Todd Murphy inquired:

**Q. **1) I'm planning to test how race affects attitude toward
abortion, seeing if the gap between whites and black is closing. I
would be controlling for religiosity, gender, and socioeconomic
status (income and education level). All of these are discrete
variables, which according to our class today means I cannot use
regression.

A.Not quite. The dictionary definition of "discrete" is "separate and distinct." So a discrete variable, as contrasted with a continuous variable, has distinct categories. A discrete variable with only two categories is adichotomy. If it has more than two, it is apolychotomy.Discrete variables also are of two types:

unorderable(also called nominal variables) andorderable(also called ordinal).A dichotomy (such as gender) can always be interpreted as ordinal. (For example, one respondent has more "femaleness" than another.) So a dichotomy can be used in regression analysis, but only as a

independentvariable. As an independent variable, a dichotomy can be best interpreted when converted into adummyvariable--coded 0 (no femaleness) or 1 (has femaleness). Recall that I usedsouthin exactly this way in my regression analysis of how states votes for Reagan in 1984.Moreover,

orderedpolychotomies (ordinal variables) can be used in regression analysisprovidedthat you assume (as is usually done) that there are equal intervals between the discrete categories. This assumption allows you to compute a mean, from which all blessings (i.e., squared deviations) flow.

**Q. **However, in the research paper handout you mention using
"thermometer" variables for regression analysis. Aren't these
thermometer variables also discrete? It seems that even in your
example today "South" and "Non-South" are discrete even after you
convert them to dummy variables (1 and 0). Did you mean that we
cannot use nominal variables?

A.Yes, the thermometer variables are discrete, but they are orderable, which makes them suitable for regression analysis, either as independent or dependent variables. The key attribute is ordinality (magnitude).

**Q. **2) If I cannot use multiple regression, can I use
Crosstabs or Chi-square instead? In other words will it affect my
grade if I stick with what I have and use these methods or should I
change my question so that I can use multiple regression? For
example, I could investigate how abortion attitudes affect voting and
convert all the independent variables to dummy variables.

A.You could use CROSSTABS, but you won't learn as much. Use regression instead.