Qualitative vs Quantitative analysis
Corpus analysis can be broadly categorised as consisting of qualitative and quantitative analysis. In this section we'll look at both types and see the pros and cons associated with each. You should bear in mind that these two types of data analysis form different, but not necessary incompatible perspectives on corpus data.
Qualitative analysis: Richness and Precision.
The aim of qualitative analysis is a complete, detailed description. No attempt is made to assign frequencies to the linguistic features which are identified in the data, and rare phenomena receives (or should receive) the same amount of attention as more frequent phenomena. Qualitative analysis allows for fine distinctions to be drawn because it is not necessary to shoehorn the data into a finite number of classifications. Ambiguities, which are inherent in human language, can be recognised in the analysis. For example, the word "red" could be used in a corpus to signify the colour red, or as a political cateogorisation (e.g. socialism or communism). In a qualitative analysis both senses of red in the phrase "the red flag" could be recognised.
The main disadvantage of qualitative approaches to corpus analysis is that their findings can not be extended to wider populations with the same degree of certainty that quantitative analyses can. This is because the findings of the research are not tested to discover whether they are statistically significant or due to chance.
Quantitative analysis: Statistically reliable and generalisable results.
In quantitative research we classify features, count them, and even construct more complex statistical models in an attempt to explain what is observed. Findings can be generalised to a larger population, and direct comparisons can be made between two corpora, so long as valid sampling and significance techniques have been used. Thus, quantitative analysis allows us to discover which phenomena are likely to be genuine reflections of the behaviour of a language or variety, and which are merely chance occurences. The more basic task of just looking at a single language variety allows one to get a precise picture of the frequency and rarity of particular phenomena, and thus their relative normality or abnomrality.
However, the picture of the data which emerges from quantitative analysis is less rich than that obtained from qualitative analysis. For statistical purposes, classifications have to be of the hard-and-fast (so-called "Aristotelian" type). An item either belongs to class x or it doesn't. So in the above example about the phrase "the red flag" we would have to decide whether to classify "red" as "politics" or "colour". As can be seen, many linguistic terms and phenomena do not therefore belong to simple, single categories: rather they are more consistent with the recent notion of "fuzzy sets" as in the red example. Quantatitive analysis is therefore an idealisation of the data in some cases. Also, quantatitve analysis tends to sideline rare occurences. To ensure that certain statistical tests (such as chi-squared) provide reliable results, it is essential that minimum frequencies are obtained - meaning that categories may have to be collapsed into one another resulting in a loss of data richness.
A recent trend
From this brief discussion it can be appreciated that both qualitative and quantitative analyses have something to contribute to corpus study. There has been a recent move in social science towards multi-method approaches which tend to reject the narrow analytical paradigms in favour of the breadth of information which the use of more than one method may provide. In any case, as Schmied (1993) notes, a stage of qualitative research is often a precursor for quantitative analysis, since before linguistic phenomena can be classified and counted, the categories for classification must first be identified. Schmied demonstrates that corpus linguistics could benefit as much as any field from multi-method research.