Student Evaluations of Faculty: A Longitudinal Study from One Department in a Business School

Structural equation modeling is used to examine the nomological relationships between the five latent constructs that comprise the Student Instructional Rating System (SIRS), a widely used student evaluation of instructor form. Our results indicate that input factors, that is, course organization and student-instructor interaction, influence the endogenous factors, that is, instructor involvement and student interest, in a positive manner. However, the factor—course demands affects these endogenous factors in a negative manner. In addition, course demands is also negatively associated with the other two input factors. Despite their apparent intuitive appeal, users of student evaluation forms often ignore these relationships. The authors hope that users of student evaluation forms take these relationships between the latent constructs into account when using the results.

Section: Studentevaluationofinstructorcreatesadebatethatstimu-mentioning

confidence: 99%

Student Evaluation of Instructor: A Nomological Investigation Using Structural Equation Modeling

Paswan

Young

2002

“…The overall rationale for this model is that some perceptions of teaching develop before others and influence the final summative evaluation. For example, the literature suggests that students lack the confidence to evaluate their learning or are unable to make that determination (Clayson and Haley King 1983;Rodewald and Carroll 1974;Shamanske 1988;Wheeler and Guerts 1986). In their meta-analysis, Dowell and Neal (1982) suggest the following:…”

Section: Theoretical Modelmentioning

confidence: 99%

“…According to Larkin et al, "A sizable body of knowledge is prerequisite to expert skill, that knowledge must be indexed to large numbers of patterns that, on recognition, guide the expert in a fraction of a second to relevant parts of the knowledge store" (p. 1336). It is not surprising, then, that the literature seems to indicate that students either lack confidence or are relatively unable to make a determination of what they learned (King 1983;Rodewald and Carroll 1974;Shamanske 1988;Wheeler and Guerts 1986). A recent study by Harvard psychologists (Ambady and Rosenthal 1993) indicated that student reactions (i.e., nonenrolled student judges) to randomly selected 30-second clips of soundless videotapes of actual class instruction were extremely accurate predictors of end-of-course evaluations, even though the sound had been turned off.…”

Section: Figure 2: Theoretical Modelmentioning

confidence: 99%

Determinants of Student Evaluations of Global Measures of Instructor and Course Value

Marks

2000

125

118

Using structural equation modeling (LISREL) with a sample of business students, this study addresses important issues with respect to student evaluations. After reviewing past research and using exploratory factor analysis, confirmatory factor analysis suggests the existence of five latent variables in student evaluations: organization, workload/difficulty, expected/fairness of grading, instructor liking/concern, and perceived learning. Next, adding structural paths, these variables are used to model the formation of student evaluations. The models developed demonstrate that student evaluations may lack discriminant validity, the extent to which a measure does not correlate with other constructs it is not supposed to measure (e.g., expected/fairness of grading does have a large impact on ratings of teaching ability). Moreover, the use of student evaluations is compounded by the common usage of global measures in student evaluation practice. Here, the use of overall course value and instructor rating are contrasted. The results are disparate, raising questions about the use of summative measures (i.e., of what should they be composed, if used at all).

“…Only a minority of studies that investigate student evaluations focuses on methodological and measurement aspects, and within these, the following problems have been identified: Student evaluations do not differentiate well between faculties (Wheeler and Geurts, 1986). They only distinguish between the very best and the very worst teachers (Wheeler and Geurts, 1986), and they capture "halo effects", which decrease as the overall rating of the instructor increases (Orsini, 1988).…”

Section: Prior Research -Student Evaluationsmentioning

confidence: 99%

“…They only distinguish between the very best and the very worst teachers (Wheeler and Geurts, 1986), and they capture "halo effects", which decrease as the overall rating of the instructor increases (Orsini, 1988). The validity of student evaluations is reduced by several language difficulties: (1) people do not have the necessary level of awareness to evaluate all aspects of a situation, (2) disagreement may exist regarding the object of the evaluation, (3) denotative and connotative meanings cannot be separated, (4) signs and meanings may be inconsistent and (5) the situation may affect the evaluation (Bertsch and Peek, 1982).…”

Section: Prior Research -Student Evaluationsmentioning

confidence: 99%

Response Style Contamination of Student Evaluation Data

Dolničar

Grün

2009

Student evaluation surveys provide instructors with feedback regarding development opportunities and they form the basis of promotion and tenure decisions. Student evaluations have been extensively studied, but one dimension hitherto neglected is the actual measurement aspect: which questions to ask, how to ask them, and what answer options to offer to students to get the most valid results. This study investigates whether crosscultural response styles affect the validity of student evaluations. If they do, then the student mix in a class can affect an instructor's evaluation, potentially producing biased feedback and prompting inappropriate decisions by university committees. This article discusses two main response styles, demonstrates the nature of the bias they can cause in student evaluation surveys using simulated artificial data, and illustrates three cases based on real student evaluation data in which marketing instructors' teaching quality assessments may be heavily biased because of response styles. The authors propose a simple method to check for response style contamination in student evaluation data and they discuss some practical implications. This paper discusses two main response styles, demonstrates the nature of the bias they can cause in student evaluation surveys using simulated artificial data and illustrates three cases based on real student evaluation data in which marketing instructors' teaching quality assessments may be heavily biased because of response styles. We propose a simple method to check for response style contamination in student evaluation data, and discuss some practical implications.