Lesson 4: Measurement Considerations Flashcards Preview

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Flashcards in Lesson 4: Measurement Considerations Deck (26):
1

Bias

Systematic error introduced by selecting or encouraging out come over others

2

Validity

Extent to which a measure actually represents what it claims to measure

 

3

Reliability

Degree to which results are stable and consistent

4

Sensitivity

Proportion of POSITIVES that are correctly identified

5

Specificity

Proportion of NEGATIVES that are correctly identified

6

Correlated

A statistical relationship existing between two variables or data sets that reflects a dependence between the two

7

Independent

The occurrence of one variable does not influence the probability of another variable

8

Parametric

Data with an underlying normal distribution

9

Nonparametric

Data for which the probability distribution is unknown or NOT known to be normal

10

When can bias occur during clinical research?

Before, During, & after the study 

A image thumb
11

Selection Bias

the error associated with how participants are selected for studies

12

Interviewer Bias

When the interviewer influences the participant response during an interview

ex: giving reactions, social cues, presenting questions in a certain way 

13

Recall Bias

the error associated with remembering

14

Publication Bias

the error associated with not selectively submitting [researchers fault] and/or publishing research [journal's fault]

*negative results are much less likely to be published

15

What does it mean when we say that something is validated?

-We made some attempt to show that our data actually represents what it claims to measure

-Most types of validity are measured with a metric or scale and then compared to some sort of standard

16

Name a few examples of threats to validity in measuring medication adherence.

Patients could lie about taking their medications

Small Sample size

17

Face Validity (for questionnaires)

do the questions look like (“at face value”) they measure what they say they are measuring?

Ex:

Measuring Necessity = My life would be impossible without my medicines

Measuring Overuse = Doctors use too many medicines

18

Construct Validity 

are we measuring distinct constructs? (i.e. are the scales correlated or independent?)

How well did it measure or test a specific construct?

Did you measure adherence? Or something else?

19

External Validity

Are these findings generalizable to beyond the sample?

20

Internal Validity 

Was the study well designed?

Did the study limit or control for possible confounders?

21

If our measuring instrument has been validated before, can we automatically use it?

No; We need to make sure our instrument is still valid when we put it in a new setting. Once is not enough. We need to revalidate the survey/instrument of measurement.

22

Which is valid? Which reliable?

Q image thumb

The left is reliable, but not valid.

The middle two are neither.

The right is both reliable and valid. 

23

Inter-Rater Reliablilty

The extent of agreement between two or more raters

24

Test-Retest Reliability 

The degree to which test or instrument scores are consistent from one point in time to the next (the test taker and test conditions must be the same at both points in time)

25

Internal Consistency Reliability 

The consistency of responses across items on a single instrument or test

26

Is nominal data usually parametric or nonparametric?

Nominal Data is always non-parametric

Nominal Data cannot by ordered by magnitude along the x-axis, thus cannot be normal.