unit 5 Data Collection and Measurements

UNIT 5 (a):
METHODOLOGY
Measurement & Data Collection
Populations & Samples
Ms. Chanda Jabeen
Lecturer
RN, RM, BSN
M.Phil. Epidemiology & Public Health
PhD (Scholar) Epidemiology & Public Health 1

OBJECTIVES
At the end of this session the students will be able to:
• Describe evaluation of observational methods.
• Discuss types of questions.
• Enlist Composite Psychosocial Scales.
• Explain Likert scale.
• Explain Semantic Differential Scales
• Explain Guttman Scale
• Explain Accuracy, Precision & Error
• Discuss Sampling & Population.
• Explain calculation of sample size.
2

Evaluation of Observational Methods
• Excellent method for capturing many clinical
phenomena and behaviors
• Potential problem of reactivity when people are aware
that they are being observed
• Risk of observational biases—factors that can interfere
with objective observation
3

Self-report
• A self-report study is a type of survey, questionnaire,
or poll in which respondents read the question and
select a response by themselves without researcher
interference.
4

A self-report is any method which involves asking a
participant about their feelings, attitudes, beliefs and so
on.
Examples of self-reports are questionnaires and
interviews; self-reports are often used as a way of
gaining participants' responses in observational studies
and experiments.
5

Structured Self-Reports (Surveys)
• Data are collected with a formal instrument.
– Interview schedule
• Questions are prespecified but asked orally.
• Either face-to-face or by telephone
– Questionnaire
• Questions prespecified in written form, to be self-
administered by respondents
8

Advantages of Interviews Compared
with Questionnaires
• Higher response rates with interviews
• Usually lower costs with questionnaires
• Interviews appropriate for more diverse audiences
• Interviews allow more opportunities to clarify
questions or to determine comprehension
• Interviews allow more opportunity to collect
supplementary data through observation, ie. body
language
• Questionnaires allow for more privacy or anonymity
• Questionnaires lack interviewer bias
9

Kinds of Questions
• Knowledge questions
• Opinion questions
• Application questions
• Analysis questions
• Synthesis questions
10

Synthesis Questions
• Synthesis questions ask you to take two kinds of
information and put them together… you compare
them, or make conclusions based on both of them, or
get new information about the reading based on
learning something new.
11

Survey Considerations
• Clarity of questions
• Reading level of subjects
• Length of survey
• Analysis method planned
• Start with least threatening questions
• Limit questions to a single concept
• Provide well written cover letter/ instructions
12

Types of Questions in a Structured
Instrument
• Closed-ended (fixed alternative) questions
– ―Within the past 6 months, were you ever a
member of a fitness center or gym?‖ (yes/no)
• Open-ended questions
– ―Why did you decide to join a fitness center or
gym?‖
13

Specific Types of Closed-Ended
Questions
• Dichotomous questions
– Yes/no; male/female
• Multiple-choice questions
• Forced-choice questions
– Which statement most closely represents you view?
• What happens to me in research class is my own doing
• What happens to me in research class is Dr. Creel’s
fault!
14

• Cafeteria questions are a special type of multiple choice
question that asks respondents to select a
response that most closely corresponds to their
view. The response options are usually full expressions
of a position on the topic.
• Rank-order questions ask respondents to rank
target concepts along a continuum, such as most
to least important.
15

Composite Psychosocial Scales
• Scales—used to make fine quantitative
discriminations among people with different attitudes,
perceptions, traits
• Likert scales—summated rating scales
• Semantic differential scales
• Guttman scale
• Visual analog scale (VAS)
18

Likert Scales
• The Likert scale is designed to determine the opinions or
attitudes of study subjects.
• This scale contains a number of declarative statements, with a
scale after each statement.
• The Likert scale is the most commonly used of the scaling
techniques.
• The original version of the scale included five response
categories. Each response category was assigned a value, with
a value of 0 or 1 given to the most negative response and a
value of 4 or 5 given to the most positive response
19

Response choices in a Likert scale usually address agreement,
evaluation, or frequency.
Agreement options may include statements such as strongly
disagree, disagree, uncertain, agree, and strongly agree.
Evaluation responses ask the respondent for an evaluative rating
along a bad-good dimension, such as negative to positive or
terrible to excellent.
Frequency responses may include statements such as never,
rarely, sometimes, frequently, and all the time.
22

A Likert scale usually consists of 10 to 20 items, each addressing
an element of the concept being measured.
Usually, the values obtained from each item in the instrument are
summed to obtain a single score for each subject.
Although the values of each item are technically ordinal-level
data, the summed score is often analyzed as interval-level data.
The CES-D is a Likert scale used to assess the
level of depression in patients in clinical practice and research.
This scale has four response options—
Rarely or none of the time (less than 1 day)¼0, Some or a little of
the time (1 to 2 days)¼1, Occasionally or a moderate amount of
time (3 to 4 days)¼2, and Most or all of the time (5 to 7 days)¼3.
23

Semantic Differential Scales
• Require ratings of various concepts
• Rating scales involve bipolar adjective pairs, with
7-point ratings.
• Ratings for each dimension are summed to compute
a total score for each concept.
24

Example of a Semantic Differential
25

Guttman Scale
• Set of items on a contiuum or statements
ranging from one extreme to another.
• Responses are progressive and cumulative
26

Guttman scale examples
The ideal Guttman scale is such that if the respondent
disagrees, for example, with statement 4 (having agreed
with statements 1 to 3) then the respondent will disagree
with statement 5 and higher as these represent more
extreme expressions of the attitude being investigated.
For example, a series of items on attitude could be
• "I am willing to be near a cat"
• "I am willing to have a cat"
• "I love to have a cat"
• "I am willing to touch a cat"
27

Guttman scale
On a Guttman scale, items are arranged in an order so
that an individual who agrees with a particular item also
agrees with items of lower rank-order. For example, a
series of items could be
(1) "I am willing to be near ice cream";
(2) "I am willing to smell ice cream";
(3) "I am willing to eat ice cream"; and
(4) "I love to eat ice cream". Agreement
with any one item implies agreement with the lower-
order items.
29

Guttman scale
The concept of Guttman scale likewise applies to series
of items in other kinds of tests, such as achievement
tests, that have binary outcomes.
For example, a test of math achievement might order
questions based on their difficulty and instruct the
examinee to begin in the middle.
30

Guttman scale
The assumption is if the examinee can successfully
answer items of that difficulty (e.g., summing two 3-
digit numbers), s/he would be able to answer the
earlier questions (e.g., summing two 2-digit numbers).
Some achievement tests are organized in a Guttman
scale to reduce the duration of the test.
31

Visual Analog Scale (VAS)
• Used to measure subjective experiences (e.g., pain,
nausea)
• Measurements are on a straight line measuring 100
mm
• End points labeled as extreme limits of sensation
32

Example of Visual Analog Scale
33

Response Set Biases
• Biases reflecting the tendency of some people to
respond to items in characteristic ways, independently
of item content
• Examples:
– Social desirability response set bias – answer in a
way that is socially acceptable
– Extreme response set – answer to shock the
researcher
– Acquiescence response set (yea- sayers) – answer
to please researcher (agree)
– Nay-sayers response set – answer to disagree or
antagonize researcher
34

Evaluation of Self-Reports
• Strong on directness
• Allows access to information otherwise not available
to researchers
• But can we be sure participants actually feel or act the
way they say they do?
35

ACCURACY, PRECISION, AND
ERROR OF PHYSIOLOGICAL
MEASURES
36

Accuracy
Accuracy is comparable to validity in that it addresses
the extent to which the instrument measures what it is
supposed to measure in a study (Ryan-Wenger, 2010).
For example, oxygen saturation measurements with
pulse oximetry are considered comparable with
measures of oxygen saturation with arterial blood gases.
Because pulse oximetry is an accurate measure of
oxygen saturation, it has been used in studies because it
is easier, less expensive, less painful, and less invasive
for research participants. 37

Precision
Precision is the degree of consistency or reproducibility
of measurements made with physiological instruments.
Precision is comparable to reliability.
The precision of most physiological equipment depends
on following the manufacturer’s instructions for care
and routine testing of the equipment. Test-retest
reliability is appropriate for physiological variables that
have minimal fluctuations, such as cholesterol (lipid)
levels, bone mineral density, or weight of adults (Ryan-
Wenger, 2010).
38

Precision
Test-retest reliability can be inappropriate if the
variables’ values frequently fluctuate with various
activities, such as with pulse, respirations, and BP.
However, test-retest is a good measure of precision if
the measurements are taken in rapid succession.
For example, the national BP guidelines encourage
taking three BP readings 1 to 2 minutes apart and then
averaging them to obtain the most precise and accurate
measure of BP.
39

Error
Sources of error in physiological measures can be
grouped into the following five categories:
I. environment,
II. user,
III. subject,
IV. equipment, and
V. interpretation.
40

Error
The environment affects the equipment
and subject. Environmental factors might include
temperature, barometric pressure, and static electricity.
User errors are caused by the person using the
equipment and may be associated with variations by the
same user, different users, or changes in supplies or
procedures used to operate the equipment.
Subject errors occur when the subject alters the
equipment or the equipment alters the subject.
In some cases, the equipment may not be used to its full
capacity. 41

Error
Equipment error may be related to calibration or the
stability of the equipment. Signals transmitted from the
equipment are also a source of error and can result in
misinterpretation.
Researchers need to report the protocols followed or
steps taken to prevent errors in their physiological and
biochemical measures in their published studies
42

Critiquing Measurement & Data
Collection
• Labeled: Methods, Measurement, Instruments
• Report on reliability/validity when instrument was
used in the past and on the population of this study
• Remember instruments should be re-evaluated if used
in different populations, for a different problem or in
a different setting.
• If a new instrument is used – a pilot study should
have been done to test reliability/validity
• Usually best to use a proven tool than try to develop a
new instrument
43

Critiquing Measurement & Data
Collection (Cont.)
• Methods; Procedures are specific enough for
replication
• Researcher should identify if primary/secondary
data used
• What was collected, how, who – training?
• Psychometric properties are identified for the
instruments used (reliability & validity)
• If psychometric properties not identified the
method of instrument development & testing is
described
44

SAMPLE & POPULATION
Sampling involves selecting a group of people,
events, objects, or other elements with which to
conduct a study.
A sampling method or plan defines the selection
process, and the sample defines the selected
group of people (or elements).
A sample selected in a study should represent an
identified population of people.

Sampling…
The process of selecting a number of
individuals for a study in such a way that
the individuals represent the larger group
from which they were selected
46

SAMPLING…….
47
TARGET POPULATION
STUDY POPULATION
SAMPLE

 A sample is ―a smaller (but hopefully
representative) collection of units from a
population used to determine truths about that
population‖
 The sampling frame
A list of all elements or other units containing the
elements in a population.
48

Population…
The larger group from which
individuals are selected to
participate in a study
49

The population is a particular group of
individuals or elements, such as people with type
2 diabetes, who are the focus of the research.
The target population is the entire set of
individuals or elements who meet the sampling
criteria such as female, 18 years of age or older,
new diagnosis of type 2 diabetes confirmed by
the medical record, and not on insulin.

Target population
A set of elements larger than or different
from the population sampled and to which
the researcher would
like to generalize
study findings.
51

The purpose of sampling…
• To gather data about the population in
order to make an inference that can be
generalized to the population
53

Define the target population
Select a sampling frame
Conduct fieldwork
Determine if a probability or nonprobability
sampling method will be chosen
Plan procedure for selecting
sampling units
Determine sample size
Select actual sampling units
Stages in the
Selection
of a Sample
54

WHAT IS SAMPLE SIZE?
• This is the sub-population to be studied in order to
make an inference to a reference population(A
broader population to which the findings from a
study are to be generalized)
• In census, the sample size is equal to the
population size. However, in research, because of
time constraint and budget, a representative
sample are normally used.
• The larger the sample size the more accurate the
findings from a study.
55

• Availability of resources sets the upper limit of
the sample size.
• While the required accuracy sets the lower
limit of sample size
• Therefore, an optimum sample size is an
essential component of any research.
56

PROCEDURE FOR CALCULATING SAMPLE
SIZE.
There are four procedures that could be used for
calculating sample size:
1. Use of formulae
2. Ready made table
3. Computer software
59

USE OF FORMULAE FOR SAMPLE SIZE
CALCULATION & POWER ANALYSIS
 There are many formulae for calculating sample
size & power in different situations for different
study designs.
 The appropriate sample size for population-based
study is determined largely by 3 factors
1. The estimated prevalence of the variable of
interest.
2. The desired level of confidence.
3. The acceptable margin of error.
60

 To calculate the minimum sample size required for accuracy, in
estimating proportions, the following decisions must be taken:
1. Decide on a reasonable estimate of key proportions (p) to be
measured in the study
2. Decide on the degree of accuracy (d) that is desired in the
study. ~1%-5% or 0.01 and 0.05
3. Decide on the confidence level(Z) you want to use. Usually
95%≡1.96.
4. Determine the size (N) of the population that the sample is
supposed to represent.
5. Decide on the minimum differences you expect to find
statistical significance.
61

1. Cochran’s Formula
 For population >10,000. (When population is unknown)
n=Z2pq/e2
n= desired sample size(when the population>10,000)
Z=standard normal deviate; usually set at 1.96(or a~2), which
correspond to 95% confidence level.
p=proportion in the target population estimated to have a
particular characteristics. If there is no reasonable estimate, use
50%(i.e 0.5)
q=1-p(proportion in the target population not having the
particular characteristics)
e= degree of accuracy required, usually set at 0.05 level
62

• E.g if the proportion of a target population with
certain characteristics is 0.50, Z statistics is 1.96
& we desire accuracy at 0.05 level, then the
sample size is
n=(1.962)(0.5)(0.5)/0.052
n=384.
63

If study population is < 10,000 or sample size is
greater than population than adjust the sample.
nf=n/1+(n)/(N)
nf= adjusted sample size, when study population
<10,000
n= desired sample size, when the study
population > 10,000
N= estimate of the population size
64

Example, if n were found to be 400 and if the
population size were estimated at 1000, then nf
will be calculated as follows
nf= 400/1+400/1000
nf= 400/1.4
nf=286
65

2. Slovin’s Formula
When population is known.
It is used to calculate the sample size (n) given the
population size (N) and a margin of error (e).
It is computed as n = N / (1+Ne2).
whereas:
• n = no. of samples
• N = total population
• e = error margin / margin of error
66

To use the formula, first figure out what you want your error of
tolerance to be. For example, you may be happy with a
confidence level of 95 percent (giving a margin error of 0.05),
or you may require a tighter accuracy of a 98 percent
confidence level (a margin of error of 0.02). Plug your
population size and required margin of error into the formula.
The result will be the number of samples you need to take.
In research methodology, for example N=1000 and e=0.05
n = 1000 / (1 + 1000 * 0.5²)
n = 1000 / (1 + 250)
n = 3.984063745 = 4 samplings
67

USE OF READYMADE TABLE FOR SAMPLE
SIZE CALCULATION
 How large a sample of patients should be followed up
if an investigator wishes to estimate the incidence rate
of a disease to within 10% of it’s true value with 95%
confidence?
 The table show that for e=0.10 & confidence level of
95%, a sample size of 385 would be needed.
 This table can be used to calculate the sample size
making the desired changes in the relative precision &
confidence level e.g if the level of confidence is
reduce to 90%, then the sample size would be 271.
 Such table that give ready made sample sizes are
available for different designs & situation
68

USE OF COMPUTER SOFTWARE FOR SAMPLE
SIZE CALCULATION & POWER ANALYSIS
The following software can be used for calculating
sample size & power;
Epi-info
nQuerry
STATA
SPSS
70

References
Polit, D. F., & Beck, C. T. (2017). Nursing research:
Generating and Assessing Evidence for Nursing
Practice (10th ed.). Philadelphia: Lippincott
Williams & Wilkins.
Polit, D. F., & Beck, C. T. (2006). Essential of nursing
research: Methods, appraisal, & utilization.
(6thed.). Philadelphia: Lippincott.
71

unit 5 Data Collection and Measurements

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