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> Design & Development > Analysing > Scaling and Measurement
Analysis Methods
Scaling and Measurement
A topic which can create a great deal of confusion
in social and educational research is that of types of scales used in measuring
behaviour.
It is critical because it relates to the types of statistics you can use
to analyse your data. An easy way to have a paper rejected is to have used
either an incorrect scale/statistic combination or to have used a low powered
statistic on a high powered set of data.
Nominal
The lowest measurement level you can use, from a statistical point of view,
is a nominal scale.
A nominal scale, as the name implies, is simply some placing of data into
categories, without any order or structure.
A physical example of a nominal scale is the terms we use for colours.
The underlying spectrum is ordered but the names are nominal.
In research activities a YES/NO scale is nominal. It has no order and there
is no distance between YES and NO.
and statistics
The statistics which can be used with nominal scales are in the non-parametric
group. The most likely ones would be:
mode
crosstabulation - with chi-square
There are also highly sophisticated modelling techniques available for
nominal data.
Ordinal
An ordinal scale is next up the list in terms of power of measurement.
The simplest ordinal scale is a ranking. When a market researcher asks you
to rank 5 types of beer from most flavourful to least flavourful, he/she
is asking you to create an ordinal scale of preference.
There is no objective distance between any two points on your subjective
scale. For you the top beer may be far superior to the second prefered beer
but, to another respondant with the same top and second beer, the distance
may be subjectively small.
An ordinal scale only lets you interpret gross order and not the relative
positional distances.
and statistics
Ordinal data would use non-parametric statistics. These would include:
Median and mode
rank order correlation
non-parametric analysis of variance
Modelling techniques can also be used with ordinal data.
Interval
The standard survey rating scale is an interval scale.
When you are asked to rate your satisfaction with a piece of software on
a 7 point scale, from Dissatisfied to Satisfied, you are using an interval
scale.
It is an interval scale because it is assumed to have equidistant points
between each of the scale elements. This means that we can interpret differences
in the distance along the scale. We contrast this to an ordinal scale where
we can only talk about differences in order, not differences in the degree
of order.
Interval scales are also scales which are defined by metrics such as logarithms.
In these cases, the distances are note equal but they are strictly definable
based on the metric used.
and statistics
Interval scale data would use parametric statistical techniques:
Mean and standard deviation
Correlation - r
Regression
Analysis of variance
Factor analysis
Plus a whole range of advanced multivariate and modelling techniques
Remember that you can use non-parametric techniques with interval
and ratio data. But non-paramteric techniques are less powerful than the
parametric ones.
Ratio
A ratio scale is the top level of measurement and is not often available
in social research.
The factor which clearly defines a ratio scale is that it has a true zero
point.
The simplest example of a ratio scale is the measurement of length (disregarding
any philosophical points about defining how we can identify zero length).
The best way to contrast interval and ratio scales is to look at temperature.
The Centigrade scale has a zero point but it is an arbitrary one. The Farenheit
scale has its equivalent point at -32o. (Physicists would probably argue
that Absolute Zero is the zero point for temperature but this is a theoretical
concept.) So, even though temperture looks as if it would be a ratio scale
it is an interval scale. Currently, we cannot talk about no temperature
- and this would be needed if it were a ration scale.
and statistics
The same as for Interval data
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