This article will help to define a cross-sectional study, outlining the ways in which cross-sectional studies can be used and what makes them valuable to marketers and researchers. It will also help you identify opportunities for a cross-sectional study in order to better understand an existing assumption regarding a specific demographic, hopefully before you undergo an extensive survey or research effort based on that assumption.
In short, think of it as a test to confirm or deny a theory so you can avoid further wasted efforts and expenses on a study that may lack correlation.
What are Cross-Sectional Studies?
A cross-sectional study is a research method that is also often called a ‘snapshot’. Apropos to its name, a cross section of a population is sampled and studied over the course of only a single contact period (one survey, one questionnaire, or one observation). In a cross-sectional study, a single variable that differs across the sample of respondents is the focal point, but can then be cross-referenced across demographic similarities among the respondents, such as, age, gender, location, etc. Only a single relevant variable is allowed, and all other key attributes must be the same in order to observe any accurate correlation.
An example might be a single survey sent out to discover the price sensitivity to a product or service of companies varying in size. Maybe you had previously assumed that larger companies are willing to pay more for your product, but before engaging a market research company, you want to know if that assumption is credible. A cross-sectional study can help you do that quickly and cheaply.
The controlled attributes would be the level of price sensitivity (only willing to pay x dollars or less), perhaps the industry (let’s choose software), and the variable would be the size of the company.
If the results of the study show there is a strong correlation to size and price sensitivity in the direction you had assumed, that information can help you to form your full research efforts that will provide significant evidence in order to change your pricing to better accommodate those companies and increase adoption.
Defining characteristics of Cross-Sectional Studies
- Takes place at a single point in time, hence “snapshot”
- Observational – does not involve manipulating subjects, variables, or the study environment
- Often used to look at the frequency of something in a given population
- Not definitive regarding causation, only degrees of correlation
What Value Does a Cross-Sectional Study Provide a Marketer or Researcher?
All too often decision makers (and marketers are particularly guilty of this) like to make changes based on assumptions. They observe a trend, let’s say in this case, having to do with the target demographic of their customers for a certain product (we’ll use a smartphone in this example). That trend may be accurate and representative of the whole, but it could just as easily be a biased observation due to selective listening.
Perhaps the marketing manager feels that the 17-21 age range, younger than expected, is buying the product more than other ages. But, that feedback is coming from social media and those most vocal on social media. Before any targeted advertising is paid for or product changes are made to redirect the product towards that younger demographic, multiple cross-sectional queries to look at the age of those who are buying that product in multiple time periods would help gauge the accuracy of that “trend” the marketing manager had observed.
(Note: I use the word queries because most companies are already tracking that data. In this example, taking a cross-section should not involve sending out surveys to collect opinions or behavior, but simply pulling existing internal information.)
Advantages of Cross-sectional Studies
- Relatively inexpensive and takes very little time to conduct
- Data from a large number of subjects for exploratory research can be gathered
- Data from a single study can be useful to many different researchers
- Insight on attitudes and behavior can be easily interpreted
Disadvantages of Cross-sectional studies
- Difficult to make causal inference
- It’s just a snapshot, results may be different based on time frame of the study
- Cannot measure change over time
- Static; cannot establish cause and effect
- Finding a sample of respondents who are very similar except in one specific variable can be difficult
Great Survey Design and Cross-Sectional Studies
A few years ago, people much smarter than I created a curriculum of best practices to follow when creating a survey from scratch. That material has been trademarked as Great Survey DesignTM and helps us guide marketers and those less familiar with market research and research methods to gather quality, actionable data.
Within that curriculum is a framework, six steps that represent the process of considering, vetting, building, and analyzing a survey and the associated data. They are:
- Need – Identify goals and your objective, brainstorm, selection and refinement
- Design – Organize, eliminate bias, re-establish focus
- Build – Construct, validate, test reports, apply & test logic
- Collect – Choose mode of survey, sample, and engage a panel (if needed)
- Report – Clean data, run initial reports, analyze data, create financial report
- Act – Empower action taking, monitor action, get feedback on study value, create final report
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How is this relevant to cross-sectional studies? Consider the Design stage in which a cross-sectional study may be most valuable. Decision makers often bypass the brainstorm and refinement steps seeing as they’ve already seen a need to learn more. Also, most people tend to think their intuition is good enough to act on. By introducing a cross-sectional study in the Design stage, you can help eliminate bias as well as re-establish focus.
Using a cross-sectional study as a litmus test, you’ll either be affirming an assumption made about a particular demographic or behavior, or discovering a lack of correlation, and thus the need to go back to the brainstorm phase to figure out exactly what it is you’re trying to change. It’s a chance to slow things down in order to avoid headaches further along the Design Cycle.
Examples of Cross-Sectional Studies in Marketing
We already touched on the hypothetical price-sensitivity study with regards to company size. But what are some other examples of cross-sectional studies that could be used to help guide marketing efforts and shape expansive research projects?
Briefly mentioned was the idea of using cross-sectional studies to learn more about target demographics. This is perhaps one of the most useful and common scenarios. Imagine that you’re the marketing director for a mobile gaming company that sells a subscription-based game that appeals to varying age ranges. We know that long time customers who hold subscriptions for eight months are more valuable to the company than a customer who subscribes for two or three months before dropping.
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What we can do is pull a cross section of three different age ranges, 300 individuals from each group: 15-24 years old, 25-34, and 35-44. All are players of the same game, and are currently subscribed. By looking at the average duration of subscription for each group, we can see correlation between how long different age ranges stay interested in that particular game. That information can then help guide more in-depth research, and eventually future development of the game itself, as well as where to target marketing and advertising dollars.
Cross-Sectional Vs. Longitudinal Studies
Cross-sectional studies differ from longitudinal research in that cross-sectional studies are designed to look at a variable at a particular point in time in order to gain insight on assumptions, not evidence. Longitudinal studies involve taking multiple identical surveys over an extended period of time, the advantage being the ability to see historical change and better understand causation between variables.
The disadvantages are expense and loss of respondents due to the extended timeline. Learn more about Longitudinal Studies and the benefits of extended research efforts here.
Key Takeaways
There are as many uses for cross-sectional studies as you can think of. Any time you need to validate an assumption before committing to a large research project, you can use cross-sectional studies to gather insight about a key variable, be it price, audience demographic, or new market penetration.
Time is not an issue. Even if your team lead is pushing to make changes, this is low-impact, low-cost, quickly gathered data. It may not be empirically sound with regards to long-term relationships, but it will give you solid ground to walk on when you decide how to design your research.