WE AUTOMATE YOUR…
Cross Tabulation
Just upload your data file, define your breakdowns and your tables will soon be ready.
Cross tabulation, cross-tabs or simply just Excel tables:
Whatever you call it – we automate it!
Just upload your data file, define your breakdowns and your tables will soon be ready.
Cross tabulation, cross-tabs or simply just Excel tables:
Whatever you call it – we automate it!
We automate your Excel Cross Tabulation. Faster delivery, with less risk of errors.
We highlight significant values for you to identify patterns and trends faster.
We enable quick insights from the survey by:
There are many different ways of presenting data for Likert Scale Questions. In our Excel Cross-tabs you always get four different measures – so you can choose the one you prefer!
Click below to download a Cross Tab example (Excel file) to see exactly how your tables will look.
Excel Cross Tabulation Automation
Order standardized cross-tabulation from our Professional service team or start using our platform. You decide!
If your Excel tables does not fit into the packages above, please contact us for a customised quote.
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Want to know more before you place an order?
Cross tabulations, commonly known as cross tabs, involve the analysis of categorical variables. Categorical variables represent distinct categories or groups, such as gender, age groups, product types, or geographic regions.
The primary output of a cross-tabulation is a table that displays the frequencies, counts, averages or other measures of observations that fall into the intersections of the categories of the variables being analyzed. The table is organized in rows and columns, with each cell representing the count or frequency of observations for a specific combination of categories.
Cross tabs are a powerful technique used to analyze the differences between groups. They comprehensively organize and interpret data, offering valuable insights into patterns, trends, and associations.
Significance tests play a crucial role in cross-tabulations by helping to determine whether the differences between variables are statistically significant or if they could have occurred by chance.
Testing the significance of a sample proportion is used to answer whether the breakdown column sample proportion is significantly greater or smaller than the total population proportion (one-tail test).
Colouring the cells or values of groups significantly higher or lower than the total values makes it easier to distinguish them visually.
Our significance tests use Significance Level 0.05 (5%) as the standard, the most commonly used level in statistical testing. The significance test level refers to the predetermined thresholds used to determine whether the statistical test results are considered statistically significant. A significance level of 0.05 indicates a 5% chance (1 in 20) of rejecting the null hypothesis when it is true (Type I error).
Other common Significance Levels are 0.01 (1%) (lower probability of making Type I errors) and 0.1 (10%) (lower probability of making Type I errors).