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Foundations of Clinical Research > Measurement/Reliability > Flashcards

Measurement/Reliability Flashcards

1
Q

Discrete Measurment

A

One Number

Example: One point in an entire cycle of gait.

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2
Q

Continuous Measurement

A

Same Scale, Multiple different data point taken in a cycle

Example: Kinematics of joint angles moving through a motion

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3
Q

What are the 4 scales of measurement?

A

Nominal
Ordinal
Interval
Ratio

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4
Q

Nominal Scale

A
  • “dummy variables”; coded 1 or 0
  • Lowest level of measurement
  • Ex: Yes - No; Male - Female; Injured - Healthy
  • Two categorical variables
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5
Q

Ordinal Scale

A
  • Categories order ranked
  • “greater than less than relationship”
  • good is better than fair which is better than poor
  • As many categories as you want
  • Ex: Foot strike patterns: forefoot vs rearfoot, high arch vs low arch
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6
Q

Interval Scale

A
  • Can be ranked, each intervals between each rank
  • Examples: Temperature
    – 1 degree is constant interval
    – 30 degrees is “hotter” than 20 degrees
  • Example: RPE
    – 1 is constant interval
    – 7 is greater than 3; therefore they are working harder
  • No zero point
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7
Q

Ratio Scale

A
  • Possess all components of a true number system
  • zero, equal intervals
  • Example: Peak ground force
    – 1000 N is twice as large as 500 N
    – 1 N is the constant interval
  • Example: Running
    – I ran 3 miles which is half of 6 miles
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8
Q

Most to Least Level of Measurement

A

Most
Ratio
Interval
Ordinal
Nominal
Least

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9
Q

What is transforming data? What are the pros and cons? What do we need to consider?

A
  • Changing high level measurements and change them into lower level meaurements
  • Example: Strong, Weak
  • Pro: Can see the bigger picture (obscures a few data points)
  • Con: Lose ability to magnity difference
  • Must think about how valid and reliable measures are. Consider if it is error or is true data. The way in which we interpret data changes the conversation (Categories vs Numerical)
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10
Q

Why do we care about the type of data and statistics used to analyze it?

A
  • Influences how you can manipulate the data and what statistics can be used
  • Some statistical tests are more “powerful” than others (aka more likely to predict a difference)
  • The more powerful the measurment the les people you need to find a difference
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11
Q

Reliability

A
  • Consistency of measurement
  • Reproducibility/dependibility of the measurement; free from error
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12
Q

Validity

A

Accuracy
Tells you if we are getting the correct awnser

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13
Q

What is a “ground truth”?

A

The measure is what actually happened.

Ex: I ran 2 miles (ground truth); I forgot to stop my watch and now it says I ran 4 (NOT ground truth)

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14
Q

Can something that is reliable be valid? Can we use it in PT practice?

A
  • Yes, if the measurement is reliable/consistent.
  • Ex: Scale is always off by 10, you can still get good measurements if you take the 10 into account.
  • Consistent but not calibrated
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15
Q

What types of tools are used in PT practice most often?

A

Those that are reliable and valid!

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16
Q

The wider the data is the ____ reliable

A

less

17
Q

Precision gets more important with discrete data. Therefore less precision = ____

A

less reliable

18
Q

Decreasing the resolution, eventually leads to..

A

a much different representation. Not accurate data representation.

19
Q

Explain what this graph is

A

Validity: mean of measure and true value

More reliability if the point gets smaller

20
Q

Measurement Error

A
  • Every measurement includes error
  • Measurement is the recorded data point and true score is the “real” or “actual” value under investigation
  • Statistical designs can help reduce error, but not remove (repeated measure, within subject)
  • The greater the variability between measurements, the lower the reliability is presumed to be

Measurement = True Score + Error

21
Q

What do we compare when estimating reliability

A

Expected (True Score) vs What happened (Data w/ Error)

22
Q

Ratios closer to ____ are reliable

A

1.0

23
Q

Primary Contributors to Measurment Errors

A

Subject
* mood, fatigue, injury type, practice, motivation, knowledge

Testing
* test instructions, multiple testers, tester skill, test environment

Instrumentation
* calibration, measurement drift

24
Q

Explain this graph in terms of reliability

A

Trained individuals are more reliable/less variant

25
Q

Types of Reliability

A
  • Greater reliability increases confidence between repeated measures
  • Intra-rater reliability (same person testing)
  • Inter-rater reliability (different people testing)
26
Q

High inter-rater reliability allows for us to…

A

make easier generalization to the public!

27
Q

What needs to be considered for generalizability of reliability?

A
  • Inter and intra rated reliability
  • Be interpreted with repect to the specific conditions in which they were obtained
  • Ex: age, different medical condition, injuries, etc.
28
Q

Correlation Coefficient

A
  • Describes the extent that one measurement varies with the other – how are different measures related
  • Values near one or negative one means there is an association but does NOT explain the extent!
  • Ex: Pearson Correlation Coefficent
29
Q

General rules for r - Correlation Coefficent

A
  • .25 to .5 fair
  • .5 to .75 moderate
  • Greater than 0.75 good
30
Q

Can see how correlation coefficent plays into relationship by taking the p and…

A
  • Multiplying it by the percent you want to see change
  • EX: As state cigarette tax goes up the % of adult smokers goes down. We have a negative correlation (one goes up, one goes down)
  • p = 0.05
  • 25% change in state tax results in a change of 25% change in % of people smoking which we got by 0.05 x 0.05 = 0.025 = 25%
31
Q

What is intraclass correlation coefficent?

A
  • Comparison of two or more repeated measurements
  • Provides an estimate of association AND agreement in the absolute magnitude of repeated measures
  • Range: 0 to 1.0
  • ICC measurements:
    – Less than 0.75 = unacceptable
    – 0.75-0.90 = good but consider other options
    – Greater than 0.90 = excellent and acceptable
32
Q

Kappa Statistic

A
  • Nominal or Ordinal Data (Categories)
  • Gives Rank, the more numbers down the middle the better

Kappa Statistic Values
* Less than 0.2 not different than expected by chance alone
* .2 to .4 minimal agreement
* .4 to .6 moderate
* .6 to .8 substantial
* Greater than 0.8 near perfect (suitable for clinical practice)

Foundations of Clinical Research (10 decks)

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