5th lecture on research methodology

Data & Variables
Dr. Md. Anisur Rahman Anjum
Prof & Head (Eye) DMC
anjumk38dmc@gmail.com

In God we trust.
All others must bring data.
Saturday, April 17, 2021 2

Mis-Interpretation of Data
“On average, my class is doing well. Half of
my students think that 2+2=3, the other half
thinks that 2+2=5.”
Saturday, April 17, 2021 3

Today’s lesson
1) Data
2) Variable
3) Levels of Measurement
4) Ambiguities in classifying a type of variable
5) Data is interchangeable.
6) Likert Scale

What is data?
Data are distinct pieces of information, usually formatted in a special
way.
Strictly speaking, data is the plural of datum, a single piece of
information.
Now data is generally used in the singular, as a mass noun.

Data vs. Information
Data is the computer’s language. Information is our translation of this
language
Data are plain facts
Data in themselves are fairly useless. But when these data
are interpreted and processed to determine their true meaning, they
become useful and can be called information.

Metadata
Metadata is “data” [information] that provides information about other data. Three
distinct types of metadata exist:
a) descriptive metadata,
b) structural metadata, and
c) administrative metadata.
Metadata summarizes basic information about data, which can make finding
and working with particular instances of data easier.

Metadata
• For example, a digital image may include metadata that describes how
large the picture is, the color depth, the image resolution, when the
image was created, the shutter speed, and other data.
• A text document's metadata may contain information about how long
the document is, who the author is, when the document was written,
and a short summary of the document

Variable
a) What is variable?
b) Example of data and variable:
c) What are the different aspects of variables?
i. Dependent and Independent Variables
ii. Experimental and Non-Experimental Research
iii. Discrete and Continuous variables

Variable
A variable is any characteristics, number, or quantity that can be
measured or counted
A variable may also be called a data item. Age, sex, business income
and expenses
It is called a variable because the value may vary between data units
in a population and may change in value over time.

Say, we have measure the blood pressure of 6 people over 40 year of age. Value of
BP of each people is the data, but if we say before measuring the BP that those
who are above 140/90 mm of Hg is hypertensive, and below 140/90 mm of Hg is
normotensive then our variable is “hypertensive” and “normotensive”

Recorded Blood Pressure Measure
Sl Data Variable
BP mm of Hg Hypertensive /
Normotensive
1 120/80 Normotensive
2 150/95 Hypertensive
4 180/95 Hypertensive
4/17/2021 12

We can divide variables into three main sections.
1) Dependent and independent variables.
2) Experimental and non-experimental research.
3) Categorical or continuous Variables
4/17/2021 13

Dependent and Independent Variables
An independent variable, sometimes called an experimental or
predictor variable, is a variable that is being manipulated in an
experiment in order to observe the effect on a dependent variable,
sometimes called an outcome variable
The dependent variable is simply that, a variable that is dependent on
an independent variable(s)
4/17/2021 14

Example of Dependent and Independent Variables
• Imagine that a tutor asks 100 students to complete a math test. The tutor wants to
know why some students perform better than others. Whilst the tutor does not
know the answer to this, s/he thinks that it might be because of two reasons:
• (1) some students spend more time revising for their test; and
• (2) some students are naturally more intelligent than others.
4/17/2021 15

• As such, the tutor decides to investigate the effect of revision time and
intelligence on the test performance of the 100 students. The dependent and
independent variables for the study are:
Dependent Variable: Test Mark (measured from 0 to 100)
Independent Variables: Revision time (measured in hours) Intelligence (measured
using IQ score)

Experimental and Non-Experimental Research
Experimental research: Manipulate an independent variable(s) and
then examine the effect on a dependent variable(s).
Experimental research has the advantage of enabling a researcher to
identify a cause and effect between variables
4/17/2021 17

Example: 100 students in your study
4/17/2021 18
50 in group: A. REVISION OF MATH EVERDAY FOR TWO HOUR FOR 7
DAYS
50 students In group B: Not to revised math FOR 7 DAYS
Take a math exam
Compare the test marks, and find out is there any difference between the two groups of test marking?

IN ABOVE EXAMPLE TEST MARK IS THE DEPENDENT VARIABLE & THE REVISION TIME IS THE
INDEPENDENT VARIABLE
4/17/2021 anjumk38dmc@gmail.com 19

Non-experimental research
In non-experimental research, the researcher does not manipulate the
independent variable(s). This is not to say that it is impossible to do
so, but it will either be impractical or unethical to do so.
For example, a researcher may be interested in the effect of illegal,
recreational drug use (the independent variable(s)) on certain types of
behavior (the dependent variable(s)).
4/17/2021 20

Non-experimental research (contd)
However, whilst possible, it would be unethical to ask individuals to take illegal
drugs in order to study what effect this had on certain behaviors.
As such, a researcher could ask both drug and non-drug users to complete a
questionnaire that had been constructed to indicate the extent to which they
exhibited certain behaviors.

Non-experimental research (contd)
Whilst it is not possible to identify the cause and effect between the variables, we
can still examine the association or relationship between them.
In addition to understanding the difference between dependent and independent
variables, and experimental and non-experimental research, it is also important to
understand the different characteristics amongst variables.

Categorical and Continuous Variables
Categorical variables are also known as discrete or qualitative variables.
Categorical variables can be further categorized as
either nominal, ordinal or dichotomous.
Variables such as number of children in a household are called discrete
variables since the possible scores are discrete points on the scale. For
example, a household could have three children or six children, but not 4.53
children.
4/17/2021 23

Levels of Measurement
4/17/2021 24

Examples of types of data
CATEGORICAL (Measurement scale is
Ordinal & Nominal)
Ordinal (Ordered
categories)
Nominal (Unordered
categories)
Grade of breast cancer
Better, same, worse.
Disagree, neutral, agree
Sex (male, female)
Alive or dead
Blood group, O, A, B, AB
4/17/2021 25

Examples of types of data
QUANTATIVE (Measurement scale is Interval & Ratio)
Continuous Discrete
Blood pressure
Height
Weight
age
Number of children.
Number of attack of
asthma per week
4/17/2021 26

VARIABLE
CATEGORICAL NUMERICAL
CATEGORICAL
ORDINAL NOMINAL RATIO
INTERVAL
With
order,
that is
military
rank
Without
order that
is, colour
of the eye
Temp
i, e
degree
C
Continuous
:
Measuring
process
Discrete:
Counting
process i, e
number of
widgets in
the
inventory.
4/17/2021 27

Types of Data & Measurement Scales
1) Nominal-Level Measurement
2) Ordinal-Level Measurement
3) Interval-Level Measurement
4) Ratio-Level Measurement
4/17/2021 28

Nominal-Level Measurement
The name 'Nominal' comes from the Latin nomen, meaning 'name'
and nominal data are items which are differentiated by a simple
naming system.
The nominal level of measurement is the "lowest" level of
measurement, because it makes no assumption whatever about the
values being assigned to the data
4/17/2021 29

Nominal-Level Measurement
• For instance gender is a nominal variable since the numerical code
assigned to the possible responses (e.g. 1 for male and 2 for female)
convey no information regarding inherent ordering or distance. That
is, we cannot say that female (code 2) is greater than male (code 1).
The fact that male may be assigned a bigger number/code (2) than
female (1) also means nothing;
4/17/2021 30

Ordinal-Level Measurement
 Ordinal scales are typically measures of non-numeric concepts like
satisfaction, happiness, discomfort, etc
“Ordinal” is easy to remember because is sounds like “order” and
that’s the key to remember with “ordinal scales”–it is the order that
matters, but that’s all you really get from these.
4/17/2021 31

Ordinal-Level Measurement
•
How do you feel today?
1―Very unhappy
2― Unhappy
3― OK
4―Happy
5―Very happy
How satisfied are you with our
service?
1― Very unsatisfied
2― Somewhat unsatisfied
3―Neutral
4― Somewhat satisfied
5― Very satisfied
4/17/2021 32

Interval-Level Measurement
 “Interval” itself means “space in between,” which is the important thing to
remember–interval scales not only tell us about order, but also about the
value between each item.
• Here’s the problem with interval scales: they don’t have a “true zero.” For
example, there is no such thing as “no temperature.” Without a true zero, it
is impossible to compute ratios. With interval data, we can add and
subtract, but cannot multiply or divide.
4/17/2021 33

Interval-Level Measurement
• Interval scales are nice because the realm of statistical analysis on these data sets
opens up. For example, central tendency can be measured by mode, median, or
mean; standard deviation can also be calculated.
• “Interval” itself means “space in between,” which is the important thing to
remember–interval scales not only tell us about order, but also about the value
between each item.

Interval -Level Measurement
 Interval data (also sometimes called integer) is measured along a
scale in which each position is equidistant from one another. This
allows for the distance between two pairs to be equivalent in some
way. Interval data cannot be multiplied or divided.
4/17/2021 35

Ratio -Level Measurement
 Ratio scales provide a wealth of possibilities when it comes to
statistical analysis. These variables can be meaningfully added,
subtracted, multiplied, divided (ratios). Central tendency can be
measured by mode, median, or mean; measures of dispersion, such as
standard deviation and coefficient of variation can also be calculated
from ratio scales.
4/17/2021 36

Difference between ratio scale and interval scale
• In an interval scale, you can take difference of two values. You may not be
able to take ratios of two values.
• Example: temperature in Celsius.
• You can say that if temperature in Delhi is 40 deg Celsius and in Dhaka is
20 deg Celsius, then Delhi is 20 deg Celsius hotter than Dhaka (taking
difference). But you cannot say Delhi is twice as hot as Dhaka (not allowed
to take ratio).

• In a ratio scale, you can take a ratio of two values. Example 40 kg is twice as
heavy as 20 kg (taking ratios).
• Also, “0” on ratio scale means the absence of that physical quantity. “0” on
interval scale doesn't mean the same. 0 kg means the absence of weight. 0 deg
Celsius doesn't mean absence of heat.

NOMI ORDI INTE RATI
The order of values is
known
 .  .  .
Frequency of distribution  .  .  .  .
Mode, median  .  . .  .
The “order” of values is
known
 . .  .
Can quantify the
difference between each
value
.  .
Can add or subtract values .  .
Can multiply and divide
values
 .
Has true zero
4/17/2021 39

4/17/2021 40

5th lecture on research methodology

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