Apples and oranges. Two fantastic fruits.

Teenagers and elderlies. Two different generations.

We’ll get two different data points if we ask about your age and the fruit you like. To analyze this data, we’ll use Cross-tabulation.

What the heck is Cross-tabulation? How does it help with the overall data analysis? And in what sectors is it predominantly used? Answers to these and a lot more quality stuff is coming your way. There is something that you definitely do not want to miss.

In this blog, we’ll cover:

1. What is Cross-Tabulation analysis?
2. Benefits of Cross-Tabulation
3. Cross-Tabulation Examples
4. Cross-Tabulation in Data Analysis

What Is Cross-Tabulation Analysis?

Cross-tabulation is a statistical tool for categorizing data and making sense of it. It involves data values that are mutually exclusive from each other. This data is collected in numbers but has no value unless it means something. Like 1, 2, and 3 are mere numbers, but 1 trousers, 2 books, and 3 pencils are meaningful data points.

Cross-tabulation, or Cross-tabulation analysis, helps you make informed decisions from raw data by identifying patterns, trends, and a correlation between parameters.

During a study, raw data can be overwhelming and almost always lead to confusing, scattered outcomes. In such situations, Cross-tab analysis helps you arrive at a single theory by drawing trends, comparisons, and correlations between two or more factors.

Benefits of Cross-Tabulation

Cross-tabulation is a fundamental tool in data analysis, particularly when working with categorical variables. Understanding its benefits is key to effectively leveraging it in research. Let’s dive into these advantages.

On a more conversational note, think of cross-tabulation as the first glance at a puzzle. Instead of pouring over each individual piece, you’re grouping similar ones together, providing a clearer picture of what you’re working with. It’s like getting a quick overview before diving deep into the details.

Cross-Tabulation Example

Conducting surveys is only half the job done without analysis. And to be honest, without proper analysis, there’s no point in collecting data through surveys. Cross-tabulation helps here, too.

Let’s say you run a supermarket with multiple outlets where you conduct employee engagement feedback surveys every quarter. Now, you want to know how the junior-level employees from your Los Angeles outlet have answered the survey against employees from other outlets.

• Go to the ‘Reports’ section to begin the process.
• Click ‘Compare’ to select the question asking employees to mark the outlet they are in.

• Select the outlet you want to see the responses for and click ‘Apply’.

• Click “+Add” to add another question and response and click Apply. Cross-tabulation with one comparison can stay active at any given time.
• Graphical correlations between the selected question against the rest will get displayed.

• Save the view, download, or schedule the report.

That’s how simple it is to use cross-tabulation analysis with SurveySparrow. The insights you’ll get from this will allow you to either pivot or make necessary changes. Additionally, these three tips will assist you further in building a crosstab report.

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 Bonus Data Insights

• Find questions you really want in your report. Choosing one too many will lead to a crowded cross-tabulation table making more valuable insights less visible.
• Apply filters to close in on the audience you care about most. You can create and save views for any combination of filters and access them in the “Crosstab Setup.” But selecting the right filters is the only key to getting the right insights.
• Figure if your results are statistically significant, meaning the results are not by a random chance. If you’re running a crucial survey, make sure to double-check this.

Speaking of surveys, I wanted to introduce SurveySparrow, one of the best online survey tools in the industry.

Using the ‘Compare’ feature in SurveySparrow, you can group responses from a specific question and cross-tabulate them to compare them with the responses from other questions. This allows you to identify customer behavioral patterns and see categorized reports for each one of them.

In short, the compare feature, using cross-tabulation, transforms a normal report into one with rich insights into customer trends and patterns. You still must collect relevant feedback of the highest order before using this feature during analysis. Read this guide on how to achieve an acceptable survey response rate every time.

For example, if you want to identify millennials’ interest in your brand from a particular geography, initiate this feature by selecting the responses against age and pin code. A report showing how millennials from a certain pin code responded to the survey will show up. You can save, download, or schedule this cross-tabulated report to reach the inbox at your convenience.

How does Cross-Tabulation Help in the Data Analysis Process?

This is a pretty valid question. How does cross-tabulation help with the data analysis process? Well, there are multiple ways it happens, starting from;

Less Confusion

One of the worst things about raw data is that it’s damn confusing. It points to different patterns at the same time with little substance. But with Cross-tabulation, this data gets segregated into less confusing categories for easy interpretations. Cross-tabulation analysis, thus, makes the data analysis process smoother and less confusing right from the start.

Reveals Insights Easily

Large data sets are overwhelming, making the entire process of analysis overwhelming. Well, not if you use a Cross-table to reduce the raw data into manageable subgroups. With crosstabs, researchers pull insight using relationships between categorical variables and can do so with greater ease. Without crosstabs, getting the same insights would’ve taken a heck lot of legwork.

Predicts The Next Step

Cross-tabulation makes it easier to interpret data and predict the next course of action, becoming beneficial for researchers with limited knowledge of statistical analysis. As with cross-tabulation, people don’t need an understanding of statistical programming to correlate categorical variables. That helps professionals evaluate current and future strategies, giving them the necessary information to make solid predictions.

Empowers Your Decision

Cross-tabs help uncover actionable insights that affect your target goals. With these insights, you can make decisions impacting your brand and business. The insights from cross-tabulation spss validate your decisions, empowering them to make your data analysis process that much more effective.

Delivers Data That Matters

In all the cross-tabulation examples, the data you and your team finally get is the data that matters. Data that are reliable to take action on. And that’s the whole point of data analysis… getting information (data) that matters, helping you bring changes.

Fewer Chances Of Error

Analyzing large sets of data isn’t easy. Errors are bound to happen in whatever analysis method you use. However, with cross-tabulation, the chances of error are the least due to converting raw data into manageable categories—another reason why cross-tabulation makes the overall data analysis super-efficient.

When To Use Cross Tabulation For Analyzing Data?

Cross-tabulation for data analysis is significant if done correctly and at the right time. Fundamentally, it measures how different variables are related to each other. Each variable has data recorded in a specific table or matrix to compare.

No doubt, Cross-tabs is an enormously complex area of work. Although it is possible to run these statistics manually using Excel, people prefer using specially designed software. Furthermore, this allows a better understanding of the data collected through questionnaires.

The statistics associated with this analysis technique are;

• Chi-square – It analyzes the statistical significance of the Cross-tabulations. Chi-squared should isn’t for percentage calculations. Before calculating chi-squared, the conversion of Cross-tabs into absolute counts (numbers) happens. Further, it is problematic when any cell has a joint frequency of less than five.
• Contingency Coefficient – It’s a variant of the Phi Coefficient that adjusts for statistical significance. Values range from 0 (no association) to 1 (the theoretical maximum possible association).
• Cramer’s V – Another variant of the Phi Coefficient, Cramer’s V regulates the number of rows and columns. Its estimates range from 0 (no association) to 1 (the theoretical maximum possible association).
• Lambda Coefficient – Evaluates the strength of association of the Cross-tabulations for variables measured at the nominal level. Here, values range from 0 (no association) to 1 (the theoretical maximum possible association). Asymmetric Lambda measures the percentage of improvement in predicting the dependent variable. Symmetric Lambda measures the percentage improvement for a prediction made in both directions.
• Tau b – Investigates the potency Cross-tabulation relationship for variables measured at the ordinal level. Formulate adjustments for ties and is most suitable for square tables. Values range from -1 (no association) to +1 (the theoretical maximum possible association).
• Tau c – In Tau c, both variables are measured at the ordinal level to examine the connection of Cross-tabulations. Points range from -1 (no association) to +1 (the theoretical maximum possible association). It makes adjustments for ties and is most suitable for rectangular tables.
• Gamma – Tests the strength of association of the Cross-tabulations when both variables are measured at the ordinal level. Utilities range from -1 (no association) to +1 (the theoretical maximum possible association).

Where Is Cross-tabulation Analysis Used?

Getting deep insights into employee survey data is one of the areas where cross-tab analysis is used, but it’s not the only one. We’re about to find what the other avenues of cross-table usage are;

Customer Behavior

Organizations globally are always working to find better ways to keep a customer for long. Just like finding new employees, acquiring new customers takes a lot more time and finances than keeping the existing ones. To do that though, you need to keep track of their behavior. Analyze related patterns, see when a certain behavior kicks in, and the time it takes a customer to change a specific behavior.

The cross-table study is best suited for this. You just gotta bring the customer data, select the parameters you wish to analyze, and employ this research method. It won’t disappoint, for sure. And if you’re using SurveySparrow for collecting customer data (which you should do!), then you already know how to perform cross-tabulation.

Market Research And More!

We’re saying market research, but truth be told, cross-tabulation is used during product and campaign testing, design changes, and even to support the sales team.

The process is the same. You and your team select relevant parameters, geography, and data for this analysis technique to find insightful patterns for you. This pattern helps bring changes that ultimately spur sustained organizational growth. Fantastico!

Large-scale Research

Didn’t think this was coming? Well, if cross-tabulation is successful at finding deep insights from just one organization’s data, it’s always going to show similar magic in large-scale university or government research. And that’s why it’s one of the first quantitative analysis techniques in a researcher’s mind.

Election Campaigns

Cross-table and other quantitative analysis techniques are widely used in all election campaigns globally. See, today is the age of data, but it’s not as good just as it is. Think of crude oil here. It’s not useful unless converted into Petrol, diesel, and gas. Big machines do this job of converting crude oil into something useful.

Consider cross-tabulation similar to these machines for data. Political parties, both ruling and opposition, have mammoth proportions of data on their people. To make sense of it, they employ analysis techniques, of which, cross-table is quite the favorite.

Go For It!

This entire article points towards one thing. What’s that?

Cross-tabulation is one of the best techniques for finding quality insights from noisy data. With the right software (like SurveySparrow) and parameters, it’s probably the best analysis technique you’ll find. Focus on the word ‘right software’, because it’s pivotal. You can’t expect your team to sit down and make the entire cross table for multiple variables. It would be an utter waste of time and efficiency, plus errors, as humans are more prone to that than computers and softwares.

Give SurveySparrow a try here. From an organization’s point of view, you’ll conduct survey campaigns for customer and employee feedback, along with market research. And SurveySparrow will help you conduct the best surveys and then use its in-built cross-table analysis tool to find all that’s relevant in them.

Don’t give it a second thought. We’re here to see you and your business flourish. So, talk to us, and go for it!

Kate Williams

Content Marketer at SurveySparrow