or more raters
2. doesnt require same numbers of raters for each subject
3. designed for interval and ratio data
4. can be used with nominal data

Front 31

If you have low power, what do you need to protect against a type two error?

Back 31

more subjects

Front 32

Name two non parametric tests for correlated paired samples

Back 32

1. wilcox
2. sign test ranked

Front 33

What test would you use for MMT ordinal data (just memorize this)

Back 33

sign

Front 34

What does wilcoxen test take into account that sign test doesnt?

Back 34

The direction and the magnitude of the differences. If the data is ordinal this test has more POWERRRRR

Front 35

Name 2 nonparametric ANOVAS

Back 35

1. Kruskal wallis One way analysis of variance of ranks
2. Friedman two-way analysis of variance by ranks

Front 36

If you had 2 levels of 1 independant variable and paired data....

1. and you had parametric data

2. And you had non parametric data (nominal )

3. and you had nonparametric data (ordinal)

Back 36

1. paired t test

2. sign test

3. U test

Front 37

After your t test, if the data now represents a different population...

Back 37

then your treatment worked

Front 38

How do we know how big the difference has to be before we can be sure that its due to treatment? t test

Back 38

statistical ratio

becuase if difference is due to sampling error then the statistical ratio will be 1.
if the difference is due to treatment effect the statistical ratio will be greater then 1!

Front 39

variability within groups means-

Back 39

this is your estimation of sample error. Stastical ratioes take this into account. This number is usually 1

Front 40

name 4 assumptions for an independant sample t test

Back 40

1. independant groups
2. RA
3. normal distrobution
4. variance of the two groups is relatively equal

Front 41

what 3 things do you need to be able to find t critical?

Back 41

1. power
2. one or 2 tailed test
3. DOF

Front 42

So running a two tailed test would make it ____ to reject the null hypothesis since...

Back 42

easier

since power is 2.5% insterad of 5%

Front 43

ANOVA means literally...

Back 43

analysis of variance

Front 44

just memorize this bullshit: for more than two groups we use __________ to represent the variablity

Back 44

sum of squares

Front 45

Standard error, generated from theortical populations, is used to find the

Back 45

confidence interval: is a range of scores with speciic boundaries that probably contains the population mean

to inc confidence you INC the interval

as sample size inc, the interval gets smaller

Front 46

____ = predetermined probability of making a type I error (alpha is chosen a priori)

Back 46

level of significance

Front 47

If your sample size was too small your descriptive test...

Back 47

fails

Front 48

coefficent of variation describes variability as a...

Back 48

proportion of the mean

Front 49

The two most common methods of data collection (I think for qual is..)

Back 49

1. interviews
2. observation

Front 50

what type of research is NOT linear?

Back 50

qualitative

Front 51

correlation magnitude =

Back 51

1.00 is perfect
0.00 is NO correlation

1.00- looks like all dots on the same line

Front 52

Which correlation to run..

1. ratio data

2. Ordinal data

3. Dichotomous Variable

4. 1 dichotomous, 2 continues

Back 52

1. (r) pearson product moment correlation coeff (measures how far each one is from a point

2. Spearman Rank correlation Coeff

3. PHI coeff

4. point diserial or rank biserial

Front 53

unspoken truth

Back 53

if a group of scores used to evaluate the reliability of a measure is too HETER, you will get a good reliability coefficient, even if the errors are large