Detecting direction in interaction evidence

IntroductionPaper (PDF)

I have previously argued (Wallis 2014) that interaction evidence is the most fruitful type of corpus linguistics evidence for grammatical research (and doubtless for many other areas of linguistics).

Frequency evidence, which we can write as p(x), the probability of x occurring, concerns itself simply with the overall distribution of linguistic phenomenon x – such as whether informal written English has a higher proportion of interrogative clauses than formal written English. In order to calculate frequency evidence we must define x, i.e. decide how to identify interrogative clauses. We must also pick an appropriate baseline n for this evaluation, i.e. we need to decide whether to use words, clauses, or any other structure to identify locations where an interrogative clause may occur.

Interaction evidence is different. It is a statistical correlation between a decision that a writer or speaker makes at one part of a text, which we will label point A, and a decision at another part, point B. The idea is shown schematically in Figure 1. A and B are separate ‘decision points’ in a given relationship (e.g. lexical adjacency), which can be also considered as ‘variables’.

Figure 1: Associative inference from lexico-grammatical choice variable A to variable B (sketch).

Figure 1: Associative inference from lexico-grammatical choice variable A to variable B (sketch).

This class of evidence is used in a wide range of computational algorithms. These include collocation methods, part-of-speech taggers, and probabilistic parsers. Despite the promise of interaction evidence, the majority of corpus studies tend to consist of discussions of frequency differences and distributions.

In this paper I want to look at applications of interaction evidence which are made more-or-less at the same time by the same speaker/writer. In such circumstances we cannot be sure that just because B follows A in the text, the decision relating to B was made after the decision at A. Continue reading

Measures of association for contingency tables

Introduction Paper (PDF)

Often when we carry out research we wish to measure the degree to which one variable affects the value of another, setting aside the question as to whether this impact is sufficiently large as to be considered significant (i.e., significantly different from zero).

The most general term for this type of measure is size of effect. Effect sizes allow us to make descriptive statements about samples. Traditionally, experimentalists have referred to ‘large’, ‘medium’ and ‘small’ effects, which is rather imprecise. Nonetheless, it is possible to employ statistically sound methods for comparing different sizes of effect by estimating a Gaussian confidence interval (Bishop, Fienberg and Holland 1975) or by comparing pairs of contingency tables employing a “difference of differences” calculation (Wallis 2011).

In this paper we consider effect size measures for contingency tables of any size, generally referred to as “r × c tables”. This effect size is the “measure of association” or “measure of correlation” between the two variables. There are more measures applying to 2 × 2 tables than for larger tables. Continue reading

A statistics crib sheet

Confidence intervalsHandout

Confidence intervals on an observed rate p should be computed using the Wilson score interval method. A confidence interval on an observation p represents the range that the true population value, P (which we cannot observe directly) may take, at a given level of confidence (e.g. 95%).

Note: Confidence intervals can be applied to onomasiological change (variation in choice) and semasiological change (variation in meaning), provided that P is free to vary from 0 to 1 (see Wallis 2012). Naturally, the interpretation of significant change in either case is different.

Methods for calculating intervals employ the Gaussian approximation to the Binomial distribution.

Confidence intervals on Expected (Population) values (P)

The Gaussian interval about P uses the mean and standard deviation as follows:

mean xP = F/N,
standard deviation S ≡ √P(1 – P)/N.

The Gaussian interval about P can be written as P ± E, where E = z.S, and z is the critical value of the standard Normal distribution at a given error level (e.g., 0.05). Although this is a bit of a mouthful, critical values of z are constant, so for any given level you can just substitute the constant for z. [z(0.05) = 1.95996 to six decimal places.]

In summary:

Gaussian intervalP ± z√P(1 – P)/N.

Confidence intervals on Observed (Sample) values (p)

We cannot use the same formula for confidence intervals about observations. Many people try to do this!

Most obviously, if p gets close to zero, the error e can exceed p, so the lower bound of the interval can fall below zero, which is clearly impossible! The problem is most apparent on smaller samples (larger intervals) and skewed values of p (close to 0 or 1).

The Gaussian is a reasonable approximation for an as-yet-unknown population probability P, it is incorrect for an interval around an observation p (Wallis 2013a). However the latter case is precisely where the Gaussian interval is used most often!

What is the correct method?

Continue reading

Goodness of fit measures for discrete categorical data

Introduction Paper (PDF)

A goodness of fit χ² test evaluates the degree to which an observed discrete distribution over one dimension differs from another. A typical application of this test is to consider whether a specialisation of a set, i.e. a subset, differs in its distribution from a starting point (Wallis 2013). Like the chi-square test for homogeneity (2 × 2 or generalised row r × column c test), the null hypothesis is that the observed distribution matches the expected distribution. The expected distribution is proportional to a given prior distribution we will term D, and the observed O distribution is typically a subset of D.

A measure of association, or correlation, between two distributions is a score which measures the degree of difference between the two distributions. Significance tests might compare this size of effect with a confidence interval to determine that the result was unlikely to occur by chance.

Common measures of the size of effect for two-celled goodness of fit χ² tests include simple difference (swing) and proportional difference (‘percentage swing’). Simple swing can be defined as the difference in proportions:

d = O₁/D₁ – O₀/D₀.

For 2 × 1 tests, simple swings can be compared to test for significant change between test results. Provided that O is a subset of D then these are real fractions and d is constrained d ∈ [-1, 1]. However, for r × 1 tests, where r > 2, we need to obtain an aggregate score to estimate the size of effect. Moreover, simple swing cannot be used meaningfully where O is not a subset of D.

In this paper we consider a wide range of different potential methods to address this problem.

Correlation scores are a sample statistic. The fact that one is numerically larger than the other does not mean that the result is significantly greater. To determine this we need to either

  1. estimate confidence intervals around each measure and employ a z test for two proportions from independent populations to compare these intervals, or
  2. perform an r × 1 separability test for two independent populations (Wallis 2011) to compare the distributions of differences of differences.

In cases where both tests have one degree of freedom, these procedures obtain the same result. With r > 2 however, there will be more than one way to obtain the same score. The distributions can have a significantly different pattern even when scores are identical.

We apply these methods to a practical research problem, how to decide if present perfect verb phrases more closely correlate with present- and past-marked verb phrases. We consider if present perfect VPs are more likely to be found in present-oriented texts or past-oriented ones.

Continue reading

z-squared: the origin and application of χ²

Abstract Paper (PDF)

A set of statistical tests termed contingency tests, of which χ² is the most well-known example, are commonly employed in linguistics research. Contingency tests compare discrete distributions, that is, data divided into two or more alternative categories, such as alternative linguistic choices of a speaker or different experimental conditions. These tests are highly ubiquitous, and are part of every linguistics researcher’s arsenal.

However the mathematical underpinnings of these tests are rarely discussed in the literature in an approachable way, with the result that many researchers may apply tests inappropriately, fail to see the possibility of testing particular questions, or draw unsound conclusions. Contingency tests are also closely related to the construction of confidence intervals, which are highly useful and revealing methods for plotting the certainty of experimental observations.

This paper is organised in the following way. The foundations of the simplest type of χ² test, the 2 × 1 goodness of fit test, are introduced and related to the z test for a single observed proportion p and the Wilson score confidence interval about p. We then show how the 2 × 2 test for independence (homogeneity) is derived from two observations p₁ and p₂ and explain when each test should be used. We also briefly introduce the Newcombe-Wilson test, which ideally should be used in preference to the χ² test for observations drawn from two independent populations (such as two subcorpora). We then turn to tests for larger tables, generally termed “r × c” tests, which have multiple degrees of freedom and therefore may encompass multiple trends, and discuss strategies for their analysis. Finally, we turn briefly to the question of differentiating test results. We introduce the concept of effect size (also termed ‘measures of association’) and finally explain how we may perform statistical separability tests to distinguish between two sets of results.


Karl Pearson’s famous chi-square test is derived from another statistic, called the z statistic, based on the Normal distribution.

The simplest versions of χ² can be shown to be mathematically identical to equivalent z tests. The tests produce the same result in all circumstances. For all intents and purposes “chi-squared” could be called “z-squared”. The critical values of χ² for one degree of freedom are the square of the corresponding critical values of z.

  • The standard 2 × 2 χ² test is another way of calculating the z test for two independent proportions taken from the same population (Sheskin 1997: 226).
  • This test is based on an even simpler test. The 2 × 1 (or 1 × 2) “goodness of fit” (g.o.f.) χ² test is an implementation of one of the simplest tests in statistics, called the Binomial test, or population z test (Sheskin 1997: 118). This test compares a sample observation against a predicted value which is assumed to be Binomially distributed.

If this is the case, why might we need chi-square? Continue reading