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