Crosstab, unparalleled insights.

Crosstab: segment, compare and analyze

Crosstab: segment, compare and analyze

IdSurvey provides tools for multi-variate analysis by crosstabbing 2 or more variables. With crosstab ( cross tabulation or contingency table), the desired confidence level can be set and associations and correlations between variables can be identified using statistical tests such as Chi-square, Z-Test and ANOVA.

In-depth information in seconds

With IdSurvey, you do not need to be an expert to get detailed information. The software takes care of all the heavy lifting, automatically running appropriate tests such as Chi-square, Z-Test and ANOVA to provide detailed information in a few moments.

Explore your data in an intuitive way

Observe different categories of respondents and discover their behavior through easy-to-interpret cross tabulations. You can visualize different data structures in one interface and even combine groups. You can easily analyze statistics related to questions, open-ended responses, or contacts fields.

Group values for flexible analysis

Create Buckets to group categories or variable values to create classes or to make the crosstab analysis clear and understandable. Also, to perform quantitative analysis, you can set a custom numerical score to each variable value.

Not only bivariate analysis

Create the crosstab by setting up the banner and adding up to five nested variables. This allows you to analyze the interactions between the variables in detail, breaking down the data into more specific groups and revealing hidden patterns and correlations between the different dimensions being analyzed.

Export data dynamically

You can export cross tabulations in Excel format with one click. Choose whether to export a single crosstab or multiple contingency tables in a single file, or use the convenient views to export multiple crosstabs at once.

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Cross tabulation: simple interface

IdSurvey’s crosstab interface is simple and intuitive.

Configure banner and stabs by grouping, crosstabbing, and sorting any variable by simply dragging and dropping it. A preconfigured list of metrics will allow you to analyze the data with a single click. You can choose to analyze all interview data or only validated interview data.

For clear and quick analysis, each category or value of crosstab variables can be customized by setting the score, including, excluding or hiding categories from the data source, and quickly creating convenient groupings of variable values (buckets).

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What is cross tabulation?

Cross tabulation, also known as “crosstab,” is a statistical method used to analyze the relationship between two or more categorical variables. It involves creating a table that displays the frequency distribution of data points for each combination of categories of the variables being analyzed. This method examines patterns, associations, or dependencies between the variables by comparing the distribution of one variable across the different categories of another variable.

How to create a crosstab?

Creating a crosstab allows you to analyze the relationship between different categorical variables in your dataset.

  • Select Variables: Begin by choosing at least one column variable (banner) and one row variable (stub). These variables will determine the structure of your crosstab.
  • Choose Metrics: Select the desired metric to display in the cells of your crosstab. Common metrics include counts, percentages, or other statistical measures.
  • View Results: Once you’ve selected your variables and metrics, a crosstab table will be displayed, showing the count of responses distributed for each combination of answer options between the stub question and the banner question.

How to read a crosstab?

When you look at a crosstab, the first step is to understand which variables are presented along the rows and columns. For example, you might have a variable representing gender along the rows and a variable representing age along the columns.

By examining the headers of the rows and columns, you can get an overview of the specific categories within each variable. These headers provide the context for the data presented in the table, helping you better understand the distribution of the data.

The cells within the table show the frequency or proportion values of the data falling into a particular combination of row and column variable categories. Each cell provides information about how many observations are in a specific cross-category. For example, you might see the number of men in a certain age group within the table.

By comparing values across rows and columns, you can identify patterns or relationships between the variables. Look for cells with particularly high or low values, as they may indicate significant associations or trends within the data.

Understanding these aspects of a crosstab allows you to extract meaningful insights and draw conclusions about the relationships between different categorical variables in your dataset.

What does it mean to add nested variables to my banner?

The banner is usually the variable or variables placed on the column axis of your crosstab. You can set it up by selecting the desired variable from the list of available variables and dragging it to the area designated for the banner.
Adding nested variables means that you can examine multiple variables simultaneously within your banner. This allows you to analyze how the subcategories of one variable relate to the subcategories of another, providing a deeper understanding of multivariate relationships.

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