research methods - measurement scales

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Measurement scales

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A topic which can create a great deal of confusion in social and educationresearch 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 usedeither an incorrect scale/statistic combination or to have used a lowpowered statistic on a high powered set of data.

NominalOrdinalIntervalRatio

Nominal

The lowest measurement level you can use, from a statistical pointview, is a nominal scale.

A nominal scale, as the name implies, is simply some placing of datinto categories, without any order or structure.

A physical example of a nominal scale is the terms we use forcolours. The underlying spectrum is ordered but the names arenominal.

In research activities a YES/NO scale is nominal. It has no order an

there is no distance between YES and NO.

and statistics

The statistics which can be used with nominal scales are in thnon-parametric group. The most likely ones would be:

modecrosstabulation - with chi-square

There are also highly sophisticated modell ing techniquesavailable for nominal data.

Ordinal

An ordinal scale is next up the list in terms of power of measuremen

The simplest ordinal scale is a ranking. When a market researcherasks you to rank 5 types of beer from most flavourful to leastflavourful, he/she is asking you to create an ordinal scale ofpreference.

There is no objective distance between any two points on yoursubjective scale. For you the top beer may be far superior to thesecond prefered beer but, to another respondant with the same topand second beer, the distance may be subjectively small.

http://www.csse.monash.edu.au/~smarkham/resources/scaling.htm

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An ordinal scale only lets you interpret gross order and not therelative positional distances.

and statistics

Ordinal data would use non-parametric statistics. These wouldinclude:

Median and moderank order correlationnon-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 softwaron a 7 point scale, from Dissatisfied to Satisfied, you are using aninterval scale.

It is an interval scale because it is assumed to have equidistant poinbetween each of the scale elements. This means that we caninterpret differences in the distance along the scale. We contrast thito an ordinal scale where we can only talk about differences in ordenot differences in the degree of order.

Interval scales are also scales which are defined by metrics such aslogarithms. In these cases, the distances are note equal but they ar

strictly definable based on the metric used.

and statistics

Interval scale data would use parametric statistical techniques:

Mean and standard deviationCorrelation - rRegressionAnalysis of varianceFactor analysis

Plus a whole range of advanced multivariate and modellingtechniques

Rememberthat you can use non-parametric techniques withinterval and ratio data. But non-paramteric techniques are lesspowerful than the parametric ones.

Ratio

A ratio scale is the top level of measurement and is not oftenavailable in social research.

The factor which clearly defines a ratio scale is that it has a true zerpoint.

The simplest example of a ratio scale is the measurement of length(disregarding any philosophical points about defining how we canidentify zero length).

The best way to contrast interval and ratio scales is to look attemperature. 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 poifor temperature but this is a theoretical concept.) So, even thoughtemperture looks as if it would be a ratio scale it is an interval scale.


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Currently, we cannot talk about no temperature- and this would beneeded if it were a ration scale.

and statistics

The same as for Interval data

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