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Validity

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Validity In our last class, we began to discuss some of the ways in which we can assess the quality of our measurements. We discussed the concept of reliability (i.e ... – PowerPoint PPT presentation

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Title: Validity


1
Validity
  • In our last class, we began to discuss some of
    the ways in which we can assess the quality of
    our measurements.
  • We discussed the concept of reliability (i.e.,
    the degree to which measurements are free of
    random error).

2
Why reliability alone is not enough
  • Understanding the degree to which measurements
    are reliable, however, is not sufficient for
    evaluating their quality.
  • In-class scale example
  • Recall that test-retest estimates of reliability
    tend to range between 0 (low reliability) and 1
    (high reliability)
  • Note An on-line correlation calculator is
    available at http//easycalculation.com/statistics
    /correlation.php

3
Validity
  • In this example, the measurements appear
    reliable, but there is a problem . . .
  • Validity reflects the degree to which
    measurements are free of both random error, E,
    and systematic error, S.
  • O T E S
  • Systematic errors reflect the influence of any
    non-random factor beyond what were attempting to
    measure.

4
Validity Does systematic error accumulate?
  • Question If we create a composite of multiple
    observations, how will systematic errors
    influence our estimates of the true score?

5
Validity Does error accumulate?
  • Answer Unlike random errors, systematic errors
    accumulate.
  • Systematic errors exert a constant source of
    influence on measurements. We will always
    overestimate (or underestimate) T if systematic
    error is present.

6
Note Each measurement is 2 points higher than
the true value of 10. The errors do no average
out.
7
Note Even when random error is present, E
averages to 0 but S does not. Thus, we have
reliable measures that have validity problems.
8
Validity Ensuring validity
  • What can we do to minimize the impact of
    systematic errors?
  • One way to minimize their impact is to use a
    variety of indicatorsdifferent sources of
    information.
  • Different kinds of indicators of a latent
    variable may not share the same systematic errors
  • If true, then S will behave like random error
    across measurements (but not within measurements)

9
Example
  • As an example, lets consider the measurement of
    self-esteem.
  • Some methods, such as self-report questionnaires,
    may lead people to over-estimate their
    self-esteem. Most people want to think highly of
    themselves.
  • Other methods, such as clinical ratings by
    trained observers, may lead to under-estimates of
    self-esteem. Clinicians, for example, may be
    prone to assume that people are not as well-off
    as they say they are.

10
Self-reports
Clinical ratings
Note Method 1 systematically overestimates T
whereas Method 2 systematically underestimates T.
In combination, however, those systematic errors
cancel out.
11
Another example
  • One problem with the use of self-report
    questionnaire rating scales is that some people
    tend to give high (or low) answers consistently
    (i.e., regardless of the question being asked).
  • This is sometimes referred to as a yay-saying
    or nay-saying bias. Acquiescence

12
1 strongly disagree 5 strongly agree
Item T S O
I think I am a worthwhile person. 4 1 5
I have high self-esteem. 4 1 5
I am confident in my ability to meet challenges in life. 4 1 5
My friends and family value me as a person. 4 1 5
Average score 4 1 5
In this example, we have someone with relatively
high self-esteem, but this person systematically
rates questions one point higher than he or she
should.
13
1 strongly disagree 5 strongly agree
If we reverse key half of the items, the bias
averages out. Responses to reverse keyed items
are counted in the opposite direction. T (4 4
6-2 6-2) / 4 4 O (5 5 6-3
6-3) / 4 4
Item T S O
I think I am a worthwhile person. 4 1 5
I have high self-esteem. 4 1 5
I am NOT confident in my ability to meet challenges in life. 2 1 3
My friends and family DO NOT value me as a person. 2 1 3
Average score 4 1 4
14
Validity
  • To the extent to which a measure has validity, we
    say that it measures what it is supposed to
    measure
  • Question How do you assess validity?

Very tough question to answer!
15
Different ways to think about validity
  • To the extent that a measure has validity, we can
    say that it measures what it is supposed to
    measure.
  • There are different reasons for measuring
    psychological variables. The precise way in which
    we assess validity depends on the reason that
    were taking the measurements in the first place.

16
Prediction
  • As an example, if ones goal is to develop a way
    to determine who is at risk for developing
    schizophrenia, ones goal is prediction.

17
Predictive Validity
  • We may begin by obtaining a group of people who
    have schizophrenia and a group of people who do
    not.
  • Then, we may try to figure out which kinds of
    antecedent variables differentiate the two groups.

18
Correct classifications
Lost a parent before the age of 10 10
Parent or grandparent had schizophrenia 50
Mother was cold and aloof to the person when he or she was a child 15
19
Predictive Validity
  • In short, some of these variables appear to be
    better than others at discriminating
    schizophrenics from non-schizophrenics
  • The degree to which a measure can predict what it
    is supposed to predict is called its predictive
    validity.
  • When we are taking measurements for the purpose
    of prediction, we assess validity as the degree
    to which those predictions are accurate or useful.

20
Reality Schizophrenic
Yes
10
No
Measure Schizophrenic
40
Yes
21
Reality Schizophrenic
Yes
No
10
10
No
Measure Schizophrenic
40
40
Yes
50 ( 40 10 / 100) people were correctly
classified (with a 50 base rate. Yuck.)
22
Reality Schizophrenic
Yes
No
0
98
No
Measure Schizophrenic
1
1
Yes
99 ( 98 1 / 100) people were correctly
classified, but note the base rate problem.
Cohens kappa is used to account for this
problem. Kappa in this example is 66
23
Construct Validity
  • Sometimes were not interested in measuring
    something just for technological purposes, such
    as prediction.
  • We may be interested in measuring a construct in
    order to learn more about it
  • Example We may be interested in measuring
    self-esteem not because we want to predict
    something with the measure per se, but because we
    want to know how self-esteem develops, whether it
    develops differently for males and females, etc.

24
Construct Validity
  • Notice that this is much different than what we
    were discussing before. In our schizophrenia
    example, it doesnt matter whether our measure of
    schizophrenia really measured schizophrenic
    tendencies per se.
  • As long as the measure helps us predict
    schizophrenia well, we dont really care what it
    measures.

25
Construct Validity
  • When we are interested in the theoretical
    construct per se, however, the issue of exactly
    what is being measured becomes much more
    important.
  • The general strategy for assessing construct
    validity involves (a) explicating the theoretical
    relations among relevant variables and (b)
    examining the degree to which the measure of the
    construct relates to things that it should and
    fails to relate to things that it should not.

26
Nomological Network
  • The nomological network represents the
    interrelations among variables involving the
    construct of interest.

achieve in school
ability to cope


self- esteem
-
distrust friends
27
Nomological Network Validity
  • The process of assessing construct validity
    basically involves determining the degree to
    which our measure of the construct behaves in the
    way assumed by the theoretical network in which
    it is embedded.
  • If, theoretically, people with high self-esteem
    should be more likely to succeed in school, then
    our measure of self-esteem should be able to
    predict peoples grades in school.

28
Construct Validity
  • Notice here that establishing construct validity
    involves prediction. The difference between
    prediction in this context and prediction in the
    previous context is that we are no longer trying
    to predict school performance as best as we
    possibly can.
  • Our measure of self-esteem should only predict
    performance to the degree to which we would
    expect these two variables to be related
    theoretically.

29
Discriminant Validity
  • The measure should also fail to be related to
    variables that, theoretically, are unrelated to
    self-esteem.
  • The ability of a measure to fail to predict
    irrelevant variables is referred to as the
    measures discriminant validity.

achieve in school
ability to cope


self- esteem
0
-
like coffee
distrust friends
30
Validity Assessing validity
  • Finally, it is useful, but not necessary, for a
    measure to have face validity.
  • Face validity The degree to which a measure
    appears to measuring what it is supposed to
    measure.
  • A questionnaire item designed to measure
    self-esteem that reads I have high self-esteem
    has face validity. An item that reads I like
    cabbage in my Frosted Flakes does not.
  • In the context of prediction, face validity
    doesnt matter. In the context of construct
    validity, it matters more.

31
A Final Note on Construct Validity
  • The process of establishing construct validity is
    one of the primary enterprises of psychological
    research.
  • When we are measuring the association between two
    variables to assess a measures predictive or
    discriminant validity, we are evaluating both (a)
    the quality of the measure and (b) the soundness
    of the nomological network.
  • It is not unusual for researchers to refine the
    nomological network as they learn more about how
    various measures are inter-related.
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