In terms of data gathering, qualitative research is valued for investigating social phenomena in natural (rather than experimental) settings. This data collection procedure gives emphasis to the meanings, experiences, and views of all the participants (Pope and Mays 1995). The researcher is able to gain an insight into another person’s views, opinions, feelings and beliefs all within natural settings (Hicks 1999). However, qualitative research has a very low reliability in that it is extremely difficult to replicate a piece of qualitative research due to the fact that it does not have a structured design or a standardised procedure. In terms of people’s daily language usage, there are simply too many ways of speaking about things, and to do them justice one would have to use all these different ways at once. When replicate the study, something is always left out precisely because something more can be said, and each new way of saying will add a new dimension. Hicks (1999) suggests that within qualitative studies as the participants are aware that they are being observed/asked questions they might change their behaviour in a way they might not have if they were not being observed or interviewed. When being interviewed there is a possibility that participants answer the questions to what the interviewer wants to here and not how they would normally answer the questions. Back in the term of discourse analysis, one can say that putting something into language changes it and being in an interview context, discourses will be altered more.
The qualitative data analysis procedure is open for much critique. The whole notion of the extent to which we can make transcription scientifically accurate is open to debate. Qualitative analysis is therefore still subject to attack for lacking rigour, objectivity and so on. It has been argued that applied linguists need to develop a reflexive approach to transcription as part of a wider reflexive discourse analysis (e.g., Edwards, 1992). It has been noted that the central process of transcribing is a ‘discovery procedure’ (Rampton, 1995). Researchers need to be very carful and thoughtful when listen and notate the discourse over and over again. They need to take time and find the patience and the tolerance to go back again and again to the original data. Qualitative data is therefore rich in details, but transcription is an immensely time-consuming process.
Quantitative research is concerned with obtaining numerical information which can be analysed using statistics, where it does not need to go beyond the use of what is stated as ‘descriptive’ (Silverman, 2000, p.26). With regard to the data gathering procedure of quantitative research, methodology section always includes an overall description of the research design, a thorough description of how the research took place and to include all the steps taken in order to allow other readers to follow the article and other researchers to replicate the study when needed (e.g., Hardey & Mulhal, 1994; Cormack, 2000). In quantitative research, language processing is quantified by participant’s reaction time to experiments that use language stimuli, for example, words, phrases and sentences. The main topic of interest here is how people identify/recognise words under various experimental circumstances. The use of lexical decision task (LDT), firstly coined by Meyer & Schvaneveldt (1971), is most extensively used in the investigation of word recognition. The basic procedure of LDT involves: subjects being presented, either visually or auditorily, with a mixture of words and nonwords; their task is to indicate, usually with a button-press, whether the presented stimulus is a word or not. For example, participants have to press one key in response to the word ‘nurse’ and another key in response to the nonword ‘murse’. The task is carried out in a laboratory setting. The ultimate aim is to measure how quickly people classify stimuli (i.e., the latency of word recognition) and how many errors they make. The analysis is based on the reaction times (and, secondarily, the error rates) for various conditions for which the words differ. A very commonly found effect is the frequency effect of the target words: words that are more frequent are recognized faster. Researchers then draw theoretical conclusion from the experimental data that frequent (common) words are responded to faster than less frequent (uncommon) words (e.g., Alija & Cuetos, 2006; Bonin et al., 2001; Connine, et al., 1990; Sears et al., 2006; Ziegler, et al., 2000).
With regard to data collection, one of the main criticisms of quantitative research in language processing is that it often takes place in an unnatural setting. That is, the researchers create an artificial environment in an attempt to control all relevant variables. Considering this, how sure can they be that the results which they obtain in the laboratory will also apply in the real world? One problem with lexical decision task (i.e., ‘is it a word’?) is that experimenters must be sensitive to the problem of finding speed-error trade-offs, such that the faster subjects respond the more errors they make (Pachella, 1974). Therefore, experimenters must be careful with the precise instructions the subjects are given. They have to respond as fast as possible and as accurately as possible. Whaley (1978) showed that frequency is the single most important factor in determining the speed of responding in the lexical decision task. However, a number of other variables correlate with word frequency. For example, frequent/common words tend to be shorter. If researchers wish to demonstrate an unambiguous effect of frequency, they must be careful to control for these other factors. Moreover, there are simply too many factors or variables that are believed to influence language processing. These factors include participants’ language proficiency, history of learning, age-of-acquisition, task demand in experimentation, types of words being tested, and individual differences and so on. Unfortunately, many studies either ignore them in experimentation or fail to take them into account in interpretation of the results (see discussion in Gorsjean, 1998). The question is, how can one control for all these variables in any study? The effect of word frequency in language processing is all pervasive.
In terms of quantitative data analysis, statistics is the bottom line in the quantitative research. To apply statistics correctly, researchers must follow the basic rules, and then use the appropriate mathematical formulas to arrive at average measures of experimental variables and their variance. Comparisons are made with the t-test or a non-parametric test, which will come up with a p (for probability) value, indicating the degree of similarity between the groups. The quantitative results can be replicated, which means that the same test applied to another sample of the same background population should yield the same results within the limits of random fluctuation. However, statistics can never be perfect and statistical decision process always involves errors. Moreover, statistical method of analysing experimental data demands the use of a larges sample; otherwise it might not be able to provide accurate or meaningful results.
Quantitative research normally involves large numbers of respondents, typically a hundred or more, and obtains results that are representative of the total population. Therefore, it is a form of research method that is considerably appropriate for generalization of descriptions. However, the corresponding disadvantage of this method is that the resulting finding usually fails to account for the unique characteristics of individual cases, which qualitative data is designed to accomplish. Quantitative research is seen to have high reliability as different researchers, using the same measurement system are likely to come up at the same measurement for same subjects. The results generated from quantitative research tend to be very simple as they are generally reduced to a few numerical data and summarised in a few short statements. This is the opposite of qualitative research, which is generally rich in information with detailed descriptions of participants’ spoken language. As language is a dynamic set of communication, the depth of understanding of language in qualitative research is therefore considerably superficial. Furthermore, quantitative research requires the advance formulation of specific hypothesis (Huysamen, 1997). With this characteristic in place, quantitative research is therefore only appropriate when the issues to be tested are known. This research method would not be appropriate to use in situations such as an initial language leaning phase or as a method to explore new ideas such as how bilinguals process their languages.
To conclude, both qualitative and quantitative methodologies are fruitfully employed in the study of language. They both have their strengths and weaknesses. I do not think that there is one ideal theoretical framework (qualitative or quantitative approach) for conducting language research. What is important is that the theoretical framework and methods match what the researcher wants to know. The researchers should also be explicit about any challenging theoretical and ideological problems encountered in their research.
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