stats final Flashcards by Hannah Nelson

correlation levels of measurement

IV and DV are interval and ratio

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pearson-product moment correlation

interval/ratio
normal distribution
r

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strength of correlation

0: null
1.0: perfect pos
-1.0: perfect neg

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assumptions for correlation

scores rep population
normal distribution
has both x and y
x and y are independent measures
x and y are observed
linear relationship

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interpretation of correlation

< .25 little to no
.25-.50 low to fair
.50-.75 moderate to good
> .75 strong relationship

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limitations of correlations

only two variables
only linear
does not tell cause and effect
does not account for agreement
influenced by range
average values can suppress variation

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coefficient of determination

square of correlation coefficient
the percent of variance in y that is explained by x

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significance of coefficient

very sensitive to sample size

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conventional effect sizes for r

small: .10
medium: .30
large: .50

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what are non parametric statistics based on?

comparisons of rank scores
comparisons of counts or signs of scores

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when do you use non parametric tests?

when you violate more than 2 parametric assumptions

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what are the advantages of non parametrics

appropriate for wide range of solutions
can use with categorical data
simple computations
outliers have less effect

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disadvantages of non parametrics

they waste information - collapsed data
less power - 65-95% of para counterparts
if outliers are not errors, effects may be underestimated

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non para for unpaired t test

Mann-Whitney U

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non para for paired t test

sign test
~ scores converted to signs
wilcoxon signed ranks test (more common)
~ gives magnitude of change

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non para for IG ANOVA

kruskal-wallis ANOVA

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non para for RM ANOVA

freidmans ANOVA

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how to rank ties

average what the two ranks would be

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spearman rank (rho) correlation coefficient

non para analog of pearson r
at least one variable will be ordinal
non normal distribution of ratio/interval data
can be used with curvilinear

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spearman value

since it is correlation -1 through +1

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chi-square

association between two categorical variables

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goodness of fit chi square

compare observed frequencies of 1 variable to uniform frequencies

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tests of association chi square

much more common
compare observed frequencies of one variable to observed frequencies of another variable

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assumptions for chi square

frequencies represent individual counts
can only be part of one category
no subject is represented twice - not for paired

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what is signal?

true score

what is noise?

error

define relative reliability

ratio of variability of scores to variability within scores unitless ICC and kappa

define absolute reliabilty

how much of a measured value is likely due to error SEM

acceptable value of reliability

0.80

define internal consistency

how well do these questions reflect the same construct not actually measuring

3 things that a valid test should do

discriminate among those who do or do not have it evaluate change in magnitude predict an outcome

concurrent validity

target test correlating to standard taken at same time

predictive validity

can target test predict standard

convergent validity

correlates with other tests of closely related constructs

divergent validity

uncorrelated with tests of distinct or contrasting constructs

ICC

for continuous scale scores values from 0-1 measures degree of relationship and agreement > 2 raters or ratings

higer ICC value

greater reliability

negative ICC value

divergence or disagreement

ICC model 1

raters chosen from larger population some subjects assessed by different raters

ICC model 2

each subject assessed by same set of raters test-retest and inter-rater can generalize to other raters

ICC model 3

same set of raters but only represent raters of interest only for intra-rater cannot generalize

ICC form 1

single measurement

ICC form k

several measurements

cohen's kappa coefficients

for categorical scale scores

ICC interpretation

> 0.90 best for clinical measurements > 0.75 good < 0.75 poor to moderate

cobach's alpha

correlation among items and correlation of each individual item with the total score simply how often raters agree recommended to be between 0.7-0.9

kappa coefficient

proportion of agreement between raters after chance agreement has been removed nominal and ordinal interpreted like ICC

weighted kappa

best for ordinal data can choose to make penalty worse for larger disagreements

kappa interpretation

<0.4 poor to fair 0.4-0.6 moderate 0.6-0.8 substantial 0.8-1.0 excellent

concurrent validity

do two criteria measured at same time correlate

predictive validity

can one criterion predict magnitude of the other

true positive

clinical test + condition present

false negative

clinical test - condition present

false positive

clinical test + condition absent

true negative

clinical test - condition absent

sensitivity

true pos / (true pos + false neg) rule out

specificity

true neg / (false pos + true neg) rule in

positive predictive value

true pos / all pos

negative predictive value

true neg / all neg

likelihood ratios

0-1 decreased probability of disease 1 null value > 1 increases probability of disease

LR+

likelihood a positive was obtained in someone with disease compared to someone without the disease

LR-

likelihood a negative was obtained in someone with disease compared to someone without the disease

large and often conclusive shift in LR

LR+ >10 LR- <0.1

moderate shift

LR+ 5 - 10 LR- 0.1 - 0.2

small: sometimes important

LR+ 2 - 5 LR- 0.2 - 0.5

small: rarely important

LR+ 1 - 2 LR- 0.5 - 1

cohort studies

based on exposure usually prospective

case-control study design

based on outcome retrospective cases selected form same population as cases

relative risk

cohort studies

odds ratio

case-control studies

RR and OR = 1

null value

RR and OR > 1

considered harmful

RR and OR < 1

considered protective

disease in exposed / disease in unexposed

odds of exposure among cases / odds of exposure among controls

experimental event rate

% pts in experimental group with bad outcome

control event rate

% pts in control group with bad outcome

number needed to treat

how many pts you have to provide treatment to in order to prevent one bad outcome closer to 1 the better if 0, NNT is infinity smaller is better

number needed to harm

measure of adverse treatment effect larger is better