What is a cross-sectional survey?
Professional researchers define cross-sectional survey – alternatively a cross-sectional or “snapshot” study – as follows: It observes a targeted population with specific demographic or behavioral characteristics to collect desired outcome data on a single date. A crucial rule of cross-sectional analysis is that it’s strictly observational without influencing the variables driving the outcome in any way.
Consider the following as a typical cross-sectional survey example:
- A demographically or behaviorally specific population: College and non-graduate adults living in Brooklyn.
- Outcome qualifying for observation: Who voted for the Democrat, Independent, or Republican candidates in a state election?
- Point in time: Immediately following an intensive political campaign on the 15th of February, 2024.
Cross-Sectional vs. Longitudinal Study
It’s a longitudinal survey program when researchers use the cross-sectional survey study tool over an extended period, taking snapshots of the same characteristics at different time points.
Longitudinal surveys, also known as longitudinal or panel studies:
- Rely on repeated observations of the same population variables to track their changes over time.
- Aim to detect sustainable trends and patterns obtainable from a single cross-sectional survey.
A case study showing the cross-sectional survey study and longitudinal survey connection:
Medical researchers wanted to know if a representative sample of a hospital chain’s heart attack patients following a physician-prescribed exercise program lived with a reduced threat of another one occurring. So, they conducted a cross-sectional survey where:
- The studied variables – the exercise program outcome – were weight loss and improved cholesterol readings.
- The results showed that younger patients responded significantly better than older ones and women more than men overall.
- The single-point-in-time data triggered motivation to learn more about how age and gender link to exercise as a heart ailment remedy. How were they going to achieve this? By initiating a longitudinal study of the same variables for long-term observation triggered by the cross-sectional survey as described.
Regarding (3) above, without the initial cross-sectional survey putting things into perspective, researchers would be taking a shot in the dark or not know if more readings were even necessary. In short, a cross-sectional study is a fast and seamless method to identify demographic and behavioral connections or correlations with outcomes, validating or invalidating ongoing longitudinal analysis.
Cross-sectional survey uses, advantages, and disadvantages
1. When you want to assess the prevalence of a population outcome (as demonstrated above), a cross-sectional survey provides, delivers, establishes, or identifies:
Medical researchers wanted to know if a representative sample of a hospital chain’s heart attack patients following a physician-prescribed exercise program lived with a reduced threat of another one occurring. So, they conducted a cross-sectional survey where:
- An initial insight into unique situations at a point in time.
- A baseline for possible long-term monitoring (i.e., merging into longitudinal studies).
- A framework for comparing multiple demographic and behavioral subgroups within a population (e.g., by age, gender, income, activities, etc.) to identify disparities.
- Relationships, connections, and correlations between population characteristics and the variables under study, thus generating hypotheses for further research.
2. It can achieve all the items in (1) above cost- and time-efficiently. How? Dealing with a single time point, a cross-sectional study can:
- Erase the traditional costs of a study program involving several repeat observations like longitudinal survey projects, making it a relatively cheap process.
- Facilitate data collection from large samples affordably and quickly, thus ideal for crucial decisions that depend on short but accurate background research horizons for validation.
3. It covers scores of industries from public health to manufacturing, retail, distribution, and services, highlighting:
- Customer and employee experience insights that impact ROI and revenues favorably or unfavorably.
- The ethical standards of entity activities.
4. Disadvantages of a cross-sectional survey are as follows:
- Identifying or confirming cause-and-effect relationships, trends, and patterns from a one-time reading is impossible. You must include several similar studies to derive such benefits, effectively converting to a longitudinal survey program.
- Single-point studies may contain biases that only long-term verification will expose.
- Any research where movement or change is the desired goal – key to the end strategy – cannot depend on a cross sectional study.
Three examples of cross-transactional surveys using quantitative questionnaires (possibly with open-ended questions for added insight) in the marketplace:
- Determining how Gen Zers and Millennials react to contactless reservations and restaurant menu ordering versus Baby Boomers.
- Creating insights into how much the sustainability concept influences the purchase behavior of different income groups in convenience stores.
- Measuring the impact of “buy-now-pay-later” options on different credit card customer categories by region, education, age, and income.
How to conduct a cross-sectional survey for your business
Step 1: Define Your Research Goals
Using our heart attack example described above to illustrate, your study objectives must sub-divide into initiatives where you:
1. Pinpoint the outcome you want to assess: In my example, this was the percentage of heart attack victims prescribed exercise programs who have reduced the threat of a repeat cardiac event.
Other outcome definitions (to mention a few) may be relevant to:
- Customer satisfaction.
- Employee engagement.
- Political affiliations (see my example above and below).
- Product usage.
- Brand perception.
2. Identify the variables that define the outcome: In my example, it was weight and cholesterol readings. Other examples are as follows:
- Customer satisfaction – Those respondents willing to recommend the brand to friends and family and those not ready to do so.
- Employee engagement – Those respondents who rate the employer highly on a corporate culture that fosters retention versus those who score it poorly or neutrally.
- Political affiliations – Those respondents voting independent, republican, or democrat after a politician campaign.
- Product usage – Those respondents buying a brand repeatedly relative to once-off purchasers.
- Brand perception – Those respondents who see a brand as trustworthy versus unreliable, masculine versus no gender connection, premium priced versus value for money, etc.
3. Decide on the respondent characteristics you want to include for comparison: It boils down to deciding on the vital demographic and behavioral influences affecting the defined outcome. In my example, it was gender and age. However, we could have extended this to include:
- Diabetes or non-diabetes sufferers.
- Those exercising every day versus intermittently.
- Personnel trainer-assisted or not.
Step 2: Choose the Right Audience & Sample Size
The population you want to target can vary from small segments to national populations. Accordingly, your sample size and methodology are crucial if you want reliability and confidence in your results. This opens an entirely new subject, “How to statistically structure your sample to provide strategists with a sound planning platform.”
I recommend several Sogolytics articles with insightful takeaways covering cluster sampling techniques for massive populations, stratified random sampling as a powerful tool, and statistical methods. Finally, the Sogolytics team is ready to help you with a suite of AI-powered audience targeting features for accurate sample selection that will cut out wasteful trial and error.
Step 3: Select Your Survey Method
When selecting your business’s survey methodology, you can’t beat the experience and broad coverage of a professional customer and employee experience entity. Again, Sogolytics is your one-stop resource for conducting a cross-sectional study that generates the feedback you expect and deserve.
The options are extensive, from NPS (Net Promoter Score) to CSAT, CES (Customer Effort Score), Census Counts, and more, conducted anonymously online using Google Forms or customized Sogolytics templates (also applicable offline). Sogolytics ensures your questionnaires are focused, simple to understand, short, and to the point, ensuring acceptable respondent responses.
Step 4: Analyze & Apply the Cross-Sectional Survey Data to Your Business Strategy
Modern surveys inextricably connect to AI automation and sophisticated data analytics that can absorb gigantic data volumes, sort the raw material into logical categories, erase field errors (like incomplete entries or repeats), and detect patterns or trends (the latter in longitudinal survey projects).
Furthermore, you can do it in a fraction of a human team’s time with the help of an in-house or contracted data scientist to iron out the process. Sogolytics covers this aspect from corner to corner with user-friendly dashboards and customization to meet every client’s needs.
Conclusion
Cross-sectional surveys fit the bill when you want insights without breaking the bank and where trend analysis is secondary. This study vertical lets you overview massive populations inexpensively on a decided date to create crucial insights. You can also use it as a precursor to potential longitudinal surveys customized to generate trends.
Sogolytics’ mission as a cross-sectional study specialist is to make your research easy and seamless no matter what industry you work in. We have AI-powered software, automation options, B2B and B2C questionnaire templates, data analytic tools, and more to provide a complete surveying process. Contact us today for a no-obligation conversation to enlighten you on uncovering hidden opportunities in your marketplace.
FAQs:
Q1: What is a cross-sectional survey?
A: It observes a targeted population with specific demographic or behavioral characteristics to collect desired outcome data on a single date. A crucial rule of cross-sectional analysis is that it’s strictly observational without influencing the variables driving the outcome in any way.
Q2: How does a cross-sectional survey differ from a longitudinal study?
A: It’s a longitudinal survey program when researchers use the cross-sectional survey study tool over an extended period, taking snapshots of the same characteristics at different time points. In other words, the latter is an overview of several cross sectional surveys focused on the same population’s metrics measured on each occasion.
Q3: What are some real-world examples of cross-sectional surveys?
A:
- Determining how Gen Zers and Millennials react to contactless reservations and restaurant menu ordering versus Baby Boomers.
- Creating insights into how much the sustainability concept influences the purchase behavior of different income groups in convenience stores.
- To measure the impact of “buy-now-pay-later” options on different credit card customer categories by region, education, age, and income.
Q4: What are the uses, advantages, and limitations of cross-sectional surveys?
A: Uses
- Assessing the prevalence of a population outcome
- Establishing a baseline for extended monitoring long-term.
- Providing a framework for comparing multiple demographic and behavioral subgroups within a population
- Identifying relationships, connections, and correlations between population characteristics and the variables under study, thus generating hypotheses for further research.
ADVANTAGES
- Cost and time efficiency.
- Covering scores of industries, multiple variables, and ethical standards of entity activities.
DISADVANTAGES
- Identifying or confirming cause-and-effect relationships, trends, and patterns from a one-time reading is impossible. To derive such benefits, you must include several similar studies, effectively a longitudinal survey program.
- Single-point studies may contain biases that only long-term verification will expose.
- Any research where movement or change is the desired goal – key to the end strategy – cannot depend on a cross-sectional study.
