Criteria for Courses which Satisfy the Quantitative Skills Requirement
Adopted Fall, 1997
I. As stated in the policy concerning Quantitative Skills (adopted by the
College Faculty on February 4, 1997), a course which satisfies the
Quantitative Skills Requirement should use mathematical or statistical
analysis of quantitative data as an important approach for understanding
another area of knowledge. Students in these courses should:
a. learn how to summarize quantitative data (e.g. in graphical or tabular
forms) and learn how to interpret summaries of quantitative data
b. understand concepts of random variability and an elementary level of
statistical analysis of data
c. develop sophistication in generating alternative hypotheses and learn
how to analyze quantitative data to evaluate alternative hypotheses
d. actively analyze quantitative data (e.g., through work with data sets),
using the skills outlined above, as well as other relevant skills
e. critically evaluate reports of inference from quantitative data by others.
Courses that fulfill the Quantitative Requirement would not necessarily
include all of these characteristics, but would include a substantial
component of quantitative analysis both in class time and in assignments
II. In order to specify these general criteria, the Quantitative Skills
Committee has decided that a course which satisfies the Quantitative Skills
Requirement MUST INCLUDE the following.
(A) All students must actively analyze and interpret quantitative data.
(B) At least some of the quantitative analysis in the course must involve
analysis of data sets and interpretation of the results of these analyses to
evaluate hypotheses and/or to understand phenomena in the real world.
(C) At least some of the quantitative analyses in the course must involve
approaches more sophisticated than simple cross-tabulations (i. e., counting
cases in various categories and calculating percentages).
In some courses it may be appropriate for the primary emphasis to be on
methods of quantitative analysis, with relatively less emphasis on the
interpretation of quantitative data. In other courses it may be appropriate
for the primary emphasis to be on the interpretation of quantitative data,
and most of the quantitative analyses may use basic skills, such as
summarizing data in tables and graphs. Strengths in one area can balance
limitations in another area. However, a course which satisfies the
Quantitative Skills Requirement must meet the minimum criteria listed in
points A-C above.
III. It is desirable, but not required, for a course which satisfies the
Quantitative Skills Requirement to include more in depth coverage of varied
aspects of the analysis and interpretation of quantitative data. Desirable
components include the following.
(A) Students should learn how to carry out more sophisticated quantitative
or statistical analyses of data and should understand the theoretical bases
for these quantitative
and statistical analyses. Relevant types of analyses include:
* statistical analyses, such as regression analyses
* quantitative modeling
* quantitative decision analysis or signal detection analysis
(B) Students should learn how to formulate a research question or
hypothesis and learn how to identify whether quantitative data are
appropriate to answer the question or test the hypothesis and, if so, what
type of quantitative data are needed. Students should learn how to
critically evaluate reports of inference from quantitative data. Attention
should be given to issues such as:
* making inferences about cause and effect from correlational data;
* deciding on appropriate control groups for an experiment;
* deciding on an appropriate sample size;
* deciding whether an apparent result could be due to random variation;
* distinguishing statistical significance from practical significance;
* deciding whether conditions of some sort of analysis are met (e.g., whether a
linear approximation is adequate);
*comparing alternative explanations of data by making inferences about the fit
of alternative models;
* deciding on an appropriate measure of the size of an effect.
* assessing the accuracy of estimation of parameters;
* distinguishing between post-hoc and planned analyses;
A course may satisfy the Quantitative Skills Requirement even if it does not
include all or most of the components listed in this section, provided that
the course devotes substantial attention to analysis of quantitative data,
both in class time and in assignments and/or exams.