“Say cheese and… got it!”
That is what you’ll say while clicking pictures of your colleagues after finally meeting. We, at SurveySparrow, did that, and it was fun. But this very activity explains what we’ll talk about here.
Cross-sectional study. Yes, for almost all your market research needs, this study technique plays a huge role. And yes, it’s like clicking a pic! From the definition, examples, types, to the pros and cons of using it, we’ll discuss everything, and It’ll all be useful to you. Ok, enough saying, get your cuppa and let’s begin.
Cross-Sectional Study – The Definition
Cross-sectional analysis or cross-sectional study is the collection of relevant information from a group at/for any specific point in time. The group is chosen from the entire population or the overall sample group, and the study is done for a particular time.
Also known as transverse or prevalence study, this technique, as we’ll see, is used extensively for healthcare and business-related studies. Generally, it takes place as physical experiments or a survey where the researcher decides the participant group and time period of evaluation.
Why Do We Use Cross-Sectional Study?
We know the definition, but why exactly is a cross-sectional study used wherever they are? What characteristics make them so useful? Let’s understand:
Observational Nature: One of the first things that make this study technique effective is its observational characteristic. New participants can be explored through similar cross-sectional studies having the same variables. So practically, a vast population can be observed through this study method.
Same Variables: However long the period of study is, the same variables can be used. Changing periods doesn’t require a change of variables.
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.
This is a topic we couldn’t discuss with the other examples. Why? Because the influence of cross-sectional study in performing market research is huge. From small market research campaigns to the big-money ones, this study technique is used massively. Here’s a quick market research survey:
Sign up for FREE here to create a similar cross-sectional research survey…
14-Day Free Trial • No Credit Card Required • No Strings Attached
Here’s an example delving deeper into the topic:
A soap manufacturer knows that his most valuable customers are those who’re using his soaps for the last 2 months or have used them for at least 2 months in the past. Now, he wishes to know the age group of his valuable customers. To find this out, his team created a survey for 500 individuals from 3 categories: 18-30 years old, 31-45, and 46-60. All 500 survey takers are the current users of his soaps. The survey collected the data on how long the respondent has been using the soaps. Plus, it asked if they had talked about the soaps in their friend and family circles?
The collected data showed that while the 18-30 age group bought the maximum soaps, it was the 31-45 age group using them the most. And they were talking about the soaps in their circles, too. The manufacturer started acting on this market research data to shift focus to the 31-40 age group, as they were his most valuable customers.
Now, he could go ahead and conduct another market research survey for the 18-30 and the 31-40 age group, where the former will be asked why they’re not using the soaps for long, and the latter will be asked how to improve the soaps to get more similar customers? The results will again allow him to pivot and make necessary changes. So, market research and cross sectional study go hand-in-hand in making your brand better. We, at SurveySparrow, believe in it, and we’re sure you do too.
Cross-Sectional Study Examples
So, 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:
For Analyzing Spending Trends
One of the most common examples of cross-sectional study comes in understanding the spending habits of a target market to identify relevant trends. Men and women from a specific age group (defined time period) are surveyed to identify where they will spend the most on? Based on this, the entire marketing structure can either be changed or prepared from scratch.
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 like morbidity, employment rate, mortality, poverty, recession numbers, and others are analyzed for the final GDP figure.
For Measuring The Spread Of A Disease
For measuring a disease’s spread, cross-sectional analysis becomes important. The total number of infected persons, along with the total population of demography, is calculated for a specific year or months to understand how quickly this disease is spreading. So naturally, cross-sectional research was used a lot in the past 2 years because of the Covid-19 pandemic.
For Understanding People
For psychological purposes too, a cross-sectional study is used. It 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.
For Educational Research
If there’s one area where cross-sectional studies are absolutely common, it’s this. Yes, in most educational research, the researcher selects students from schools or colleges who scored in a particular grade range in the same course. This is then used to analyze how they will perform in a new curriculum. Or it can analyze which subjects are making the maximum sense for students from the same grade range.
For Preparing Financial Datasheets
Cross-sectional data becomes 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.
For Knowing The Employment Status
The graphical fluctuation of employment status in different industries is found after a cross-sectional study. The total employed versus the total employed in a specific time in the past is found, compared, and compiled to get the employment ratio or status.
Cross-Sectional Study – The Types
We’ve covered almost everything about the cross-sectional study. Just the types along with the pros and cons remain. Time to discuss the former:
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. And when this is the case, it helps researchers identify the problem areas in the taking part group. An example of this can come from medical research; where the descriptive type cross-sectional study determines how a population reacts to biotech equipment used in hospitals.
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. An example of this again comes from the medical sector, where to investigate if smokers can develop cancers require the researcher to look at 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 based on the requirements.
5 Pros & Cons Of Cross-Sectional Study
The definition of cross-sectional study, why it’s used, the examples, types, and its use in the market research, we’ve covered all this here. But to be honest, we can’t end without talking about the pros and cons of this analysis technique. So, here are the 5 pros and cons of cross-sectional study you should know about before using it for the next survey or research.
A cross-sectional study is super affordable compared to the other available study designs, mainly longitudinal studies. The reason is that most of the data here are collected from self-report surveys that are taken by a suitable participant group. Once this data is collected, no follow-up is required before analyzing it. So, the cross-sectional data can be analyzed immediately and doesn’t require any extra, significant investment.
One of the biggest pros of cross-sectional study is the excellent control it gives to the researchers. 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.
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 time you researched. No demographical analysis over an extended period is required in this.
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.
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 majority characteristics in a population because of their focus on the individual.
With cross-sectional analysis, the risk of missing critical data points is highly reduced, making the entire process efficient. 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.
Researcher’s Personal Bias
The survey taker’s or researcher’s personal preferences affect the overall cross-sectional data. This is a disadvantage. Measures are taken to make sure there are no biases, but saying there aren’t any would not be true. For example, if a researcher chooses only men for conducting a cross-sectional study, the data points will definitely be biased towards what men think on the survey or research topic.
Not Revealing The True Story
Researchers can shape the entire study based on their requirements here. They can ask specific questions in a way that leads to specific results.
Need Of Large Samples
A large sample size is necessary for a cross-sectional study to yield fruitful results. This is because, otherwise, the needed efficiency and credibility of data will not be established. See, when the sample size is small, the risk of errors affecting the data increases dramatically. Also, credibility is not established because in most cases, there is no obvious pattern in such data. The chances for coincidence are more with a smaller sample in a cross-sectional study.
No Information On Causal Relationships
This 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
At SurveySparrow, we’ve seen little focus from our clients in determining the ideal demography for a cross-sectional survey. Just the target market is surveyed for a specific time period or age group are surveyed. This leads to the collection of redundant data, too. And such data comes of no use at all.
Well, well… we covered it all. Before wrapping up here, we’ll quickly summarize it for you.
- We defined cross-sectional study in layman terms after giving a cool introduction!
- Gave reasons why cross-sectional research or survey is so crucial? Basically, we explained why businesses and researchers like this study technique so much.
- Then came the examples. We tried to go as diverse as we can with it. Picking different industries and segments for it.
- A cross-sectional study in market research. This was a very in-demand topic, and we covered it with a detailed example.
- The 2 types of cross-sectional study came next.
- Lastly, 5 pros and cons were discussed, in-depth.
So, what remains now? Trying it yourself. Yes, that’s what remains for you. We’ve given you the A to Z guide on cross-sectional study, but you gotta use it in your next survey or research to understand it better.
Before doing that, check out SurveySparrow’s market research surveys. It’ll make the entire process so much easier and more effective. So, start using this study technique. You’ll thank us later! Ciao.