To gather information about plant responses over time, you can fill out the same data sheet every few days until the end of the experiment. This example sheet is color-coded according to the type of variable: nominal , continuous , ordinal , and binary.
Experiments are usually designed to find out what effect one variable has on another — in our example, the effect of salt addition on plant growth.
You manipulate the independent variable the one you think might be the cause and then measure the dependent variable the one you think might be the effect to find out what this effect might be. You will probably also have variables that you hold constant control variables in order to focus on your experimental treatment. In this experiment, we have one independent and three dependent variables. However, there might be cases where one variable clearly precedes the other for example, rainfall leads to mud, rather than the other way around.
In these cases you may call the preceding variable i. Scribbr editors not only correct grammar and spelling mistakes, but also strengthen your writing by making sure your paper is free of vague language, redundant words and awkward phrasing.
See editing example. Once you have defined your independent and dependent variables and determined whether they are categorical or quantitative, you will be able to choose the correct statistical test. But there are many other ways of describing variables that help with interpreting your results.
Some useful types of variable are listed below. You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect. In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:.
Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design. A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.
A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable. Quantitative variables are any variables where the data represent amounts e.
Categorical variables are any variables where the data represent groups. This includes rankings e. You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results. Discrete and continuous variables are two types of quantitative variables :.
Have a language expert improve your writing. 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, 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. We illustrate the two types of continuous variable i. Interval variables have a numerical value and can be measured along a continuum. Some examples of interval variables are:. 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 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:. 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. However, when a Likert scale has seven or more values e. Nonetheless, this is a matter of dispute.
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. 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 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:.
Therefore, the tutor decides to investigate the effect of revision time and intelligence on the test performance of the students.
As such, the dependent and independent variables for the study are:. In our case, the test mark i. Whilst revision time and intelligence i. 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. 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.
You can find out more about the different uses of variables, especially in quantitative research designs i. 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.
Categorical and continuous variables Dependent and independent variables Ambiguities in classifying variables.
Categorical and continuous variables There are two groups of variables that you need to know about: categorical variables and continuous variables. Here is an example:. This statement creates a variable named homerun and assigns it the value 6. When Python Interpreter encounter statements like this it does the following things behind the scenes. The important thing to understand here is that variable homerun itself doesn't contain any value, it only refers to a memory location which contains the actual value.
In fact, it a Syntax error if you try to do so. If you don't do this you will get a syntax error as follows:. Python automatically detects the type of the variable and operations that can be performed on it based on the type of the value it contains.
In programming jargon this behavior is known as Dynamic Typing. It means that we could use the same variable to refer to a data of completely different type than it initially points to. For example:. When we assign a new value to a variable the reference to old value is lost. For example, when "now" is assigned to the homerun , the reference to value 6 is lost. At this point, no variable is pointing to that memory location. When this happens Python Interpreter automatically removes the value from the memory through a process known as garbage collection.
If you try to access a variable before assigning any value to it. You will get NameError error like this:. Python is case-sensitive language which means that HOME and home are two different variables.
Python Keywords are words that denote something specific in a Python language. That's why, we are not allowed to use them as variables names. Here is the list of Python keywords:. Comments are used to add notes to a program. In a large program, comments may describe the purpose of the program and how it works. They are solely intended for the people who are trying to understand the source code of the program. They are not programming statements so they are ignored by the Python Interpreter while executing the program.
In Python, everything from to end of the line is considered a comment. Constants are variables whose values do not change during the lifetime of the program.
Unlike languages like C or Java; Python doesn't have a special syntax to create constants. We create constants just like ordinary variables. However, to separate them from an ordinary variable, we use all uppercase letters. It has no other special properties. We can also use print statement to print multiple items in a single call by separating each item by a comma ,. When multiple arguments are passed to print function, they are printed to the console separated by spaces. We can assign values to multiple variables at once using simultaneous assignment which has the following syntax:.
0コメント