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RESEARCH > lecture 8 > Flashcards

lecture 8 Flashcards

1
Q

what is a statistical technique that predicts an outcome?

A

regression

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2
Q

for a regression the predictors can be ____ , ___ or____

A

I/R , ordinal or nominal

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3
Q

what type of regression analyses has one predictor (any level of data) and one I/R outcome.

A

linear

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4
Q

what type of regression analyses has multiple predictor (any level of data) and one I/R outcome.

A

multiple linear

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5
Q

what type of regression analyses has One or
more predictors (any level of data) and one categorical outcome, two levels only.

A

logistic

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6
Q

what type of regression analyses has One or
more predictors (any level of data) and one categorical outcome, multiple levles

A

multinomial logistic

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7
Q

ex: does GPRE score significantly
predict PT school GPA?

what type of regression would u run

A

linear bc one predictor and one I/R outcome

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8
Q

Ex/ Does age predict days to
clearance following a concussion in
adolescent athletes?

what type of regression would u run

A

linear bc one predictor and one i/r data

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9
Q

Ex/ Does disease category predict
distance on the 6MWT?

what regression would u run

A

lienar bc one predictor and one I/R outcome

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10
Q

if we are dong a pearson correlations and we state for our scientific hypothesis that “There will be a significant relationship between time 1and time
2 at r >+.80 or r<-.80. what are we saying we believe ??? rom is being recorded

A

that the ROM recorded at test 1 can predict 64% of the variance at time 2

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11
Q

would u reject the null hypothesis if the p= < .001

A

yes bc since the p=<.05 (the alpha) it is saying that there is a significant difference so u reject the null

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12
Q

what kind of test is a dublin watson test

A

assumption test for linear regression

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13
Q

what is a concurrent validity study

A

testing 2 tools ( neither fro the gold standard) and u want to see if there is a relationship between them

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14
Q

if the relationship is strong is the effect size small or larger

A

larger

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15
Q

assumptions for regression…. data must be ____ not curvilinear (scattterplot)

A

linear

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16
Q

another assumption for regression … the data much have ____

A

normality and look at histograms , skewness, kurtosis , box plots and shapiro wilk test

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17
Q

when is the skewness/kurtosis a problem for normality

A

> +2 or < -2

18
Q

when looking at a shapiro wilk test for normality what kind of result do we want compared to the alpha of .05

A

a non significant results

19
Q

if the skewness is .-281 and .337 is there a problem

A

no bc only a problem is >2 or < -2

20
Q

if the shapiro wilk test is .012 is that okay

A

NOOOOO bc we don’t want it to be significant bruhhhh

21
Q

if the shapiro wilk test is .012 but the sample is small does it matter

A

no not really bc it is designed for a large sample

22
Q

another assumption for regression is that the data must have ______

A

homoscedasticity

23
Q

In relationship designs, the variance of the
outcome variable should be approximately the
same at all levels of the predictor variable… what is this called

A

homoscedasticity

24
Q

when do u want HOV

A

for a independent t test

25
Q

what test is used for HOV

A

levenes test (used when testing for differences)

26
Q

do we want the levenes test to be significant

A

no no no no

27
Q

levenes test if the variances in different groups are approximately the ___

A

sane

28
Q

what is an example of a problem with homoscedasticity

A

if the spread to the data is no even around the best fit line and if it goes into t funnel shape

29
Q

another assumptions for regression is that the data must be free of influential ____

A

outliers (cooks distance )

30
Q

a point is considered an outliear if the cooks distance is what

A

greater then 1 - then we have to eleimaite one participant

31
Q

another assumption for regression is that all data must be ____ of each other

A

independent ( participants cant be influenced by each other and no trends over time ) (dburbin watson test )

32
Q

what is one way to check for independence of observations for regression

A

durbin watson test

33
Q

what are the values ranges for the durbin watson test and what is it checking for

A

0-4 (2 is perfrect) .. checking for possible correlations between the participants which would violate our assumption

34
Q

when running the regression analysis what does the R squared mean

A

then the % of y could be predicted from X

35
Q

Ex/ Does UG GPA and GRE score significantly
predict PT school GPA?

this is an exmaple of what kind of regression

A

multiple linear bc multiple predictors and one I/R outcome

36
Q

Ex/ Does age and sport predict days to
clearance following a concussion in adolescent
athletes?

this is an exmaple of what kind of regression

A

multiple linear bc multiple predictors and one I/R outcome

37
Q

what is the new assumptions for multiple regression

A

multicolinearity

38
Q

do u want to have multicolineartiy

A

no

39
Q

for multicolinearityi for multiple regression the VIF would be ___ and the tolerance (TOL) should be what

A

VIF- should be <10
TOL- should be >.1

40
Q

Ex/ Does TUG score predict fallers and non-
fallers?

this is an example of what type or regression

A

logisitc bc one or more predictors (tug) and one categorical outcome , 2 levels only (fallers and non fallers)

41
Q

Ex/ Does a score on the VOM (Vestibular
Ocular Motor) Screening test predict return to
sport in the next 30 days? (yes/no)

this is na exmaple of what type of regression

A

logisitc bc one or more predictors (VOM) and one categorical outcome , 2 levels only (yes or no)

42
Q

Ex/ Does age and type of cardiac procedure
(value repair vs bypass) predict discharge
location to home vs. other?

this is an example of what type of regression

A

logisitc bc one or more predictors (age and type of caradic procedure) and one categorical outcome , 2 levels only (home vs other)

RESEARCH (11 decks)

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