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Unit Four

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Week Four Discussion One

What is measurement, most of us think of measurement in terms of objects such as rulers, scales, gauges, and thermometers? In research, measurement takes on a somewhat different meaning. Measurement is limiting data of any phenomenon substantial or insubstantial so that those data may be interpreted and will ultimately, compared to an acceptable quantitative standard.

There are four scales of measurement or scales (1) nominal, (2) ordinal, (3) interval, and (4) ratio. The scale of measurement will dictate the statistical procedures that can be used if any, in processing the data (Leedy and Ormrod, 2005, p. 25).

Nominal Scale of measurement means a method for sorting objects into categories according to some distinguishing characteristic, and attaching a name, or label to each category; considered the weakest measurement. Only a few statistics are appropriate for analyzing nominal data.

Ordinal Scale of measurement we can think in terms of > greater than or < less than. We can compare various pieces of data in terms of one of them being greater or higher than another. This scale allows us to rank-order the data. An example would be how you would rank pain on a scale of 1 to 10, 1 being the least and 10 being the worst.

Interval Scale of Measurement characterized by two features (1) it has an equal number of units of measurement (2) it has a zero point that has been established arbitrarily. An example would be students are given a rating scale to evaluate their teacher's ability to teach.

Ratio Scale of Measurement means are similar to interval scale and it also has two other characteristics (1) equal measurements units and (2) an absolute zero point such that 0 on the scale reflects a total absence of the quantity being measured.

When measuring variables to understand how important it is to measure variables at an interval and ratio data are sometimes grouped together and referred to as a fundamental data, or continuous data. It is possible to convert data from one level of measurement to another, but only in one direction. If you have data measured on a ratio scale you can convert them into interval data, by ignoring the zero. If you have data that are measured on a ratio scale you can convert them to ordinal data, by converting them to ranks (Stevens, 2007, p. 4).

If you have data that are measured on an ordinal scale, you can convert them to nominal data, by grouping them into high or low categories. However, this can be normally a bad thing to do, you can convert in one direction, but you cannot convert in the other direction, in looking at this it can involve discarding information, which cannot be retrieved (Stevens, 2007, p. 4).

In comparing the concept of validity for design to the concept of validity for measurement, the key concepts that are relevant to research methodology is that of validity. When an individual asks, is this study valid, they are questioning the validity of at least one aspect of the study, there are four types of validity that can be discussed in relation to research and statistics (Webster, 2001, p. 1).

Statistical Conclusion Validity is a statistical conclusion, the question that is being asked are the variables under study related, or is variable A correlated with variable B, when looking at this question we should be able to answer yes. Some issues that could threaten these results are if the sample is a small and it would be complicated to find meaningful relationships



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