As a previous reply mentioned, yes it is and the technical description is at SPSS's support page: https://www-304.ibm.com/support/docview.wss?uid=swg21477269

This is a useful statistic for those who understand it.  Suppose we investigate whether 78 employees' promotion (yes/no) is related to their performance ranking in the previous year (1-4, 1=low), as follows:

Ranking 1: Not promoted 17, Promoted 2, Total 19. 
<br>Ranking 2: Not promoted 16, Promoted 4, Total 20.
<br>Ranking 3: Not promoted 14, Promoted 6, Total 20.
<br>Ranking 4: Not promoted 10, Promoted 9, Total 19.

SPSS shows a significant linear-by-linear association (p=.008) showing that there is a significant association between the ranking and being promoted.  

Some useful details of how this works are:
<br>1.  The test relates to the odds.  Odds are used for their statistical properties, and are not quite the same as probabilities.  For ranking 1, the odds of being promoted are 2:17, as opposed to the probability which is 2:19.
<br>2.  Then, the test is on the odds ratios; e.g. if you move from rank 1 to rank 2, the odds ratio is 4:16/2:17 = 0.250/0.118 = 2.12.  (The null hypothesis is that the odds ratio is 1, i.e. a change in ranks makes no difference to the odds.)
<br>3.  The procedure presumes that the odds ratios (in the population) are the same for all steps (i.e. if moving from rank 1 to rank 2 doubles the odds of promotion, moving from rank 2 to rank 3 would also double the odds of promotion).  That is why there is only 1 degree of freedom.  (This assumption is known as "linearity in the logit".)
<br>4.  The test is therefore conceptually the same (and gives a similar answer) to doing logistic regression with just one covariate. (In logistic regression, "covariate" means a variable like this one).  In this case the covariate would be ranking, and the DV would be promotion decision.