1 Introduction

This tutorial introduces the \(\chi\)2 family of tests. The entire code for the sections below can be downloaded here.

2 Preparation

As all caluculations and visualizations in this tutorial rely on “R”, it is necessary to install “R”, “RStudio”, and “Tinn-R”. If these programms (or, in the case of “R”, environments) are not installed yet, please search for them in your favorite search engine and add the term “download”. Open any of the first few links and follow the installation instructions (they are easy to follow, do not require any specifications, and are pretty much self-explanatory).

In addition, certain “libraries” need to be installed so that the scripts shown below are executed without errors. Before turning to the code below, please install the libraries needed for running the code below. If you have already installed the libraries mentioned below, then you can skip ahead ignore this section. To install the necessary libraries, simply run the following code - it may take some time (between 1 and 5 minutes to install all of the libraries so you do not need to worry if it takes some time).

# clean current workspace
rm(list=ls(all=T))
# set options
options(stringsAsFactors = F)
# install libraries
install.packages(c("stringr", "cfa", "dplyr"))

Once you have installed “R”, “R-Studio”, “Tinn-R”, and have also initiated the session by executing the code shown above, you are good to go.

3 (Pearsons’s) Chi-Square Test

This tutorial focuses on the most frequently used statistical test in linguistics: the \(\chi\)2 test (or Pearsons’s chi-square test, chi-squared test, or chi-square test). We will use a simple, practical example to explore how this test works. In this example, we will test whether speakers of American English (AmE) and speakers of British English (BrE) differ in their use of the near-synonyms sort of and kind of as in “He’s sort of stupid” and “He’s kind of stupid”. As a first step, we formulate the hypothesis that we want to test (H1) and its Nullhypothesis (H0). The Alternative- or Test Hypothesis reads:

H1: Speakers of AmE and BrE differ with respect to their preference for sort of und kind of.

while the Null Hypothesis (H0) states

H0: Speakers of AmE and BrE do not differ with respect to their preference for sort of und kind of.

The H0 claims the non-existence of something (which is the more conservative position) and in our example the non-existence of a correlation between variety of English and the use of sort of und kind of. The question now arises what has to be the case in order to reject the H0 in favour of the H1.

To answer this question, we require information about the probability of error, i.e. the probability that the H0 does indeed hold for the entire population. Before performing the chi-square test, we follow the convention that the required significance level is 5 percent. In other words, we will reject the H0 if the likelihood for the H\(_{0}\) being true is less than 5 percent given the distribution of the data. In that case, i.e. in case that the likelihood for the H0 being true is less than 5 percent, we consider the result of the chi-square test as statistically significant. This means that the observed distribution makes it very unlikely that there is no correlation between the variety of English and the use of sort of and kind of.

Let us now assume that we have performed a search for sort of and kind of in two corpora representing American and British English and that we have obtained the following frequencies:

Observed frequencies of sort of and kind of in American and British English
BrE AmE
kindof 181 655
sortof 177 67

In a first step, we now have to calculate the row and column sums of our table.

Observed frequencies of sort of and kind of in American and British English with row and column totals
BrE AmE Total
kindof 181 655 836
sortof 177 67 244
Total 358 722 1080

Next, we calculate, the values that would have expected if there was no correlation between variety of English and the use of sort of and kind of. In order to get these “expected” frequencies, we apply the equation below to all cells in our table.

\(\frac{Column total*Row total}{Overall total}\)

In our example this means that for the cell with [+]BrE [+]kindof we get:

\(\frac{836*358}{1080} = \frac{299288}{1080} = 277.1185\)

For the entire table this means we get the following expected values:

Expected frequencies of sort of and kind of in American and British English with row and column totals
BrE AmE Total
kindof 277.11850 558.8815 836
sortof 80.88148 163.1185 244
Total 358.00000 722.0000 1080

In a next step, we calculate the contribution of each cell to the overall \(\chi\)2 value (\(\chi\)2 contribution). To get \(\chi\)2 contribution for each cell, we apply the equation below to each cell.

\(\frac{(observed – expected)^{2}}{expected}\)

In our example this means that for the cell with [+]BrE [+]kindof we get:

\(\frac{(181 – 277.1185)^{2}}{277.1185} = \frac{-96.1185^{2}}{277.1185} = \frac{9238.766}{277.1185} = 33.33868\)

For the entire table this means we get the following \(\chi^{2}\) values:

Chi values of sort of and kind of in American and British English with row and column totals
BrE AmE Total
kindof 33.33869 16.53082 49.86951
sortof 114.22602 56.63839 170.86440
Total 147.56470 73.16921 220.73390

The sum of \(\chi\)2 contributions in our example is 220.7339. To see if this value is statistically significant, we need to calculate the degrees of freedom because the \(\chi\) distribution differs across degrees of freedom. Degrees of freedom are calculated according to the equation below.

\(DF = (rows -1) * (columns – 1) = (2-1) * (2-1) = 1 * 1 = 1\)

In a last step, we check whether the \(\chi\)2 value that we have calculated is higher than a critical value (in which case the correlation in our table is significant). Degrees of freedom are relevant here because the critical values are dependent upon the degrees of freedom: the more degrees of freedom, the higher the critical value, i.e. the harder it is to breach the level of significance.

Since there is only 1 degree of freedom in our case, we need to consider only the first column in the table of critical values below.

Critical chi values for 1 to 5 degrees of freedom
DF p<.05 p<.01 p<.001
1 3.84 6.64 10.83
2 5.99 9.21 13.82
3 7.82 11.35 16.27
4 9.49 13.28 18.47
5 11.07 15.09 20.52

Since the \(\chi\)2 value that we have calculated is much higher than the critical value provided for p<.05, we can reject the H0 and may now claim that speakers of AmE and BrE differ with respect to their preference for sort of und kind of.

Before we summarize the results, we will calculate the effect size which is a measure for how strong the correlations are.

3.1 Effect Sizes in Chi-Square

Effect sizes are important because they correlations may be highly significant but the effect between variables can be extremely weak. The effect size is therefore a measure how strong the correlation or the explanatory and predictive power between variables is.

The effect size measure for \(\chi\)2 tests can be either the \(\phi\)-coefficient (phi-coefficient) or Cramer’s \(\phi\) (Cramer’s phi). The \(\phi\)-coefficient is used when dealing with 2x2 tables while Cramer’s \(\phi\) is used when dealing with tables with more than 4 cells. The \(\phi\) coefficient can be calculated by using the equation below (N = overall sample size).

\(\phi = \sqrt{\frac{\chi^{2}}{N}}\)

In our case, this means:

\(\phi = \sqrt{\frac{220.7339}{1080}} = \sqrt{0.2043832} = 0.4520876\)

The \(\phi\) coefficient varies between 0 (no effect) and 1 (perfect correlation). For the division into weak, moderate and strong effects one can follow the division for \(\omega\) (small omega), so that with values beginning with .1 represent weak, values between 0.3 and .5 represent moderate and values above .5 represent strong effects (Bühner and Ziegler 2009, 266). So, in this example we are dealing with a medium-sized effect/correlation.

3.2 Chi-Square in R

Before we summarize the results, we will see how to perform a chi-square test in R. In addition to what we have done above, we will also visualize the data. To begin with, we will have a look at the data set (which is the same data we have used above).

chidata              # inspect data
##        BrE AmE
## kindof 181 655
## sortof 177  67

We will now visualize the data with an association. Bars above the dashed line indicate that a feature combination occurs more frequently than expected by chance. The width of the bars indicates the frequency of the feature combination.

The fact that the bars are distributed complimentarily (top left red and below bar; top right black above bar; bottom left black above bar; bottom right red below bar) indicates that the use of “sort of” and “kind of” differs across AmE and BrE. We will check whether the mosaic plot confirms this impression.

mosaicplot(chidata, shade = TRUE, type = "pearson", main = "")  # mosaic plot

The colour contrasts in the mosaic plot substantiate the impression that the two varieties of English differ significantly. To ascertain whether the differences are statistically significant, we can now apply the chi-square test.

chisq.test(chidata, corr = F)  # perform chi square test
## 
##  Pearson's Chi-squared test
## 
## data:  chidata
## X-squared = 220.73, df = 1, p-value < 2.2e-16

The results reported by R are identical to the results we derived by hand and confirm that BrE and AmE differ significantly in their use of “sort of” and “kind of”. In a next step, we calculate the effect size.

# calculate effect size
sqrt(chisq.test(chidata, corr = F)$statistic / sum(chidata) * (min(dim(chidata))-1))
## X-squared 
## 0.4520877

The phi coefficient of .45 shows that variety of English correlates moderately with the use of “sort of” and “kind of”. We will now summarize the results.

3.3 Summarizing Chi-Square Results

The results of our analysis can be summarised as follows: A \(\chi\)2-test confirms a highly significant correlation of moderate size between the variety of English and the use of the near-synonymous hedges sort of and kind of (\(\chi\)2 = 220.73, df = 1, p < .001***, \(\phi\) = .452).

3.4 Requirements of Chi-Square

Chi-square tests depend on certain requirements that, if violated, negatively affect the reliability of the results of the test. To provide reliable results, 80 percent of cells in a table to which the chi-square test is applied have to have expected values of 5 or higher and at most 20 percent of expected values can be smaller than 5 (see Bortz, Lienert, and Boehnke 1990, 98). In addition, none of the expected values can be smaller than 1 (see Bortz, Lienert, and Boehnke 1990, 136) because then, the estimation, which relies on the \(\chi\)2-distribution, becomes too imprecise to allow meaningful inferences (Cochran 1954).

If these requirements are violated, then the Fisher’s Exact Test is more reliable and offers the additional advantage that these tests can also be applied to data that represent very small sample sizes. When applying the Fisher’s Exact Test, the probabilities for all possible outcomes are calculated and the summed probability for the observed or more extreme results are determined. If this sum of probabilities exceeds five percent, then the result is deemed statistically significant.

3.5 Chi-Square Exercises

  1. Imagine you are interested in whether older or younger speakers tend to refer to themselves linguistically. The underlying hypothesis is that - contrary to common belief - older people are more narcissistic compared with younger people. Given this research question, perform a chi-square test and summarize the results on the data below.
Table adapted from Gries (2014: 9)
1SGPN PN without 1SG Total
Young 61 43 104
Old 42 36 78
Total 103 79 182
  1. Imagine you are interested in whether young men or young women exhibit a preference for the word whatever because you have made the unsystematic, anecdotal observation that young men use this word more frequently than young women. Given this research question, perform a chi-square test and summarize the results on the data below.
Observed frequency with row- and column totals for the use of whatever by male and female speakers.
YoungMales YoungFemales Total
whatever 17 55 71
other words 345128 916552 1261680
Total 345145 916607 1261752
  1. Find a partner and discuss the relationship between significance and effect size. Then, go and find another partner and discuss problems that may arise when testing the frequency of certain words compared with the overall frequency of words in a corpus.

4 Extensions of Chi-Square

In the following, we will have a look at tests and methods that can be used if the requirements for ordinary (Pearson’s) chi-square tests are violated and their use would be inappropriate

4.1 The Yates-Correction

If all requirements for ordinary chi-square tests are acceptable and only the sample size is the issue, then applying a so-called Yates-correction may be appropriate. This type of correction is applied in cases where the overall sample size lies in-between 60 and 15 cases ((Bortz, Lienert, and Boehnke 1990) 91). The difference between the ordinary chi-square and a Yates-corrected chi-square lies in the fact that the Yates-corrected chi-square is calculated according to the equation below.

\(\frac{(|observed – expected|-0.5)^{2}}{expected}\)

According to this formula, we would get the values shown below rather than the values tabulated above. It is important to note here that this is only a demonstration because a Yates-Correction would actually be inappropriate as our sample size exceeds 60 cases.

Corrected chi-square values for sort of and kind of in BrE and AmE
Variant BrE AmE Total
kind of 32.9927 113.0407 146.0335
sort of 16.3593 56.0507 72.41
Total 49.352 169.0914 218.4434

If the Yates-correction were applied, then this results in a slightly lower \(\chi\)2-value and thus in more conservative results compared with the traditional test according to Pearson.

4.2 Chi-Square within 2*k Tables

Although the \(\chi\)2-test is widely used, it is often used inappropriately. This is especially the case when chi-square tests are applied to data representing tables with more than two rows and more than two columns. It is important to note that applying the common Pearson’s’ chi-square test to sub-tables of a larger table is inappropriate because, in such cases, a modified variant of Pearson’s’ chi-square test is warranted. We will go through two examples that represent different scenarios where we are dealing with subsamples of larger tables and a modified version of the \(\chi\)2-test should be used rather than Pearson’s’ chi-square.

In this first example, we are dealing with a table consisting of two columns and multiple rows, a so-called 2*k table (two-by-k table). In order to test if a feature combination, that is represented by a row in the 2*k table, is significantly more common compared with other feature combinations, we need to implement the \(\chi\)2-equation from (Bortz, Lienert, and Boehnke 1990, 126–27).

In this example, we want to find out whether soft and hard X-rays differ in their effect on grasshopper larva. The question is whether the larva reach or do not reach a certain life cycle depending on whether they are exposed to soft X-rays, hard X-rays, light, or beta rays. The data for this example is provided below.

Data adapted from Bortz (1990: 126)
Mitosis not reached Mitosis reached Total
X-ray soft 21 14 35
X-ray hard 18 13 31
Beta-rays 24 12 36
Light 13 30 43
Total 76 69 145

If we would apply an ordinary chi-square test, we would ignore that all data were collected together and using only a subsample would ignore the data set of which the subsample is part of. In other words, the subsample is not independent from the other data (as it represents a subsection of the whole data set). However, for exemplary reasons, we will apply an ordinary chi-square test first and then compare its results to results provided by the correct version of the chi-square test. In a first step, we create a table with all the data.

# create tdata
wholetable <- matrix(c(21, 14, 18, 13, 24, 12, 13, 30), byrow = T, nrow = 4)
colnames(wholetable) <- c("reached", "notreached")           # add column names
rownames(wholetable) <- c("rsoft", "rhard", "beta", "light") # add row names
wholetable                                                   # inspect data
##       reached notreached
## rsoft      21         14
## rhard      18         13
## beta       24         12
## light      13         30

Now, we extract the subsample from the data.

subtable <- wholetable[1:2,] # extract subtable
subtable                     # inspect subtable
##       reached notreached
## rsoft      21         14
## rhard      18         13

Next, we apply the ordinary chi-square test to the subsample.

# simple x2-test
chisq.test(subtable, corr = F)
## 
##  Pearson's Chi-squared test
## 
## data:  subtable
## X-squared = 0.025476, df = 1, p-value = 0.8732

Finally, we perform the correct chi-square test.

# load function for correct chi-square
source("https://slcladal.github.io/rscripts/x2.2k.r") 
x2.2k(wholetable, 1, 2)
## $Description
## [1] "rsoft  against  rhard  by  reached  vs  notreached"
## 
## $`Chi-Squared`
## [1] 0.025
## 
## $df
## [1] 1
## 
## $`p-value`
## [1] 0.8744
## 
## $Phi
## [1] 0.013
## 
## $Report
## [1] "Conclusion: the null hypothesis cannot be rejected! Results are not significant!"

Below is a table comparing the results of the two chi-square tests.

Table adapted from Bortz (1990: 126)
chi-square chi-square in 2*k-tables
chi-squared 0.0255 0.025
p-value 0.8732 0.8744

The comparison shows that, in this example, the results of the two tests are very similar but this may not always be the case.

4.3 Chi-Square within z*k Tables

Another application in which the \(\chi\)2 test is often applied incorrectly is when ordinary Parsons’s \(\chi\)2 tests are used to test portions of tables with more than two rows and more than two columns, that is z*k tables (z: row, k: column). An example is discussed by Gries (2014) who also wrote the R Script for the correct version of the \(\chi\)2 test.

Let’s first load the data discussed in the example of Gries (2014) 9. The example deals with metaphors across registers. Based on a larger table, a \(\chi\)2 confirmed that registers differ with respect to the frequency of EMOTION metaphors. The more refined question is whether the use of the metaphors EMOTION IS LIGHT and EMOTION IS A FORCE OF NATURE differs between spoken conversation and fiction.

# create table
wholetable <- matrix(c(8, 31, 44, 36, 5, 14, 25, 38, 4, 22, 17, 12, 8, 11, 16, 24), ncol=4)
attr(wholetable, "dimnames")<-list(Register=c("acad", "spoken", "fiction", "new"),
Metaphor = c("Heated fluid", "Light", "NatForce", "Other"))

Based on the table above, we can extract the following subtable.

Table adapted from Gries (2014: 9)
Register Heated fluid Light NatForce Other
acad 8 5 4 8
spoken 31 14 22 11
fiction 44 25 17 16
new 36 38 12 24

If we used an ordinary Pearson’s \(\chi\)2 test (the use of which would be inappropriate here), it would reveal that spoken conversations do not differ significantly from fiction in their use of EMOTION IS LIGHT and EMOTION IS A FORCE OF NATURE (\(\chi\)2=3.3016, df=1, p=.069, \(\phi\)=.2057).

# create table
subtable <- matrix(c(14, 25, 22, 17), ncol=2)
chisq.results <- chisq.test(subtable, correct=FALSE) # WRONG!
phi.coefficient = sqrt(chisq.results$statistic / sum(subtable) * (min(dim(subtable))-1))
chisq.results
## 
##  Pearson's Chi-squared test
## 
## data:  subtable
## X-squared = 3.3016, df = 1, p-value = 0.06921
phi.coefficient
## X-squared 
## 0.2057378

The correct analysis takes into account that it is a subtable that is not independent of the overall table. This means that the correct analysis should take into account the total number of cases, as well as the row and column totals (vgl. Bortz, Lienert, and Boehnke 1990, 144–48).

In order to perform the correct analysis, we must either implement the equation proposed in Bortz, Lienert, and Boehnke (1990) 144-148 or read in the function written by Gries (2014) and apply it to the subtable.

# load function for chi square test for subtables
source("https://slcladal.github.io/rscripts/sub.table.r") 
# apply test
results <- sub.table(wholetable, 2:3, 2:3, out="short")
# inspect results
results
## $`Whole table`
##          Metaphor
## Register  Heated fluid Light NatForce Other Sum
##   acad               8     5        4     8  25
##   spoken            31    14       22    11  78
##   fiction           44    25       17    16 102
##   new               36    38       12    24 110
##   Sum              119    82       55    59 315
## 
## $`Sub-table`
##          Metaphor
## Register  Light NatForce Sum
##   spoken     14       22  36
##   fiction    25       17  42
##   Sum        39       39  78
## 
## $`Chi-square tests`
##                                   Chi-square Df    p-value
## Cells of sub-table to whole table  7.2682190  3 0.06382273
## Rows (within sub-table)            0.2526975  1 0.61518204
## Columns (within sub-table)         3.1519956  1 0.07583417
## Contingency (within sub-table)     3.8635259  1 0.04934652

The results show that the difference is, in fact, statistically significant (\(\chi^{2}\)=3.864, df=1, p=.049*).

4.4 Configural Frequency Analysis (CFA)

# load library
library(cfa)
# load data
cfadata <- read.delim("https://slcladal.github.io/data/cfadata.txt", 
                      header = T, sep = "\t")
# inspect data
head(cfadata)
##    Variety Age Gender   Class Frequency
## 1 American Old    Man  Middle         7
## 2  British Old  Woman  Middle         9
## 3  British Old    Man  Middle         6
## 4 American Old  Woman Working         2
## 5 American Old    Man  Middle         5
## 6  British Old    Man  Middle         8

In a next step, we define the configurations and separate them from the counts.

# load library
library(dplyr)
# define configurations
configs <- cfadata %>%
  select(Variety, Age, Gender, Class)
# define counts
counts <- cfadata$Frequency

Now that configurations and counts are separated, we can perform the configural frequency analysis.

# perform cfa
cfa(configs,counts) 
## 
## *** Analysis of configuration frequencies (CFA) ***
## 
##                           label   n   expected            Q       chisq
## 1  American Old   Man   Working   9  17.269530 0.0074991397 3.959871781
## 2  American Young Man   Middle   20  13.322419 0.0060338993 3.346996519
## 3  British  Old   Woman Working  33  24.277715 0.0079603059 3.133665860
## 4  British  Young Woman Middle   12  18.728819 0.0061100471 2.417504403
## 5  American Young Woman Middle   10   6.362422 0.0032663933 2.079707490
## 6  British  Old   Man   Working  59  50.835658 0.0076361897 1.311214959
## 7  British  Young Man   Middle   44  39.216698 0.0044257736 0.583424432
## 8  American Old   Woman Middle   81  76.497023 0.0043152503 0.265066491
## 9  British  Old   Woman Middle  218 225.181379 0.0080255135 0.229025170
## 10 American Old   Man   Middle  156 160.178850 0.0043537801 0.109020569
## 11 American Old   Woman Working   8   8.247454 0.0002225797 0.007424506
## 12 British  Old   Man   Middle  470 471.512390 0.0023321805 0.004851037
##       p.chisq sig.chisq          z        p.z sig.z
## 1  0.04659725     FALSE -2.1267203 0.98327834 FALSE
## 2  0.06732776     FALSE  1.7026500 0.04431680 FALSE
## 3  0.07669111     FALSE  1.6871254 0.04578962 FALSE
## 4  0.11998594     FALSE -1.6845116 0.95395858 FALSE
## 5  0.14926878     FALSE  1.2474422 0.10611771 FALSE
## 6  0.25217480     FALSE  1.1002146 0.13561931 FALSE
## 7  0.44497317     FALSE  0.6962784 0.24312726 FALSE
## 8  0.60666058     FALSE  0.4741578 0.31769368 FALSE
## 9  0.63224759     FALSE -0.5726832 0.71657040 FALSE
## 10 0.74126197     FALSE -0.3993470 0.65518123 FALSE
## 11 0.93133480     FALSE -0.2612337 0.60304386 FALSE
## 12 0.94447273     FALSE -0.1217934 0.54846869 FALSE
## 
## 
## Summary statistics:
## 
## Total Chi squared         =  17.44777 
## Total degrees of freedom  =  11 
## p                         =  2.9531e-05 
## Sum of counts             =  1120 
## 
## Levels:
## 
## Variety     Age  Gender   Class 
##       2       2       2       2

4.5 Hierarchical Configural Frequency Analysis (HCFA)

# load library
library(cfa)
# load data
cfadata <- read.delim("https://slcladal.github.io/data/cfadata.txt", 
                      header = T, sep = "\t")
# inspect data
head(cfadata)
##    Variety Age Gender   Class Frequency
## 1 American Old    Man  Middle         7
## 2  British Old  Woman  Middle         9
## 3  British Old    Man  Middle         6
## 4 American Old  Woman Working         2
## 5 American Old    Man  Middle         5
## 6  British Old    Man  Middle         8

In a next step, we define the configurations and separate them from the counts.

# load library
library(dplyr)
# define configurations
configs <- cfadata %>%
  select(Variety, Age, Gender, Class)
# define counts
counts <- cfadata$Frequency

Now that configurations and counts are separated, we can perform the hierarchical configural frequency analysis.

# perform cfa
hcfa(configs,counts) 
## 
## *** Hierarchical CFA ***
## 
##                      Overall chi squared df          p order
## Variety Age Class              12.218696  4 0.01579696     3
## Variety Gender Class            8.773578  4 0.06701496     3
## Variety Age Gender              7.974102  4 0.09253149     3
## Variety Class                   6.078225  1 0.01368582     2
## Variety Class                   6.078225  1 0.01368582     2
## Age Gender Class                5.164357  4 0.27084537     3
## Variety Age                     4.466643  1 0.03456284     2
## Variety Age                     4.466643  1 0.03456284     2
## Age Gender                      1.934543  1 0.16426233     2
## Age Gender                      1.934543  1 0.16426233     2
## Age Class                       1.673538  1 0.19578534     2
## Age Class                       1.673538  1 0.19578534     2
## Gender Class                    1.546666  1 0.21362833     2
## Gender Class                    1.546666  1 0.21362833     2
## Variety Gender                  1.120155  1 0.28988518     2
## Variety Gender                  1.120155  1 0.28988518     2

According to the HCFA, only a single configuration (Variety : Age : Class) is significant (X2 = 12.21, p = .016).

References

Bortz, J, GA Lienert, and K Boehnke. 1990. Verteilungsfreie Methoden in Der Biostatistik. Berlin: Springer Verlag.

Bühner, Markus, and Matthias Ziegler. 2009. Statistik Für Psychologen Und Sozialwissenschaftler. München: Pearson Studium.

Cochran, W. G. 1954. “Some Methods for Strengthening the Common \(\chi\)^2tests.” Biometrics 10: 417–51.

Gries, Stefan Thomas. 2014. “Frequency Tables: Tests, Effect Sizes, and Explorations.” In Polysemy and Synonymy: Corpus Methods and Applications in Cognitive Linguistics., edited by Dylan Glynn and Justyna Robinson, 365–89. Amsterdam: John Benjamins.