Community health nursing 2

1. DATA MEASUREMENT

2. Directness of Measurement
-Direct measurement
-Indirect measurement
Measurement Error
-Systematic Error
-Random Error
Levels of Measurement
Concepts of Measurement
Theory

3. Measurement
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 The assignment of numbers to represent the
amount of an attribute present in an object or
person, using specific rules OR
 Measurement is the process of assigning numbers to
variables. These ways of assigning numbers include:-
 counting,
 ranking and
 comparing objects or events.
 It implies quantification of information.

4. DATA MEASUREMENT
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 Advantages:
 Removes guesswork
 Provides precise information
 Less vague than words

5. Errors of Measurement
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Obtained Score = True score + Error
Obtained score: An actual data value for a
participant (e.g., anxiety scale score)
True score: The score that would be
obtained with an infallible measure
Error: The error of measurement, caused by
factors that distort measurement

6. Factors That Contribute to Errors of
Measurement
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 Situational contaminants
 Transitory personal factors (e.g.,
fatigue)
 Response-set biases
 Administration variations
 Item sampling

7. DATA MEASUREMENT
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It implies that some kind of
comparison is made between
piece of information.
Numbers are the means of
comparing this information.

8. Levels of Measurement
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Nominal
Ordinal
Interval
ratio

9. LEVEL OF MEASUREMENT/MEASUREMENT
SCALE
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a). Nominal level:- Objects or events
are “named” or categorized. The
categories must be distinct from
each other and include all the
possible ways of categorizing the
data. Numbers are obtained
through counting the frequency or
percentages of objects in each
category.

10. a). Nominal level:-
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 Examples- sex, religion, marital status, political
party.
 Some types of nominal data appear to contain
“real” numbers e.g. postal codes.
 These numbers are symbols and can only be
placed into categories. Its the lowest level or the
weakest of the measurement levels.

11. b. ordinal level:–
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b. ordinal level:– Data can be rank-ordered as well
as placed into categories. The exact differences
between the ranks cannot be specified with this
type of data.
The numbers obtained indicate the order rather
than exact quantity of the variables, e.g. anxiety
levels could be mild, moderate and severe.
Frequency distributions and percentages are used
here.

12. c. Interval level of
measurement:–
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 c. Interval level of measurement:– consists of ‘
real’ numbers. The data can be placed in
categories, ranked and the distance,
 the ranks specified are the categories and the
actual numbers on the scale e.g. Thermometer.
The categories are the actual numbers.

13. d. Ration level:–
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 d. Ration level:– considered the ‘highest’ level of
data. These data can be categorized , ranked,
and the distance between the ranks specified and
a ‘true’ or natural zero point can be identified. The
zero point on the ration scale means that there is
total absence of the quantity being measured.

14. CONVERTING DATA TO A LOWER LEVEL OF
MEASUREMENT.
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 Data can always be converted to a lower level of
measurement but not to a higher level.
 Internal and ratio data can be converted to ordinal
or nominal data, and ordinal data can be
converted to nominal data. Rarely do researchers
convert higher levels of data to lower levels,
however, because precision is always lost.

15. DETERMINING THE APPROPRIATE LEVEL OF
MEASUREMENT.
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If concerned about precision of
the data, choose internal or ratio
level of measurement.
If concerned with ranks or
categories, ordinal data will be
needed.

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If only categories of data is
needed, nominal data will be
appropriate.
Your operational definition of the
variable will determine the level
of data that will be gathered in a
research study.

17. DETERMINING THE APPROPRIATE LEVEL OF
MEASUREMENT.
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Some variables by their very
nature can only be measured at
one level. For example, sex can
only be measured at a nominal
level.

18. DETERMINING THE APPROPRIATE LEVEL OF
MEASUREMENT.
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 The main considerations in determining the level
of measurement for data are:-
a. The levels of measurement that is appropriate for
the type of data being sought
b. The degree of precision being desired when it is
possible to consider the data at more than one
level of measurement.

19. DATA COLLECTION INSTRUMENTS/TOOLS.
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 Research instruments:- are the devices used to
collect data.
 The instrument facilitates the observation and
measurement of variables and will be determined
by the data-collection method(s)

20. Use of the existing instruments.
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 When conducting literature review, researcher
may discover that an instrument is available to
measure the variable(s).
 some of the best sources are published
compilation of instruments.

21. developing an instrument
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 The researcher can develop a new instrument or
an existing instrument can be revised for use in
the study.
 New reliability and validity testing will need to be
conducted. Also permission to revise the
instrument will have to be obtained from the
developer of the tool.

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When a completely new
“instrument is under
consideration, volumes of books
have been written concerning
tool development

23. Pilot study.
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 It is a small-scale, trial run of the actual research
study.
 Primary reason for a pilot study is conducted is to
pretest a newly designed instrument.
 A group of people similar to the study subjects
should be tested in conditions similar to those
that will be used in the actual research study.

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 There is no set number of persons needed but
time, cost and availability of persons similar to the
study subjects will help determine the size of the
pilot group.

25. CRITERIA FOR SELECTION OF A DATA
COLLECTION INSTRUMENT.
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1. Practicality
2. Reliability
3. Validity