Is There Anything Good About Rate My Professor?

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AN EXPLORATORY STUDY:
IS THERE ANYTHING GOOD ABOUT RATEMYPROFESSOR?
Richard L. Peterson
Department of Management & Information Systems
Montclair State University, Montclair, NJ 07043, 973-655-7038
[email protected]
Mark L. Berenson
Department of Management & Information Systems
Montclair State University, Montclair, NJ 07043, 973-655-6857
[email protected]
Risha Aijaz
Department of Management & Information Systems
Montclair State University, Montclair, NJ 07043, 973-655-4335
[email protected]
ABSTRACT
Professor rating services such as RateMyProfessor.com are frequently
dismissed as invalid due to their inherent nature of self selection. Only raters
at the extremes, so the theory goes, contribute to the site as they have axes to
grind or high praises to deliver. The middle, supposedly more even handed
raters don’t bother to offer ratings or comments. While rater bias on these
sites seems logical in theory, is it actually true? RateMyProfessor provides an
opportunity to test for bias in the comments offered by the raters. If these
verbal evaluations are biased the language used would be extreme and outside
the boundaries of normal written discourse. The work of Hart [8] and others
provides both a software tool for textual analysis and normative data on nine
―dimensions of language‖. The exploratory research reported here examines
written comments for individual professors. We evaluate the comments of
positive and negative raters to the norms of discourse across a variety of
genres and identify language dimensions where raters truly are extreme and
where they are not.
KEYWORDS: Professor Ratings, Text Analysis, Content Analysis
Author Notes: Correspondence concerning this manuscript should be sent to: Mark Berenson, Department of
Management and Information Systems, School of Business, Montclair State University, Montclair, NJ 07043; Phone:
(973) 655-6857; E-mail: [email protected]
INTRODUCTION
Ten years after being created, RateMyProfessor.com (RMP) today is the most
popular and widely used website by students to provide ratings and comments
about their professors; with more than 10 million opinions of over 1 million
professors. The ratings cover more than 6,000 schools across the United
States, Canada, England, Scotland, and Wales*. It’s appreciated by students
―shopping‖ for professors, but dismissed by empiricists for its self-selection of
raters violates the bedrock principle of random selection
RMP offers two sources of data: numeric ratings and textual comments.
Raters are presented with a 1-5 scale:
Easiness
Hard
Easy
Helpfulness:
Extremely
Helpful
Useless
Clarity:
Incomprehensible
Crystal
Interest level prior to attending
class:
None at all
It's my world!
Appearance: (just for fun)
Hot
Not
An Overall Quality rating is calculated from Helpfulness and Clarity. Other
information solicited—but not displayed--includes: Textbook Use (5-point
scale from ―Low‖ to ―High,‖ Textbook Used (by name or ISBN), Grade,
Attendance (Mandatory or Not Mandatory), Professor Status (Still Teaching,
Retired/Gone), and Class and Section (both fill-in). Finally, RMP includes a
350 character text box for students to type comments. The text box includes a
prominent note ―Please keep comments clean. Libelous comments will be
deleted.‖
Also included are warnings ―Remember, YOU ARE
RESPONSIBLE for what you write here. Submitted data become the
property of RateMyProfessors.com. IP addresses are logged.‖ Adjacent to
the Comments field is a label ―Guidelines‖ that links to page of ―Dos‖ and
―Don’ts‖ for raters which is reproduced as Appendix A.
Despite the best intentions of those responsible for the development of
RateMyProfessors.com, one must seriously question the value of the
numerical ratings provided because the raters ―self-select,‖ violating the tenet
of random selection needed for drawing overall inferences from any survey or
designed experiment. It has long been surmised that the majority of those
students who decide to participate in the evaluation process hold distinctly
bipolar views of the faculty member they are rating. Students with a
legitimate or perceived gripe are more likely to participate in the ratings, as
are students who very much appreciate or value what the faculty member has
contributed to the course. A third group of raters, asserted to be far fewer in
number, believe the faculty member being rated is ―okay/average‖ but feel
obligated to participate in the rating process because of their responsibility to
fraternity, sorority or fellow classmates. Thus it is conjectured that the
distribution of RMP evaluations will be U-shaped for most faculty members,
the majority of ratings being ―Good‖ or ―Poor,‖ with a minority of ratings
being ―average.‖ On the other hand, had universal/mandatory ratings been
required, as may be the case at various institutions of higher learning, whether
a 3-point rating scale is used, or more popular 5 or 7-point Likert-type rating
scales are used, one would hypothesize the distribution of ratings for most
faculty would be unimodal with one of the tails (highest rating or lowest
rating) being very limited in frequency count.
Interestingly, and despite the problems of self-selection bias in the RMP
ratings, Jaschik [12] has shown that there is a significantly positive correlation
between the numerical RMP quality rating and the average rating computed
from the student evaluations of the faculty by a whole class. As outlined in
the Methodology, using the RMP quality rating data for the faculty in the
Management and Information Systems Department in the School of Business
at Montclair State University along with the corresponding student evaluation
ratings on campus, one aspect of this overall study will be to attempt to
corroborate the findings reported in the study by Jaschik [12].
If we can’t trust the ratings per se due to bias, is it also the case that we can’t
trust the comments? Are the comments also biased such that the language of
the comments is somehow different from ―normal‖ language? Are raters
somehow different from the ―general‖ population in terms of language usage?
To make this determination we might compare the language of the comments
of the raters on RMP to a variety of language samples.
Content analysis of text is a research methodology that uses a set of
procedures to analyze and categorize communication [20]. The methodology
offers a number of potential benefits including the identification of individual
differences among communicators [20], avoidance of recall biases [1], and the
ability to obtain otherwise unavailable information [13]. In the business
disciplines content analysis has been used in accounting [17], management
[2], marketing [19], and corporate strategy [15] [21].
There are three types of approaches to language analysis 16]: human-scored
procedures, artificial intelligence systems, and individual word count systems.
With the first approach, coding rules are established, human coders are
trained, and then the coders classify selected aspects of the text. Artificial
intelligence approaches consider the lexicon, syntax, and semantics of text
[18]. With respect to individual word count methodology, individual words in
the text are counted and the frequency of each word is compared to the
frequency of these same words in other communication samples. Word
frequencies outside of the range of frequencies in these comparative samples
are an indication of differences between the samples.
DICTION [8] is one of a number of word frequency programs. In DICTION
the frequency of word usage in the analyzed text is compared to the frequency
of word usage across various genres studied by Hart [7] [4] [5] [9] [10]. The
genre(s) to which the analyzed text is compared may be selected from
business, daily life, entertainment, journalism, literature, politics, and
scholarship. Hart [11] analyzed from one to six sub-genres to derive word
frequency norms for each genre. In total, the norms are based on the analysis
of 22,027 texts of various genres written between 1948 and 1998.
The words from these genres are arranged in 33 dictionaries or word lists
ranging in size from 10 to 745 words. No word appears in more than one
dictionary. Brief descriptions of each dictionary may be found in Appendix
B.
In addition to the absolute frequency counts, DICTION calculates four
variables based on word ratios. These calculated variables are:
Insistence, a measure of ―code-restriction‖ that indicates a ―preference for
a limited, ordered world‖;
Embellishment, a measure of the ratio of adjectives to verbs;
Variety, a measure of conformity to, or avoidance of, a limited set of
expressions (different words/total words); and
Complexity, a measure of word size based on the Flesch[3] method.
Frequency counts from the various dictionaries along with the four calculated
compose five master variables. Hart’s [11] master variables, intended to
capture the tonal features of the text, are defined and formulated as follows:
Certainty is a measure of language ―indicating resoluteness, inflexibility,
and completeness and a tendency to speak ex cathedra.‖
Certainty = [Tenacity. + Leveling. + Collectives. + Insistence] [Numerical Terms + Ambivalence. + Self Reference + Variety]
Activity is a measure of ―movement, change, [and] the implementation of
ideas and the avoidance of inertia;
Activity = [Aggression. + Accomplishment. + Communication. + Motion]
- [Cognitive Terms. + Passivity. + Embellishment]
Optimism is a measure of ―language endorsing some person, group,
concept or event or highlighting their positive entailments.‖
Optimism = [Praise + Satisfaction + Inspiration] - [Blame + Hardship
+Denial]
Realism is a measure of language ―describing tangible, immediate,
recognizable matters that affect people’s everyday lives.‖
Realism = [Familiarity + Spatial Awareness. + Temporal Awareness.
+Present Concerns. + Human Interest + Concreteness] - [Past Concern +
Complexity]
Commonality is a measure of language ―highlighting the agreed-upon
values of a group and rejecting idiosyncratic modes of engagement."
Commonality = [Centrality. + Cooperation. + Rapport] - [Diversity +
Exclusion + Liberation]
SPECIFIC RESEARCH QUESTIONS
In RMP an Overall Quality rating is calculated from the raters’ numerical
evaluations of ratings of professors’ Helpfulness and Clarity. For each
professor rated, RMP categorizes the professor’s Overall Quality as ―Good,‖
―Average,‖ or ―Poor.‖ In this exploratory study of the textual comments of
raters we questioned whether there would be significant differences among the
comments created by raters classified in each category of Overall Quality
compared to the general population as defined by the dictionaries included in
DICTION. Specifically, the questions addressed were:
Are there significant differences in the commentary provided by RMP
evaluators who rate professors ―Good‖ versus those who rate
professors ―Poor‖ with respect to the master variables?
Are there significant differences in the commentary provided by RMP
evaluators who rate professors ―Good‖ versus those who rate
professors ―Poor‖ with respect to the five content-analysis master
variables?
Are there significant differences in the commentary provided by RMP
evaluators who rate professors ―Good‖ versus those who rate
professors ―Poor‖ with respect to the four content-analysis calculated
variables?
For the Master variables of DICTION, we hypothesized as follows:
Certainty. As a measure of resoluteness, the group of raters rating
professors as ―Poor‖ will be more certain in their language than those
rating the professor as ―Good.‖ This certainty will be revealed in higher
scores for tenacity, leveling, collectives, insistence, numerical terms,
ambivalence and self-references. ―Poor‖ raters will also show this
certainty by a reduced variety of words.
Activity. This measure of movement will show ―Poor‖ raters with greater
activity. Words of aggression, accomplishment, and communication will
be higher for this group. They will also use more terms of passivity, and
more embellishments. ―Good‖ and ―Poor‖ raters will use more cognitive
terms than the ―Average‖
Optimism. ―Good‖ and ―Poor‖ raters will reflect their ratings in their
choice of words in their comments. ―Good‖ raters will use more terms of
praise, satisfaction, and inspiration. ―Poor‖ raters will do the opposite;
fewer terms of praise, satisfaction, and inspiration. In addition, while
:Good‖ raters will not avoid blame, hardship, and denial, ―Poor‖ raters
will use more of these terms than would be usual.
Realism. There will be no significant differences in the frequency of
word use in any dictionary making up this master variable either for
―Good‖ or ―Poor‖ raters.
Commonality. ―‖Good‖ raters will use above average numbers of words
of centrality, cooperation and rapport while ―Poor‖ raters will use fewer of
these words. These raters will indicate their feelings of exclusion and
liberation by more frequent use of these terms.
METHODOLOGY
For this exploratory study we restricted the data set to all full-time,
tenure/tenure-track faculty teaching in a department within a school of
business at a public university who had entries on RateMyProfessor. Of the
23 faculty members in the department over the period, 100 percent had ratings
on RMP. As we were interested in studying raters who were or planned to be
business students, given that some professors also had expertise in other
disciplines and taught some courses outside the school of business, we
eliminated all ratings and comments for any professor where the reported
experience with the professor was in a course not offered by the department.
This resulted in the elimination of 18 RMP records of the 700 total. Ratings
without comments (a total of 17) were also eliminated as these comments
were the focus of current study. Finally as our interest was on ratings and
comments at the extremes we eliminated 106 raters whose Overall Quality
rating was ―Average.‖
The comments of the remaining 559 raters were then cleaned to correct
misspellings and abbreviations that would impact the word frequency counts.
No other changes were made to the corpus. Text files of all the comments
from ―Good‖ and ―Poor‖ raters and a combined file were created and
submitted to DICTION. All words were processed (DICTION allows
sampling of the corpus) and no custom dictionaries were created for the
analysis.
AN EXPLORATORY ANALYSES OF SPECIFIC RESEARCH
HYPOTHESIS
Table 1 displays the DICTION reported results for the two groups of ratings,
―Good‖ versus ―Poor,‖ for each of the five master variables and their
corresponding component variables. Also indicated is whether or not our
hypothesized directions of results were confirmed. The results indicate that for
the five master variables of DICTION our combined hypotheses were not
consistently confirmed, nor were they for the component variables.
Table 1 – Dictionary, Constructed, and Master Variable Results for ―Good‖
and ―Poor‖ Raters
Variable
Frequency
Good
Frequency2
Poor
Hypothesis: Poor vs.
Good
6.09
7.25
Ambivalence
18.09
17.30
Self-reference
7.80
8.58
Tenacity
36.01
46.70*
Leveling Terms
11.52
15.21*
Collectives
12.57
8.85
17.01*
5.99
Satisfaction
2.82
3.67
Inspiration
1.22*
1.31*
Blame
6.20*
15.53*
Hardship
1.16*
3.32
1.28
0.05*
Accomplishment
3.65*
3.87*
Communication
10.20
11.97*
Cognition
10.27
19.70*
Passivity
2.23
2.82
2.09*
4.37
80.85*
89.63*
Temporal Terms
4.56*
8.56
Present Concern
13.09
19.66*
Human Interest
41.20
58.05*
Concreteness
10.24*
16.01
Past Concern
3.03
2.44
Centrality
0.26*
1.11*
Rapport
0.17*
1.56
15.09*
11.09*
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0.39
0.52
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Numerical Terms
Praise
Aggression
Spatial Terms
Familiarity
Cooperation
Diversity
Exclusion
0.29
0.31
Liberation
0.53
0.30
Denial
9.43*
20.95*
Motion
2.44
1.61
Calculated Variables
Good
Poor
Insistence
73.04
2.51
Embellishment
1.55*
1.00
0.47
0.46
3.93*
4.25*
Variety
Complexity
Master Variables
Good
Poor
Activity
47.60
6.32
Optimism
50.20
9.31
Certainty
51.97
2.87
Realism
46.00
9.92
Commonality
52.53
2.53
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Hypothesis: Poor vs. Good
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Hypothesis: Poor vs.Good
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The first surprising result occurred with the master variable certainty where
the ―Good‖ raters scored higher than the ―Poor‖ raters. However, for the eight
component variables we correctly hypothesized the result five times and were
wrong three times.
The second surprising result occurred with the master variable activity. The
―Good‖ raters again scored higher than the ―Poor‖ raters. However, for the
seven component variables we correctly hypothesized the result three times
and were wrong twice. We did not hypothesize and difference in results for
two component variables.
For the master variable optimism our hypothesis was confirmed. The ―Good‖
raters again scored higher than the ―Poor‖ raters. On the other hand, for the six
component variables we correctly hypothesized the result four times and were
wrong twice.
For the master variable realism we did not hypothesize any difference in
direction of results for ―Good‖ versus ―Poor‖ raters, nor did we make any
hypotheses for the eight component variables.
For the master variable commonality our hypothesis was confirmed. The
―Good‖ raters again scored higher than the ―Poor‖ raters. On the other hand,
for the six component variables we correctly hypothesized the result only two
times and were wrong three times. We did not hypothesize the direction of
the results for one of the component variables.
DISCUSSION
From Table 1 it is observed that the ―Good‖ raters outscored the ―Poor‖ raters
on four of five master variables and our hypothesized results were only
confirmed twice. For one master variable, realism, we did not specify a
preconceived difference in direction and that was the only master variable that
demonstrated higher scores for the ―Poor‖ raters. Breaking these results down
by the components of the five master variables, we correctly hypothesized
results 14 times, we were incorrect 10 times and on 11 occasions we did not
attempt to predict the direction of the results.
The question that must be pondered is why such unexpected results? A few
possibilities must be thoroughly examined.
1 – As asked rhetorically in the Introduction section, if we can’t trust
the numerical ratings per se due to bias, is it also the case that we can’t
trust the comments? Are the comments also biased such that the
language of the comments is somehow different from ―normal‖
language?
Are raters somehow different from the ―general‖
population in terms of language usage? To make this determination
we may need to compare the language of the comments of the raters
on RMP to a variety of language samples, not just the DICTION
program..
2 – Although much has been written about the DICTION program in
various articles by Hart [4] [5] [6] [7] [8] [9] [10] and others who have
used it for research one must question its validity reliability which
have not been reported. Furthermore, there is no readily found
description of the computation of mean and standard deviation, or
computation of the standard Z scores and Hart fails to demonstrate
why he breaks with long-held convention and describes absolute Z
scores greater than 1.0 as outside the normal range and thereby
significant.
3 – Hart’s [10] five master variables appear to be independent
constructs arising from a factor analysis – there is little to no
correlation among these constructs. The component variables
comprising the five master variables did not seem to consistently
display the direction of difference expected by our hypotheses, perhaps
a misunderstanding on our part of the definition of the involved terms?
4 – It is impossible for us to determine how the DICTION program
searches for discrepancies in various commentary that could result in
misclassification. For example, it is not known whether the DICTION
program can properly classify a comment about teacher performance
that says ―the teacher is easy‖ versus ― the teacher is not easy‖ versus
―I was told the teacher was easy but I don’t think this is so.‖ If the
DICTION program cannot properly distinguish among such responses
both its reliability and validity as a measuring instrument can be
questioned.
Further exploration will provide answers to the above.
CONCLUSIONS
Despite the surprising findings which indicated discrepancies with several of
our hypotheses we remain encouraged by the results of this exploratory study.
Once we satisfactorily address the aforementioned dilemmas described in the
Discussion section we plan to extend the study and address additional
questions in two phases:
:Phase I Questions
Is there a significantly positive correlation between the biased RMP
quality ratings of the faculty in the Management and Information
Systems Department in the School of Business at Montclair State
University and the corresponding campus student evaluations received
by these faculty? That is, do the MGIS Department data corroborate
the findings reported in the study conducted by Jaschik [12]?
Omitting ―Average‖ ratings, are the percentage of ―Good‖ to ―Good‖
or ―Poor‖ ratings significantly higher from the class evaluations than
from the RMP evaluations? An affirmation of this hypothesis is proof
of the negative self-selection bias in the RMP ratings.
Phase II Questions
Given the self-selection bias issues, using the Management and
Information Systems Department faculty ratings in the School of
Business at Montclair State University as a base, is there a statistically
significant difference between the RMP quality ratings of these faculty
and those given to a randomly selected sample of similar faculty from
corresponding/similar AACSB-International public universities?
Are there significant differences in the commentary provided by RMP
evaluators who rate professors ―Good‖ versus those who rate
professors ―Poor‖ with respect to the 33 content-analysis dictionary
variables between the aforementioned Montclair State University
faculty and the randomly selected faculty?
Are there significant differences in the commentary provided by RMP
evaluators who rate professors ―Good‖ versus those who rate
professors ―Poor‖ with respect to the five content-analysis master
variables between the aforementioned Montclair State University
faculty and the randomly selected faculty?
Are there significant differences in the commentary provided by RMP
evaluators who rate professors ―Good‖ versus those who rate
professors ―Poor‖ with respect to the four content-analysis calculated
variables between the aforementioned Montclair State University
faculty and the randomly selected faculty?
REFERENCES
[1]
Barr, P. S., Stimpert, J. L., & Huff, A. S. (1992). Cognitive Change,
Strategic Action, and Organizational Renewal. Strategic Management
Journal, 13, 15-36.
[2]
Fiol, C. M. (1989). A semiotic analysis of corporate language:
Organizational boundaries and joint venturing. Administrative Science
Quarterly, 34, 277-303.
[3]
Flesch, R. (1951). The Art of Clear Thinking. New York: Harper.
[4]
Hart, R. P. (1984a). Systematic analysis of political discourse: The
development of DICTION. K. Sanders, L. Kaid, & D. Nimmo (Eds.),
Political Communication Yearbook (pp. 97-134). Carbondale: Southern
Illinois University Press.
[5]
Hart, R. P. (1984b). Verbal Style and the Presidency: A Computerbased Analysis. New York: Academic.
[6]
Hart, R. P. (1987). The Sound of Leadership: Presidential
Communication in the Modern Age. Chicago, IL: University of Chicago
Press.
[7]
Hart, R. P., and S. Jarvis. (1997). Political Debate: Forms, Styles, and
Media. American Behavioral Scientist 40: 1095-1122.
[8]
Hart, R. P. (2000a). DICTION 5.0: The Text-analysis Program.
Thousand Oaks, CA: Scolari/Sage Publications.
[9]
Hart, R. P. (2000b). Campaign Talk: Why Elections are Good for Us.
Princeton, NJ: Princeton University Press.
[10] Hart, R. P. (2001). Redeveloping DICTION: Theoretical
Considerations. In M. West (Ed.), Theory, Method, and Practice of
Computer Content Analysis (pp. 26-55). New York: Ablex.
[11] Hart, R. P. (2003). DICTION http://rhetorica.net.htm
[12] Jaschik, S. (2007). Could RateMyProfessors.com be Right? Inside
Higher Education. June:1–2.
[13] Kabanoff, B., Waldersee, R., & Cohen, M. (1995). Espoused values
and organizational change themes. Academy of Management Journal, 38,
1075-1104.
[14] McCain, J. (2010) 4 Great Sites to Rate and Review Teachers &
Professors
http://www.makeuseof.com/tag/4-great-sites-rate-reviewteachers-professors/
[15] Merchant, H. (2004). Revisiting Shareholder Value Creation via
International Joint Ventures: Examining Interactions among Firm- and
Context-specific Variables. Canadian Journal of Administrative Sciences, 21,
129-145.
[16] Morris, R. (1994). Computerized Content Analysis in Management
Research: A Demonstration of Advantages & Limitations. Journal of
Management, 20, 903-931.
[17] Rogers, R. K., Dillard, J., & Yuthas, K. (2005). The Accounting
Profession: Substantive Change and/or Image Management. Journal of
Business Ethics, 58, 159-176.
[18] Rosenberg, S. D., Schnurr, P. P., & Oxman, T. E. (1990). Content
Analysis: A Comparison of Manual and Computerized Systems. Journal of
Personality Assessment, 54, 298-310.
[19] Simon, M., & Houghton, S. M. (2003). The Relationship Between
Overconfidence and the Introduction of Risky Products: Evidence from a
Field Study. Academy of Management Journal, 46, 139-149.
[20] Weber, R. P. (1990). Basic Content Analysis. Newbury Park, CA:
Sage.
[21] Yuthas, K., Rogers, R., & Dillard, J. F. (2002). Communicative Action
and Corporate Annual Reports. Journal of Business Ethics, 41, 141-157.
APPENDIX A
RateMyProfessor.com Posting Guidelines
As a user of RateMyprofessors.com, you agree and accept the terms and
conditions of the site. This site is a resource for students to provide and
receive feedback on professor's teaching methods and insight into the courses.
Comments should only be posted by students who have taken a class from the
professor. Please limit one comment per person per course.
The following guidelines are intended to protect all users-students and
professors. Please review before posting on RateMyProfessors.com
DOs:
o
o
o
Be honest.
Be objective in your assessment of the professor.
Limit your comments to the professor's professional abilities.
Do not to get personal.
o Proof your comments before submitting. Poor spelling WILL
NOT cause your rating to be removed; however, poor spelling
may result in your rating being discredited by those who read
it.
o Leave off your Name, Initials, Pseudo Name, or any sort of
identifying mark when posting.
o Refer to the Rating Categories to help you better elaborate your
comments.
o Remember that negative comments that still offer constructive
criticism are useful. Comments that bash a professor on a
personal level are not.
o Submit helpful comments that mention professor's ability to
teach and/or communicate effectively, course load, type of
course work and course topics.
DO NOTs:
o State something as a fact if it is your opinion.
o Post a rating if you are not a student or have not taken a class
from the professor.
o Post ratings for people who do not teach classes at your college
or university.
o Input false course or section codes for a class that does not
exist.
o Rate a professor more than once for the same class.
o Make references to other comments posted.
o Professors : Do not rate yourselves or your colleagues.
Comments will be deemed inappropriate that are libelous, defamatory,
indecent, vulgar or obscene, pornographic, sexually explicit or sexually
suggestive, racially, culturally, or ethnically offensive, harmful, harassing,
intimidating, threatening, hateful, objectionable, discriminatory, or
abusive, or which may or may appear to impersonate anyone else.
COMMENTS THAT CONTAIN THE FOLLOWING WILL BE
REMOVED :
Profanity, name-calling, vulgarity or sexually explicit in nature
Derogatory remarks about the professor's religion, ethnicity or race,
physical appearance, mental and physical disabilities.
References to professor's sex life (Including sexual innuendo, sexual
orientation or claims that the professor sleeps with students).
Claims that the professor shows bias for or against a student or specific
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Claims that the professor has been or will be fired, suspended from
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Claims that the professor engages or has previously engaged in illegal
activities (drug use, been incarcerated.)
Includes a link/URL to a webpage or website that does not directly
pertain to the class.
Any piece of information including contact info that enables someone
to identify a student.
Any piece of information about the professor that is not available on
the school's website and allows someone to contact them outside of
school. This also includes remarks about the professor's family and
personal life.
Accusations that the professors is rating themselves or their
colleagues.
Is written in a language other than English? Unless you attend a
French-Canadian school.
The Do Nots of these Posting Guidelines will be enforced and violations will
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Please do not flag a rating just because you disagree with it.
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you. IP addresses will also be turned over to the proper authorities when
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Multiple Ratings
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APPENDIX B
Descriptions of the Dictionaries and Scores
Accomplishment: Words expressing task-completion (establish, finish,
influence, proceed) and organized human behavior (motivated, influence,
leader, manage). Includes capitalistic terms (buy, produce, employees, sell),
modes of expansion (grow, increase, generate, construction) and general
functionality (handling, strengthen, succeed, outputs). Also included is
programmatic language: agenda, enacted, working, leadership.
Aggression: A dictionary embracing human competition and forceful action.
Its terms connote physical energy (blast, crash, explode, collide), social
domination (conquest, attacking, dictatorships, violation), and goaldirectedness (crusade, commanded, challenging, overcome). In addition,
words associated with personal triumph (mastered, rambunctious, pushy),
excess human energy (prod, poke, pound, shove), disassembly (dismantle,
demolish, overturn, veto) and resistance (prevent, reduce, defend, curbed) are
included.
Ambivalence: Words expressing hesitation or uncertainty, implying a
speaker's inability or unwillingness to commit to the verbalization being
made. Included are hedges (allegedly, perhaps, might), statements of
inexactness (almost, approximate, vague, somewhere) and confusion (baffled,
puzzling, hesitate). Also included are words of restrained possibility (could,
would, he'd) and mystery (dilemma, guess, suppose, seems).
Blame: Terms designating social inappropriateness (mean, naive, sloppy,
stupid) as well as downright evil (fascist, blood-thirsty, repugnant, malicious)
compose this dictionary. In addition, adjectives describing unfortunate
circumstances (bankrupt, rash, morbid, embarrassing) or unplanned
vicissitudes (weary, nervous, painful, detrimental) are included. The
dictionary also contains outright denigrations: cruel, illegitimate, offensive,
miserly.
Centrality: Terms denoting institutional regularities and/or substantive
agreement on core values. Included are indigenous terms (native, basic,
innate) and designations of legitimacy (orthodox, decorum, constitutional,
ratified), systematicity (paradigm, bureaucratic, ritualistic), and typicality
(standardized, matter-of-fact, regularity). Also included are terms of
congruence (conformity, mandate, unanimous), predictability (expected,
continuity, reliable), and universality (womankind, perennial, landmarks).
Cognitive Terms: Words referring to cerebral processes, both functional and
imaginative. Included are modes of discovery (learn, deliberate, consider,
compare) and domains of study (biology, psychology, logic, economics). The
dictionary includes mental challenges (question, forget, re-examine,
paradoxes), institutional learning practices (graduation, teaching, classrooms),
as well as three forms of intellection: intuitional (invent, perceive, speculate,
interpret), rationalistic (estimate, examine, reasonable, strategies), and
calculative (diagnose, analyze, software, fact-finding).
Collectives: Singular nouns connoting plurality that function to decrease
specificity. These words reflect a dependence on categorical modes of
thought. Included are social groupings (crowd, choir, team, humanity), task
groups (army, congress, legislature, staff) and geographical entities (county,
world, kingdom, republic).
Communication: Terms referring to social interaction, both face-to-face
(listen, interview, read, speak) and mediated (film, videotape, telephone, email). The dictionary includes both modes of intercourse (translate, quote,
scripts, broadcast) and moods of intercourse (chat, declare, flatter, demand).
Other terms refer to social actors (reporter, spokesperson, advocates, preacher)
and a variety of social purposes (hint, rebuke, respond, persuade).
Complexity: A simple measure of the average number of characters-per-word
in a given input file. Borrows Rudolph Flesch's (1951) notion that convoluted
phrasings make a text's ideas abstract and its implications unclear.
Concreteness: A large dictionary possessing no thematic unity other than
tangibility and materiality. Included are sociological units (peasants, AfricanAmericans, Catholics), occupational groups (carpenter, manufacturer,
policewoman), and political alignments (Communists, congressman,
Europeans). Also incorporated are physical structures (courthouse, temple,
store), forms of diversion (television, football, CD-ROM), terms of
accountancy (mortgage, wages, finances), and modes of transportation
(airplane, ship, bicycle). In addition, the dictionary includes body parts
(stomach, eyes, lips), articles of clothing (slacks, pants, shirt), household
animals (cat, insects, horse) and foodstuffs (wine, grain, sugar), and general
elements of nature (oil, silk, sand).
Cooperation: Terms designating behavioral interactions among people that
often result in a group product. Included are designations of formal work
relations (unions, schoolmates, caucus) and informal associations (chum,
partner, cronies) to more intimate interactions (sisterhood, friendship,
comrade). Also included are neutral interactions (consolidate, mediate,
alignment), job-related tasks (network, détente, exchange), personal
involvement (teamwork, sharing, contribute), and self-denial (public-spirited,
care-taking, self-sacrifice).
Denial: A dictionary consisting of standard negative contractions (aren't,
shouldn't, don't), negative functions words (nor, not, nay), and terms
designating null sets (nothing, nobody, none).
Diversity: Words describing individuals or groups of individuals differing
from the norm. Such distinctiveness may be comparatively neutral
(inconsistent, contrasting, non-conformist) but it can also be positive
(exceptional, unique, individualistic) and negative (illegitimate, rabble-rouser,
extremist). Functionally, heterogeneity may be an asset (far-flung, dispersed,
diffuse) or a liability (factionalism, deviancy, quirky) as can its
characterizations: rare vs. queer, variety vs. jumble, distinctive vs.
disobedient.
Exclusion: A dictionary describing the sources and effects of social isolation.
Such seclusion can be phrased passively (displaced, sequestered) as well as
positively (self-contained, self-sufficient) and negatively (outlaws,
repudiated). Moreover, it can result from voluntary forces (secede, privacy)
and involuntary forces (ostracize, forsake, discriminate) and from both
personality factors (small-mindedness, loneliness) and political factors (rightwingers, nihilism). Exclusion is often a dialectical concept: hermit vs. derelict,
refugee vs. pariah, discard vs. spurn).
Familiarity: Consists of a selected number of C.K. Ogden's (1968)
"operation" words which he calculates to be the most common words in the
English language. Included are common prepositions (across, over, through),
demonstrative pronouns (this, that) and interrogative pronouns (who, what),
and a variety of particles, conjunctions and connectives (a, for, so).
Hardship: This dictionary contains natural disasters (earthquake, starvation,
tornado, pollution), hostile actions (killers, bankruptcy, enemies, vices) and
censurable human behavior (infidelity, despots, betrayal). It also includes
unsavory political outcomes (injustice, slavery, exploitation, rebellion) as well
as normal human fears (grief, unemployment, died, apprehension) and
incapacities (error, cop-outs, weakness).
Human Interest: An adaptation of Rudolf Flesch's notion that concentrating
on people and their activities gives discourse a life-like quality. Included are
standard personal pronouns (he, his, ourselves, them), family members and
relations (cousin, wife, grandchild, uncle), and generic terms (friend, baby,
human, persons).
Inspiration: Abstract virtues deserving of universal respect. Most of the
terms in this dictionary are nouns isolating desirable moral qualities (faith,
honesty, self-sacrifice, virtue) as well as attractive personal qualities (courage,
dedication, wisdom, mercy). Social and political ideals are also included:
patriotism, success, education, justice.
Leveling: Words used to ignore individual differences and to build a sense of
completeness and assurance. Included are totalizing terms (everybody,
anyone, each, fully), adverbs of permanence (always, completely, inevitably,
consistently), and resolute adjectives (unconditional, consummate, absolute,
open-and-shut).
Liberation: Terms describing the maximizing of individual choice
(autonomous, open-minded, options) and the rejection of social conventions
(unencumbered, radical, released). Liberation is motivated by both personality
factors (eccentric, impetuous, flighty) and political forces (suffrage, liberty,
freedom, emancipation) and may produce dramatic outcomes (exodus, riotous,
deliverance) or subdued effects (loosen, disentangle, outpouring). Liberatory
terms also admit to rival characterizations: exemption vs. loophole, elope vs.
abscond, uninhibited vs. outlandish.
Motion: Terms connoting human movement (bustle, job, lurch, leap),
physical processes (circulate, momentum, revolve, twist), journeys
(barnstorm, jaunt, wandering, travels), speed (lickety-split, nimble, zip,
whistle-stop), and modes of transit (ride, fly, glide, swim).
Numerical Terms: Any sum, date, or product specifying the facts in a given
case. This dictionary treats each isolated integer as a single "word" and each
separate group of integers as a single word. In addition, the dictionary
contains common numbers in lexical format (one, tenfold, hundred, zero) as
well as terms indicating numerical operations (subtract, divide, multiply,
percentage) and quantitative topics (digitize, tally, mathematics). The
presumption is that Numerical Terms hyper-specify a claim, thus detracting
from its universality.
Passivity: Words ranging from neutrality to inactivity. Includes terms of
compliance (allow, tame, appeasement), docility (submit, contented,
sluggish), and cessation (arrested, capitulate, refrain, yielding). Also contains
tokens of inertness (backward, immobile, silence, inhibit) and disinterest
(unconcerned, nonchalant, stoic), as well as tranquillity (quietly, sleepy,
vacation).
Present Concern: A selective list of present-tense verbs extrapolated from
C.K. Ogden's list of "general" and "picturable" terms, all of which occur with
great frequency in standard American English. The dictionary is not topicspecific but points instead to general physical activity (cough, taste, sing,
take), social operations (canvass, touch, govern, meet), and task-performance
(make, cook, print, paint).
Past Concern: The past-tense forms of the verbs contained in the Present
Concern dictionary.
Praise: Affirmations of some person, group, or abstract entity. Included are
terms isolating important social qualities (dear, delightful, witty), physical
qualities (mighty, handsome, beautiful), intellectual qualities (shrewd, bright,
vigilant, reasonable), entrepreneurial qualities (successful, conscientious,
renowned), and moral qualities (faithful, good, noble). All terms in this
dictionary are adjectives.
Rapport: This dictionary describes attitudinal similarities among groups of
people. Included are terms of affinity (congenial, camaraderie, companion),
assent (approve, vouched, warrants), deference (tolerant, willing, permission),
and identity (equivalent, resemble, consensus).
Satisfaction: Terms associated with positive affective states (cheerful,
passionate, happiness), with moments of undiminished joy (thanks, smile,
welcome) and pleasurable diversion (excited, fun, lucky), or with moments of
triumph (celebrating, pride, auspicious). Also included are words of
nurturance: healing, encourage, secure, relieved.
Self-Reference: All first-person references, including I, I'd, I'll, I'm, I've, me,
mine, my, myself. Self-references are treated as acts of "indexing" whereby
the locus of action appears to reside in the speaker and not in the world at
large (thereby implicitly acknowledging the speaker's limited vision).
Spatial Awareness: Terms referring to geographical entities, physical
distances, and modes of measurement. Included are general geographical
terms (abroad, elbow-room, locale, outdoors) as well as specific ones (Ceylon,
Kuwait, Poland). Also included are politically defined locations (county,
fatherland, municipality, ward), points on the compass (east, southwest) and
the globe (latitude, coastal, border, snowbelt), as well as terms of scale
(kilometer, map, spacious), quality (vacant, out-of-the-way, disoriented) and
change (pilgrimage, migrated, frontier.)
Temporal Awareness: Terms that fix a person, idea, or event within a
specific time-interval, thereby signaling a concern for concrete and practical
matters. The dictionary designates literal time (century, instant, mid-morning)
as well as metaphorical designations (lingering, seniority, nowadays). Also
included are calendrical terms (autumn, year-round, weekend), elliptical terms
(spontaneously, postpone, transitional), and judgmental terms (premature,
obsolete, punctual).
Tenacity: All uses of the verb "to be" (is, am, will, shall), three definitive
verb forms (has, must, do) and their variants, as well as all associated
contractions (he'll, they've, ain't). These verbs connote confidence and totality.
Source: Hart [11]
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