http://www.hvsinternational.com/Emails/Announcements/110705/

Looks like Dr. Houran found a much better position, even though he’s still in the online testing field.

Congratulations, Dr.!

Randy

I am hearing True.com is having money problems, so they cut you because they couldn’t afford to pay you?

Or, did you figure out the Vest is a Psycho?

http://www.rasch.org/memos.htm
http://www.rasch.org/rmt/index.htm
Institute for Objective Measurement, Inc.
http://www.rasch.org/

T
E.G: Suppose you have PATTERN#Xprofile::4.9.5.4.1.3.4.9.7.8.7.5.6.7.9.10 and PATTERN#Yprofile::8.6.3.5.2.9.6.9.3.6.7.5.5.7.7.4

Which is the probability that #X is similar to #Y?
Using the method I had invented: 62.18120805%

Kindest Regards,

Fernando Ardenghi.
Buenos Aires.
Argentina.
ardenghifer@gmail.com

You summed up my continued push in this industry perfectly, namely the adoption of professional testing standards when it comes to the tests online dating sites offer as “scientific.” It comes down to a simple question: “How can anyone have faith in research findings, or for that matter, their implications, if it is not clear whether the measures used are even psychometrically valid?”

Unfortunately, the traditional way in which researchers construct and validate their assessment instruments relies on classical test theory — and this goes for virtually all compatibility tests and the theoretical and applied research on which they are based. The usual approach within classical test theory is to develop a test consisting of a number of items, and to assume that the sum of the scores received on the test items defines the latent trait (e.g., cognitive impairment or improvement). Such techniques essentially treat all items as equivalent and ignore the possibility that some items may be more difficult (or, diagnostic of individuals exceptionally high on the particular construct) than other items.

Another major flaw of this approach is that summed scores do not provide linear (i.e., interval-level) measures of the underlying trait. In addition, the standard raw score approach does not recognize that some items may be biased such that subjects with identical trait levels receive systematically different scores. This might be the case for instance when women (or younger respondents) endorse some questions more (less) often then do men (or older respondents) with equal trait levels.

Thus, traditional scaling approaches offer no indicators of the true internal validity of respondents’ scores. Furthermore, response biases can systematically distort research findings thereby leading to spurious correlations or factor structures (Lange et al., 2000).

Professional tests, and indeed all relationship research, would be better if they were based on Rasch scaling techniques (for reviews, see Wright & Stone, 1979; Bond & Fox, 2001). In brief, Rasch scaling allows for the quantification of the response consistency of items and persons, thus yielding important diagnostic information that goes far beyond deriving just “scores� or “measures.� Specifically, it is possible to determine the fit of each response record and to identify malingering or otherwise deviant respondents. Additionally, Rasch scaling enables researchers to identify item and response biases. Although item biases within a test are generally considered undesirable as they distort the estimates of individuals’ and groups’ (average) trait levels, such biases can actually be integrated into the into the test and used as an additional diagnostic tools. Finally, the Rasch approach provides “fit� information that enables researchers to judge the internal validity of respondents’ answers.

Accordingly, misfit is a property of the data, rather than the model. As Bond and Fox (2001) explained, “the goal is to create abstractions that transcend the raw data, just as in the physical sciences, so that inferences can be made about constructs rather than mere descriptions about raw data� (p. 3). Researchers are then in a position to formulate initial theories, validate the consequences of theories on real data, refine theories in light of empirical data, and follow up with revised experimentation in a dialectic process that forms the essence of scientific discovery.

It’s too bad that there are very few experts in the world when it comes to applied Rasch scaling. The leader in Rasch scaling as it applies to compatibility testing (long-term or casual relationships) is Dr. Rense Lange of Integrated Knowledge Systems (IKS: http://www.iknowsys.org). His mathematical foundations combined with the expertise of my team produced the True Compatibility Test, as well as the new Sexploration test (i.e., sexual compatibility).

But even those testing products are essentially outdated by my (and his) standards. You see, the best part is… technologies and research now exist to produce even more sophisticated and precise compatibility matching (and other types of tests) that companies using classical test theory methods can’t duplicate. And, these data collected can then be used for accurate psychographic profiling and targeted marketing!

Compatibility testing, indeed all online testing, is still in its infancy — but just wait and see what’s coming to the right company that’s seriously interested in the state-of-the-art of applied psychology :) !!!

Thx, Dr. Jim

References

Bond, T. G., & Fox, C.M. (2001). Applying the Rasch model: Fundamental Measurement in the Human Sciences. Mahwah, NJ: Lawrence Erlbaum.

Lange, R., Irwin, H. J., & Houran, J. (2000). Top-down purification of Tobacyk’s Revised Paranormal Belief Scale. Personality and Individual Differences, 29, 131-156.

Wright, B. D., & Stone, M. H. (1979). Best Test Design. Chicago, IL: MESA Press.

http://www.grabsomeone.com