prof.evaltns.beauty.public: Instructor beauty scores and instruction ratings
In JECheadle/RSOC317L: Data Sets and Supplemental Functions from David Diez's 'OpenIntro' Textbook
Description
Usage
Format
Source
See Also
Examples
Description
Many college courses conclude by giving students the opportunity to evaluate the course and the instructor anonymously. However, the use of these student evaluations as an indicator of course quality and teaching effectiveness is often criticized because these measures may reflect the influence of non-teaching related characteristics, such as the physical appearance of the instructor. Researchers at University of Texas, Austin collected data on teaching evaluation score (higher score means better) and standardized beauty score (a score of 0 means average, negative score means below average, and a positive score means above average) for a sample of 463 professors. The scatterplot below shows the relationship between these variables, and also provided is a regression output for predicting teaching evaluation score from beauty score.
Usage
1
data("prof.evaltns.beauty.public")
Format
A data frame with 463 observations on the following 64 variables.
tenureda numeric vector
profnumbera numeric vector
minoritya numeric vector
agea numeric vector
beautyf2uppera numeric vector
beautyflowerdiva numeric vector
beautyfupperdiva numeric vector
beautym2uppera numeric vector
beautymlowerdiva numeric vector
beautymupperdiva numeric vector
btystdavea numeric vector
btystdf2ua numeric vector
btystdfla numeric vector
btystdfua numeric vector
btystdm2ua numeric vector
btystdmla numeric vector
btystdmua numeric vector
class1a numeric vector
class2a numeric vector
class3a numeric vector
class4a numeric vector
class5a numeric vector
class6a numeric vector
class7a numeric vector
class8a numeric vector
class9a numeric vector
class10a numeric vector
class11a numeric vector
class12a numeric vector
class13a numeric vector
class14a numeric vector
class15a numeric vector
class16a numeric vector
class17a numeric vector
class18a numeric vector
class19a numeric vector
class20a numeric vector
class21a numeric vector
class22a numeric vector
class23a numeric vector
class24a numeric vector
class25a numeric vector
class26a numeric vector
class27a numeric vector
class28a numeric vector
class29a numeric vector
class30a numeric vector
courseevaluationa numeric vector
didevaluationa numeric vector
femalea numeric vector
formala numeric vector
fulldepta numeric vector
lowera numeric vector
multipleclassa numeric vector
nonenglisha numeric vector
onecredita numeric vector
percentevaluatinga numeric vector
profevaluationa numeric vector
studentsa numeric vector
tenuretracka numeric vector
blkandwhitea numeric vector
btystdvariancea numeric vector
btystdaveposa numeric vector
btystdavenega numeric vector
Source
Daniel S Hamermesh and Amy Parker. Beauty in the classroom: Instructors pulchritude and putative pedagogical productivity. In: Economics of Education Review 24.4 (2005), pp. 369-376.
See Also
See also evals for simplified version of dataset.
Examples
1
2
data(prof.evaltns.beauty.public)
## maybe str(prof.evaltns.beauty.public) ; plot(prof.evaltns.beauty.public) ...
JECheadle/RSOC317L documentation built on May 15, 2019, 4:02 a.m.
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Description Usage Format Source See Also Examples
Description
Many college courses conclude by giving students the opportunity to evaluate the course and the instructor anonymously. However, the use of these student evaluations as an indicator of course quality and teaching effectiveness is often criticized because these measures may reflect the influence of non-teaching related characteristics, such as the physical appearance of the instructor. Researchers at University of Texas, Austin collected data on teaching evaluation score (higher score means better) and standardized beauty score (a score of 0 means average, negative score means below average, and a positive score means above average) for a sample of 463 professors. The scatterplot below shows the relationship between these variables, and also provided is a regression output for predicting teaching evaluation score from beauty score.
Usage
1 | data("prof.evaltns.beauty.public")
|
Format
A data frame with 463 observations on the following 64 variables.
tenureda numeric vector
profnumbera numeric vector
minoritya numeric vector
agea numeric vector
beautyf2uppera numeric vector
beautyflowerdiva numeric vector
beautyfupperdiva numeric vector
beautym2uppera numeric vector
beautymlowerdiva numeric vector
beautymupperdiva numeric vector
btystdavea numeric vector
btystdf2ua numeric vector
btystdfla numeric vector
btystdfua numeric vector
btystdm2ua numeric vector
btystdmla numeric vector
btystdmua numeric vector
class1a numeric vector
class2a numeric vector
class3a numeric vector
class4a numeric vector
class5a numeric vector
class6a numeric vector
class7a numeric vector
class8a numeric vector
class9a numeric vector
class10a numeric vector
class11a numeric vector
class12a numeric vector
class13a numeric vector
class14a numeric vector
class15a numeric vector
class16a numeric vector
class17a numeric vector
class18a numeric vector
class19a numeric vector
class20a numeric vector
class21a numeric vector
class22a numeric vector
class23a numeric vector
class24a numeric vector
class25a numeric vector
class26a numeric vector
class27a numeric vector
class28a numeric vector
class29a numeric vector
class30a numeric vector
courseevaluationa numeric vector
didevaluationa numeric vector
femalea numeric vector
formala numeric vector
fulldepta numeric vector
lowera numeric vector
multipleclassa numeric vector
nonenglisha numeric vector
onecredita numeric vector
percentevaluatinga numeric vector
profevaluationa numeric vector
studentsa numeric vector
tenuretracka numeric vector
blkandwhitea numeric vector
btystdvariancea numeric vector
btystdaveposa numeric vector
btystdavenega numeric vector
Source
Daniel S Hamermesh and Amy Parker. Beauty in the classroom: Instructors pulchritude and putative pedagogical productivity. In: Economics of Education Review 24.4 (2005), pp. 369-376.
See Also
See also evals for simplified version of dataset.
Examples
1 2 | data(prof.evaltns.beauty.public)
## maybe str(prof.evaltns.beauty.public) ; plot(prof.evaltns.beauty.public) ...
|
R Package Documentation
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