Although questionnaires show a great variety, they elicit only four main types of data: nominal (categorical), ordinal, interval, and textual. As will be discussed in Section 4.4, the last type - open-ended and sometimes extensive verbal response - is usually converted to quantifiable categories, that is, to one of the first three data types. The main difference between the three types of quantitative data lies in the precision of the measurement:
• Nominal or categorical data come from scales that have no numerical value, such as gender or race. Here the assigned values are completely arbitrary; for example, for the gender variable male is usually coded '1' and female '2,' which does not indicate any difference in size or salience.
• Ordinal data are similar to nominal data except that greater numbers refer to the order of the values on a continuum. In other words, ordinal data involves ranked numbers. For example, a multiple-choice item with options such as 'once a day,' 'once a week,' 'once a month,' 'once a year,' and 'never' will produce ordinal data because the answers can be placed on a 'frequency' continuum.
• Interval data can be seen as ordinal data in which the various values are at an equal distance - or intervals - from each other on a continuum. That is, equal numerical differences in the coding imply equal differences in the degree/salience/size of the variable being measured. An example of such data would be L2 proficiency test scores.
The separation of these three types of measure becomes important when we select the statistical techniques to be used with our data. Certain types of data can be analyzed only by means of certain types of techniques: The big dividing line is between parametric procedures, which require interval data, and non-parametric procedures which can be applied to ordinal and even nominal data. Statistical computer packages contain a variety of procedures belonging to both types.
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