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Crosstab is one of the most useful analytical tools in the industry. Crosstab analysis is most often used to analyze categorical (nominal measurement scale) data.
At their core, cross-tabulations are simply data tables that present the results of total respondents or subgroups. You can examine relationships within & across different segments that might not be readily obvious when only looking at total survey responses.
Statistical analysis methods
Regression is one of the most common statistical technique used to understand corelation between two (or more) variables.
The T-test is a significant testing tool between two data groups with different mean values. The T-test allows the user to interpret whether differences are real or not.
Cluster analysis is a way of processing datasets by identifying how similar data sets are. You can identify whether there are similar groups (clusters) amongst a large database, or if the data is evenly spread out.
Like the T-test, ANOVA (analysis of variance) is a way of testing the differences between groups to see if they’re statistically significant. The difference is it allows you to compare three or more groups rather than just two.
Factor analysis is a way to reduce the complexity of your research findings by reclassifying a large number of initial variables to few more meaningful variables. You can uncover “hidden” factors that explain variance (difference from the average) in your data.
Researchers like to understand and explain how people make the complex choices. Conjoint analysis simulate this: it asks people to make trade-offs with test choices similar to the real world, followed by modeling to get insightful results based on people's choices.
MaxDiff is ideal to rank importance of multiple items such as brands, product features, ad claims, side effects, etc. MaxDiff is also known as "best-worst scaling".
Price Sensitivity Measurement (PSM)
PSM is a research method that allows you to determine the most optimum consumer price range based on the perceived value of the product. It is can be used in the development of a new product or perception of price for existing product.
Imagine that you have a product usage of men and women equal split. But when your survey returns, 80% of your respondents were female, and 20% were male. If you analyze these results with no weighting, data will be bias towards women.
Weighting solve this bias by allocating representative percentage across different fields that is correct for the market.