# Measuring Reliability Methods

Methods of Measurement Reliability II Reliability Of Measuring Instruments ,split-half technique.

In other words, the proportion of true score component in an obtained score varies from one person to the next. Many factors contribute to errors of measurement. The most common are the following:

1. Situational contaminants. Scores
can be affected by the conditions under which they are produced. A
participant's awareness of an observer's presence (reactivity) is one source of
bias. The anonymity of the response situation, the friendliness of researchers,
or the location of the data gathering can affect subjects' responses. Other environmental
factors, such as temperature, lighting, and time of day, can represent sources
of measurement error.

2. Temporary personal factors. A
person's score can be influenced by such temporary personal states as fatigue,
hunger, anxiety, or mood. In some cases, such factors directly affect the
measurement, as when anxiety affects a pulse rate measurement. In other cases,
personal factors can alter scores by influencing people's motivation to
cooperate, act naturally, or do their best.

3. Response-set biases. Relatively
enduring characteristics of respondents can interfere with accurate measures.
Response sets such as social desirability, acquiescence, and extreme responses
are potential problems in self-report measures, particularly in psychological
scales.

4. Administration variations.
Alterations in the methods of collecting data from one person to the next can
result in score variations unrelated to variations in the target attribute. If
observers alter their coding categories, if interviewers improvise question
wording, if test administrators change the test instructions, or if some
physiologic measures are taken before a feeding and others are taken after a
feeding, then measurement errors can potentially occur.

5. Instrument clarity. If the
directions for obtaining measures are poorly understood, then scores may be
affected by misunderstanding. For example, questions in a self-report
instrument may be interpreted differently by different respondents, leading to
a distorted measure of the variable. Observers may miscategorize observations
if the classification scheme is unclear.

6.Item sampling. Errors can be
introduced as a result of the sampling of items used in the measure. For
example, a nursing student's score on a 100-item test of nursing knowledge will
be influenced somewhat by which 100 questions are included. A person might get
95 questions correct on one test but only 92 right on another similar test.

7. Instrument format. Technical
characteristics of an instrument can influence measurements. Open-ended
questions may yield different information than closed-ended ones. Oral
responses to a question may be at odds with written responses to the same
question. The ordering of questions in an instrument may also influence
responses.

## Reliability Of Measuring Instruments

The reliability of a quantitative
instrument is a major criterion for assessing its quality and adequacy. An
instrument's reliability is the consistency with which it measures the target
attribute. If a scale weighed a person at 120 pounds one minute and 150 pounds
the next, we would consider it unreliable. The less variation an instrument
produces in repeated measurements, the higher its reliability.

Thus,
reliability can be equated with a measure's stability, consistency, or dependability.
Reliability also concerns a measure's accuracy. An instrument is reliable to
the extent that its measures reflect true scores that is, to the extent that
errors of measurement are absent from obtained scores.

A reliable measure
maximizes the true score component and minimizes the error component. These two
ways of explaining reliability (consistency and accuracy) are not so different
as they might appear. Errors of measurement that impinge on an instrument's
accuracy also affect its consistency.

The example of the scale with variable
weight readings illustrates this point. Suppose that the true weight of a
person is 125 pounds, but that two independent measurements yielded 120 and 150
pounds. In terms of the equation presented in the previous section, we could
express the measurements as follows:

The errors of measurement for the
two trials (5 and 25, respectively) resulted in scores that are inconsistent
and inaccurate. The reliability of an instrument can be assessed in various
ways. The method chosen depends on the nature of the instrument and on the
aspect of reliability of greatest concern. Three key aspects are stability,
internal consistency, and equivalence.

internal consistency

Scales and tests that involve
summing items are often evaluated for their internal consistency. Scales
designed to measure an attribute ideally are composed of items that measure
that attribute and nothing else. On a scale to measure nurses' empathy, it
would be inappropriate to include an item that measures diagnostic competence.
An instrument may said to be internally consistent or homogeneous to the extent
that its items measure the same trait.

Internal consistency reliability is the
most widely used reliability approach among nurse researchers. Its popularity
reflects the fact that it is economical (it requires only one test
administration) and is the best means of assessing an especially important
source of measurement error in psychosocial instruments, the sampling of items.

One of the oldest methods for
assessing internal consistency is the split-half technique. For this approach,
items on a scale are split into two groups and scored independently. Scores on
the two half tests then are used to compute a correlation coefficient.

Let us say
that the total instrument consists of 20 questions, and so the items must be
divided into two groups of 10. Although many splits are possible, the usual
procedure is to use odd items versus even items. One half-test, therefore,
consists of items 1, 3, 5, 7, 9, 11, 13, 15, 17, and 19, and the even numbered
items compose the second half-test. The correlation coefficient for scores on
the two half-tests gives an estimate of the scale's internal consistency. If
the odd items are measuring the same attribute as the even items, then the
reliability coefficient should be high.

The correlation coefficient computed on
these fictitious data is67. The correlation coefficient computed on split
halves tends to underestimate the reliability of the entire scale. Other things
being equal, longer scales are more reliable than shorter ones. A correction formula has been developed to
give a reliability estimate for the entire test. The equation, known as the
Spearman-Brown prophecy formula, is as follows for this situation.

where r the correlation coefficient
computed on the split halves r1 the estimated reliability of the entire test
Using the formula, the reliability for our hypothetical 20-item measure of
self-esteem

The split-half technique is easy to
use, but is handicapped by the fact that different reliability estimates can be
obtained with different splits. That is, it makes a difference whether one uses
an odd even split, a first -half second-half split, or some other method of
dividing items into two groups. The most widely used method for evaluating
internal consistency is coefficient alpha (or Cronbach's alpha).

Coefficient
alpha can be interpreted like other reliability coefficients described here;
the normal range of values is between .00 and 1.00, and higher values reflect a
higher internal consistency. Coefficient alpha is preferable to the split-half
procedure because it gives an estimate of the split-half correlation for all
possible ways of dividing the measure into two halves.

It is beyond the scope
of this text to explain this method in detail, but more information is
available in textbooks on psychometrics (eg, Cronbach, 1990; Nunnally &
Bernstein, 1994).

In summary, indices of homogeneity
or internal consistency estimate the extent to which different subparts of an instrument
are equivalent in measuring the critical attribute.

The split-half technique
has been used to estimate homogeneity, but coefficient alpha is preferable.
Neither approach considers fluctuations over time as a source of unreliability.

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