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ASSIGNMENT ON RESEARCH METHODS

FOR MANAGEMENT

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TYPES OF SCALING

TECHINQUES

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TYPES OF SCALING

Nominal

Ordinal

Interval

Ratio

http://www.csse.monash.edu.au/~smarkham/resources/nominalhttp://www.csse.monash.edu.au/~smarkham/resources/ordinalhttp://www.csse.monash.edu.au/~smarkham/resources/intervalhttp://www.csse.monash.edu.au/~smarkham/resources/ratiohttp://www.csse.monash.edu.au/~smarkham/resources/ratiohttp://www.csse.monash.edu.au/~smarkham/resources/intervalhttp://www.csse.monash.edu.au/~smarkham/resources/ordinalhttp://www.csse.monash.edu.au/~smarkham/resources/nominal

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Nominal scale

NominalThe lowest measurement level you can use,

from a statistical point of view, is a nominal scale. Anominal scale, as the name implies, is simply some placingof data into categories, without any order or structure.

A physical example of a nominal scale is theterms we use for colors. The underlying spectrum isordered but the names are nominal. In research activities aYES/NO scale is nominal. It has no order and there is nodistance between YES and NO.

statistics :The statistics which can be used with nominal scalesare in the non-parametric group. The most likely oneswould be:

mode

cross tabulation - with chi-square

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Ordinal scale

.

Ordinal

An ordinal scale is next up the list in terms ofpower of measurement. The simplest ordinal scale is a

ranking. When a market researcher asks you to rank 5types of beer from most flavorful to least flavorful,he/she is asking you to create an ordinal scale ofpreference.

There is no objective distance between any two

points on your subjective scale. For you the top beermay be far superior to the second preferred beer but, toanother respondent with the same top and second beer,the distance may be subjectively small. An ordinal scaleonly lets you interpret gross order and not the relativepositional distances.

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Statistics:Ordinal data would use non-parametric statistics.These would include:

Median and moderank order correlation

non-parametric analysis of variance

Modeling techniques can also be used with ordinal data.

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INTERVAL SCALE

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 pointscale, from Dissatisfied to Satisfied, you are using an interval scale.

It is an interval scale because it is assumed to haveequidistant points between each of the scale elements. This means that

we can interpret differences in the distance along the scale. Wecontrast this to an ordinal scale where we can only talk aboutdifferences in order, not differences in the degree of order.

Interval scales are also scales which are defined by metricssuch as logarithms. In these cases, the distances are note equal but theyare strictly definable based on the metric used.

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statistics:Interval scale data would use parametricstatistical techniques:

Mean and standard deviationCorrelation - rRegressionAnalysis of varianceFactor analysis

Plus a whole range of advanced multivariate and modelingtechniques

Remember that you can use non-parametric techniques withinterval and ratio data. But non-parametric techniques areless powerful than the parametric ones.

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RATIO SCALE

RatioA 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 ithas 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 anarbitrary one. The Fahrenheit scale has its equivalent point at -32o.(Physicists would probably argue that Absolute Zero is the zero pointfor temperature but this is a theoretical concept.) So, even thoughtemperature looks as if it would be a ratio scale it is an interval scale.Currently, we cannot talk aboutno temperature - and this would beneeded if it were a ration scale.

STATISTICS

The same as for Interval data

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