Relationships are the most important thing in our lives—and it turns out the same is true for data. Without an understanding the relationships between various data sets, you get only a superficial view of your data, not the deep dive you were hoping for.
Crosstab software is one way researchers can see how different variables relate to each other. For example, instead of simply knowing that 54% of respondents intend to purchase your product in the next year and 33% don’t (13% are unsure), you can cross-tabulate by gender and discover that more women than men intend to purchase your product, and that the majority of men are not sure about making a purchase.
The good news about crosstab software is that it allows you to easily look at a large number of variables: things like age and intent to purchase, income levels and gender, income levels and occupation, occupation and intent to purchase, and so on. This lets you form hypotheses based on stand-out statistics and check their validity by performing more crosstab operations.
The not-so-good news about crosstab software is that it requires some data prep. First, the data must be in a format supported by your software, and all compressed data must be extracted. Second, the data must be prepared with variables in columns and identifiers in rows and reviewed to ensure no data is missing. Along the way, you’ll need to understand crosstab’s unique terminology, including banners (variables listed in column), stubs (variables listed in rows), and expected values (results you expect, like a 50% chance of getting heads on a coin).
Of course, for serious researchers and those faced with organizing massive amounts of data, that prep work is completely worth it. Crosstab software allows huge data sets to be arranged in a concise, spreadsheet-like format. Just as importantly, it helps you confirm your hypotheses, turning what was just an educated guess into a scientifically proven fact.
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