Hi. Good afternoon.
The next talk is by a colleague. Beatriz Rodríguez.
She works at UNED's Philology and Education.
-The button, right there. -Now?
Hello. I'll start again.
Welcome. Beatriz Rodríguez
our colleague from the Department of Languages.
She works with Margarita Pino.
She teaches at the Faculty of Sports Science and Education.
They'll talk about their research.
Content Analysis in documents as a scientific research tool.
They'll keep it short. And then we'll have a debate.
They have 15 minutes. Let's see if they can manage.
Let's try. Excuse me if I leave in a hurry.
I'm not feeling well.
If I run to the door, don't mind me.
Today, we have chosen
Content Analysis as a technique.
One of the research techniques.
In the field of research there are techniques, methodologies
and tools available to the researcher
to do research and study.
Content Analysis is widespread in the field of Linguistics.
Perhaps the most widespread. I haven't got hard data.
Let's start with two definitions which seem to us
very straightforward as regards Content Analysis.
"The process to identify, codify and categorize
primary data patterns". Creating word groups.
Establishing categories. Analysing them
in search of parameters. Common or not.
Depending on our goals. Using our own criteria.
And Marshall and Rossman's definition
They talk about "a technique that lets us
examine data to decide if they support a hypothesis or not".
We are looking for a text. A piece of writing.
My colleague will talk about types of text
as we're looking for data to try to corroborate
our main goals or hypotheses.
That's what we do with Content Analysis.
There are three types of approaches
positive, interpretative and critical.
Content Analysis falls into the critical approach.
The definition is there.
This is the Power Point. We're cutting the talk down by half.
Everything we say is already there.
Don't rush taking notes. It's all there.
Santos-Guerra's definition. A very clear one at that.
It fits into the critical method
as it doesn't intend to...
Content Analysis doesn't generalize
or quantify results so that we can say
"It's like this for these reasons. We prove this with these data".
We simply analyse a situation
and get to the bottom of it
and gather information to corroborate our objectives or hypotheses
as we had them established. For instance...
when we do research in the Humanities. Inside a classroom, if you're teachers
you try to improve the situation by analysing
students' discourse when they answer
your questions.
I mean official school documents such as the syllabus or...
Any type of document. There are many different kinds.
And we analyse that very carefully so
we improve that situation. At an individual scale. Locally.
A clear purpose in a concrete situation.
The result of our research's not intended
to be extrapolated. If my study deals with a Galician school
then I'm trying to improve that particular school.
I'll use a tool to achieve that. But it's not my intention
to apply it to... that piece of research cannot be extrapolated.
Our aim is not to universalise or generalise.
As is the case in quantitative approaches.
With this in mind, research methods...
they are either qualitative to study human beings
in their normal lives. Their routine. Understanding the situation.
The ultimate goal of qualitative methods.
Quantitative methods establish relations. They measure and quantify.
Content Analysis is closely related
to qualitative methods in scientific research.
That's not to mean that results
cannot be quantified.
Quantitative methods can be applied to some aspects.
At the centre of our research
our aim is not to quantify. It's to analyse and clarify.
That's what we mean
in several slides. We'll skip over them. Now Margarita can speak.
We point to qualitative and quantitative aspects
which are applicable to one side of Contents Analysis...
A qualitative process which may contain...
quantifiable aspects.
If we're working with the use of the article "the" in Shakespeare
perhaps we want to quantify
how many times it emerges as well as any other aspect.
Especially in corpora. Quantification is fundamental.
The aim of our study is not to quantify
but to achieve a deep analysis. Knowing why
the article is used that way and not differently.
Any other aspect.
That's what we mean.
There are several charts drawing parallels
between our results from qualitative and quantitative viewpoints.
To see the pros and cons. So that results and model fit together
to make research decisions
and have a clear view of their properties.
There are some charts. Four. This is the last one.
My colleague will now talk about types of documents
and the steps of this technique.
Morning. Expanding on what Beatriz is saying...
I'll be synthetic. The slide contains the text.
If anybody's working in Content Analysis
pay attention to the process. Right?
After this talk there'll be another one
dealing with corpora. A much deeper
analysis of all these questions. Ours is a cursory look
at how it's done simply and didactically.
What types of document?
To be clear, any type will do.
Graphical or iconical. The same with literary texts.
A PhD thesis in my faculty
it is about Telva magazine. The one you know.
Telva's historical background.
The materials used in fashion design.
The fabrics. Interesting conclusions.
A study of drawings and pictures as featured in Telva magazine.
Really interesting, the faculty. Up to the brim with magazines.
They say: "Such fun!" It's not like that at all.
It requires some serious, careful study. Lots of dedication.
This is a list of things
that you can study. From work reports.
In the field of Philology.
Notes authors take to write their work.
To learn about character psychology.
I don't know much about it but it needs to be done.
Specialists do that.
This classification is found in many textbooks.
To classify documents in Content Analysis.
Printed documents.
I must be pressing something.
Printed or graphical. Direct or indirect.
There are several classifications. It doesn't matter.
They're different. You can study...
pictures, films or literary texts.
What to do with it? Very important.
Four things.
Comparing documents from the same source.
The works of one author.
Comparing messages from one source in different contexts.
Longitudinal life histories.
The same goes for literature.
Authors write at different points in time.
Classifying stages in the author's life...
"by classifying features which are present in these writings".
In the Arts. Picasso's Cubist period.
We look for and identify the features of a document.
Comparing messages from two sources.
What two authors have in common.
What scientific documents have in common.
We're studying school education programmes.
In healthy, education-friendly cities.
What do school programmes have in common?
Healthy, education-friendly cities they're expected to follow...
they should follow certain criteria.
It points to incongruities between theory
and practice.
Comparing messages with standard models.
There is a method to follow. That's what we use.
We assess the criteria for education-friendly cities.
And then we decide how decisions are made.
How? Using school programmes.
Types of document analysis.
First. I'll try to get it right.
You don't need just one type of analysis.
The researcher decides. They can use one
or every one of them. It depends on how careful
they want to be with a given document.
Exploratory analysis, here's one example.
We've used it for questionnaires.
A project that's become a PhD thesis.
It looked for teachers' criteria in attention to diversity.
There were no questionnaires for that context
in the Galician framework.
Seven discussion groups across Galicia.
Five members each.
With teachers and counsellors.
Content Analysis made of their discussions.
Of their conversations.
We created our tool. A questionnaire.
A quantitative one used in all Galicia.
To generalise our results. That's our tool.
Contents Analysis provides us with a quantitative tool.
That's all it was.
That's applicable to other environments.
Verifying the contents. Having a goal.
Using life histories.
We've done it repeatedly.
For example, how senior citizens
created their own health codes.
We did interviews in health centres.
And there it was. Our work hypothesis.
Later, those interviews were analysed
to compile all the points and draw conclusions.
There are many examples.
No need to be confined to one type of analysis.
The more you analyse, the deeper you get.
How to do Content Analysis? I'll skip that. Each step...
They've been explained here.
Many documents give a clear explanation.
The next talk is much more interesting.
Is the speaker in the room? Lovely.
It goes much further at the quantitative level.
Very interesting. The first part...
it is complex, but not difficult. It takes some time.
First. Reading the documents.
Reading several times takes long.
The first time you can't see as much
as you do the second time, or the third.
Many people read it. Several times if possible.
And they obtain more units of analysis from the text.
Then comes "separating units".
Segments of analysis. With a set of goals.
Sometimes there aren't any. Or, from our goals
we draw our "segments of analysis"
and later enlarge them depending on that reading.
Thus obtaining richer information.
Three steps. Creating corpora of analysis.
Transcribing corpora.
There are computer tools for that.
And to select methods of analysis.
Third step. Choosing units of analysis.
What will we study? Where to begin?
Saturating information.
The larger the corpus the more information it contains.
More saturated and better quality.
Then comes organising and exploiting the results.
With a computer tool we transcribe data.
We focus on Ethnograph.
There are others.
In selecting our tool validity and reliability are paramount.
Obviously, any tool's exposed to
researchers having their own take
on information gathering. That's right.
The more information you gather the smaller the risk...
of subjectivity.
One must be careful with these criteria.
Thoroughness. Representativity.
Documents related to the topic.
Homogeneity.
Pertinence. And univocality.
Researchers tend to reach the same conclusions
when reading something for the first time. They get it right
with the categories and the units of analysis.
There are pros and cons.
Pros. Very rich analyses.
The situation becomes clear.
Conclusion can be drawn
correctly about categories of analysis.
The problem? Codification. We might be wrong.
We are subjective in our classification.
But with plenty of information
when analysed by many researchers
who are using computer tools
we reduce subjectivity. It's normal
to draw valid and reliable conclusions.
Too fast?
Questions? Are you tired?
Let's go on with the next talk.
It might be clarifying. Then we debate.
-I'm not sure. -It's fine.
Any question... Perhaps later.
You may ask questions now. But the next talk...
it'll focus on quantitative analysis.
Later you can ask questions about both talks.
Thank you very much. Let's welcome the next speaker.