Similarly research ranking RESEARCH was not significant in t

Similarly, research ranking (RESEARCH) was not significant in the model so H7 is not supported. Contrary to the findings of Bline et al., 2016b, Boone et al., 2006, the current study did not find an association between faculty research productivity and exam pass rate. This could be due to differences in how exam performance was measured (overall institutional pass rates vs. section-by-section individual candidate exam scores). Finally, contrary to H8, public versus private institutions (PRIVATE) was not significant in the model. While private schools may have smaller class sizes that provide more opportunities for interactions between students and faculty, the current results did not support the notion that this po1 receptor is associated with enhanced exam performance. While Trinkle et al. (2016) did find such an association, the conflicting results may once again be explained by differences in how CPA exam performance was measured. The regression model included the number of exam sections attempted (ATTEMPTS) as a control variable. The results indicated that ATTEMPTS was highly significant in the model (p < .001). It is possible that ATTEMPTS proxies for the size of an institution’s accounting program given that larger programs produce more graduates, who in turn sit for the CPA exam. Larger schools may have more diverse faculty that could enhance students’ preparation compared to smaller schools. ATTEMPTS may also proxy for the proportion of a school’s graduates that sit for the exam. For instance, two schools may be comparable in size, but one school has a higher proportion of graduates sitting for the exam. This could be a function of the extent to which a school’s faculty emphasizes the importance of taking the exam which in turn may influence performance. The results of the supplemental analyses, reported in Table 5, are similar to the main analysis. GRAD, AACC, SELECT, and CPA were all highly significant (p < .01) in both supplemental analysis. BACC was significant in the ATE model (p < .05) but could not be included in the matched sample model given all schools in this latter sample were AACSB business accredited. Also consistent with the main analysis, PHD, RESEARCH, and PRIVATE were all insignificant (p > .10).
Conclusion
Acknowledgements
Introduction In recent years, fast progress has been made toward using the supercritical technology in industrial separation processes. Apart from a green extraction process that replaces organic solvents, applications involving supercritical fluids include supercritical fluid fractionation [[1], [2], [3]], supercritical fluid chromatography [[4], [5], [6]], use as anti-solvent for micro- and nanoparticle formation [7,8] and reaction in supercritical medium [[9], [10], [11]]. Thus, chemical, petroleum, pharmaceutical, food, biotechnology, biomedical, nuclear, material and polymer processing industries are examples of application areas for separation processes using this technology [12].