  The vast availability of large scale, massive and big data has increased the
computational cost of data analysis. One such case is the computational cost of
the univariate filtering which typically involves fitting many univariate
regression models and is essential for numerous variable selection algorithms
to reduce the number of predictor variables. The paper manifests how to
dramatically reduce that computational cost by employing the score test or the
simple Pearson correlation (or the t-test for binary responses). Extensive
Monte Carlo simulation studies will demonstrate their advantages and
disadvantages compared to the likelihood ratio test and examples with real data
will illustrate the performance of the score test and the log-likelihood ratio
test under realistic scenarios. Depending on the regression model used, the
score test is 30 - 60,000 times faster than the log-likelihood ratio test and
produces nearly the same results. Hence this paper strongly recommends to
substitute the log-likelihood ratio test with the score test when coping with
large scale data, massive data, big data, or even with data whose sample size
is in the order of a few tens of thousands or higher.
