Linear trend test on proportions
A more powerful form of chisquare specifically tests for a linear trend in proportions across groups. For example, proportion remembered correctly using a memory aid.
Example

Time 1 
Time 2 
Time 3 

Correct 
3 
6 
10 

Incorrect 
9 
6 
2 
Does the proportion correct change linearly over time?
The chisquare testing the presence of a linear trend is outputted by SPSS CROSSTABS as the LinearbyLinear association term ( also called the MantelHaenszel statistic).
Linearbylinear association = $$r^text{2} (N1)$$
where r is the Pearson correlation of the rows (correct/incorrect) with the columns (group) and N is the total sample size. Bruce Weaver has shown that provided all expected cell counts are greater than 1 the LinearbyLinear association is the most powerful preferred chisquare for 2x2 tables (see here).
In particular for a 2x2 table Bruce shows that the linearbylinear chisquare has the special form equal to N(adbc)^2 / (mnrs)
where: * N is the total number of observations * a, b, c, and d are the observed counts in the 4 cells * ^2 means "squared"
* m, n, r, s are the 4 marginal totals
For a 2x2 table (only) the regular Pearson chisquare (e.g., in the output from statistical software), can be converted to the 'N  1' chisquare as follows:
'N 1' chisquare = LinearbyLinear chisquare = Pearson chisquare x (N 1) / N
The lack of fit is the difference between the Pearson chisquare value and the linearbylinear one.
Model 
Chisquare 
Df 
pvalue 

Linear 
7.96 
1 
0.005 

Lack of Fit 
0.29 
1 
0.130 

Total 
8.25 
2 
0.004 


(Pearson Chisquare) 


So there is a linear trend providing a reasonable fit.
Denoting the time points by –1,0 and 1 and regressing these on the observed proportions correct give regression estimates of the above linear trend. The Pearson chisquare lack of fit term is (OE)*(OE)/E where O are observed table frequencies and E are expected frequencies using the expected proportions from the linear regression.
Observed proportion correct 
0.33 
0.50 
0.83 

Expected proportion correct 
0.30 
0.55 
0.80 

(Fitting a linear trend) 



You can also compare linear trends of proportions in SPSS LOGISTIC.
References:
Agresti, A (2013) Categorical Data Analysis. Third Edition. Wiley:New York. Pages 8687 mention the above testing for linear trend.
Everitt, BS and Wykes T.(1999) A Dictionary for Psychologists. Arnold:London. (See page 31).