Lesson 4: Measurement Considerations

Question 1

Bias

Answer 1

Systematic error introduced by selecting or encouraging out come over others

Question 2

Validity

Answer 2

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

Question 3

Reliability

Answer 3

Degree to which results are stable and consistent

Question 4

Sensitivity

Answer 4

Proportion of POSITIVES that are correctly identified

Question 5

Specificity

Answer 5

Proportion of NEGATIVES that are correctly identified

Question 6

Correlated

Answer 6

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

Question 7

Independent

Answer 7

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

Question 8

Parametric

Answer 8

Data with an underlying normal distribution

Question 9

Nonparametric

Answer 9

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

Question 10

When can bias occur during clinical research?

Answer 10

Before, During, & after the study

Question 11

Selection Bias

Answer 11

the error associated with how participants are selected for studies

Question 12

Interviewer Bias

Answer 12

When the interviewer influences the participant response during an interview

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

Question 13

Recall Bias

Answer 13

the error associated with remembering

Question 14

Publication Bias

Answer 14

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

Question 15

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

Answer 15

• 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

Question 16

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

Answer 16

Patients could lie about taking their medications

Small Sample size

Question 17

Face Validity (for questionnaires)

Answer 17

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

Question 18

Construct Validity

Answer 18

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?

Question 19

External Validity

Answer 19

Are these findings generalizable to beyond the sample?

Question 20

Internal Validity

Answer 20

Was the study well designed?

Did the study limit or control for possible confounders?

Question 21

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

Answer 21

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.

Question 22

Which is valid? Which reliable?

Answer 22

The left is reliable, but not valid.

The middle two are neither.

The right is both reliable and valid.

Question 23

Inter-Rater Reliablilty

Answer 23

The extent of agreement between two or more raters

Question 24

Test-Retest Reliability

Answer 24

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)

Question 25

Internal Consistency Reliability

Answer 25

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

Question 26

Is nominal data usually parametric or nonparametric?

Answer 26

Nominal Data is always non-parametric.

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