measures of sequential variation I have psychological data that consists of series of binary choices (e.g., 1001010101) made by research subjects, where 1 and 0 reflect two different options that can be chosen.
Are there any known measures of the variation in such sequences?
For example, the sequence of 1s and 0s in 0101011010 is more variable than the one in 001111110. 
Edit: to clear up any confusion in relation to the comments below, I noted in the original post that the series in my data not only vary in the sequences of 1/0s but they also vary in the proportion of 1s and 0s (i.e., the sample variance), just in case this info happened to be relevant to answering the inquiry.
 A: *

*If I have correctly understood, it seems that you can possibly measure it by something like $\frac{1}{n-1}\sum_{t=2}^n (y_t-y_{t-1})^2$.

*This measure is also equal to "The number of changes from 1 to 0 or 0 to 1" divided by "the number of times it could possibly have changed" --- i.e. the proportion of times it changed from the previous value. (So it's clearly between 0 and 1.)

*This is also -- up to end effects and possible differences in denominator-correction terms $(1-r_1(y))/2$, where $r_1$ is the lag 1 sample autocorrelation. Since those diminish with sample size, for large samples they should be essentially equivalent.

You might also consider a (somewhat) related quantity which is the number of runs in a Wald-Wolfowitz type runs test (many runs = lots of sequential variation, few runs means little sequential variation); note that the number of changes discussed in 2. above is one fewer than the number of runs. However, something based on this measure would condition on the actual number of 1s and the number of 0's (e.g. if there are nine 1's and one 0, then taking that as given, the only possible number of changes are 1 or 2). Consequently if you divide the number of runs by the largest possible number of runs this may be somewhat larger than the above measure (which divides by the total number of changes possible ignoring how many 0s and 1s there were), especially when there is a strong imbalance in the numbers of 1's and 0's.
