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 and/or exams.

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.

 

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