Class Attendance and Students’ Evaluations of Teaching

Background: Many university departments use students’ evaluations of teaching (SET) to compare and rank courses. However, absenteeism from class is often nonrandom and, therefore, SET for different courses might not be comparable. Objective: The present study aims to answer two questions. Are SET positively biased due to absenteeism? Do procedures, which adjust for absenteeism, change course rankings? Research Design: The author discusses the problem from a missing data perspective and present empirical results from regression models to determine which factors are simultaneously associated with students’ class attendance and course ratings. In order to determine the extent of these biases, the author then corrects average ratings for students’ absenteeism and inspect changes in course rankings resulting from this adjustment. Subjects: The author analyzes SET data on the individual level. One or more course ratings are available for each student. Measures: Individual course ratings and absenteeism served as the key outcomes. Results: Absenteeism decreases with rising teaching quality. Furthermore, both factors are systematically related to student and course attributes. Weighting students’ ratings by actual absenteeism leads to mostly small changes in ranks, which follow a power law. Only a few, average courses are disproportionally influenced by the adjustment. Weighting by predicted absenteeism leads to very small changes in ranks. Again, average courses are more strongly affected than courses of very high or low in quality. Conclusions: No-shows bias course ratings and rankings. SET are more appropriate to identify high- and low-quality courses than to determine the exact ranks of average courses.

[1]  S. Adams,et al.  Student Withdrawal from Higher Education , 1996 .

[2]  William D. Bosshardt.  Student Drops and Failure in Principles Courses , 2004 .

[3]  Jeffrey M. Wooldridge,et al.  Solutions Manual and Supplementary Materials for Econometric Analysis of Cross Section and Panel Data , 2003 .

[4]  H. Boer,et al.  On the Way towards New Public Management? The Governance of University Systems in England, the Netherlands, Austria, and Germany , 2007 .

[5]  F. Hénard.  Learning Our Lesson , 2010 .

[6]  Uwe Wilkesmann,et al.  The impacts of new governance on teaching at German universities. Findings from a national survey , 2012 .

[7]  David Draper,et al.  Statistical analysis of performance indicators in UK higher education , 2004 .

[8]  Ann Kirby,et al.  The Effect of Attendance on Grade for First Year Economics Students in University College Cork , 2003 .

[9]  P. Allison.  Fixed Effects Regression Models , 2009 .

[10]  R. Weisheit,et al.  Self-generated identification codes for anonymous collection of longitudinal questionnaire data. , 1984, Public opinion quarterly.

[11]  Mantz Yorke.  Leaving Early: Undergraduate Non-completion in Higher Education , 1999 .

[12]  J. S. Long,et al.  Regression Models for Categorical and Limited Dependent Variables , 1997 .

[13]  H. Rindermann,et al.  Generalizability of Multidimensional Student Ratings of University Instruction Across Courses and Teachers , 2001 .

[14]  Joshua D. Angrist,et al.  Mostly Harmless Econometrics: An Empiricist's Companion , 2008 .

[15]  J. C. Smart,et al.  Higher Education: Handbook of Theory and Research , 2001, Higher Education: Handbook of Theory and Research.

[16]  S. Schwartz,et al.  Leaving College: Rethinking the Causes and Cures of Student Attrition , 1987 .

[17]  J. Reed.  Dropping a College Course: Factors Influencing Students' Withdrawal Decisions. , 1981 .

[18]  M. E. J. Newman,et al.  Power laws, Pareto distributions and Zipf's law , 2005 .

[19]  Mark E. J. Newman,et al.  Power-Law Distributions in Empirical Data , 2007, SIAM Rev..

[20]  Pravin K. Trivedi,et al.  Regression Analysis of Count Data , 1998 .

[21]  Pravin K. Trivedi,et al.  Regression Analysis of Count Data , 1998 .

[22]  J. Biggs.  THE ROLE OF METALEARNING IN STUDY PROCESSES , 1985 .

[23]  S. Devadoss,et al.  Evaluation of Factors Influencing Student Class Attendance and Performance , 1996 .

[24]  A. Cameron,et al.  Microeconometrics: Methods and Applications , 2005 .

[25]  F. Hénard,et al.  Learning Our Lesson: Review of Quality Teaching in Higher Education Draft Report , 2022 .

[26]  Philip G. Altbach,et al.  International handbook of higher education , 2007 .

[27]  Paul Ramsden,et al.  Student learning and perceptions of the academic environment , 1979 .

[28]  R. Todd Jewell,et al.  Leveling the Playing Field: Should Student Evaluation Scores be Adjusted?* , 2007 .

[29]  P. Abrami,et al.  Students' Evaluations of University Teaching: Research Findings, Methodological Issues, and Directions for Future Research , 1987 .

[30]  Richard G. Kohlan.  A Comparison of Faculty Evaluations Early and Late in the Course. , 1973 .

[31]  Wiji Arulampalam,et al.  Am I Missing Something? The Effects of Absence from Class on Student Performance , 2012, SSRN Electronic Journal.

[32]  Daniel S. Hamermesh,et al.  Beauty Pays: Why Attractive People Are More Successful , 2011 .

[33]  D. Rubin,et al.  Statistical Analysis with Missing Data. , 1989 .

[34]  J. Kulik.  Student Ratings: Validity, Utility, and Controversy , 2001 .

[35]  Elisha Y. Babad,et al.  Antecedents and Correlates of Course Cancellation in a University “Drop and Add” Period , 2008 .

[36]  L. A. Yurek,et al.  The Use of Self-Generated Identification Codes in Longitudinal Research , 2008, Evaluation review.

[37]  William E. Becker,et al.  Student performance, attrition, and class size given missing student data , 2001 .

[38]  D. Romer,et al.  Do Students Go to Class? Should They? , 1993 .

[39]  Charles E. Packard,et al.  Beauty in the Classroom , 1935 .

[40]  Marc Keuschnigg.  Das Bestseller-Phänomen : die Entstehung von Nachfragekonzentration im Buchmarkt , 2012 .

[41]  Frank Costin,et al.  A Graduate Course in the Teaching of Psychology: Description and Evaluation , 1968 .

[42]  William E. Becker,et al.  Data Loss from Pretest to Posttest as a Sample Selection Problem , 1990 .

[43]  D. J. Merritt.  Bias, the Brain, and Student Evaluations of Teaching , 2007 .

[44]  Harvey Goldstein,et al.  League Tables and Their Limitations: Statistical Issues in Comparisons of Institutional Performance , 1996 .

[45]  D. Hamermesh,et al.  Beauty in the Classroom: Professors&Apos; Pulchritude and Putative Pedagogical Productivity , 2003 .

[46]  B. Sporn.  Governance and Administration: Organizational and Structural Trends , 2007 .

[47]  D. Jansen,et al.  New Forms of Governance in Research Organizations , 2007 .

[48]  J. Angrist.  Mostly Harmless Econometrics , 2008 .

[49]  J. J. Deely,et al.  Quantitative refinements for comparisons of institutional performance , 1998 .

[50]  Gordon L. Patzer,et al.  The Physical Attractiveness Phenomena , 1985 .

[51]  Michael Mitzenmacher,et al.  A Brief History of Generative Models for Power Law and Lognormal Distributions , 2004, Internet Math..

[52]  J. Heckman.  Sample selection bias as a specification error , 1979 .

[53]  D. Rubin.  INFERENCE AND MISSING DATA , 1975 .

[54]  Jason E. Lane,et al.  Interests, Information, and Incentives in Higher Education: Principal-Agent Theory and Its Potential Applications to the Study of Higher Education Governance , 2008 .

[55]  P. Ramsden.  A performance indicator of teaching quality in higher education: The Course Experience Questionnaire , 1991 .

[56]  V. Schumpelick.  Spieglein, Spieglein an der Wand. . . . . , 1998, Der Chirurg.