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Data Theory and Dimensional Analysis
By examining some of the basic scaling questions, such as the importance of measurement levels, the kinds of variables needed for Likert or Guttman scales and when to use multidimensional scaling versus factor analysis, Jacoby introduces readers to the most appropriate scaling strategies for different research situations. He also explores data theory, the study of how real world observations can be transformed into something to be analyzed, in order to facilitate more effective use of scaling techniques.
96 pages, Paperback
First published February 28, 1991
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December 12, 2016
Interestingly, few books are readily available on Data Theory on Amazon. This was approachable and discusses a few of the major topics I was interested in.
Sadly, only the front 15 or so pages talk about Data theory. I'm sure the author cares about the topic as this is 15 pages more than I typically see, thus making this one of the better guides out there.
That said, there are some great citations I can use and some wonderful leads. For my purposes it was a 3 star, but really, if you're interested in data theory, this is one of few options.
The best part of theory is the portion which describes Measurement as Theory Testing. this is often confused in this day and age, since Data Theory is missing entirely out of the curriculum of a tools based approach to analysis.
Interestingly, this book describes data in two ways, superfluous info vs the data that is used for measurement. This is a bit of an odd definition that I will likely argue in my papers and works going forward, but good to know that's where the discourse is starting as of 2016. It kind of makes sense though, as I can see how a measurement based "purpose" to data would indeed result in assuming that which cannot be measured is superfluous to a data analysis.
Sadly, only the front 15 or so pages talk about Data theory. I'm sure the author cares about the topic as this is 15 pages more than I typically see, thus making this one of the better guides out there.
That said, there are some great citations I can use and some wonderful leads. For my purposes it was a 3 star, but really, if you're interested in data theory, this is one of few options.
The best part of theory is the portion which describes Measurement as Theory Testing. this is often confused in this day and age, since Data Theory is missing entirely out of the curriculum of a tools based approach to analysis.
Interestingly, this book describes data in two ways, superfluous info vs the data that is used for measurement. This is a bit of an odd definition that I will likely argue in my papers and works going forward, but good to know that's where the discourse is starting as of 2016. It kind of makes sense though, as I can see how a measurement based "purpose" to data would indeed result in assuming that which cannot be measured is superfluous to a data analysis.
Displaying 1 of 1 review