Did you know that a cross sectional study is like clicking a picture? “Say cheese, and… got it!”

How? Well, cross sectional data plays a vital role in market research because it gives a snapshot of your target audience. So in this blog, we’ll go through:

Cross sectional study: Meaning
5 defining characteristics of cross sectional research
How to apply a cross sectional study design with example + free template
Seven more cross sectional study examples
Types of cross sectional research
Advantages and disadvantages of cross sectional studies

What is a cross sectional study?

A cross sectional study (or cross sectional analysis) is a type of research that observes and collects data about a specific group at a particular point in time. In other words, it’s an observational descriptive research method.

In simple terms, researchers select a group from a larger population or sample, focusing on certain variables they want to study. They then conduct the research within a defined time period.

Cross sectional studies, also referred to as transverse or prevalence study are commonly used in fields like healthcare, clinical research, population studies and business studies. Usually, these studies involve conducting surveys or physical experiments. The researcher decides who the participants will be and the timeframe for the study.

5 defining characteristics of cross sectional research

What characteristics make the cross sectional design so useful?

Observational nature: As a first characteristic, cross sectional analysis is effective because it is observational. A researcher records information and characteristics about a population, but does not alter the variables in any way.
Consistent variables: However long the period of study is, the same variables can be used. Changing periods doesn’t require a change of variables. By using similar cross-sectional studies with the same variables, new participants can be explored. As a result, this method of study can be applied to a vast number of people.
Well-defined extremes: The starting and ending extremes are well-defined in cross-sectional research that allows all variables to remain the same. This is in contrast with longitudinal research, where they change during the entire course.
Singular instances: With cross-sectional study, only singular instances or topics can be analyzed. These topics are rigidly defined, which allows for more accurate data collection.
Cause-effect analysis: Here, one independent variable is kept as the main, and its effects are examined on different dependent variables. This lets the researcher understand the cause-effect relationship between the variables clearly.

Cross sectional study design: A market research example

This is a topic we couldn’t discuss with the other examples. Why? Because the influence of cross-sectional data on market research is huge. Whether it’s for a small market research campaign or a big one, cross-sectional studies are widely used.

Check out this template for a cross-sectional market research survey by SurveySparrow.

Templates like this can help you launch a cross-sectional research survey 2x faster. Plus, SurveySparrow survey software provides features like online panel services and data dashboards that lets you run an effective cross-sectional study – from start to finish.

We offer a Forever Free pricing plan with limited features as well as a free trial for testing the product. Sign up below to try it out.

How to perform a cross-sectional study

Example:

A soap manufacturer identified their most loyal customers as those who have been using their soaps for the last two months or longer.
Now, they need to determine the age groups of these loyal customers. To do this, they surveyed 500 current soap users from three age groups: 18-30, 31-45, and 46-60.
The survey collected data on how long each respondent has been using the soaps and if they’ve discussed the soaps with their friends and family.
The results revealed that while the 18-30 age group purchased the most soaps, the 31-45 age group used the soaps the most. And they were talking about the soaps in their circles, too.
Based on this market research data, the manufacturer adjusted their marketing strategy to focus more on the 31-45 age group, recognizing them as the most valuable customers.

The manufacturer now has the opportunity to conduct another market research survey. This time, he could ask the 18-30 age group why they don’t use the soap for longer periods. The 31-40 age group could be asked for suggestions on how to improve the soap to attract more customers like them.

The results from this second survey will again provide valuable insights, allowing the manufacturer to make informed changes. Therefore, market research and cross sectional study work together to improve and grow a brand.

7 more cross-sectional study examples

In a cross-sectional study, the variables remain the same throughout. This makes it useful in many sectors and circumstances – mainly in financial and healthcare areas. Let’s discuss a few examples for better clarity:

1. For Analyzing Spending Trends

Understanding the spending habits of a target market is one of the most common examples of cross-sectional studies.
In a cross sectional survey, brands ask men and women of a specific age group (defined time period) where they will spend most of their money.
Based on this, you can change your entire marketing structure or prepare a new one from scratch.

2. For GDP Measurement

Governments all around the world use cross-sectional research to come up with the quarterly and annual GDP numbers.
During this GDP measurement, counting for the total population is made for a particular year and variables.
These include variables like morbidity, employment rate, mortality, poverty, recession numbers, etc. Analysis of this data leads to the final GDP figure.

3. For Measuring The Spread Of A Disease

For measuring a disease’s spread, cross-sectional analysis is vital.
Medical researchers calculate the total number of infected persons, along with the total population of demography for a specific year or months to understand how quickly this disease is spreading.
Naturally, cross-sectional research was used a lot in the past three years because of the Covid-19 pandemic.

4. For Understanding People

Cross-sectional studies are widely used in psychology.
A cross-sectional study involves a set of people who do not share the same variables but come from a time that is relevant for the psychologist to study.
This helps them in finding common patterns for better treatments and sessions.

5. For Educational Research

If there’s one area where cross-sectional studies are widespread, it’s this.
In most educational research, the researcher selects students from schools or colleges who scored in a particular grade range in the same course.
The cross sectional data is then used to analyze how they will perform in a new curriculum. It can also analyze which subjects are making the maximum sense for students from the same grade range.

6. For Preparing Financial Datasheets

Cross-sectional data is crucial in preparing the financial datasets for a company.
Statistics of profits or loss, growth figures, or other parameters are prepared using cross-sectional research on the sale/revenue figures for a quarter or financial year.

7. For Knowing The Employment Status

Cross sectional research is useful for pinpointing the graphical fluctuations of the employment status in different industries.
Researchers find, compare and compile the numbers on the total number of employed versus the total unemployed in a specific time in the past to get the employment ratio or status.

Types of cross sectional study

I. Descriptive research

A cross-sectional study or survey is descriptive when it assesses how frequently, widely or commonly the variable of interest occurs in the selected demographic.
When this is the case, it helps researchers identify the problem areas in the participant group.
An example of this comes from medical research. A descriptive type cross-sectional study determines how a population reacts to biotech equipment in hospitals.

II. Analytical research

The analytical type of cross-sectional research studies or investigates the association between two related or completely unrelated parameters.
This type isn’t exactly safe from outside variables which are simultaneously occurring while the study is going on.
An example of this again comes from the medical sector. To investigate if smokers can develop cancers, the researcher looksat the variables in the cigarette content. What it doesn’t account for is that cancers can be formed because of genetic reasons, too.

In almost all cross-sectional research cases, both the descriptive and analytical types go hand in hand. It’s up to the researcher to choose the right one for their requirements.

Cross-Sectional Study: Advantages and Disadvantages

Here are the 5 pros and cons of cross-sectional study you should know about before using it for your next survey or research.

Pros:

#1. Affordability

A cross-sectional study is super affordable in comparison to the other available study designs – mainly longitudinal studies. The reason is that most of the data here are from self-report surveys by a suitable participant group.

Once this data is at hand, you don’t need a follow-up before analyzing it. So, you can analyze cross-sectional data immediately without any extra, significant investment.

#2. Excellent Control

One of the biggest pros of cross-sectional study is the excellent control it gives to the researchers. Additionally, they don’t have to care about long-term considerations and there’s a specified period for which the data is collected.

This allows them to collect, analyze, and start using the data quickly while keeping excellent control over the entire process.

#3. Real-time Updates

Cross-sectional study is a snapshot of a group of people at a specific point in time. Therefore, you can look at what’s happening in the present compared to the specific research period. Demographical analysis beyond this period isn’t necessary.

To give an example, a cross-sectional study will look at a person’s past eating habits to determine if there’s any relation with a recent illness. Although it won’t give a cause-effect explanation, it will, however, look at potential correlations.

#4. Focus On Individual

Researchers prefer cross-sectional analysis because they can look at many characteristics simultaneously. Instead of focusing on just income, age, or gender, this study technique focuses on each survey taker as an individual.

That allows for including useful characteristics that benefit from changing variables. Researchers often use cross-sectional analysis to look at the dominant characteristics in a population because of their focus on the individual.

#5. Efficiency

Cross-sectional analysis reduces the risk of missing critical data points. This leads to a more efficient data process.

Moreover, researchers maximize their use of information because there are no time variables here. This leads to a lower error rate compared to other study techniques.

Cons:

#1. Researcher’s Personal Bias

The survey taker’s or researcher’s personal preferences affect the overall cross-sectional data. This is a disadvantage.

There are measures to reduce bias in your cross sectional survey. But saying there isn’t any survey bias would be untrue.

For example, if a researcher chooses only men for conducting a cross-sectional study, the data points will definitely skew towards what men think on the survey or research topic.

#2. Not Revealing The True Story

Researchers can shape the entire cross sectional study design according to their requirements here. In other words, hey can ask specific questions in a way that leads to specific results.

#3. Need Of Large Samples

A large sample size is necessary for a cross-sectional study to yield fruitful results. Otherwise, it’s hard to establish the efficiency and credibility of the data.

See, when the sample size is small, the risk of errors affecting the data increases dramatically. Also, it’s hard to establish credibility because in most cases, there is no obvious pattern in such data. So the chances for coincidences are more with a smaller sample in a cross-sectional study.

No Information On Causal Relationships

The cross sectional study technique offers no information about causal relationships between an individual or the population group.

Such information becomes useful while finding relevant information. So, it only shows that a causal relationship exists, but it does not tell why.

Less Focus On Respondent’s Quality

Based on our experience, many businesses put minimal effort in determining the ideal demography for a cross-sectional survey. In othere words, researchers just survey the target market or age group for a specific time period.

This leads to the collection of data that will quickly become redundant. And such data is of no use at all.

Wrapping Up

Let’s summarize. We talked about:

Cross-sectional study: a definition
The characteristics that make cross sectional research studies unique.
A few examples of cross sectional research design in action, along with a free template.
A cross-sectional study in market research.
Types of cross-sectional study.
Pros and cons of this technique.

Next up: here’s a detailed guide on how to do market research surveys with SurveySparrow.

It’ll make the entire process so much easier and more effective. So, start using this study technique. You’ll thank us later! Ciao.

Exploring Cross Sectional Study: A Comprehensive Guide with Examples