Harvard psychologist Daniel Gilbert (2007) explores the way people find themselves more often Stumbling on Happiness than successfully planning for and achieving it. Gilbert's main argument as to why we more often stumble on happiness than arrive at it deliberately follows from the firmness with which we all believe our individual uniqueness makes comparison impossible. On page 252, Gilbert cites a series of research studies showing that "the average person doesn't see herself as average." The Lake Wobegon effect apparently extends into almost every area of life, with most people thinking they are more intelligent, fair, attractive, skilled, etc. than average.
Gilbert offers three reasons why we think of ourselves as uniquely special: 1) because we know ourselves so much better than we know anyone else; 2) because we value individuality and are uncomfortable with too much conformity; and 3) because we focus more on the interesting features that set individuals apart from others than we do on what everyone has in common.
We are so tuned in to differences, and we blow them so wildly out of proportion relative to what we have in common, that we wind up unable to learn as much from others' experiences as we ought to. The book's key point comes on pp. 255-6, where Gilbert says [my emphasis]:
"Our mythical belief in the variability and uniqueness of individuals is the main reason why we refuse to use others as surrogates. After all, surrogation is only useful when we can count on a surrogate to react to an event roughly as we would, and if we believe that people's emotional reactions are more varied than they actually are, then surrogation will seem less useful to us than it actually is. The irony, or course, is that surrogation is a cheap and effective way to predict one's future emotions, but because we don't realize just how similar we all are, we reject this reliable method and rely instead on our imaginations, as flawed and fallible as they may be."
The Afterword of the book touches on the key issues, too, addressing "a formula for predicting utility" (p. 262) after introducing Daniel Bernoulli's ideas on the probabilistic estimation of utilities. Gilbert concludes that we are left dependent on our fallible imaginations for predicting future happiness.
Gilbert could have reached a far different conclusion if his research had been pushed so far as to have found Wright (1997), which traces developments from Daniel Bernoulli's father, Jacob. Wright makes two relevant points. First, any measurement worthy of the name has to produce the same results no matter which particular instrument is used to measure the construct of interest. That is, we have to a) be able to conceive of any given collection of statements concerning a coherent domain of utilities, for instance, as representing the entire universe or population of all possible ways of articulating that domain, and then b) show that the same measures are in fact produced by different collections of those statements.
Gilbert's overall point as to our unwillingness to rely on surrogates for information on the choices likely to make us happy stems from the fact that a) and b) are so rarely undertaken in psychology and social science. Everyone is using different words, phrases, and languages to talk about the same thing, and we focus on widely different ranges of the overall continuum of less and more utility. We naturally assume, as Gilbert says, that the myth of variability and individual uniqueness makes it impossible to apply a variation on the Golden Rule (Fisher, 1994 in RMT 7:4) and so take a surrogate's sense of what's good for them as an analogy for what's good for us.
A properly constituted economic science, however, builds on proven instances in which a) and b) hold, which leads to Wright's second point, namely, that our goal in science is to learn from the data we have in order to make inferences about data we don't have. A measuring instrument is a tool that embodies a formula for predicting utility. What we would need to do is research into the differences between what we say we want, what we objectively get, and what we subjectively experience. First, we would establish that the differences exist and in what forms. Second, we would measure those differences, and third, we would study variation in the differences by various demographics. The results would be the information we need to trust surrogates and let go of the myth of incomparable individual variability.
The existence of a formula for predicting utility will not result in simplistic or obvious recommendations for choices any more than it will result in unidimensional reductions of individual uniqueness to homogenized sameness. Anyone who has much experience at all with test, survey, or assessment data has likely been struck by the fact that good model fit in no way entails some kind of rigid conformity with an externally imposed standard. Rather, natural laws of human behavior are defined by and emerge from within the behaviors themselves.
There has never been greater potential for the emergence of a science of psychology capable of bringing useful technologies to bear on the life problems of everyday people. But as long as even the Harvard psychologists studying those problems themselves buy into the myth of the variability and uniqueness of individuals, we will not see much progress in the direction of using others' experiences as surrogates for our own.
William P. Fisher, Jr.
Gilbert, Daniel. (2007). Stumbling on Happiness. New York: Vintage.
Wright, B. D. (1997). A history of social science measurement. Educational Measurement: Issues and Practice, 16(4), 33-45,52. www.rasch.org/memo62.htm
Alternative Approaches to Finding Happiness. W.P. Fisher, Jr. Rasch Measurement Transactions, 2008, 21:4, 1137
|Rasch Measurement Transactions (free, online)||Rasch Measurement research papers (free, online)||Probabilistic Models for Some Intelligence and Attainment Tests, Georg Rasch||Applying the Rasch Model 3rd. Ed., Bond & Fox||Best Test Design, Wright & Stone|
|Rating Scale Analysis, Wright & Masters||Introduction to Rasch Measurement, E. Smith & R. Smith||Introduction to Many-Facet Rasch Measurement, Thomas Eckes||Invariant Measurement: Using Rasch Models in the Social, Behavioral, and Health Sciences, George Engelhard, Jr.||Statistical Analyses for Language Testers, Rita Green|
|Rasch Models: Foundations, Recent Developments, and Applications, Fischer & Molenaar||Journal of Applied Measurement||Rasch models for measurement, David Andrich||Constructing Measures, Mark Wilson||Rasch Analysis in the Human Sciences, Boone, Stave, Yale|
|in Spanish:||Análisis de Rasch para todos, Agustín Tristán||Mediciones, Posicionamientos y Diagnósticos Competitivos, Juan Ramón Oreja Rodríguez|
|Forum||Rasch Measurement Forum to discuss any Rasch-related topic|
Go to Top of Page
Go to index of all Rasch Measurement Transactions
AERA members: Join the Rasch Measurement SIG and receive the printed version of RMT
Some back issues of RMT are available as bound volumes
Subscribe to Journal of Applied Measurement
Go to Institute for Objective Measurement Home Page. The Rasch Measurement SIG (AERA) thanks the Institute for Objective Measurement for inviting the publication of Rasch Measurement Transactions on the Institute's website, www.rasch.org.
|Coming Rasch-related Events|
|July 30 - Nov., 2018||Online Introduction to Classical and Rasch Measurement Theories (D.Andrich), University of Western Australia, Perth, Australia, http://www.education.uwa.edu.au/ppl/courses|
|Oct. 12 - Nov. 9, 2018, Fri.-Fri.||On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com|
|Jan. 25 - Feb. 22, 2019, Fri.-Fri.||On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com|
|April 4 - 8, 2019, Thur.-Mon.||NCME annual meeting, Toronto, Canada.https://ncme.connectedcommunity.org/meetings/annual|
|April 5 - 9, 2019, Fri.-Tue.||AERA annual meeting, Toronto, Canada.www.aera.net/Events-Meetings/Annual-Meeting|
|May 24 - June 21, 2019, Fri.-Fri.||On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com|
|June 28 - July 26, 2019, Fri.-Fri.||On-line workshop: Practical Rasch Measurement - Further Topics (E. Smith, Winsteps), www.statistics.com|
|Aug. 9 - Sept. 6, 2019, Fri.-Fri.||On-line workshop: Many-Facet Rasch Measurement (E. Smith, Facets), www.statistics.com|
|Oct. 11 - Nov. 8, 2019, Fri.-Fri.||On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com|
|Jan. 24 - Feb. 21, 2020, Fri.-Fri.||On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com|
|May 22 - June 19, 2020, Fri.-Fri.||On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com|
|June 26 - July 24, 2020, Fri.-Fri.||On-line workshop: Practical Rasch Measurement - Further Topics (E. Smith, Winsteps), www.statistics.com|
|Aug. 7 - Sept. 4, 2020, Fri.-Fri.||On-line workshop: Many-Facet Rasch Measurement (E. Smith, Facets), www.statistics.com|
|Oct. 9 - Nov. 6, 2020, Fri.-Fri.||On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com|
|June 25 - July 23, 2021, Fri.-Fri.||On-line workshop: Practical Rasch Measurement - Further Topics (E. Smith, Winsteps), www.statistics.com|
The URL of this page is www.rasch.org/rmt/rmt214d.htm