Non experimental design1

Non-experimental design


Table of Contents

What is non-experimental design?

While working with non-experimental design, researchers do not evaluate the independent variable. So what do we understand from this? It is clearly a major difference between the experimental design  and non-experimental design. Meaning, unlike the experimental research design, non-experimental design does not progress on the grounds of independent variables, dependent variables or their cause-effect relationships. 

The non-experimental study totally depends on the variables that are out of the scope of the researcher’s control. They cannot control, manipulate or alter the subjects by any means. So that leaves them with just keep observing and interpreting their subjects along the research. 

Hence, it will be safe to say that the researchers use non-experimental research design when they do not have any particular cause-effect research problem at hand and they just want to understand a topic in depth without bounding it with variables. They study the matter naturally as they occur.

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Difference between experimental design and non-experimental design?

Just to make it more clear how both the methods are different from each other, let us take a look at a tabular representation to understand it more neatly:

Experimental Design

Non-experimental Design

Uses a scientific approach to manipulate variables.

Does not involve the manipulation of variables. 

Researchers can control the variables.

Researchers have no control over the variables/

Experimental research is usually quantitative.

Non-experimental research can be more quantitative and qualitative.

Independent variables can be changed.

Independent variables are generally not involved, but if they are, they cannot be manipulated.

There is tampering with natural setting.

These experiments are carried out in natural setting, hence not tampering is done.

Focuses on cause-effect of two variables.

They may not provide any information about the causal factors in the study.

Answers questions regarding the “WHY” of the study.

As it is more descriptive, it answers the “WHAT”s of the study.

Mostly used to get to scientific innovations.

Used to define a subject, compare situations and measure data trends. 

Are conducted in unnatural settings.

They are conducted in natural settings. 

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Given the differences, your research question will tell you which approach to use. If you have to explain the relationship between two things in your research question, then experimental design is more suitable. But if you have to predict or explain something deeply, then go for non-experimental design. 

Although, both of the approaches can be used for the same research question, if the problem looks something like “a researcher wants to know “what are the factors from today’s educational system that affects the children not being able to ace their practical lives as well.” A researcher may want to compare two variables and study their cause-effect relationships. Or the researcher may want to just observe those factors and how they affect the children in their natural environment. 

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When to use the non-experimental design?

Want to know the right time to use a non-experimental design? Here are some factors that will hint you to better turn to a non-experimental research design:

  • When the research question is just about exploring one variable and not anything about the relationship between two variables.
  • When the variables (provided there are more than one) in the research question do not have any cause-effect relationships. 
  • When there is a cause-effect relationship but the independent variable is out of manipulation or the participants cannot be assigned randomly.
  • When the research question is focused more on how it is like to have a particular experience.
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What are the examples of non-experimental design?

Based on the above scenarios, let us look at some examples for each of them:

Example 1: How practical is the current education system?

Here, you just have one variable “current education system” and you have to understand it to determine how practical it is for the kids.

Example 2: Is there a relationship between the current education system and children’s ability to deal with the practicality of life?

As you can see, there exist two variables, BUT the research questions are not concerning how either one of them affects the other. 

Example 3: Does a dyslexic child find it hard to deal with its practical life?

Here, there are both independent and dependent variables, but neither can we manipulate the independent variable (a dyslexic child), nor can we assign random children to the experiment. 

Example 4: What is it like to be a dyslexic child in the current education system?

Here, the researcher is only concerned about what a dyslexic child feels and goes through while adjusting to the currently fast running education system.

Types of non-experimental design

There are three types of non-experimental design, namely:

  • Cross-sectional research

It studies the experiment by comparing two already existing groups. This comparison is done at the same time. As it is a non-experimental design, it does not involve the manipulation of independent variables and does not assign participants randomly. 

Example: we want to compare the IQs of students who scored below 60% in exams with those who scored more than 60%. We will start the study on both of them at the same time. Also, we cannot randomly assign the students to their respective groups. Also, we will not consider changing the independent variable as we just want to get to know their IQs. 

Cross-sectional research can be further divided into:

Descriptive- observation of values in presence of one or more variables.

Causal- to explain the relationship between the existing various variables. 

  • Correlational research

It is the most commonly used type of non-experimental design in the psychology field. It majorly focuses on the statistical relationships between variables and does not manipulate the independent variable. The researchers try to study the variables without controlling them and result in the relationship between them. 

Example: a researcher is interested in the relationship between a student’s reading habits and their concentration. This will need the researcher to gather the details about the reading habits among students and their ability to concentrate on a certain thing. This doesn’t involve manipulating any of the variables but just observing them and getting to the results. 

Both correlation research can be used in exchange for the cross-sectional research, but the difference is cross-section compares two pre-existing groups, whereas correlational research compares two continuous variables and no groups. 

  • Observational research

It focuses on observing the behaviour of the subject in its natural or laboratory setting. And obviously, it does not manipulate the variables as it just observes them. 

Example: a doctor observes the patient after his surgery. 

In this case, the doctor does not perform any medications or operations on the patient while in the observation phase. As most of the observational studies are qualitative, they are descriptive. 

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Explore all the survey question types possible on Voxco

Advantages and disadvantages of non-experimental design?


Let us look at some characteristics of non-experimental design which makes it the best choice when it comes to researching (obviously only after looking out for the above factors);

  • It helps when the studies happen in the past, but they are analysed later. 
  • The main factors that should be considered during carrying out any experiments are ethics and morals. The on-experimental approach takes both of them into consideration. 
  • There is no need to generate samples for the population as the participants grow and develop in their environments.
  • The researchers cannot control the experiment variables.
  • This study progresses as the experiment occurs naturally. 


All that being said, let us look at some of the cons of non-experimental design;

  • The groups may not be representing the entire population. 
  • Biases can happen due to errors in methodology. 

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