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VW NCS Prep > Research and Statistics > Flashcards

Research and Statistics Flashcards

1
Q

Nominal

A

Labels, mutually exclusive, exhaustive

Ex: male and female

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2
Q

Ordinal

A

Rank ordering, distance between rating not equal

Ex: 1st, 2nd, 3rd place in a race

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3
Q

Interval

A

Equal intervals between ratings, no true zero point

Ex: temperature

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4
Q

Ratio

A

Equal intervals, has true zero point

Ex: 10MWT

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5
Q

Reliability

A

Consistent and dependable- results can be reproduced under same conditions

Random errors limit reliability
Systematic errors limit validity

Data must be reliable before it can be considered valid

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6
Q

SEM

A

Repeated measures on the same instrument tend to be distributed around the “true” score

Large SEM = low reliability
Small SEM = high reliability

Ex: BP readings 120, 140, 160 vs. 102, 104, 106

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7
Q

Validity

A

The extent to which a test measures what it is purported to measure

A test must be reliable to be valid - although a highly reliable test may be invalid

Ex: bull’s eye with x’s

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8
Q

Construct Validity

A

How well a test measures the concept it was designed to measure

Ex: pain, intelligence, QoL

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9
Q

Content Validity

A

Assesses whether a test is representative of all aspects of the construct

Usually refers to surveys/questionnaires

Ex: QoL outcome measures

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10
Q

Criterion-related validity

A

Compares a test with other valid measures- gold standard

Concurrent- measures done at same time yield same results

Predictive- comparison btw the test and another measure administered in the future

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11
Q

Floor Effect

A

A measure’s lowest score does not capture patient’s level of ability

Ex: FGA has a floor effect for complete SCI

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12
Q

Ceiling Effect

A

A measure’s highest score is unable to assess a patient’s level of ability

Ex: FIST to young athletes post concussion

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13
Q

Minimal detectable change

A

The minimum amount of change in a patient’s score that ensures the change is not the result of measurement error

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14
Q

Minimal Clinically Important Difference

A

The smallest amount of change in outcomes that might be considered meaningful to patient/clinician

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15
Q

Sensitivity

A

True positive rate

How good a test is at determining who has the disease- test is positive for those who have the condition

A test with high sensitivity can be used to r/o the disease–

SnNout it out

SenSitivity = Screening

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16
Q

Specificity

A

True negative rate

A test that is good at finding those who do not have the condition

How often a test gives a negative result when the person does not have it

High specificity = can Spin it in

SpeCificity = Confirming

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17
Q

Positive Predictive Value

A

Percentage of people who are positive on the diagnostic test who have the condition

Ex 100% of people with vestibular disorder test (+), then high positive predictive value

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18
Q

Negative Predictive Value

A

Percentage of people who are negative on the diagnostic test who do not have the condition

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19
Q

Positive Likelihood Ratio

A

Indicates how many times more or less likely a positive test result will occur in someone with the condition than in someone without the condition

True positive rate compared to false positive rate

> 10: large, often conclusive likelihood disorder is present
5-10: moderate likelihood disorder is present
2-5: small likelihood disorder is present
1: normal (useless test)

20
Q

Negative likelihood ratio

A

Indicates how many times more or less likely a negative test result will occur in someone without the condition than in someone with the condition

True negative rate compared to the false negative rate

1: neutral (useless test)
0.2-0.5: small decrease in likelihood of the disorder
0.1-0.2: moderate decrease in likelihood of disorder
<0.1: large, often conclusive decrease in likelihood of disorder

21
Q

Mean

A

Sum all scores and divide by number of scores

Best used with interval/ratio data (unskewed)

22
Q

Mode

A

The scores which are most frequently represented

Best used with nominal data

23
Q

Median

A

Middle score for a set of data

Best used with ordinal data, skewed interval/ratio

24
Q

Range

A

Difference between the highest value and the lowest value

No insight into distribution of scores

25
Q

Percentiles/quartiles

A

Value below which a given percentage of scores fall

Ex: 70th percentile- value below which 70% of scores fall

25 percentile = 1st quartile (Q1), 50 percentile = 2nd quartile, 75 percentile = 3rd quartile

26
Q

Standard deviation

A

Measure of the distribution of scores around the mean

Low SD: scores tend to be close to the mean
High SD: scores spread out over a wider range of values

Ex: 6MWT mean distance of 1,200ft, SD: 30ft vs. SD: 300ft

27
Q

Intraclass correlation coeficient

A

Measure of reliability of ratings

Describes how strongly units in the same group resemble eachother

Ranges from 0-1 (low to high agreement)

Generally ICC>0.75 is good reliability

28
Q

Interferential statistics

A

Techniques that allow us to make generalizations about the populations the samples represent.

29
Q

Descriptive statistics

A

Summarizes a sample rather than the population the sample represents

Descriptive statistics do not allow us to make conclusions about the population

30
Q

Null hypothesis

A

Assumes there is no relationship between X and y

31
Q

Experimental hypothesis

A

Assumes there is a relationship between X and Y

32
Q

P-value

A

The probability that your results are d/t chance and are actually real regardless of test type.

P= 0.10 means there is a 10% probability your results are due to chance (and thus may be incorrect)
P= <0.05 means there is a 5% probability…- most commonly used
P= <0.01 means there is a 1% probability…

Used with tests of significant diff, relationships and tests that predict

33
Q

Parametric statistics

A

Relies on assumptions about the population

interval and ratio data

Parametric data
2 IND groups: unpaired t-test
2 related groups: paired t-test
=/>3 IND groups: One-way analysis of variance (ANOVA)
=/>3 related groups: one-way repeated measures (ANOVA)

34
Q

Nonparametric statistics

A

Does not rely on assumptions about the population

nominal or ordinal scales

2 IND groups: Mann-Whitney U test
2 related groups: Wilcoxon signed rank test
=/> 3 IND groups: Kruskal-Wallis analysis of variance by ranks
=/>3 related groups: Friedman two-way analysis of variance by ranks

35
Q

Significance testing

A

Determines if groups are significantly different from each other

36
Q

r2

A

Measures the % of variation in the values of the dependent variable that can be explained by the variation in the independent variable

Ranges from 0-1 expressed in %

Ex: r2= 0.8419 means that 84.19% of the variance in Y can be explained by changes in X

37
Q

Confidence interval

A

Range of numbers of which you expect the true difference to fall.

Typically 95%, can be 98 and 99%

Measure of precision, range

Ex: CI=95% gives you 95% confidence that the true difference btw groups will fall btw a and b

38
Q

Type 1 error

A

False positive

Ex: fire alarm goes off when there is no fire

39
Q

Type 2 error

A

False negative

Fire alarm fails to sound when there is a fire

More severe- condition is present but test is telling us its not there

40
Q

Statistical power

A

Alpha= 5% chance of incurring a Type 1 error

Beta= 20% chance of incurring a Type 2 error
Power= 1-beta

Increasing power:
-increase sample size
-increase alpha (from p0.5 to p0.1)
-larger effect size

41
Q

Case Control Study

A

Patients with condition are compared to people without

Often rely on patient recall and medical records

Always retrospective

Cannot prove cause and effect

42
Q

Case Report/Series

A

Detailed description of a series or single case = selection bias

No statistical comparison, only course of care

Cannot prove cause/effect

43
Q

Cohort Study

A

Pt’s with the condition who undergo specific treatment are compared to those who either do not have the condition or did not have treatment

Can be prospective or retrospective

Provides evidence for cause and effect

44
Q

Randomized Controlled Trials

A

Randomly assigns patients to treatment group or comparative group

Control: no treatment or sham
Treatment: other treatment

Strong evidence for cause and effect

45
Q

Chi Square Test

A

Tests for the frequency of distribution.

Uses nominal data

Is the distribution of those categories any different?

Ex: are there a different amount of men in group A as compared with women in group B

46
Q

Effect size

A

How big is the difference between groups? How much better did my patient get?

Larger effect size = larger difference

Ex: Cohen’s D, Hedges’ G, odd ratio, Pearson’s r

VW NCS Prep (5 decks)

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