- Is age nominal or ordinal in SPSS?
- What are the 5 types of variables?
- Is yes or no nominal or ordinal?
- Is age categorical or numerical?
- What is ordinal data type?
- What type of variable is yes or no?
- What kind of variable is age?
- What type of data is age?
- Is yes or no discrete or continuous?
- What type of variable is eye color?
- Is data nominal or ordinal?
- What level of measurement is yes or no?
- Is ordinal qualitative or quantitative?
- What are the 3 types of variables?

## Is age nominal or ordinal in SPSS?

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..

## What are the 5 types of variables?

There are six common variable types:DEPENDENT VARIABLES.INDEPENDENT VARIABLES.INTERVENING VARIABLES.MODERATOR VARIABLES.CONTROL VARIABLES.EXTRANEOUS VARIABLES.

## Is yes or no nominal or ordinal?

In research activities a YES/NO scale is nominal. It has no order and there is no distance between YES and NO. There are also highly sophisticated modelling techniques available for nominal data. An ordinal scale is next up the list in terms of power of measurement.

## Is age categorical or numerical?

Quantitative variables take numerical values and represent some kind of measurement. In our medical example, age is an example of a quantitative variable because it can take on multiple numerical values. It also makes sense to think about it in numerical form; that is, a person can be 18 years old or 80 years old.

## What is ordinal data type?

Ordinal data is a statistical type of quantitative data in which variables exist in naturally occurring ordered categories. The distance between two categories is not established using ordinal data.

## What type of variable is yes or no?

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.

## What kind of variable is age?

Numerical variablesIn statistics, there are broadly 2 types of variables: Numerical variables: Numbers which should be treated as they usually are in mathematics. For example, age and weight would be considered numerical variables, while phone number and ZIP code would not be considered numerical variables.

## What type of data is age?

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.

## Is yes or no discrete or continuous?

Discrete data can be further sub-divided into three categories: binary, nominal and ordinal. Binary Data: A binary data only takes on two possible values. For example, lamp is on or lamp is off, answer is true or false, 0 or 1, yes or no etc.

## What type of variable is eye color?

nominalCertainly, eye color is a nominal variable, since it is multi-valued (blue, green, brown, grey, pink, black), and there is no clear scale on which to fit the different values.

## Is data nominal or ordinal?

In statistics, nominal data (also known as nominal scale) is a type of data that is used to label variables without providing any quantitative value. … Unlike ordinal data. One of the most notable features of ordinal data is that, nominal data cannot be ordered and cannot be measured.

## What level of measurement is yes or no?

NominalNominal Scale Level Data that is measured using a nominal scale is qualitative. Categories, colors, names, labels and favorite foods along with yes or no responses are examples of nominal level data.

## Is ordinal qualitative or quantitative?

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. Data at the interval level of measurement are quantitative. They can be ordered, and meaningful differences between data entries can be calculated.

## What are the 3 types of variables?

There are three main variables: independent variable, dependent variable and controlled variables.