Which of the following may be an example of an ordinal variable measurement

Ordinal variables are variables that are categorized in an ordered format, so that the different categories can be ranked from smallest to largest or from less to more on a particular characteristic. Examples of ordinal variables include educational degree earned (e.g., ranging from no high school degree to advanced degree) or employment status (unemployed, employed part-time, employed full-time). Numeric variables that are presented in categories or ranges are also considered ordinal as it is not possible to perform mathematical functions on the grouped numbers. Examples of this type of ordinal variable include age ranges (<18, 19-34, >35) or income presented in ranges (<$20k, $20k-50k, >$50k). The examination of statistical relationships between ordinal variables most commonly uses crosstabulation (also known as contingency or bivariate tables).  Chi Square tests-of-independence are widely used to assess relationships between two independent nominal variables.

Questions answered:

Does a relationship exist between income level and highest degree earned?

Is there an association between BMI scales and height categories?

Table 2. Multiple response set iconsMultiple response set typeIconMultiple response set, multiple categories

Measurement level

A variable's measurement level is important when you create a chart. Following is a description of the measurement levels. You can temporarily change the measurement level in the Chart Builder by right-clicking the variable in the Variables list and choosing an option. You can also permanently change a variable's measurement level in the Variable View of the Data Editor. See the topic for more information.

Categorical. Data with a limited number of distinct values or categories (for example, gender or religion). Categorical variables can be string (alphanumeric) or numeric variables that use numeric codes to represent categories (for example, 0 = male and 1 = female). Also referred to as qualitative data. Categorical variables can be either nominal or ordinal

• Nominal. A variable can be treated as nominal when its values represent categories with no intrinsic ranking (for example, the department of the company in which an employee works). Examples of nominal variables include region, postal code, and religious affiliation.
• Ordinal. A variable can be treated as ordinal when its values represent categories with some intrinsic ranking (for example, levels of service satisfaction from highly dissatisfied to highly satisfied). Examples of ordinal variables include attitude scores representing degree of satisfaction or confidence and preference rating scores.

Scale. Data measured on an interval or ratio scale, where the data values indicate both the order of values and the distance between values. For example, a salary of $72,195 is higher than a salary of $52,398, and the distance between the two values is $19,797. Also referred to as quantitative or continuous data.

Categorical variables define categories in the chart, typically to draw separate graphic elements or to group graphic elements. Scale variables are often summarized within categories of categorical variables. For example, a default chart of income for gender categories would display the mean income for males and the mean income for females. The raw values for scale variables can also be plotted, as in a scatterplot. For example, a scatterplot may show the current salary and beginning salary for each case. A categorical variable could be used to group the cases by gender.

Defined categories and labels

A variable's defined categories are displayed in the Categories list and on the canvas when you use the categorical variable in a chart. If the variable has no defined categories, the canvas pane will display two placeholder categories: Category 1 and Category 2.

The defined categories displayed in the Chart Builder are based on value labels, descriptive labels assigned to different data values (for example, numeric values of 0 and 1, with value labels of male and female). You can define value labels in Variable View of the Data Editor or with Define Variable Properties on the Data menu in the Data Editor window.

Multiple Response Sets

Custom Tables and the Chart Builder support a special kind of "variable" called a multiple response set. Multiple response sets aren't really "variables" in the normal sense. You can't see them in the Data Editor, and other procedures don't recognize them. Multiple response sets use multiple variables to record responses to questions where the respondent can give more than one answer. Multiple response sets are treated like categorical variables, and most of the things you can do with categorical variables, you can also do with multiple response sets.