Statistics

Question 1

why are statistics important?

Answer 1

• some stats are predictive ( have to be reliable and valid )
• we are interested in the “application” of statistics

Question 2

how can we represent DATA?

Answer 2

• mean (average)
• standard deviation
• standard error

Question 3

standard error

Answer 3

• represents lots of individuals
• shows how much we can trust our mean (accuracy)
• the n variable rises as the number of people being measured increases
• used to generalize the mean to other cohorts or a population
• is dependent on SD and the sample size

Question 4

standard deviation

Answer 4

• measure of variability within the cohort being assessed
• 2 x SD = 95% of the range of data
• used to describe the cohort of data

Question 5

measuring variability

Answer 5

• includes the coefficient of variation which is Relative Standard Deviation
• RSD is the magnitude of the mean
• The SD is dependent in the mean.
• CV accounts for this
• CV% = SD / mean * 100

Question 6

factors influencing variability

Answer 6

• biological
• technical
• testing
• environmental
• unknown

Question 7

biological variability

Answer 7

• physiological and psychological fluctuations of the individual - circadian rhythms, mood, etc.

Question 8

technical variability

Answer 8

precision and accuracy of the instruments

Question 9

testing variability

Answer 9

instructions and manner of administering the test
( how you give tests/performances)

Question 10

environmental variability

Answer 10

temperature and humidity

Question 11

odds ratios and risk factors

Answer 11

• how something influences another outcome
• represents the effect of an “intervention” on a particular outcome
• both attempt to describe the same effect
• normalize the occurrence of an outcome in reference to a control group
• commonly used in medicine

Question 12

validity

Answer 12

• accuracy and correctness
• does a test measure what it is supposed to measure?
• valid - invalid

Question 13

reliability

Answer 13

• precision and repeatability
• consistency or replicability of a measurement
• type I error : not getting the outcome you should
• type II error: should be getting the outcome you want but don’t

Question 14

logical or face value validity

Answer 14

• can be claimed when the measure appears to obviously assess the target variable/performance
• ex. balance test obviously measures balance
• weakest form of validity b/c it is difficult to quantify
• no statistical verification
• established by expert opinion or judges

Question 15

content validity

Answer 15

• attempts to measure the desired parameter or a defined domain of context.
• applies to written tests or questionnaires
• often a table of specifications or diagrams are developed to act as a blueprint
• validity is established through published literature or curriculum content
• often no statistical verification is required
  ex. visual rating scale for body comp

Question 16

construct validity

Answer 16

• claimed when the measures permit inferences to be made about an underlying trait
• variable of interest is multi-factorial / multi-dimensional
• ## requires more complex statistical procedures such as factor analysis, multiple regression, etc.-

Question 17

criterion validity

Answer 17

• the extent to which the results of a standard test can be compared to some criterion ( another test), or used to predict a practical outcome
• can be claimed when a test measure provides an outcome similar to a standard/criterion or previously validated test measure
• can ALSO be claimed when the measure taken, successfully predicts the criterion measure or gold standard

Question 18

systematic error

Answer 18

situations that result in a unidirectional change in scores on repeated testing

Question 19

random error

Answer 19

variability in a random manner, both increase and decrease test scores on repeated testing

Question 20

testing reliability

Answer 20

• inter-rater
• intra-rater
• test-retest

Question 21

inter-rater

Answer 21

• a measure of consistency used to evaluate the extent to which different judges agree in their assessment decisions.
  ex. giving a score

Question 22

intra-rater

Answer 22

comparison of two (or more) measures made by the same tester
- how good you are at getting an outcome/measuring

Question 23

test-retest

Answer 23

• repeated testing on two or more occasions
• used to test the reliability of the technique (repeatability)
• getting similar results if the test is done twice

Question 24

repeatability

Answer 24

1. the same experimental tools
2. the same observer
3. the same measuring instrument, under the same conditions
4. the same location
5. repetition over a short period of time
6. same objectives

Question 25

intra-class correlation

Answer 25

interpreting the ICC
- less than 0.40 = poor
- btw 0.40 and 0.59 = fair
- btw 0.60 and 0.74 = good
- btw 0.75 and 1.00 = excellent

Question 26

correlation

Answer 26

• describes the strength of the relationship between two variables of interest
• should be physiological basis fundamentally linking the variables of interest
• correlation IS NOT causation
  ex. HR vs VO2 is valid because the VO2 equation has HR in it
• does NOT describe the pattern of relationships

Question 27

regression

Answer 27

• numerical relationship btw two variables
• how the variables line up with each other
• the simplest regression is line of best fit

Question 28

multiple-linear regression

Answer 28

• many factors can be co-related, influencing the relationship of interest

Question 29

bland-Altman test

Answer 29

• see PowerPoint slides

Question 30

meta-analysis

Answer 30

• use to get at a big question
• pulls all the information together in a domain and normalizes a ton of information
• gold standard to defining where risks come from
• strict, defined process for conducting analysis
• pool data to provide a larger sample size
• highly regarding technique for interpreting variability / controversy in data
• validity of outcomes across studies
• basis for clinical practice guidelines