+13236381128
+15199002991
+61480040096
Book a Demo
en_US English
en_US English
+13236381128
+15199002991
+61480040096
en_US English
Correlation vs Causation2

Dive Deep: Understanding Causation in Modern Research

SHARE THE ARTICLE ON

Table of Contents

What is Causation?

Causation is an important and widely used term in research, and it refers to the phenomenon that causes a change in a second event or action. In research, when we say two variables have a causal relationship (or a cause-and-effect relationship), we mean that a change in one variable (known as the independent variable) causes a change in the other (the dependent variable).

Transform your insight generation process

Create an actionable feedback collection process.

Download Now
online survey
Read More

Causal Research

Causal research, also referred to as explanatory research, is used to identify the extent of the cause-and-effect relationship between two variables. Experiments are one of the most popular methods of carrying out causal research through primary data. 

There are three integral components to causal relationships: 

  1. Temporal Sequence: The cause must take place before the effect. 
  2. Nonspurious Association: The covariation between the causal relationship must be true and not due to an intervening or unaccounted variable that influences the relationship. 
  3. Concomitant Variation: The variation between two variables must be systematic, and must therefore occur or vary together. 
Start collecting insights at scale!

Correlation vs Causation

Although causation and correlation can exist at the same time, it is important to note that correlation does not imply causation. Correlation simply means that there is a statistical association or pattern between two variables, while causation not only implies a specific kind of relationship, known as a cause-and-effect relationship. This means that a change in one variable is causing a change in the other.  

There are two main reasons why correlation does not imply causation: 

  • Existence of a Confounding Variable: A confounding variable is a third variable that affects both the variables, making it seem as though they are causally related even though they are not. In this case, the statistical association is due to the third variable rather than due to the existence of a cause-and-effect relationship.
  • Directionality Problem: Sometimes two variables may indeed have a cause-and-effect relationship, however, it isn’t possible to determine which variable is the cause (independent variable) and which is the effect (dependent variable).

Download Market Research Toolkit

Get market research trends guide, Online Surveys guide, Agile Market Research Guide & 5 Market research Template

Download Now
Making the most of your B2B market research in 2021 PDF 3 s 1.png

The Advantages and Disadvantages of Conducting Causal Research

Advantages 

  • Can be Replicated: Causal research facilitates the creation of repeatable processes. 
  • High Levels of Internal Validity: Causal research is often conducted using true experiments and is therefore carried out very systematically. This provides it with higher levels of internal validity. 
  • Can be Used to Assess Impacts/Outcomes: Causal research is a useful tool for impact evaluation as it can be used to study the outcome of changes in existing methods and processes. 

Disadvantages 

  • Difficult to Execute: Causal research is often tedious to execute, especially in uncontrolled environments, as it is impossible to account for or control all the extraneous variables. 
  • Expensive to Conduct: Causal research is one of the most expensive types of research to conduct as it requires a lot of time and plenty of resources. 
  • Directionality Issues: Causal research is not always successful in conclusion which variable is the dependent variable and which is the independent one. 

See Voxco survey software in action with a Free demo.

Book a Free Demo

FAQs on Causation

Causation occurs when a change in one variable (the independent variable) leads to a change in the other (the dependent variable).

Correlation between two variables simply implies a statistical association between the two. Causation, on the other hand, implies not only that the two are related, but that one causes a change in the other.

Correlation does not always imply causation for the following reasons;

  • The existence of a confounding variable (third variable) may be causing the statistical association between the variables making them seem causally related. 
  • Directionality issues may make it impossible to determine which variable is the independent variable (cause) and which is the dependent variable (effect).

Causal research involves the investigation of the relationship between two variables, dependent and independent.

The relationship between two variables can only be a causal one if it satisfies the three following conditions;

  • Nonspurious Association: Relationship must not be due to a third variable. 
  • Concomitant Variation: Variations must occur together. 
  • Temporal Sequence: Cause must occur before effect.

Net Promoter®, NPS®, NPS Prism®, and the NPS-related emoticons are registered trademarks of Bain & Company, Inc., Satmetrix Systems, Inc., and Fred Reichheld. Net Promoter Score℠ and Net Promoter System℠ are service marks of Bain & Company, Inc., Satmetrix Systems, Inc., and Fred Reichheld.

Explore all the survey question types
possible on Voxco

Try a Sample Survey

Explore Voxco Survey Software

Online page new product image3 02.png 1

+ Omnichannel Survey Software 

+ Online Survey Software 

+ CATI Survey Software 

+ IVR Survey Software 

+ Market Research Tool

+ Customer Experience Tool 

+ Product Experience Software 

+ Enterprise Survey Software 

Get a Free Demo

Read more