Types of variables

Understanding the types of variables you are investigating in your dissertation is necessary for all types of quantitative research design, whether you using an experimental, quasi-experimental, relationship-based or descriptive research design. When you carry out your dissertation, you may need to measure, manipulate and/or control the variables you are investigating. In the section on Research Designs, you can learn more about the various types of quantitative research design. In this article, we present and illustrate the different types of variables you may come across in your dissertation. First, we discuss the main groups of variables: categorical variables and continuous variables. Second, we explain what dependent and independent variables are. This will provide you with one of the foundations required to tackle a dissertation based on a quantitative research design.

Categorical and continuous variables

There are two groups of variables that you need to know about: categorical variables and continuous variables. We use the word groups of variables because both categorical and continuous variables include additional types of variable. However, there can also be some ambiguities when deciding whether a variable is categorical or continuous. We discuss the two groups of variable, as well as these potential ambiguities, in the sections that follow:

Categorical variables

Categorical variables are also known as qualitative (or discrete) variables. These categorical variables can be further classified as being nominal, dichotomous or ordinal variables. Each of these types of categorical variable (i.e., nominal, dichotomous and ordinal) has what are known as categories or levels. These categories or levels are the descriptions that you give a variable that help to explain how variables should be measured, manipulated and/or controlled. Take the following example:

Career choices of university students
You are interested in the career choices of university students. You could ask university students a number of closed questions related to their career choices. For example:

What is your planned occupation?
What is the most important factor influencing your career choice?

The first question highlights the use of categories and the second question levels. For example:

Question 1: What is your planned occupation?
Variables with categories

Architect
Attorney
Biochemist
Engineer
Dentist
Doctor
Entrepreneur
Social Worker
Teacher
ETC...

Question 2: On a scale of 1 to 5, how important are the following factors in influencing your career choice [1 = least important; 5 = most important]?
Variables with levels

Career prospects
Nature of the work
Physical working conditions
Salary and benefits
ETC...

What is important to note about the categories in question 1 and the levels in question 2 is that these will be created by you. Ideally, you will have included these categories or levels based on some primary or secondary research. Ultimately, you choose which categories or levels to include and how many categories or levels there should be.

Each of these types of categorical variable (i.e., nominal, dichotomous and ordinal) are described below with associated examples:

Nominal variables

The following are examples of nominal variables. These nominal variables could address questions like:

 Question: What is your gender? Answer: I am male (or female, bisexual, transsexual) Nominal variable: Gender Category: Male, Female, Bisexual, Transsexual

 Question: What type of property are you interested in? Answer: A house (or an apartment, or a bungalow) Nominal variable: Type of property (the customer is interested in) Category: House, Apartment, Bungalow

 Question: What is your hair colour? Answer: I have black hair (or blond, brown, red hair, etc.) Nominal variable: Hair colour Category: Black, Blond, Brown, Red, etc.

 Question: What is your blood type? Answer: I have blood type A (or B, AB, O, etc.) Nominal variable: Blood type Category: A, B, AB, O, etc.

These examples highlight two core characteristics of nominal variables:

1. Nominal variables have two or more categories.

2. Nominal variables do not have an intrinsic order.

When we talk about nominal variables not having an intrinsic order, we mean that they can only have categories (e.g., black, blond, brown and red hair); not levels (e.g., a Likert scale from 1 to 5).

Dichotomous variables

The following are examples of dichotomous variables. These dichotomous variables could address questions like:

 Question: Are you male or female? Answer: I am male (or I am female) Dichotomous variable: Sex Category: Male, Female

 Question: Do you like watching television? Answer: Yes I do (or No I don't) Dichotomous variable: Opinion about watching television Category: Yes, No

 Question: What type of property are you interested in? Answer: A residential property (or a commercial property) Dichotomous variable: Type of property the customer is interested in Category: Residential, Commercial

 Question: What is your employment status? Answer: I am employed (or I am unemployed) Dichotomous variable: Employment status Category: Employed, Unemployed

Dichotomous variables are nominal variables that have just two categories. They have a number of characteristics:

• Dichotomous variables are designed to give you an either/or response

For example, you are either male or female. You either like watching television (i.e., you answer YES) or you don't (i.e., you answer NO).

• Dichotomous variables can either be fixed or designed

For example, some variables (e.g., your sex) can only be dichotomous (i.e., you can only be male or female). They are therefore fixed. In other cases, dichotomous variables are designed by the researcher. For example, take the question: Do you like watching television? We have determined that the respondent can only select YES (i.e., I like watching television) or NO (i.e., I don't like watching television). However, another researcher could provide the respondent with more than two categories to this question (e.g., most of the time, sometimes, hardly ever). Where more than two categories are used, these variables become known as nominal variables rather than dichotomous ones.

Ordinal variables

Just like nominal variables, ordinal variables have two or more categories. However, unlike nominal variables, ordinal variables can also be ordered or ranked (i.e., they have levels). For example, take the following example of an ordinal variable:

 Question: Do you like the policies of the Democratic Party? Answer: Not very much (or They are OK, or Yes, a lot) Ordinal variable: Opinions towards Democratic Party policies Level: Not very much, They are OK, Yes, a lot

So if you asked someone if they liked the policies of the Democratic Party and you presented them with the following three categories: Not very much, They are OK, or Yes, a lot; you have an ordinal variable. Why? Because you have 3 categories ? namely Not very much, They are OK, and Yes, a lot ? and you can rank them from the most positive (Yes, a lot), to the middle response (They are OK), to the least positive (Not very much). However, whilst we can rank the three categories, we cannot place a value to them. For example, we cannot say that the response, They are OK, is twice as positive as the response, Not very much.

Other examples of ordinal variables are:

 Question: In what year did you start university? Answer: I started in 2006 (or 2007, 2008, 2009, 2010) Ordinal variable: Year of university entry Level: 2006, 2007, 2008, 2009, 2010

 Question: Do you like watching television? Answer: Most of the time, sometimes or hardly ever) Ordinal variable: Opinion about watching television Level: Most of the time, Sometimes, Hardly ever

 Question: To what extent do you agree or disagree with the following statement: Going to university is important to get a good job [based on a 5-point Likert scale of 1 = strongly agree, 2 = agree, 3 = neither agree nor disagree, 4 = disagree, 5 = strongly disagree] Answer: 2 = I agree (or 1, 3, 4 or 5 on the 5-point Likert scale) Ordinal variable: The importance of university to getting a good job Level: 1 = strongly agree, 2 = agree, 3 = neither agree nor disagree, 4 = disagree, 5 = strongly disagree

When it comes to Likert scales, as highlighted in the previous example, there can be some disagreement over whether these should be considered ordinal variables or continuous variables [see the section: Ambiguities in classifying variables].

Continuous variables

Continuous variables, which are also known as quantitative variables, can be further classified a being either interval or ratio variables. Each of these types of continuous variable (i.e., interval and ratio) has numerical properties. These numerical properties are the values by which continuous variables can be measured, manipulated and/or controlled. We illustrate the two types of continuous variable (i.e., interval and ratio) and some associated values in the sections that follow:

Interval variables

Interval variables have a numerical value and can be measured along a continuum. Some examples of interval variables are:

 Interval variable: Temperature (measured in degrees Celsius or Fahrenheit) Explanation: The difference between 20C and 30C is the same as 30C to 40C

However, temperature measured in degrees Celsius or Fahrenheit is NOT a ratio variable. This is because temperature measured in degrees Celsius or Fahrenheit is not a ratio variable because 0C does not mean there is no temperature.

Ratio variables

Ratio variables are interval variables that meet an additional condition: a measurement value of 0 (zero) must mean that there is none of that variable. Some examples of ratio variables are:

 Ratio variable: Temperature measured in Kelvin Explanation: 0 Kelvin, often called absolute zero, indicates that there is no temperature whatsoever. A temperature of 10 Kelvin is four times the temperature of 2.5 Kelvin

 Ratio variable: Distance Explanation: If two houses are joined together (e.g., terraced housing), the distance between the adjoining walls is 0 (i.e., there is no distance whatsoever). On the other hand, a distance of 10 meters between the houses would be twice the distance of a 5 meter gap between the houses (i.e., a distance of 10 metres is twice the distance of 5 metres).

 Other ratio variables: Height, mass/weight, etc.

Ambiguities in classifying variables

Sometimes, the measurement scale for data is ordinal, but the variable is treated as though it were continuous. This is more often the case when using Likert scales. When a Likert scale has five values (e.g., strongly agree, agree, neither agree nor disagree, disagree, and strongly disagree), it is treated as an ordinal variable. However, when a Likert scale has seven or more values (e.g., strongly agree, moderately agree, agree, neither agree nor disagree, disagree, moderately disagree, and strongly disagree), the variable is sometimes treated as a continuous variable. Nonetheless, this is a matter of dispute. Some researchers would argue that a Likert scale should never be treated as a continuous variable, even with seven levels/values.

Since you are responsible for setting the measurement scale for a variable, you will need to think carefully about how you characterise a variable. For example, social scientists may be more likely to consider the variable gender to be a nominal variable. This is because they view gender as having a number of categories, including male, female, bisexual and transsexual. By contrast, other researchers may simply view gender as a dichotomous variable, having just two categories: male and female. In such cases, it may be better to refer to the variable gender as sex.

Dependent and independent variables

A variable is not only something that you measure, but also something that you can manipulate and control for. An independent variable (sometimes called an experimental or predictor variable) is a variable that is being manipulated in an experiment in order to observe the effect this has on a dependent variable (sometimes called an outcome variable). The dependent variable is simply that; a variable that is dependent on an independent variable(s). We discuss these concepts in the example below:

For example:
Imagine that a tutor asks 100 students to complete a maths test. The tutor wants to know why some students perform better than others. Whilst the tutor does not know the answer to this, she thinks that it might be because of two reasons:

1. Some students spend more time revising for their test; and

2. Some students are naturally more intelligent than others.

Therefore, the tutor decides to investigate the effect of revision time and intelligence on the test performance of the 100 students. As such, the dependent and independent variables for the study are:

 Dependent Variable: Test Mark (measured from 0 to 100) Independent Variables: Revision time (measured in hours) Intelligence (measured using IQ score)

The dependent variable is simply that; a variable that is dependent on an independent variable(s). In our case, the test mark (i.e. the dependent variable) that a student achieves is dependent on revision time and intelligence (i.e., the independent variables). Whilst revision time and intelligence (i.e., independent variables) may (or may not) cause a change in the test mark (i.e., the dependent variable), the reverse is implausible. In other words, whilst the number of hours a student spends revising and the higher a student's IQ score may (or may not) change the test mark that a student achieves, a change in a student's test mark has no bearing on whether a student revises more or is more intelligent. This would not make any sense.

Therefore, the aim of the tutor's investigation is to examine whether these independent variables (i.e., revision time and IQ) result in a change in the dependent variable (i.e., the students' test scores). However, it is also worth noting that whilst this is the main aim of the experiment, the tutor may also be interested to know if the independent variables (i.e., revision time and IQ) are also connected in some way.

You can find out more about the different uses of variables, especially in quantitative research designs (i.e., descriptive, experimental, quasi-experimental and relationship-based research designs), in the section on Research Designs.