Lie through which genius tells truth By Prof Dr Sohail Ansari & Variables 1
“As to the ones who forge such lies, they are
the ones who do not believe in the revealed signs of God. Thus it is they who
are the liars." (16:105)
· An innocent journalist
finds truth about lies. A stupid journalist finds truth about truth. An intelligent
journalist creates lie about truth. A genius journalist concocts ‘The lie’ and then
builds up on it so that it runs sprints, and when it happens, he creates more lies
about lie. A genius journalist does not waste time in seeking truth out there,
but relies on lies inside. He tells a lie and has a good enough memory to
remember everything. A genius journalist knows that People are too busy to seek truth; therefore it is not for
them, and must apologies for the truth if he ever happens to show to people not
worthy of it.
· The Prophet (pbuh) said: "The complete believer may have any
characteristic in his nature except treachery and lying." A Muslim can be a coward or a miser but can never be
a liar. The
Messenger of Allah (pbuh) was asked, "Can the believer be a coward?"
He said, "Yes." He was asked, "Can the believer be a
miser?" He said, "Yes." He was asked, "Can the believer be
a liar?" He said, "No."
Quote
· The truth may be out
there, but the lies are inside your head.” Terry Pratchett
·
The truth is not for all men but only for those who seek it.” Ayn Rand
· “Never apologies for
showing feeling. When you do so, you apologies for the truth.” Edmud Burke
·
“If you tell the
truth, you don’t have to remember anything.” Mark Twain
·
No man has a good enough memory to be a
successful liar. Abraham Lincoln
·
“Lies run sprints,
but the truth runs marathons.” Michael Jackson
· “Never tell the truth to people who
are not worthy of it.” »
Mark Twain
Categorical and continuous variables
There are
two groups of
variables that you need to know about: categorical variables and continuous variables. We use the
word groups of
variables because both categorical and continuous variables include additional types of
variable. However, there can also be some ambiguities when deciding whether a
variable is categorical or continuous. We discuss the two groups of variable,
as well as these potential ambiguities, in the sections that follow:
Categorical variables
Categorical
variables are also known as qualitative (or discrete) variables. These categorical variables can be further classified as
being nominal, dichotomous or ordinal variables. Each of these types of
categorical variable (i.e., nominal, dichotomous and ordinal) has what are known as categories or levels. These categories or levels are the descriptions that you give a
variable that help to explain how variables should be measured, manipulated and/or
controlled. Take the following example:
Career choices of university students
You are interested in the career choices of university students. You could ask university students a number of closed questions related to their career choices. For example:
What is your planned occupation?
What is the most important factor influencing your career choice?
You are interested in the career choices of university students. You could ask university students a number of closed questions related to their career choices. For example:
What is your planned occupation?
What is the most important factor influencing your career choice?
The first
question highlights the use of categories and the second
question levels. For
example:
Question 1: What is your
planned occupation?
Variables with categories
Variables with categories
Architect
Attorney
Biochemist
Engineer
Dentist
Doctor
Entrepreneur
Social Worker
Teacher
ETC...
Attorney
Biochemist
Engineer
Dentist
Doctor
Entrepreneur
Social Worker
Teacher
ETC...
Question 2: On a scale of 1 to 5, how important are the following factors in influencing your
career choice [1 = least important; 5 = most important]?
Variables with levels
Career prospects
Nature of the work
Physical working conditions
Salary and benefits
ETC...
Nature of the work
Physical working conditions
Salary and benefits
ETC...
What is
important to note about the categories in question 1 and
the levels in
question 2 is that these will be created by you. Ideally, you will have
included these categories or levels based on some primary
or secondary research. Ultimately, you choose which categories or levels to include and
how many categories or levels there should be.
Each of
these types of
categorical variable (i.e., nominal, dichotomous and ordinal) are described below with
associated examples:
The
following are examples of nominal variables. These nominal variables could
address questions like:
|
Question:
|
What
is your gender?
|
|
Answer:
|
I am male (or female,
bisexual, transsexual)
|
|
Nominal variable:
|
Gender
|
|
Category:
|
Male, Female, Bisexual,
Transsexual
|
|
Question:
|
What
type of property are you interested in?
|
|
Answer:
|
A house (or an
apartment, or a bungalow)
|
|
Nominal variable:
|
Type of property (the
customer is interested in)
|
|
Category:
|
House, Apartment, Bungalow
|
|
Question:
|
What
is your hair colour?
|
|
Answer:
|
I have black hair
(or blond,
brown, red hair, etc.)
|
|
Nominal variable:
|
Hair colour
|
|
Category:
|
Black, Blond, Brown, Red,
etc.
|
|
Question:
|
What
is your blood type?
|
|
Answer:
|
I have blood type A (or B, AB,
O, etc.)
|
|
Nominal variable:
|
Blood type
|
|
Category:
|
A, B, AB, O, etc.
|
These
examples highlight two core characteristics of
nominal variables:
1.
Nominal variables have two or more categories.
2.
Nominal variables do not have an intrinsic order. (a fixed or ordered structure or
order/ the arrangement)
When we
talk about nominal variables not having an intrinsic order, we mean that they can only have categories (e.g., black, blond, brown and red hair); not levels (e.g.,
a Likert scale from 1 to 5).
The
following are examples of dichotomous variables. These dichotomous variables
could address questions like:
|
Question:
|
Are
you male or female?
|
|
Answer:
|
I am male (or I am
female)
|
|
Dichotomous variable:
|
Sex
|
|
Category:
|
Male, Female
|
|
Question:
|
Do
you like watching television?
|
|
Answer:
|
Yes I do (or No I
don't)
|
|
Dichotomous variable:
|
Opinion about watching
television
|
|
Category:
|
Yes, No
|
|
Question:
|
What
type of property are you interested in?
|
|
Answer:
|
A residential property (or a commercial
property)
|
|
Dichotomous variable:
|
Type of property the
customer is interested in
|
|
Category:
|
Residential, Commercial
|
|
Question:
|
What
is your employment status?
|
|
Answer:
|
I am employed (or I am
unemployed)
|
|
Dichotomous variable:
|
Employment status
|
|
Category:
|
Employed, Unemployed
|
Dichotomous
variables are nominal variables that have just two categories. They have a number
of characteristics:
§ Dichotomous variables are designed to give you an either/or response
For example, you are either male or female.
You either like
watching television (i.e., you answer YES) or you
don't (i.e., you answer NO).
§ Dichotomous variables can either be fixed or designed
For
example, some variables (e.g., your sex) can
only be dichotomous (i.e., you can only be male or female). They are therefore fixed. In other cases, dichotomous
variables are designed by
the researcher. For example, take the question: Do you like watching television? We have
determined that the respondent can only select YES (i.e., I like watching television)
or NO (i.e., I don't
like watching television). However, another researcher could provide the
respondent with more than two
categories to this question (e.g., most of the time, sometimes, hardly ever). Where more than two
categories are used, these variables become known as nominal variables rather
than dichotomous ones.
Just like
nominal variables, ordinal variables have two or more categories. However, unlike
nominal variables, ordinal variables can also be ordered or ranked (i.e., they have levels). For example,
take the following example of an ordinal variable:
|
Question:
|
Do
you like the policies of the Democratic Party?
|
|
Answer:
|
Not very much (or They
are OK, or Yes,
a lot)
|
|
Ordinal variable:
|
Opinions towards Democratic
Party policies
|
|
Level:
|
Not very much, They are OK,
Yes, a lot
|
So if you
asked someone if they liked the policies of the Democratic Party and you presented them with the following three
categories: Not very much, They are
OK, or Yes, a
lot; you have an ordinal variable. Why? Because you have 3 categories
? namely Not very much, They are
OK, and Yes, a lot ? and you can rank them
from the most positive (Yes, a lot), to the middle response (They are OK), to the least positive (Not very much). However, whilst we can rank the
three categories, we cannot place a value to them. For example, we cannot say
that the response, They are OK, is twice as positive as the
response, Not very much.
Other
examples of ordinal variables are:
|
Question:
|
In
what year did you start university?
|
|
Answer:
|
I started in 2006 (or 2007,
2008, 2009, 2010)
|
|
Ordinal variable:
|
Year of university entry
|
|
Level:
|
2006, 2007, 2008, 2009,
2010
|
|
Question:
|
Do
you like watching television?
|
|
Answer:
|
Most of the time,
sometimes or hardly
ever)
|
|
Ordinal variable:
|
Opinion about watching
television
|
|
Level:
|
Most of the time,
Sometimes, Hardly ever
|
|
Question:
|
To what extent do you agree
or disagree with the following statement:
Going to university is important to get a good job [based on a 5-point Likert scale of 1 = strongly agree, 2 = agree, 3 = neither agree nor disagree, 4 = disagree, 5 = strongly disagree] |
|
Answer:
|
2 = I agree (or 1, 3, 4
or 5 on the 5-point Likert scale)
|
|
Ordinal variable:
|
The importance of
university to getting a good job
|
|
Level:
|
1 = strongly agree, 2 =
agree, 3 = neither agree nor disagree, 4 = disagree, 5 = strongly disagree
|
When it
comes to Likert scales, as highlighted in the previous example, there can be
some disagreement over whether these should be considered ordinal variables or continuous variables.
Continuous variables
Continuous variables, which are also
known as quantitative variables, can be further classified as being either interval or ratio variables. Each of these types of
continuous variable (i.e., interval and ratio) has numerical properties. These numerical properties are
the values by
which continuous variables can be measured, manipulated and/or controlled. We
illustrate the two types of continuous variable (i.e., interval and ratio) and some associated values in the
sections that follow:
Interval
variables
Interval
variables have a numerical
value and can be measured along a continuum.( a continuous sequence) Some examples of interval variables are:
|
Interval variable:
|
Temperature (measured in
degrees Celsius or Fahrenheit)
|
|
Explanation:
|
The difference between 20C
and 30C is the same as 30C to 40C
|
However,
temperature measured in degrees Celsius or Fahrenheit is NOT a ratio variable.
This is because temperature measured in degrees Celsius or Fahrenheit is not a
ratio variable because 0C does not mean there is no temperature.
Ratio
variables
Ratio
variables are interval variables that meet an additional condition: a
measurement value of 0
(zero) must mean that there is none of that variable. Some examples of ratio variables are:
|
Ratio variable:
|
Temperature measured in
Kelvin
|
|
Explanation:
|
0 Kelvin, often
called absolute
zero, indicates that there is no temperature whatsoever.
A temperature of 10 Kelvin is four times the temperature of 2.5 Kelvin |
|
Ratio variable:
|
Distance
|
|
Explanation:
|
If two houses are joined
together (e.g., terraced housing), the distance between the adjoining walls
is 0 (i.e., there is no distance whatsoever).
On the other hand, a distance of 10 meters between the houses would be twice the distance of a 5 meter gap between the houses (i.e., a distance of 10 metres is twice the distance of 5 metres). |
|
Other ratio variables:
|
Height, mass/weight, etc.
|
Ambiguities in classifying variables
Sometimes,
the measurement scale for data is ordinal, but the
variable is treated as though it were continuous. This is more often the case when using Likert scales. When a
Likert scale has five values (e.g., strongly agree, agree, neither agree nor
disagree, disagree, and strongly disagree), it is treated as an ordinal variable. However, when a Likert scale has seven or more values (e.g., strongly agree, moderately agree, agree, neither
agree nor disagree, disagree, moderately disagree, and strongly disagree), the
variable is sometimes treated as a continuous variable.
Nonetheless, this is a matter of dispute. Some researchers would argue that a
Likert scale should never be treated as a continuous variable, even with seven
levels/values.
Since you
are responsible for setting the measurement scale for a variable, you will need
to think carefully about how you characterise a variable. For example, social
scientists may be more likely to consider the variable gender to
be a nominal variable. This is because they view gender as having a number of
categories, including male, female, bisexual and transsexual. By contrast,
other researchers may simply view gender as a dichotomous variable, having just two categories: male and female. In
such cases, it may be better to refer to the variable gender as sex.
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