  Word complexity is defined in a number of different ways. Psycholinguistic,
morphological and lexical proxies are often used. Human ratings are also used.
The problem here is that these proxies do not measure complexity directly, and
human ratings are susceptible to subjective bias. In this study we contend that
some form of 'latent complexity' can be approximated by using samples of simple
and complex words. We use a sample of 'simple' words from primary school
picture books and a sample of 'complex' words from high school and academic
settings. In order to analyse the differences between these classes, we look at
the letter positional probabilities (LPPs). We find strong statistical
associations between several LPPs and complexity. For example, simple words are
significantly (p<.001) more likely to start with w, b, s, h, g, k, j, t, y or
f, while complex words are significantly (p<.001) more likely to start with i,
a, e, r, v, u or d. We find similar strong associations for subsequent letter
positions, with 84 letter-position variables in the first 6 positions being
significant at the p<.001 level. We then use LPPs as variables in creating a
classifier which can classify the two classes with an 83% accuracy. We test
these findings using a second data set, with 66 LPPs significant (p<.001) in
the first 6 positions common to both datasets. We use these 66 variables to
create a classifier that is able to classify a third dataset with an accuracy
of 70%. Finally, we create a fourth sample by combining the extreme high and
low scoring words generated by three classifiers built on the first three
separate datasets and use this sample to build a classifier which has an
accuracy of 97%. We use this to score the four levels of English word groups
from an ESL program.
