Column significativity (AskiaAnalyse)

You should use column significativity when you want to compare proportions between two independent samples. In the table, letters such as “a", "A" or "A+" are displayed to indicate the level of significance. For example:

To define where the letters in the header will be displayed, in the general tab of table definition, click settings... next to  tab template, and open the total and caption tab. Select as desired from show a column order letter (for col significativity), show a column order letter in edge total column, show a column order letter in total column, show a row order letter (for row significativity).

Once you have defined where the letters will appear, select the calculation column significativity or row significativity in the general tab of table definition:

Note that the calculation column significativity for proportions is found in the section for closed questions.

To define the parameters of the test, in the general tab right-click column significativity, select properties..., then click advanced options.... Then, set the options as follows:

Below is an example of how column significativity would appear if applied to a table. In this example, the columns for comparison are defined from three variables: Age and Social Category within Gender

Specific information on how to select which columns to apply the test to, which test to apply and how to show which cells are significantly different are described in Advanced options for significativity calculations. The options available vary on the type of calculation being defined. 

Four tests are currently supported:

The formulas and the basis on which a difference will be considered significant for each test is set out below.

Classical Student's t-test

This test describes the Z-test using unpooled variance:

Where

We compare the Z value > tα
 = 1.65
 = 1.96
 = 2.576

If Z >  then there is a significant difference.

Student's t-test Using Estimator

This test describes the Z-test using pooled variance:

Where

 , and  is the count observed in the cell  and  is the sample size for the column j.

We compare the Z value > tα
 = 1.65
 = 1.96
 = 2.576

If Z >  then there is significant difference.

Student's t-test Using Efficiency Coefficient

This test is used when we want to reduce the effect on the weighting. Leslie Kish has analysed the effect of unequal weights in the accuracy of estimations through the ‘Unequal Weighting Effect’ (UWE). (Kish L., Weighting for Unequal Pi, Journal of Official Statistics, Vol. 8, N°2, 1992, pp. 183-200).

Where

 , and  is the count observed in the cell  and  is the sample size for the column j.

We compare the Z value > tα
 = 1.65
 = 1.96
 = 2.576 

If Z >  then there is significant difference.

Student's t-test Using Estimator and Efficiency Coefficient

This test is used when we want to reduce the effect on the weighting. Leslie Kish has analysed the effect of unequal weights in the accuracy of estimations through the ‘Unequal Weighting Effect’ (UWE). (Kish L., Weighting for Unequal Pi, Journal of Official Statistics, Vol. 8, N°2, 1992, pp. 183-200).

Where

 , and  is the count observed in the cell  and  is the sample size for the column j.

We compare the Z value > tα
 = 1.65
 = 1.96
 = 2.576

If Z >  then there is a significant difference.

 

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