In what ways would you measure causality in research? select all that apply.

By Indeed Editorial Team

Published June 29, 2021

Causal research can help you assess marketing initiatives, improve internal processes and create more effective business plans. Learning how one situation affects another can help you determine the best strategies for addressing your needs. Since many industries and academic fields use causal research, it's important to develop a foundational understanding of its concepts so you can decide which aspects to use. In this article, we define causal research, discuss its core components, list its benefits, describe some examples and include some key tips.

What is causal research?

Causal research, sometimes referred to as explanatory research, is a type of study that evaluates whether two different situations have a cause-and-effect relationship. Since many alternative factors can contribute to cause-and-effect, researchers design experiments to collect statistical evidence of the connection between the situations. Afterward, they typically analyze the data to determine why the relationship developed, learn more about how it works and determine how it might apply to a larger context. They can also modify the circumstances of the first situation to observe any new effects on the second.

Here are some key terms people use for conducting causal research:

• Hypothesis: A testable prediction that describes the outcome an individual expects to occur during certain experiments or situations. In causal research, the hypothesis uses variables to understand if one variable is causing a change in another.

• Experimental design: A type of design researchers use to define the parameters of the experiment. They may sometimes use it to categorize participants into different groups, if applicable.

• Independent variable: A variable that may cause direct changes in another variable. For example, in an experiment about whether class attendance affects grade point average, your independent variable would be class attendance.

• Dependent variable: A measurable variable that may change or receive effects from the independent variable. For example, in an experiment about whether coffee consumption increases productivity, your dependent variable would be productivity.

• Control variable: Components that remain unchanged during the experiment so researchers can better understand what conditions create the cause-and-effect relationship.

• Confounding variable: A variable that exists outside the experiment's parameters and influences both the independent and dependent variables. Researchers typically identify confounding variables before beginning an experiment.

• Causation: Describes the cause-and-effect relationship. When researchers find causation, it means they've conducted all necessary processes to determine it exists.

• Correlation: Any relationship between two variables in the same experiment. Researchers typically establish correlation before they attempt to prove a cause-and-effect relationship.

Related: Types of Research: Definitions and Examples

What are the components of causal research?

To properly identify a cause-and-effect relationship, it's important to gather some data to assess whether certain conditions are true. This information can help you develop a hypothesis about the cause-and-effect relationship and produce more comprehensive results. Here are the core components of causal research:

The timeline of events

Review the timeline of the two experimental events to determine the independent and dependent variables prior to developing a hypothesis. For example, a business might observe an increase in sales over the course of three months and decide to assess what factors could have caused this change to see if they can reproduce it.

After reviewing the sales data and marketing schedule, they might discover a promotional sale occurred the week before the first day of notable sales increases. The team can use this time-based information to identify whether the promotion is the independent variable that caused a change in revenue, the dependent variable.

Evaluation of confounding variables

It's important to identify any variables that could be the true source of a cause-and-effect relationship so you can achieve more accurate conclusions. For instance, an office supplies brand observes a correlation between the sale of a specific notebook brand and the fall season and initially concludes that more people buy notebooks during the fall because students purchase them for the fall semester.

However, the brand launched a new advertising campaign on social media during the summer. To address their initial hypothesis, they can research data on demographics to determine whether the students or advertisement caused the increase in notebook sales.

Related: What Is a Spurious Correlation? (Definition and Examples)

Observation of changes

To test the validity of a cause-and-effect relationship, you can test whether the independent variable produces a change in the dependent variable. You can also adjust parameters to measure how changing the independent variable affects the dependent variable. For instance, if a marketing company seeks to validate that using digital advertising causes an increase in customer engagement, they can test print advertising to see if it produces a similar result. If they observe a decrease or an unchanged status, they can better verify the cause-and-effect relationship between digital advertising and new customer engagement.

Benefits of causal research

Common benefits of using causal research in your workplace include:

• Understanding more nuances of a system: Learning how each step of a process works can help you resolve issues and optimize your strategies.

• Developing a dependable process: You can create a repeatable process to use in multiple contexts, as you can better understand which aspects to change to be successful.

• Updating an existing process: To create effective systems, you can use causal research to determine whether a process is useful.

• Getting more objective results: Researchers often use random sampling techniques to select subjects or participants for experiments, reducing the possibility of outside influences.

Causal research examples

As different industries and fields may conduct causal research, it can serve many different purposes. Here are some examples of various applications of causal research:

Advertising research

Companies can use causal research to enact and study advertisement campaigns. For example, six months after a company releases a new commercial in one region, they observe a 5% increase in sales revenue. To assess whether the commercial caused the increase, they release the same commercial in randomly selected regions so they can compare sales data between regions for another six-month-long period. When the sales increase again in these regions, they can conclude that the commercial and sales have a valuable cause-and-effect relationship.

Related: Research and Development: What It Is and When To Use It

Customer loyalty research

Companies can use causal research to determine the best strategies for retaining customers. They monitor interactions between associates and customers to identify cause-and-effect patterns, like a product demonstration technique leading to an increase or decrease in sales from the same customers. For instance, a company implements a new one-to-one marketing strategy for a small group of customers and observes a measurable increase in monthly subscriptions. After they receive identical results from multiple groups, they conclude that the one-to-one marketing strategy has the causal relationship they intended.

Related: What Is User Research?

City planning research

Town councils and other local legislators often use causal research to learn how their policy initiatives affect their communities. For instance, six months after the council expands the operation hours of the local parks, they observe a 70% increase in reports from surrounding homeowners about noise in the parks during the evening. After eliminating the possibility that a local athletics club uses the park at night to practice and conducting survey research in the community, they conclude that the change in hours caused the increase in reports. This causes them to re-address the issue.

Employee productivity research

Businesses can use causal research to measure how employees learn protocol and other skills during training sessions. For instance, a technology company holds a training session for all employees to learn a new scheduling software. Ten months later, upper management observes an increase in reports of scheduling errors, including overlapping meeting times and double-booking rooms. After examining whether the software is causing the errors, the company hosts a second training session using updated guidelines and observes a statistical decrease in reports.

Food industry research

Restaurants and other food-based companies can use causal research to understand if customers are enjoying menu items more than others. For example, a candy company receives feedback from customers that a new dark chocolate product contains pieces of plastic. Since they recently changed suppliers, they decide to remove the chocolate from the shelves and replace it with products from their previous supplier. When they still receive the same feedback, they evaluate their production protocols and discover that a malfunction in the packaging machine caused the issue.

Education research

Learning specialists, scholars and teachers use causal research to learn more about how policy affects students and to identify possible trends in student behaviors. For instance, a university administration realizes that more science students withdraw from their program in their third year at a 7% higher rate than any other year. They interview a randomized group of science students and discover many factors that could generate these circumstances, including components outside of the university's scope. Through an in-depth statistical analysis, researchers discover the top three factors and the administration creates a committee to address them in the future.

Related: How To Calculate the Necessary Sample Size for Your Survey or Study

Entertainment industry research

Television and film content strategists can use causal research to identify which types of media techniques and story topics most resonate with viewers. For example, a television network analyzes the viewer trends of a program that just premiered their sixth season. Using feedback surveys, they learn many viewers prefer longer scenes with more character interaction and have the writers include them in the next three episodes. During the last air date, the network observes an 8% increase in viewership. Strategists decide to further study a hypothesis that longer scenes cause a measurable increase in viewer engagement.

Tips for implementing causal research

Review these tips to successfully conduct casual research:

• Know the parameters of your study. Identify any design methods that alter your data interpretation, including how you collected data and any situations where your findings apply in practice more than others.

• Choose a randomized sampling procedure. When you have participants or subjects, it's important to pick a technique that works best for you. You can generate a random list using a database, pick random samples from already separated groups or construct your own systematic process.

• Identify all potential correlations. Analyze the different correlations between your independent and dependent variables to develop more nuanced interpretations and conclusions.