GENERAL SOCIAL SURVEY ANALYSIS

General Social Survey Analysis



General Social Survey Analysis

a) Nominal, one ordinal and one interval/ratio level variable

Variables can be measured at different levels of precision.  Various statistics have been invented to deal with each level of measurement.  In order to choose the proper statistics to examine data, we first have to figure out at what level each variable is measured. (Hacking 1990 pp.89-91)

Variable chosen

In the GSS 2002, the variable “Adults” is the Nominal level variable., “RS UNDERSTANDING OF QUESTIONS” is the ordinal level variable and “NUMBER OF PEOPLE IN INFORMANT'S HOUSEHOLD” is the interval/ratio level.

The Nominal Level

The least precise level of measurement is the nominal level.  (The word "nominal" means "in name.").  Examples of nominal-level variables are Sex (with the categories of male and female), Ethnicity (categories could include African American, Latino, and white), Political Party Identification (Democrat, Republican, Independent, etc.) and Religion (Catholic, Protestant, Jewish, Hindu, Buddhist, etc.) (Moses 1986 pp. 15 - 29).  The categories of these variables are just names for the pigeonholes we can create when we classify people by sex, ethnicity, or religion. The categories don't have any particular order from more to less or higher to lower.  That is, someone in the category Latino does not have more or less "ethnicity" than an African American or a white, just a different ethnicity.  A Republican does not have more or less "party identification" than a Democrat.  A Catholic does not have more or less "religion" than a Protestant or a Jew.  And a woman does not have more or less "sex" than a man.  Since there is no order inherent in the categories, we treat these variables as nominal-level.  

A common practice among statistical analysts is to convert nominal-level variables to what are called dummy variables so they can be used as if they were ratio-level. (Anderson Sweeney Williams pp. 55 - 67)  A dummy variable has two categories, one of which is coded 0 and the other is coded 1.  (Dummy variables are used only as independent variables, not as dependent variables.)   For instance, with the variable "Sex," instead of coding males as 1 and females as 2 (or the other way around), we could code males as 0 and females as 1(Lindley 1985 Pp.76-81). 

The Ordinal Level

If I had a variable whose categories did have an order, I might have an ordinal-level variable (assuming the categories meet none of the additional criteria).  (The word "ordinal" means "in order.")  An example would be the variable "fear of crime" with categories such as very afraid, somewhat afraid, and not afraid.  These categories have names (as does a nominal-level variable), but they also have something more.  The categories have an inherent order from more to less fear.  Another example is "social class," with categories such as lower class, working class, middle class, and upper class. Almost any method of measuring attitudes results in ordinal-level variables, even if the variables include only two categories. For example, I could categorize the variable "attitudes toward capital punishment" into those ...