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The ABCs of Survey Analysis

A laptop is resting on top of a stack of papers, displaying the ABCs of Survey Analysis.

Adebisi Adewusi

February 10 2020

Obtaining responses from your painstakingly constructed consumer surveys is half the battle when it comes to gathering insights. The other part is knowing how to analyze the data in a way that yields actionably useful information. 75% of marketers agree that lack of education on data analysis is the biggest barrier to making key data-based business decisions. In this article, we’ll take a look at how to analyze survey data for the most effective application on your marketing strategy.

Clean Your Data

When it comes to analyzing survey research, a basic prerequisite is quality input data. If you use poor data for your analysis, you’ll simply emerge with faulty and unreliable insights.  91% of marketers believe poor data leads to wasted resources, lost productivity, or wasted marketing and communications spend.

As such, you’ll need to get rid of data that can skew your analysis. Here are some data-cleaning tips:

Remove Speeders: Investigate responses from individuals who finish your survey in less than half the median completion time. But before deleting those responses outright, examine your data to see whether their results look different than those of other respondents. Try not to discard more than 10% of your survey sample for speeding.

Review Open-Ended Questions: If your survey includes open-ended questions, weed out responses that don’t match the questions asked, nonsense inputs that suggest keyboard mashing, and contradictory answers which might indicate that respondents didn’t provide truthful answers elsewhere in the survey.

Look Out For Straightliners: Straightliners answer questions uniformly, e.g., choice ‘A’ for every single question – which might signal inadmissible data. To spot straightliners, look for respondents that complete your survey faster than 90%-95% of the rest — but again, keep in mind that some straightlined answers might actually be genuine. As such, try to have solid criteria for identifying straightliners before carrying out your survey.

Be Aware of Data Mistakes

When analyzing and interpreting survey data, some people take shortcuts or make mistakes that could render their survey useless. Below are some data mistakes you should be alert to.

Cherry Picking: Emphasizing data that suits your purposes at the expense of the entire body of results is known as cherry picking, and reduces the credibility of your findings. To avoid cherry picking, ensure you paint a complete picture by offering consistent reports on all important figures, and share the underlying data for context and transparency.

 Implying That Correlation = Causation: When interpreting survey results, remember that correlation doesn’t necessarily imply causality. To avoid drawing wrong conclusions, take time to understand underlying factors and verify their relevance. If you need to confirm causality, build an experimental study that is repeated over time.

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Use Cross Tabulation

When conducting a survey analysis, use cross tabulation to depict the relationship between two or more survey questions. This can mean correlating responses to a yes/no item with stated gender, for example. This type of analysis can help you understand your audience, investigate data sets at a granular level, and uncover actionable insights. Below are some points to keep in mind when using cross tabulation:

Cross Tabulation Software: Use a survey tool with a cross tabulation feature to compare how multiple groups of respondents answer your questions. Ensure the software you choose can automatically run appropriate tests like P-values and Chi-square to surface insights in seconds, export your data into other programs, and perform detailed statistical analyses on dozens of variables.

Remember Statistical Significance: A statistically significant difference means the result is unlikely due to random chance. If your data has statistical significance, it means your survey results are meaningful to a large degree. When evaluating statistical significance, make sure you have a representative sample and remember the importance of keeping an open mind to alternative factors.

Filter Results: Instead of comparing subgroups to one another, narrow your focus to how one particular subgroup answered a question. This will help you to cut through the noise and make sense of your responses. Your sample size decreases every time you apply a filter or cross tab. Use a sample size calculator to validate the statistical significance of your results.

Interrogate Your Data

Transform your data into insights that can help you grow your business with data interrogation. This is the process of manually confirming your source data and processes, a key step in verifying the results and drawing accurate and meaningful conclusions.

Ask the Right Questions: Transform your data into useful information by asking carefully defined questions. For instance, what are the most common responses to question X? What stories emerge from the data set? The questions you ask should help identify significant themes, patterns, and relationships emerging in your data set.

Look for Insightful Data: The real value of data analysis lies in its ability to deliver real-world insights. Since the best insights are actionable and prescriptive, keep an eye out for emerging trends, findings that contradict your experience, or strong relationships between variables.

Make Comparisons: Compare survey results against previously collected data to help you to evaluate how customer responses have changed over time and spot emerging trends. When making comparisons, make sure the question wording and scales are equivalent. You can also compare different slices of data such as two different time periods, or two groups of respondents.

Use Data Visualization

90% of information processed by the brain is visual. Bring your data to life and communicate your survey findings with visual reports. While graphical displays of data can be powerful communication tools, they can also be confusing to your readers if demonstrated imprecisely. Here are three tips to help you build easily graspable visualizations:

Keep it Simple: To make your charts clean and easy to understand, remove unnecessary elements, emphasize the most important data, use visual hierarchy, and avoid effects that may distort your data, such as extraneous 3D. Also, use fonts that are easy to read and choose shape fills and backgrounds which support numbers and text.

Choose the Right Chart: In selecting a chart for your data visualization, think about the message you want to share with your audience and choose a visualization that clearly depicts the relationships in your data. For example, scatter plots work well with two pieces of quantitative data, whereas line charts work best for date ordinal data.

Mind Your Colors: Color can transform a boring visualization into an inspiring one. Use no more than two color palettes: hues to show changes and axles, and dark grey for labels. Erratic graphics can make visualizations hard to interpret, so use colors consistently.

Thinking through your survey analysis ahead of time will ensure you get the actionable insights necessary to make informed business decisions. In addition to the tips mentioned above, keep the purpose of your survey at the forefront of your mind so as to easily find what you’re looking for, and when presenting your findings, go beyond sharing percentage values and highlight the insights derived from your data.

Adebisi Adewusi

Adebisi Adewusi is a freelance B2B writer and a Huffington Post Contributor. When she’s not creating compelling content for businesses, you’ll find her capturing moments with her Nikon d600.

This article was originally published by our friends at PostFunnel.

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