Measurement Error in U.S. Regional Economic Data

Author(s):  
Craig Wesley Carpenter ◽  
Anders Van Sandt ◽  
Scott Loveridge
2018 ◽  
Vol 41 (11) ◽  
pp. 1309-1335 ◽  
Author(s):  
Benjamin Yeo

PurposeThis study aims to use university patent and regional economic data to investigate the current and future impact of university innovation, measured using multiple variables, on real economic productivity.Design/methodology/approachUsing university patent and regional economic data, regression models are built to determine the impact of university innovation on current and future regional economic performance.FindingsThe findings demonstrate that university innovation generates sustained impact on economic performance, but by itself, is insufficient in driving economic performance; and different measures of university innovation have different degrees of impact. University innovation makes up a small, albeit significant, proportion of the drivers of economic performance.Research limitations/implicationsThere are four implications. First, developing countries can leverage university–industry collaborations for economic growth. Second, innovation management must encourage continuous university innovation for sustainable economic productivity. Third, Science, Technology, Engineering and Mathematics (STEM) and non-STEM innovation warrant attention. Fourth, successful innovation policies should be tailored to their unique societal contexts.Originality/valueAlthough innovation is a driver of economic performance, there is a lack of studies that focus specifically on universities, operationalize performance using gross domestic product measures and take into account impact lags by exploring universities’ current and future impacts.


2003 ◽  
Vol 20 (1) ◽  
pp. 49-66
Author(s):  
George W Oldham ◽  
Michael M Hickson

1999 ◽  
Vol 15 (2) ◽  
pp. 91-98 ◽  
Author(s):  
Lutz F. Hornke

Summary: Item parameters for several hundreds of items were estimated based on empirical data from several thousands of subjects. The logistic one-parameter (1PL) and two-parameter (2PL) model estimates were evaluated. However, model fit showed that only a subset of items complied sufficiently, so that the remaining ones were assembled in well-fitting item banks. In several simulation studies 5000 simulated responses were generated in accordance with a computerized adaptive test procedure along with person parameters. A general reliability of .80 or a standard error of measurement of .44 was used as a stopping rule to end CAT testing. We also recorded how often each item was used by all simulees. Person-parameter estimates based on CAT correlated higher than .90 with true values simulated. For all 1PL fitting item banks most simulees used more than 20 items but less than 30 items to reach the pre-set level of measurement error. However, testing based on item banks that complied to the 2PL revealed that, on average, only 10 items were sufficient to end testing at the same measurement error level. Both clearly demonstrate the precision and economy of computerized adaptive testing. Empirical evaluations from everyday uses will show whether these trends will hold up in practice. If so, CAT will become possible and reasonable with some 150 well-calibrated 2PL items.


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