Universities Benefit From Their Faculties’ Unionization, Study Finds

Posted on: April 10th, 2013 by Taylor Fontes No Comments

The Chronicle reported yesterday about a new study that was just released on the effects of faculty unionization on public universities concluding that universities with professors who form collective-bargaining units are more efficient and effective than their non-unionized counterparts.  The study was conducted by Mark K. Cassell, a professor of political science at Kent State University, and analyzes 23 years of federal data from 432 public universities obtained through the Delta Cost Project at the American Institutes for Research.  The article quotes a couple of critics who study higher education unions for a living as voicing their skepticism about the reliability of the study.  Their primary argument is the difficulty in measuring the effects of unionization, as there are cultural, economic and academic standard disparities among institutions, which may distort the accuracy of the findings. Mr. Cassell did, however, statistically control for many of the variables that tend to skew research on the effects of faculty unions on things such as budgets, spending on administration, and student outcomes.   In spite of the critics’ skepticism, Stephen G. Katsinas, a professor of higher education and director of the Education Policy Center at the University of Alabama at Tuscaloosa, said,“Mr. Cassell is to be commended for tackling a tough and important topic.”

Editorial Comment:

All scientific research requires precision and rigor in order to be definitive of a causal relationship between independent and dependent variables.  And in much of social research, perhaps with the exception of psychology, it is often not possible.  However, with proper research design and methodology it is possible to determine strong, statistically significant relationships.

Social scientists whose lab is the real world are faced with innumerous extraneous variables all of which they neither can nor should control for.  Furthermore, they do not have the advantage of conducting the type of controlled experiments that lend themselves to empirical cause-and-effect relationships.  However, they do have at statistical methods at their disposal that help to isolate the variable(s) between which they seek to determine a relationship.

Few would attempt to make the argument that “collective-bargaining units” as a variable has a direct effect on universities – either negative or positive – but their existence can be related to other variables that affect institutions of higher education.  Because there is no direct effect, per se, of collective bargaining units on universities, the presence of a faculty union as a variable might fit best nicely into a path analysis model[1] with “collective bargaining units” being the exogenous variable and factors such as full-time/part-time faculty composition, faculty salary, funds toward instruction, etc. would constitute endogenous variables.   One drawback of path analysis in this case is that it is intended to determine causal relationships.  Indeed the article acknowledges the difficulty in determining the direction of causality in this research scenario: “It might be the case, for example, that dealing with faculty unions hinders college administrators, but it also might be that dealing with dysfunctional administrations makes faculty members more likely to form union.”  One way to mitigate this complication in future research would be to do a preliminary assessment of a university just as it is embarking on forming a collective bargaining unit and compare it to those with similar features that either already have collective bargaining units or do not, never did and have not yet made the decision to form one.

To use an example from the article, if a well-designed, well executed study of a university that has a collective bargaining unit and a high composition of full-time professors who can devote more time to advising students, leading to higher graduation rates and greater and career opportunities.  If this is indeed the case, then these results could be explained by the existence of a collective bargaining unit, since CBU’s work hard to defend and protect tenure.  Another example is that unions can, through the collective bargaining process, negotiate with the administration to allocate more resources for instruction and research, as opposed to inflated salaries for executive administrators, and toward interests that do not directly benefit the university or its students.

Because funding for differs greatly between public and private institutions it would be interesting to see how private universities such as Hofstra, which is the largest private university with a collective bargaining chapter in the country, stacks up against other private institutions that benefit from union representation.   In today’s academic environment, however, there is probably less of a disparity between public and private institutions considering the massive defunding of higher education on the part of state and federal governments.

To read the full article by Peter Schmidt go to:

http://chronicle.com/article/Universities-Benefit-From/138353/?key=SjgiJ1I2Yy0QZn03aG4XYToHP3I9MxonZiAYP3opbl9TEA==



[1] In statistics, path analysis is used to describe the directed dependencies among a set of variables. This includes models equivalent to any form of multiple regression analysis, factor analysis, canonical correlation analysis, discriminant analysis, as well as more general families of models in the multivariate analysis of variance and covariance analyses (MANOVA, ANOVA, ANCOVA).  In addition to being thought of as a form of multiple regression focusing on causality, path analysis can be viewed as a special case of structural equation modeling (SEM) – one in which only single indicators are employed for each of the variables in the causal model. That is, path analysis is SEM with a structural model, but no measurement model. Other terms used to refer to path analysis include causal modeling, analysis of covariance structures, and latent variable models.

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