Always Requested To Accept Than..BY Prof Dr Sohail Ansari
“Sometimes, poor
people don't smell too good, so love can have no nose”. Tammy Faye Bakker
“To speak a kind word and to forgive
people's faults is better than charity followed by hurt. Allah is
All-Sufficient, All-Forbearing”
Al- Quran
Benefits of appearing poor
o Appearing poor has its own benefits. You are always requested to accept than to give donation.
o Appearing poor has its own benefits. You are always requested to accept than to give donation.
Quotes:
·
It is difficult to get a man to understand
something when his salary depends upon his not understanding it. Upton
Sinclair
·
“It is always a much
easier task to educate uneducated people than to re-educate the mis-educated.”
― Herbert M. Shelton, Getting Well
― Herbert M. Shelton, Getting Well
There
are four measurement scales (or types of data): nominal, ordinal, interval and
ratio.
Nominal scales are used for labeling variables, without
any quantitative value. “Nominal” scales could simply be
called “labels.” Here are some examples, below. Notice that all of
these scales are mutually exclusive (no overlap) and none of them have any (numerical
significance. Numerology is
any belief in the divine, mystical relationship between a number and
one or more coinciding events. It
is also the study of the numerical value
of the letters in words, names and ideas. In Numerology, Number 9 is
the symbol of wisdom and initiation.
A good way to remember all of this is
that “nominal” sounds a lot like “name” and nominal scales are kind of like
“names” or labels.
Examples of Nominal Scales
 |
Ordinal
With ordinal scales, it is the order of the values is what’s important and significant, but the differences between each one is not really known. Take a look at the example below. In each case, we know that a #4 is better than a #3 or #2, but we don’t know–and cannot quantify–how much better it is. For example, is the difference between “OK” and “Unhappy” the same as the difference between “Very Happy” and “Happy?” We can’t say.
With ordinal scales, it is the order of the values is what’s important and significant, but the differences between each one is not really known. Take a look at the example below. In each case, we know that a #4 is better than a #3 or #2, but we don’t know–and cannot quantify–how much better it is. For example, is the difference between “OK” and “Unhappy” the same as the difference between “Very Happy” and “Happy?” We can’t say.
Ordinal
scales are typically measures of non-numeric concepts like satisfaction, happiness, discomfort,
etc.
“Ordinal” is easy to remember because it
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.
Example
of Ordinal Scales
Interval
Interval scales are numeric scales in which we know not only the order, but also the exact differences between the values. The classic example of an interval scale is Celsius temperature because the difference between each value is the same. For example, the difference between 60 and 50 degrees is a measurable 10 degrees, as is the difference between 80 and 70 degrees. Time is another good example of an interval scale in which the increments are known, consistent, and measurable.
Interval scales are numeric scales in which we know not only the order, but also the exact differences between the values. The classic example of an interval scale is Celsius temperature because the difference between each value is the same. For example, the difference between 60 and 50 degrees is a measurable 10 degrees, as is the difference between 80 and 70 degrees. Time is another good example of an interval scale in which the increments are known, consistent, and measurable.
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.
Like
the others, you can remember the key points of an “interval scale” pretty
easily. “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. Confused? Ok, consider this: 10 degrees + 10 degrees =
20 degrees. No problem there. 20 degrees is not twice as hot as
10 degrees, however, because there is no such thing as “no temperature” when it
comes to the Celsius scale. I hope that makes sense. Bottom line,
interval scales are great, but we cannot calculate ratios, which brings us to
our last measurement scale…
Ratio
Ratio scales are the ultimate nirvana when
it comes to measurement scales because they tell us about the order, they tell
us the exact value between units, AND they also have an absolute
zero–which allows for a wide range of both descriptive and inferential statistics to be applied. Everything above about interval data
applies to ratio scales + ratio scales have a clear definition of zero.
Good examples of ratio variables include height and weight.
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.)
Questionnaires are an
effective way of quantifying data from a sample group, and testing emotions or
preferences. This method is very cheap and easy, where budget is a problem,
and gives an element of scale to opinion and emotion. These
figures are arbitrary, but at least give a directional method of measuring
intensity.
Quantifying behavior is another way of performing this research,
with researchers often applying a ‘numerical scale’ to the
type, or intensity, of behavior. The Bandura Bobo Doll experiment and
the Asch
Experiment were examples of opinion based research.
By definition, this experiment method must be used where
emotions or behaviors are measured, as there is no other way of defining
the variables.
Whilst not as robust as experimental research, the
methods can be replicated and the results falsified.
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