Age can be both nominal and ordinal data depending on the question types. I.e "How old are you" is a used to collect nominal data while "Are you the first born or What position are you in your family" is used to collect ordinal data. Age becomes ordinal data when there's some sort of order to it.
Age is frequently collected as ratio data, but can also be collected as ordinal data. This happens on surveys when they ask, “What age group do you fall in?” There, you wouldn't have data on your respondent's individual ages – you'd only know how many were between 18-24, 25-34, etc.
Likert items may be regarded as true ordinal scale, but they are often used as numeric and we can compute their mean or SD. This is often done in attitude surveys, although it is wise to report both mean/SD and % of response in, e.g. the two highest categories.
Interval data is like ordinal except we can say the intervals between each value are equally split. The most common example is temperature in degrees Fahrenheit.
A dichotomous variable is one that takes on one of only two possible values when observed or measured. The value is most often a representation for a measured variable (e.g., age: under 65/65 and over) or an attribute (e.g., gender: male/female).
A closed question where there can be only two answers, commonly 'yes' or 'no' . This type of questioning may be used in questionnaires during focus grouping or other market research.
An experiment usually has three kinds of variables: independent, dependent, and controlled. The independent variable is the one that is changed by the scientist.
Dichotomous variables are categorical variables with two levels. These could include yes/no, high/low, or male/female. To remember this, think di = two. Ordinal variables have two are more categories that can be ordered or ranked.
There are six common variable types:
- DEPENDENT VARIABLES.
- INDEPENDENT VARIABLES.
- INTERVENING VARIABLES.
- MODERATOR VARIABLES.
- CONTROL VARIABLES.
- EXTRANEOUS VARIABLES.
Binary variables are a sub-type of dichotomous variable; variables assigned either a 0 or a 1 are said to be in a binary state. For example Male (0) and female (1). Dichotomous variables can be further described as either a discrete dichotomous variable or a continuous dichotomous variable.
Nominal: Unordered categorical variables. These can be either binary (only two categories, like gender: male or female) or multinomial (more than two categories, like marital status: married, divorced, never married, widowed, separated). The key thing here is that there is no logical order to the categories.
There are three general classifications of variables: 1) Discrete Variables: variables that assume only a finite number of values, for example, race categorized as non-Hispanic white, Hispanic, black, Asian, other. Dichotomous variables. Categorical variables (or nominal variables)
qualitative) Data that represent categories, such as dichotomous (two categories) and nominal (more than two categories) observations, are collectively called categorical (qualitative). Data that are counted or measured using a numerically defined method are called numerical (quantitative).
A dummy variable (binary variable) D is a variable that takes on the value 0 or 1. • Examples: EU member (D = 1 if EU member, 0 otherwise), brand (D = 1 if product has a particular brand, 0 otherwise), gender (D = 1 if male, 0 otherwise)
One method of converting numbers stored as strings into numerical variables is to use a string function called real that translates numeric values stored as strings into numeric values Stata can recognize as such.
For clarity, a dichotomous variable is defined as a variable that splits or groups data into 2 distinct categories. An example would be employed and unemployed. This process is known as “dummy coding.” IBM SPSS makes dummy coding an unpretentious practice. Let's walk through the steps!
In terms of the title question: can, yes; should, no. Standardizing binary variables does not make any sense. The values are arbitrary; they don't mean anything in and of themselves. There may be a rationale for choosing some values like 0 & 1, with respect to numerical stability issues, but that's it.
Dummy variables are often used in multiple linear regression (MLR). There is some redundancy in this dummy coding. For instance, in this simplified data set, if we know that someone is not Christian and not Muslim, then they are Atheist. So we only need to use two of these three dummy-coded variables as predictors.
Categorical variables are also called qualitative
variables or attribute
variables. The values of a
categorical variable are mutually exclusive categories or groups.
Examples of categorical variables.
| Data type | Examples |
|---|
| Date/time | Days of the week (Monday, Tuesday, Wednesday) Months of the year (January, February, March) |
The coefficient on a dummy variable with a log-transformed Y variable is interpreted as the percentage change in Y associated with having the dummy variable characteristic relative to the omitted category, with all other included X variables held fixed.
Video on Dummy Variable Regression in RNote that in the video, Mike Marin allows R to create the dummy variables automatically. You can do that as well, but as Mike points out, R automatically assigns the reference category, and its automatic choice may not be the group you wish to use as the reference.
In linear regression the independent variables can be categorical and/or continuous. But, when you fit the model if you have more than two category in the categorical independent variable make sure you are creating dummy variables.
Data at the nominal level of measurement are qualitative. Data at the ordinal level of measurement are quantitative or qualitative. They can be arranged in order (ranked), but differences between entries are not meaningful.
Nominal and ordinal are two of the four levels of measurement. Nominal level data can only be classified, while ordinal level data can be classified and ordered.
