8 Data Reduction Techniques To Transform Your Survey Analysis

You've heard the saying more is less.

When it comes to working with survey data - especially on large-scale projects - more data can often become less helpful. It can be overwhelming, confusing, and, most of all, time-consuming to handle.

That's why data reduction is so important. It is how researchers cut through the unnecessary noise to find the most valuable nuggets of information. This not only saves time and resources but also helps improve the accuracy of the final results.

Of course, too much data reduction opens up the possibility of getting rid of important information in the name of reducing the amount of data.

In this guide, we'll show you how to effectively reduce data on your survey results without accidentally losing critical information.

What Is Data Reduction?

The process of data reduction has been compared to Michelangelo's David - he created it by chipping away everything that was not David. This not only shows us the simple theory behind data reduction, but it's also a reminder that there is a certain art involved in data reduction.

By definition, data reduction is the process of limiting the amount of data that is analyzed. When talking about data reduction from a market research standpoint this means getting rid of all of the uninteresting stuff, so all that's left is of interest.

8 Data Reduction Techniques

1. Delete Uninteresting Analyses 

The first data reduction technique is the simplest - delete anything that is not interesting. Studies can often have hundreds or thousands of different crosstabs, so manually reviewing this much data is not feasible.

Rather than subjectively deciding what is and isn't of interest, this is where we can use stat testing to identify whether results are likely due to real effect rather than random chance. By identifying results that are statistically significant, you can quickly and easily remove all that are not.

2. Remove Visual Clutter

Often when working with crosstabs, you'll find columns with no data or bright colors that do nothing but distract the eye. This is what we refer to as visual clutter.

Detail and design are what help us tell a great data story. However, too much detail can make it hard to see the key insights, while excessive design elements can be distracting. Removing this visual clutter - or reducing the amount of 'ink' on the page - will make your tables, charts, and reports easier to understand and interpret.

3. Merge Similar Things

When multiple categories or variables tell a similar story, it's helpful to group them. This reduces complexity and highlights broader trends.

Examples:

• Combining multiple response categories into broader themes (e.g., merging "very satisfied" and "somewhat satisfied" into "satisfied").
• Merging adjacent age group columns that more or less show the same results.

Merging categories or variables reduces the overall amount of information we look at without actually taking away any data.

4. Change the Scale (a.k.a. Recoding)

Another way to reduce the amount of ink on the page is by changing the scale - otherwise known as recoding.

A common example of this is the Top 2 Box score, which combines the top two responses of categorical scale questions. In the case of a 5-point satisfaction scale, the Top 2 Box score would simply be the percentage of respondents who selected either the top box (Extremely satisfied) or the second box (Satisfied) response.

By generating a Top 2 Box score, you are left with a single metric rather than the original five options on the scale. This is not only much easier to interpret, but it also provides the flexibility to crosstab against other variables in a banner.

5. Summarize

When reducing the overall amount of data in an analysis, summarization is an effective way to condense large datasets into digestible insights. And with AI becoming increasingly prevalent across the market research workflow, summarization is now one of the easiest and most effective data reduction techniques you can use.

Researchers can quickly extract key insights by selecting the relevant exploratory analyses and choosing an AI-driven interpretation tool. Additionally, AI can refine summaries further by identifying surprising, counterintuitive, or inconsistent results.

Beyond text-based summarization, numerical summaries—such as averages, percentages, top-two box scores, and correlations—further reduce complexity while retaining valuable information.

6. Reorder

Changing the order of data can significantly impact clarity. Of course, how we choose to sort data (alphabetically, highest to lowest, etc) makes a difference here, but when it comes to data reduction, a more advanced technique is diagonalization.

Diagonalization involves rearranging rows and columns in a table so that small values are clustered into the corners. By changing the order of the data in this way, you can generate a heatmap to visualize the data.

7. Decompose

Decomposition is when you replace one single number with multiple numbers. Although this may sound counterintuitive when the aim is data reduction, it allows us to see patterns in the data a little clearer.

Decomposition can happen theoretically (i.e., profit is divided into revenue and cost) or algorithmically (i.e., seasonal decomposition separates data into trend + seasonal component + random noise).  

By breaking down data into smaller, more meaningful segments, we reveal insights that might have otherwise vanished in the data reduction process.

8. Apply Common Sense

Something as simple as taking the time to look at your data and asking if it makes sense is invaluable regarding data cleaning. This is sometimes referred to as looking for APEs (alternative plausible explanations).

For example, a study could show a positive correlation between the amount of firefighters that go to a fire and the extent of the property damage.

Therefore, a reasonable recommendation could be to send fewer firefighters to fires to reduce damage - right? Obviously not. The correlation between firefighters and damage is because more firefighters are sent to worse fires.

Another common APE is response bias - the factors that lead to someone responding inaccurately. This might be because certain respondents tend to answer "yes" to every question, or they choose only the highest or lowest response available.

Once you've identified an APE, you want to test it to rule it out.

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