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Survey Features
Cross-tabulation analysis
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What do you mean by cross-tabulation analysis?
You must have come across tabular layouts of information many times. Just like the one you see in a report card or the ingredients behind a chips packet and so on. You can see two-dimensional or even three-dimensional tables spreading out such information. In short, cross-tabulation states the relationship between variables.
Talking in survey terms, cross-tabulation refers to the number of respondents having the mentioned characters as table cells. And cross-tabulation analysis is nothing but studying the tabulated information to derive conclusions out of it. Cross-tabulation analysis also uses terms like banners, stubs, chi-square statistics and expected values.
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Example of cross-tabulation analysis
The above image shows a report of frequency of visits by people further bifurcated by age group. Cross-tabulation analysis will bring out a conclusion that people form the age group “Under 18” have comparatively more percentages of visiting in every category on the left hand-side.
The chart can be put into visual representation as:
Uses of cross-tabulation analysis in surveys
Surveys can be cross tabulated only when you have categorized variables or data. Meaning, your data needs to be able to divide into groups having something in common. Let us look at some areas where you can use cross-tabulation analysis for your survey:
- Employee engagement and satisfaction surveys – you can get to know what employees feel and think about working in your workplace and about their job profiles. You can compare variables and conduct this survey in various departments.
- Exit surveys – conduct surveys when employees are exiting your company. More of these and you can tabulate the information and try to figure out the reason behind their exit.
- Customer behavior – you can keep track of customer performance in your business and understand their feedback with what most commonly appears in data.
- Product research – using demographic factors, you can see differences in how people feel about your brand and serve them accordingly.
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Advantages of cross-tabulation analysis
- Reducing error – it is not always easy to analyze large data and represent them accurately to derive conclusions from it. Although, with cross-tabulation analysis, your data sets become more manageable thus reducing any errors.
- Accurate insights – relationships between variables are represented accurately in cross-tabulation. This gives a straightforward analysis of data and easy to gather insights from.
- Quality information – you get a simplified version of your data, only the part that matters the most. With analyzing important data, you get information which is reliable and actionable too.
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