2. • Data analysis or data collection consists in organizing,
summarizing and synthetizing the data to get the results.
• The data analysis technique will depend on the research
problem, the design chosen and the type of data collected
3. Data collection in quantitative research
In quantitative research the data will be numerical or the
data will be converted in numbers and the analysis will need
statistics
Qualitative data will deal with non-numerical data. Most
likely, in linguistic units of oral or written speech.
4. It is usually taught that not using statistics makes the research
easier to conduct. On the contrary, using them makes the
research more manageable. There are many statistical packages
which can help to manage the data.
5. Data collection in qualitative research
Qualitative requires intuition and understanding concerning the
data. It is a more complex task.
There are several assumptions related to using parametric
statistics. They are not strong enough to reject a whole hypothesis.
One assumption is that the variable studied is normally distributed
in the population.
Since most variables are normally distributed the assumption is
usually met.
A second one is that data represent an interval or radio scale of
measurement. Measures used in second language acquisition
represent interval data. The assumption is also usually met.
The third one is that subjects are selected independently to study.
So, selection of one subject does not affect the selection of others.
This will be the case whenever the sample is randomly selected.
6. Qualitative requires intuition and understanding concerning the data. It is a
more complex task.
Two main techniques can be identified when analyzing qualitative data.
Deriving a set of categories for dealing with text segments. This is a
procedure merely inductive. Once the categories have been set, they are
applied to the reminder data, which leads to the refinement of categories and
the discovery of new patterns. This type of research is descriptive and
exploratory.
These studies are more confirmatory and aim at some kind of explanation.
7. Descriptive research
Data is generally analyzed with descriptive statistics. These will provide
information such as how often certain language phenomena occur, typical
use of language elements, and the relationship between variables, among
others.
The types of statistics used in descriptive research are frequencies, central
tendencies and variabilities.
The Frequencies are used to know how often a phenomenon occurs and are
based on counting the number of occurrences. Very useful in second language
acquisition research, where the main interest lies in how often elements of
language are used. They also provide information about the performance of
the subjects on tests and questionnaires before the results are used for
analyzing the data.
8. Correlational data
It is obtained from the descriptive research and examines the relationship
between variables without manipulate them.
Multivariate data
Obtained from the multivariate research. It can be analyzed through a set
of techniques where a number of dependent and independent variables
are analyzed simultaneously.
These techniques can be applied when researching language aptitude,
personality or learner’s background.
9. There are three multivariable procedures.
The first is multiple regression. This permits examine the relationship
and predictive power of one or more independent variables with the
dependent variable.
The second is discriminant analysis. This is about which combination of
independent variables distinguish the most between two or more
categories of the dependent variable. An example could be male/female,
monolingual/bilingual, etc.
The third is the factor analysis. This helps the researcher to manage
larger sets of data by identifying factors that underline the data. This
type of analysis is based on the assumption that variables that measure
the same factor will be highly related, while the ones which measure
different factors will have low correlations.
This kind of analysis has been used in second language learning to
validate factors that are believed to underline different language
constructs such as proficiency, aptitude and attitude to learn the second
language.
10. Experimental data
In experimental research, when two groups (experimental and control)
are being compared, the researcher will use something called the t-test.
This is used to compare the means of two groups. The results gotten
with this test are called t-value. That value is entered in t-values chart.
One way analysis of variance is another technique used to collect
experimental data. This analysis is performed on the variance of the
groups and is focused on whether the variability between the different
groups is greater than the variability within each of the groups. The F
value is the radio of the between variance over the within variance. A
significant F will occur when the variability among the group is greater
that the variability within the group.
11. Chi square
This data analysis procedure helps the researcher to address
questions about relations between two nominal variables.
During the procedure, the researcher compares the
frequencies observed in a sample with the expected
frequencies.
Using the computer for data analysis
Most of the data analysis techniques described here can be
performed with the computer. There are many packages which
can help you to do it. Nevertheless, it is important to know
how to use them in order to have good results. A computer
analysis must be planned and attention to small details must
be given.
