How do children overcome hazardous experiences to succeed in life? What can be done to protect young people at risk from trauma, war, disasters, and other adversities? Learn about the importance of fostering resilience in children at risk. During this course, participants will: learn how trauma can affect children and the systems they depend on, gain insight into core concepts, research methods and lessons learned in last 50 years of resilience research, learn how research is being applied in the real world through interventions that promote resilience, and engage in discussions with others who are working with children at risk around the world Participants are welcome to take the MOOC at no cost or to register for a Course Certificate ($49). Those who register and earn a Course Certificate from Coursera also are eligible to sign up for continuing education clock hours through the University of Minnesota. Participants can earn 10 clock hours of continuing education credit (added cost $99) from the College of Education and Human Development at the University of Minnesota. Go here http://z.umn.edu/1a5q to register for continuing education clock hours for completing this course.
2.7 Combined methods
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En provenance du cours de University of Minnesota
Resilience in Children Exposed to Trauma, Disaster and War: Global Perspectives
110 notes
University of Minnesota
110 notes
À partir de la leçon
Week 2: Methods and Models of Research on Resilience (including case studies)
This module highlights the models and methods used in resilience science, including person-focused methods and variable-focused methods. The case study of Dr. Maddaus continues and the case of resilience in early childhood is presented.
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Ann S. Masten, Ph.D., LPRegents Professor, Irving B. Harris Professor of Child Development
Institute of Child Development
Hello. We've been talking about person focused
and variable focused methods in resilient science.
Let's take a look at some combined approaches that investigators have
used in their research.
And these include two different strategies.
One way to use combined methods is
simply to use two different kinds of methods in the same research project.
For example, in the project competence longitudinal study,
which I was involved in for more than 20 years,
we included case studies, variable focused analysis, and
person focused examinations of, of a variety of kinds.
We used many different kinds of methods.
So we just combined methods by using different strategies to analyze our data,
trying to get a deeper understanding about resilience from
the lives of this group of people.
But there are some new statistical techniques available now that make it
possible to have a combination in a single analysis,
where people look at the trajectories of individual lives through time.
So they're measuring variables through time and individuals, but
they are lot trying to figure out the patterns that are in
the data showing different groups or different kinds of trajectories.
So these methods, and I've listed several of them here,
often relying on growth curve analysis or mixed modeling,
combine features of both variable and person focused strategies, and
I, I'm going to show you a few examples of this in this segment and
then in other segments going forward.
What I want to start with, is an example from a recent study we've been doing in
collaboration with a large school district here in the community where I live.
And we're interested in understanding how risk, economic risk and homelessness
are related to academic achievement and learning in school, over time.
And we know from lots of research, that poverty, disadvantage,
economic disadvantage, are, is a risk factor for development.
But particularly, it poses some risk to learning and academic achievement.
We also have learned in our research on homelessness, which you've been hearing
about as we go along, that children who are residentially mobile, who are saying
in a, emergency shelter, often have many other risk factors in their lives.
And that they seem to be at very high risk for having difficulties at school.
We were very concerned about that and we wanted to understand it better so
that we can work with the school to help these children have better achievement.
So what I'm going to show you is some data that was made possible by the,
the quality of information collected in this school district.
And they measure, every fall they give a,
a very good test to measure learning in the children.
And it, this particular test is designed for these new strategies
of analysis that measure change over time in individual children,
but you can do look at change over time in large groups of children.
And we're particularly interested in reading and math.
And I'm going to show you some of the results from math.
But let me explain the four groups you're going to be seeing.
We were expecting that, because of our other research, that homeless and
highly mobile children, children who had ever experienced homelessness during
the study, would be at very high risk,
because there was a lot of evidence that that's the case.
So, we were expecting that they would be the highest risk group if we
put their results together.
The next highest risk group we thought would be children who were very low
income, they were qualified for free lunch at school, which in,
in this country you get eligible for that by having a very low income.
So many of these children are living in poverty and
they really count on having free lunch at school.
They're at moderately high risk because they have a lot of poverty but
they also have other risk factors in their lives in many cases.
Then we have children who are not qualified for free lunch, but
they get a reduced price meal because they have low income, but they're not as,
as poor, their families, as the children who receive free lunch.
And we thought that they would have less risk for
academic achievement than these first two groups.
And then the other group was our low risk group.
These are children who were never qualified for free lunch or
reduced price meals, and never, as far as we know, had been homeless or
living in an emergency shelter.
These are just ordinary children who appear to be more advantaged.
And what I want to show you is their scores on this standardized achievement
test, that as I told you, is a very good measure, variable measuring learning.
And here are the results of this study.
First I want you to notice that's a very large sample size.
This, this data that you're looking at reflects the achievement of
more than 26,000 children in the school district.
This is all.
We analyzed all the data that were available.
And the data we looked at was repeated measures, over time.
Each fall, when the children were in grades 3, 4, 5, 6,
7, and 8, the, they, they were tested on this measure.
And the measure is designed to make it possible so
you can see how much they're learning over time.
And their scores are over here.
And don't worry what the actual numbers are, as you go get higher and
higher, your, your achievement is better and better.
And what you're looking at here is the four different groups.
And it does appear that we were right about our expectations of risk,
because this is the homeless highly mobile group.
The group we expected to have the highest overall risk.
And this is their average ver time of, of scores on this measure of achievement.
And what you can see here is at the beginning of this window of time when all,
the children in third grade they scored at the twelfth percentile on this measure.
This is nationally standardized measure used all over the United States.
This solid black line here represents the average national score so
this is your reference point,
this is the normative level, the average level you would expect and
you can see that this homeless group has much lower scores than average.
And as you follow them over time, they continue to learn.
On average, this group is learning and
growing their achievement and reading is getting better.
However, they never catch up to the other groups, on average.
On average, their scores show a steady course of learning,
but never closing what looks like a big achievement gap here.
The other high risk group, the children who have experienced poverty, but
haven't been homeless, also is showing high risk on achievement.
They start off at the 21st percentile,
which is significantly better than the homeless group.
But they, also, are very, doing much poorly in
reading than you would hope for, compared to the national average.
And you can see that they also show studying improvement over the years they
are learning something in school, but
again, on average these children do not catch up to other groups of children.
The reduced price meal group is an interesting one here because they have,
we though they would have lower risk and they are tracking very closely the reading
level, the national average on this test, so they're doing pretty well.
So having a little bit less poverty does appear to make a difference.
These children have better reading scores.
And then we have the low-risk group.
And what you can see in this group, who haven't experienced poverty or
homelessness, is that they start off much,
much higher on this national test, and they continue to stay very high.
They start at the 75th percentile and they do very,
very well as they go forward in time in reading achievement.
And what this, this picture represents is a risk gradient in a new form.
Before we looked at risk gradients at one point in time.
Now, we're looking at risk gradient over time,
where this is the very highest level of risk, and the achievement is lower.
And this is the lowest level of risk, and the achievement is higher.
But this, this class is about resilience.
And, so,
I want to know more than just the average level of how these children are doing.
What I'm going to show you next is the achievement scores not of the average
of the, this group of homeless highly mobile children, the highest risk group,
but how their data look when you portray it individually for each child.
And that's shown in this picture.
This is one of the famous spaghetti looking pictures from these new
modern strategies.
And the, this is the national average on this test of reading.
But this, instead of showing the average of all the children who have earned this
high risk group, this is showing their individual test performance and what you,
what strikes you immediately is the variability.
There are some children that are doing very, very poorly and
going along at you know, at the first or second percentile here.
And there are other children that are doing well,
they are doing better than the national average.
So it's misleading to think that all the children are, who are in
that category of risk that we called homeless highly mobile, are the same.
There, there's tremendous variation among those children.
And in fact, if, if you think of this as the average range,
children who are within, this is considered average range on this test.
Above this line, you can see that a lot of the children are doing quite well.
And it's hard to tell where the individual lines are, because there's so
many of them.
But, about 45% of the children stay above this
line throughout all the tests that they've taken over time,
which suggests to me that they're showing academic resilience.
And, it leads you to ask the question of what accounts for this variation?
What are the differences among the children in this high risk category who,
who are doing well, and the children who are not doing so well?
That may be critically important for us to understand in order to
design interventions to help the children that aren't doing so well.
So, in this data set, we were able to go back and
look at first grade reading skills, as a possible predictor of
later achievement, and we did find that how well the children were
doing earlier in reading was a very good predictor of how they would do later.
Though it was a good predictor both of where they would start on that test in
third grade, and
then how well, how quickly they would continue to learn over time.
In terms of growth curve analysis, early reading skills predicted both
the intercept, where the children would start in our analysis in third grade,
and also how quickly their reading skills would grow over time as individuals.
And this effect of early reading
was more pronounced, it was important for the high risk children.
So, in the, in the analysis we did, both for homeless children and for
children on free lunch, reading skills was an even better predictor of
how they would do in later achievement than it was for more advantaged students.
And that suggest to us that reading level in
first grade is a very important protective factor.
And that might have implications for
how you might focus your attention as children enter school.
But that's not the only hypothesis we might consider.
As we're going to see in the next few segments,
there're a lot of other influences on children where you might want to consider
as we think about what to do to close this achievement gap in children.
But first, in the next segment I want to take a closer look with you at
pathway models because what we've just been looking at,
is an example of pathway models.
And let's take a closer look.
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