This document discusses various methods for evaluating the reliability of measurement instruments, including internal consistency, test-retest reliability, interrater reliability, split-half methods, and alternate forms methods. It provides details on calculating and interpreting each type of reliability. Factors that can influence reliability are also examined, such as the number of items, characteristics of test takers, heterogeneity of items and groups, and time between test administrations. The document emphasizes that reliability is important for ensuring measurement tools provide consistent results.

1. Reliability

2. Evaluation of Measurement Instruments
• Reliability has to do with the consistency of the instrument.
- Internal Consistency (Consistency of the items)
- Test-retest Reliability (Consistency over time)
- Interrater Reliability (Consistency between raters)
- Split-half Methods
- Alternate Forms Methods
• Validity of an instrument has to do with the ability to
measure what it is supposed to measure and the extent to
which it predicts outcomes.
- Face Validity -
Construct & Content Validity
- Convergent & Divergent Validity
- Predictive Validity
- Discriminant Validity

3. Reliability
• Reliability is synonymous with consistency. It is the degree to
which test scores for a an individual test taker or group of test
takers are consistent over repeated applications.
• No psychological test is completely consistent, however, a
measurement that is unreliable is worthless.
For Example
A student receives a score of 100 on one intelligence tests and
114 in another or imagine that every time you stepped on a
scale it showed a different weight.
Would you keep using these measurement tools?
• The consistency of test scores is critically important in
determining whether a test can provide good measurement.

4. Reliability (cont.)
• Because no unit of measurement is exact, any time you measure
something (observed score), you are really measuring two things
1. True Score - the amount of observed score that truly represents
what you are intending to measure.
2. Error Component - the amount of other variables that can impact
the observed score
Observed Test Score = True Score + Errors of Measurement
For Example - if you weigh yourself today and weigh 140 lbs. and
then weigh yourself tomorrow and weigh 142 lbs., is the 2 pound
increase a true measure of your weight gain or could other variables
be involved?
Other variables may include: food intake, placement of scale, error
in the scale itself.

5. Why Do Test Scores Vary?
Possible Sources of Variability of Scores (pg. 110)
- General Ability to comprehend instructions
- Stable response sets (e.g., answering “C” option more frequently)
- The element of chance of getting a question right
- Conditions of testing
- Unreliability or bias in grading or rating performance
- Motivation
- Emotional Strain

6. Measurement Error
• Any fluctuation in test scores that results from factors related to
the measurement process that are irrelevant to what is being
measured.
• The difference between the observed score and the true score is
called the error score. S true = S observed - S error
• Developing better tests with less random measurement error is
better than simply documenting the amount of error.
Measurement Error is Reduced By:
- Writing items clearly
- Making instructions easily understood
- Adhering to proper test administration
- Providing consistent scoring

7. Determining Reliability
• There are several ways that a measurements reliability
can be determined, depending on the type of
measurement the and the supporting data required.
They include:
- Internal Consistency
- Test-retest Reliability
- Interrater Reliability
- Split-half Methods
- Odd-even Reliability
- Alternate Forms Methods

8. Internal Consistency
• Measures the reliability of a test solely on the number of items on
the test and the intercorrelation among the items. Therefore, it
compares each item to every other item.
• If a scale is measuring a construct, then overall the items on that
scale should be highly correlated with one another.
• There are two common ways of measuring internal consistency …
1. Cronbach’s Alpha: .80 to .95 (Excellent)
.70 to .80 (Very Good)
.60 to .70 (Satisfactory)
<.60 (Suspect)
2. Item-Total Correlations - the correlation of the item with the
remainder of the items (.30 is the minimum acceptable item-total
correlation).

9. Internal Consistency (cont.)
Internal consistency estimates are a function of:
The Number of Items - if we think that each test item is an
observation of behaviour, high internal consistency strengthens
the relationship --- i.e., There is more of it to observe.
Average Intercorrelation - the extent to which each item represents
the observation of the same thing observed.
The more you observe a construct, with greater consistency
=
Reliability

10. Split Half & Odd-Even Reliability
Split Half - refers to determining a correlation between the first
half of the measurement and the second half of the measurement
(i.e., we would expect answers to the first half to be similar to the
second half).
Odd-Even - refers to the correlation between even items and odd
items of a measurement tool.
• In this sense, we are using a single test to create two tests,
eliminating the need for additional items and multiple
administrations.
• Since in both of these types only 1 administration is needed and
the groups are determined by the internal components of the test,
it is referred to as an internal consistency measure.

11. Split Half & Odd-Even Reliability
Possible Advantages
• Simplest method - easy to perform
• Time and Cost Effective
Possible Disadvantages
• Many was of splitting
• Each split yields a somewhat different reliability estimate
• Which is the real reliability of the test?

12. Test-retest Reliability
• Test-retest reliability is usually measured by computing
the correlation coefficient between scores of two
administrations.

13. Test-retest Reliability (cont.)
• The amount of time allowed between measures is critical.
• The shorter the time gap, the higher the correlation; the longer
the time gap, the lower the correlation. This is because the two
observations are related over time.
• Optimum time betweem administrations is 2 to 4 weeks.
• If a scale is measuring a construct consistently, then there should
not be radical changes on the scores between administrations ---
unless something significant happened.
• The rationale behind this method is that the difference between
the scores of the test and the retest should be due to measurement
solely.

14. Test-retest Reliability (cont.)
• It is hard to specify one acceptable test-retest correlation
since what is considered acceptable depends on the the
type of scale, the use of the scale, and the time between
testing.
For example - it is not clear whether differences in test
scores are regarded as sources of measurement error or
as sources of real stability.
Possible difference in scores between tests? : experience,
characteristic being measured may change over time
(e.g. reading test), carryover effects (e.g., remember test)

15. Test-retest Reliability (cont.)
• A minimum correlation of at least .50 is expected.
• The higher the correlation (in a positive direction) the
higher the test-retest reliability
• The biggest problem with this type of reliability is what
called memory effect. Which means that a respondent
may recall the answers from the original test, therefore
inflating the reliability.
• Also, is it practical?

16. Interrater Reliability
• Whenever you use humans as a part of your measurement
procedure, you have to worry about whether the results you get
are reliable or consistent. People are notorious for their
inconsistency. We are easily distractible. We get tired of doing
repetitive tasks. We daydream. We misinterpret.

17. Interrater Reliability (cont.)
• For some scales it is important to assess interrater
reliability.
• Interrater reliability means that if two different raters
scored the scale using the scoring rules, they should
attain the same result.
• Interrater reliability is usually measured by computing
the correlation coefficient between the scores of two
raters for the set of respondents.
• Here the criterion of acceptability is pretty high (e.g., a
correlation of at least .9), but what is considered
acceptable will vary from situation to situation.

18. Parallel/Alternate Forms Method
Parallel/Alternate Forms Method - refers to the
administration of two alternate forms of the same
measurement device and then comparing the
scores.
• Both forms are administered to the same person and
the scores are correlated. If the two produce the
same results, then the instrument is considered
reliable.

19. Parallel/Alternate Forms Method (cont.)
• A correlation between these two forms is computed just
as the test-retest method.
Advantages
• Eliminates the problem of memory effect.
• Reactivity effects (i.e., experience of taking the test) are
also partially controlled.
• Can address a wider array of sampling of the entire
domain than the test-retest method.

20. Parallel/Alternate Forms Method (cont.)
Possible Disadvantages
• Are the two forms of the test actually measuring
the same thing.
• More Expensive
• Requires additional work to develop two
measurement tools.

21. Factors Affecting Reliability
• Administrator Factors
• Number of Items on the instrument
• The Instrument Taker
• Heterogeneity of the Items
• Heterogeneity of the Group Members
• Length of Time between Test and Retest

22. • Poor or unclear directions given during
administration or inaccurate scoring can affect
reliability.
For Example - say you were told that your scores on
being social determined your promotion. The result
is more likely to be what you think they want than
what your behavior is.
Administrator Factors

23. • The larger the number of items, the greater the
chance for high reliability.
For Example -it makes sense when you ponder that
twenty questions on your leadership style is more
likely to get a consistent result than four questions.
• Remedy: Use longer tests or accumulate
scores from short tests.
Number of Items on the Instrument

24. For Example -If you took an instrument in August
when you had a terrible flu and then in December
when you were feeling quite good, we might see a
difference in your response consistency. If you were
under considerable stress of some sort or if you were
interrupted while answering the instrument
questions, you might give different responses.
The Test Taker

25. Heterogeneity of the Items -- The greater the
heterogeneity (differences in the kind of questions or
difficulty of the question) of the items, the greater
the chance for high reliability correlation
coefficients.
Heterogeneity of the Group Members -- The greater
the heterogeneity of the group members in the
preferences, skills or behaviors being tested, the
greater the chance for high reliability correlation
coefficients.
Heterogeneity

26. • The shorter the time, the greater the chance for high
reliability correlation coefficients.
• As we have experiences, we tend to adjust our views a little
from time to time. Therefore, the time interval between the
first time we took an instrument and the second time is
really an "experience" interval.
• Experience happens, and it influences how we see things.
Because internal consistency has no time lapse, one can
expect it to have the highest reliability correlation
coefficient.
Length of Time between Test and Retest

27. How High Should Reliability Be?
• A highly reliable test is always preferable to a test with
lower reliability.
.80 > greater (Excellent)
.70 to .80 (Very Good)
.60 to .70 (Satisfactory)
<.60 (Suspect)
• A reliability coefficient of .80 indicates that 20% of the
variability in test scores is due to measurement error.

28. Generalizability Theory
Theory of measurement that attempts to determine the
sources of consistency and inconsistency
• It is necessary to obtain multiple observations for the sample
group of individuals on all the variables that might contribute to
causing measurement error (e.g., scores across occasions, across
scorers, across alternative forms).
• Allows for the evaluation of interaction effects from different
types of error sources.
• Allows for the evaluation of interaction effects from different
types of error sources.

29. Generalizability Theory (cont.)
• If feasible, it is a more thorough procedure for identifying the
error component that may enter scores.
• Useful when associated with complex methods:
1. The conditions of measurement affect test scores.
2. Test scores are used for several different purposes.
For Example - measurement involving subjectivity (e.g.,
interviews, rating scales) involve bias. Therefore, human
judgement could be considered “conditions of measurement”

30. Standard Error of Measurement (SEM)
• SEM is a statistic that obtains the confidence interval for many
obtained scores. It represents the hypothetical distribution we
would have if someone took a test an infinite # of times.
• A measure that allows one to predict the range of fluctuation that
is likely to occur in a single individual's score because of
irrelevant, chance factors. This measurement is used in analyzing
the reliability of the test in obtaining the "true" score.
• Indicates how much variability in test scores can be expected as
a result of measurement error.
• SEM is a function of two factors: reliability of test & variability
of test scores. Formula for SEM is :
SM = SD(Sq root of 1 minus reliability)

31. Standard Error of Measurement (cont.)
• The most common use of the SEM is the production of the
confidence intervals. The SEM is an estimate of how much
error there is in a test.
• The SEM can be looked at in the same way as Standard
Deviations. Sixty eight percent of the time the true score
would be between plus one SEM and minus one SEM. We
could be 68% sure that the students true score would be
between +/- one SEM. Between +/- two SEM the true score
would be found 96% of the time (e.g., SEM x +/- two SEM)
• Or, if the student took the test 100 times, 64 times the true
score would fall between +/- one SEM.