For details of the advanced properties available for significance testing, see advanced.
In the results, significant values will be indicated by symbols:
High threshold: +++ or ---
Medium threshold: ++ or --
Lower threshold: + or -
The test allows comparison of test values with threshold values.
N= Total base
Note: it is possible to define a threshold to be 0, so that the test is not run at that threshold. For example, if you want "+" to appear at 90%, specify 0,0,90. If you only want "++" to appear at 95%, specify 0,95,0.
Counts when independent
EffInd(i,j) = Total(i) * Total(j) /
PrcInd (i,j) = EffInd(ij) /
PrcObs (i,j) = EffObs(ij) /N
|
(PrcObs(ij)-PrcInd(ij)) Test value =-------------------------------------------------- squared root(PrcInd(ij)*(1-PrcInd(ij))/N) |
=> if the test value > threshold = Significant difference, upwards if the coefficient is positive or downwards if the coefficient is negative.
Compared to all other columns
N1 = Total(j)
N2 = N - N1
If N1 and N2 >
P1 = Observe(i,j) / N1
P2 = (Total(i) - Observe(i,j) ) / N2
If the standard deviation is known, we calculate 'D', which follows a normal mathematical expectation law = 0 ( p1-p2=0), and standard deviation s'd=ROOT(f*(1-f) * (1/N1+1/N2)), where f is an estimate calculated as follows:
f= (p1*n1+p2*n2)/n1+n2
s'd = Squared root(f*(1-f) * (1/N1+1/N2))
|
f D= ------------ s'd |
If the standard deviation is not known, we calculate 'D', which follows a normal mathematical expectation law p1-p2, and standard deviation sd=Squared ROOT((p1*(1-p1))/n1 + (p2*(1-p2))/n2),
|
(P1-P2) Test Value = -------------------------------------------------------- P1*(1-P1) P2 * (1 - P2) Squared Root (----------------- + ------------------- ) N1 N2 |
=> if the test value> threshold = Significant difference, upwards if the coefficient is positive or downwards if the coefficient is negative.
Compared to all other rows
N1 = Total(i)
N2 = dBase - N1
If N1 an N2 >
P1 = Observe(i,j) / N1
P2 = (Total(j) - Observe(i,j) ) / N2
If the standard deviation is known, we calculate 'D', which follows a normal mathematical expectation law = 0 ( p1-p2=0), and standard deviation s'd=ROOT(f*(1-f) * (1/N1+1/N2)), where f is an estimate calculated as follows:
f= (p1*n1+p2*n2)/n1+n2
s'd = ROOT(f*(1-f) * (1/N1+1/N2))
|
f D= ------------ s'd |
If the standard deviation is not known, we calculate 'D', which follows a normal mathematical expectation law p1-p2, and standard deviation sd=Squared ROOT((p1*(1-p1))/n1 + (p2*(1-p2))/n2),
|
(P1-P2) Test Value = -------------------------------------------------------- P1*(1-P1) P2 * (1 - P2) Root (----------------- + ------------------- ) N1 N2 |
=> if the test value> threshold = Significant difference, upwards if the coefficient is positive or downwards if the coefficient is negative.