Beyond the Academy: Let’s See If What We Think Matches What We Know

Beyond the Academy: Let’s See If What We Think Matches What We Know

[David] All righty.
So again, thank you Dory. For those who don’t know me, I’m David Thomas, and this is gonna be this session is let’s see if what we think matches what we know um so
when we came up with this session we thought about it came out of an idea
about policy and linking policy and data and seeing where we are based on some
current events and just some of our new holdings and some of our older holdings
and all of what’s going on. We’re gonna take a little stroll through this like Dory said. Please ask if you have questions ask them at the end or you can
ask them any time but we’re gonna probably ‘trust them at the end. And so,
now let’s go through it and see where this takes us.
All right, so here’s the plan for today: First off, it’s always nice to get to
know the person talking to you. You don’t see my face so at least you get some
information about me. Not very much though. Then we’re gonna get into how we
got to this space or really how I got to this space. We’re gonna review a few
archives or talk about some archives that might be relevant although because
we’re ICPSR we think all our archives might be relevant, but there are some
that are kind of more relevant or more geared or inclined toward this meeting
of policy and data. We’re gonna go through a little exercise and then we’re gonna wrap up. So, let’s get going. All right. So who is the presenter? Who am I? My name is David Thomas and as you’re seeing this I’ve heard many a Wendy’s
joke in my life. Many people think oh I bet you get that a lot, and I do.
I’m a senior data project manager here at ICPSR. I’ve been with the Resource
Center for minority data, tagline really cool minority data, and with open data
Flint project, which would be relevant as we go through this. This is my 16th year
at ICPSR. I started out as a curator, which is someone who takes the data that
either you’re using or you provide to us and I take those data or I used to take
those data and convert them into the different packages that we release at
ICPSR, so I have a statistical background to a certain degree. I more have a data
management background. I’m not a statistician by training, and so that’s
just a little bit about me. Kind of what you need to know.
All right. So, how we and by we I really mean how I got to this point. Right? So, a
few years ago working with our CMD we came across some funding and some ideas
that were about that coming out of a Robert Wood Johnson Foundation grant
that talked about well how do we cross those boundaries between academia and
activism or action. Sometimes people don’t like the word activism, but to have
an effect on the world. Many times you’ll hear from many different areas well you
know we fund all this data, we being the US government or whatever, what do we get out of it? What do we get out of this? What do you get out of that return on
investment? And so, as we started thinking about that and being more aware of that
and being more aware of the fact that the world in many ways is getting bigger
at the same time it’s getting smaller. Right? So, we’re able to connect more
things together. We are able to talk to more people, and learn about more things at a quicker pace. Just as a side note, I mean that also means we’re getting inundated with information all the time. How do we deal with it? What’s
true? What’s not? That’s not really where we’re going with this one, but it helps
when we’re talking about policy and data to figure out like what can we prove. You know going back through some of my memories and stuff like that. This is just a old pop-culture reference but talking like I used to watch a lot of crime shows and lawyer shows. You know, in many cases it’s not what you know it’s what you can
prove. Right? This was also spurred on by, at the time,
the Flint water crisis if you want to call it that. So, again, this was another
instance it was a particular instance of researchers taking their academic skills
and applying it and bringing issues up to the forefront of our public
understanding and our public knowledge. As we think about that further one of
the challenges that we all should be considering, right, is that data should
inform our thinking but also our thinking informs what data we collect,
and we won’t have any data for the questions we don’t ask. So, I want you to keep that in the back of your mind and thinking about all
right so because I’m gonna give you examples. It’s not going to be exhaustive.
It can’t be exhaustive, but it’ll be a little run-through of like okay well
this is where I’m going with this, this is some of the thinking, these are some
of the ways it might help. This is just going to be a kind of get you started
and get people going kind of thing. When we think about data should
inform our thinking, you know, it used to be there was a time if you
really wanted to find out a lot about Congress or legislatures, you know, you
would go on public access TV or maybe c-span as with current events. Now we
have a lot more access and visibility for those that we elect higher offices
into public office into nation level offices. Thinking particularly of the
last few months, weeks, years, all those things, last few decades, we see more of
our elected officials and they’re making more policies and we’re having more
policies and we want to make sure. And also, as we consider and as we think
about our own interactions with, you know, policies, what is going into
those policies? Are we able to have access to those data to see whether or
not what we’re being told is also something that we think the data bear
out? Right, let’s see if what we think of what we see is what we know and all that
stuff. The other thing that comes into play though is also we can’t get
information if we don’t ask the right question. So, if I never asked you about…I’m a medical provider, for example, I can miss key information if I’m doing an
interview or some sort of intake if I don’t ask you, you know. I don’t know…if
you’re diabetic or something I ask you about your foot care and how
your feet are doing. I might miss a key indicator, if I don’t ask you who seen
many commercials for drugs these days or people doctors have to ask questions in
order to get the information they need. And if we don’t ask those questions just
like anyone, you know we can’t get that information you need. All right. So, sticking with our plan, I’m just gonna run into…run through some of the
archives that I think might be particularly relevant. Two, we’re going to
start with are the Health and Medical Care Archive, HMCA, and the other one
is the Archive of Data on Disability to Enable Policy and research, ADDEP.
Now if you’re looking at the slides, you see that HMCA, lo and behold, is also funded by the Robert Wood Johnson Foundation. And then the ADDEP archive is a joint initiative of the Center for Large Data Research
and Data Sharing in Rehabilitation and ICPSR. That works because one of the
things we’re going to get into, now you’re going to see both HMCA and ADDEP,
you’re gonna see the same study. So, that’s just a little you know visual
reminder that yes, you can get to our studies from different places and
still get to the same information the same studies. We’re particularly gonna look at the health reform monitoring survey I chose this
one partly because of its relevance and its ease of use for me to make some
questions and to pull some information up. Also, because we don’t want to
shy away from a big topic I also forgot that this survey, now
what you’re seeing actually, is the main site of that survey from the Urban
Institute because I wanted to give them credit as well. This is actually about
the Affordable Care Act, you know, because what is it? Go big or go home. So, why
not just dive right into it about something that you know hits home and
people are interested in. And we can have, now we can say, “Oh, well what’s going on
there, what do we see, what don’t we see are we getting is what this person says
over here correct, and it could be just not really correct versus interpretation.”
Right, is this interpretation one we would share or we have a different
interpretation, you know. So let’s get into it. Just focusing right here, you’ll see that it’s IPCSR 36842. You don’t have to remember that, but just as we share the slides we just want to remind
people so if they wanted to go along. It’s almost like play along at home…they
could do that too. And if you look, this is the third quarter survey for 2016 and
this is a survey of the non-elderly population to look at the cutting edge
internet these survey methods to monitor the Affordable Care Act. And this
happened…this was done before federal government surveys are available so we
can get that earlier information. You know, so one idea as we look thinking
ahead to other sessions, thinking about linking data, and thinking
about bringing studies together…just think about that in the back your head
as well. Like now, you have comparisons that you could use. The topics covered in this survey, as you can read, are gonna be like health status, type of health insurance coverage, access to
health care, how literate, and how aware people are of their health insurance,
opportunities, needs, whether or not they feel that their treatment by the doctors
and other health care providers is fair or unfair, how they feel about the
marketplace is that something you’ve heard a lot about, what you know about
the Affordable Care Act provisions, and neighborhood characteristics. Some other
things we’re going to look at or that are in the survey, these are going to be
important, are going to be like things like age and gender and orientation and
marital status and race and ethnicity, citizenship, your housing type, your
ownership, your BMI…you know, and whether or not respondent reported some sort of additional condition of mental condition,
behavioral condition, ambulatory care sensitive condition, because all these
things could come into play to how you view the data that we’re going to look
at but also on when you’re listening to other people and talking about the
Affordable Care Act and whether it’s good or bad or it’s a fact. You can
also think about the lens from which they are viewing this because that
matters too. You know…as we all know, our perspective matters. And so, it
becomes super important to figure all that out. All right so now as a little
exercise what we’re going to do, is we’re gonna go out into the survey then play
around a bit. The first thing we’re gonna do is kind
of look at a study page and also the variable in short and then we’re going
to come back to some of these other ones about health insurance and then do some
cross tabulations. We’re gonna play on the site and so you know and kind of get
that started…that idea. Now as you know, by now, all of us…you know…anytime you
add technology to something you always gotta prepare for what could happen. So,
we’re all gonna cross our fingers and make sure this works out. Here we go…alrighty. Here I am. The HMCA site. Alright, so I’m feeling good. Feeling good. Alright…so now with anything, you’re going to read the summary and do all
that, but in the interest of time and moving forward, what I’m going to do
first I’m going to go over to this tab over here…variables here…and I know that
this is about insurance, so one thing I’m going to look at is I’m gonna say, “Okay,
well I want to know well how many people in this study are insured?” Because that’s
gonna help me to know. So because I already know that, I can just type in
“insured” and I come back with the first variable “insured.” I can click on that,
and I can see yes or no. So, that gives me a framework for what I’m looking at
right now I’m going, “Okay. At the time of this survey, 90% of this
population is insured. Okay…good to know.” My next question will be, “All right. So
hmm…how do I want to think about this relative to the Affordable Care Act?
Because if I’m gonna make some policy and some decisions about the Affordable
Care Act, it needs to be specific to that. It’s useful to know how many people in this survey are insured but I need more information.
Well…because I’ve already done some work I know that’s gonna be variable QAC.” So
I’m gonna type that in, and then I’m gonna see that there’s a question here,
and I can say is your current coverage a health insurance plan through the marketplace. I’m gonna click on that. Alright, so in this particular one,
because I’ve done some work on this…I know that one is yes and two
is no. And so going forward, I’m gonna remember this number right here.
This unweighted frequency right here of about 1,085. That’s gonna be important for
me as we move forward. All right, so now, what I did was I just went straight to
the variables because I just needed to know kind of basic information. Um…you
know like okay, like where are we, what’s going on, what are the questions asked…
those sorts of things. As I want to delve deeper, and I’m
just waiting for things to catch up to me. Now I want to think about, alright, I maybe want to do some more analysis and
go, “Okay look…wait…what do I need to know?” “What if I want to know some cross
tabulations? “What if I want to know some more information about this? What am I
going to do?” So then in this case, I could download the data, and you can go to
download right up here. You can also access it in “Data & Documentation” and
do some things here. But again, for the purpose of just what I’m trying to do…I’m just gonna click on “simple crosstab/frequency”. You might be saying, “Wait
how did he get there?” I went to the “Analyze Online” right here. And I can
tell that there’s some information here because of this one here…and then I
click on that and that comes up. And now I’m going to do a simple crosstab. Now… It’s always one of those things: we’re gonna
log in. And I’m going to carefully read the Terms of Use. I’m gonna go back
and make sure I know about redistribution of data, and I’m gonna agree to the Terms of Use. Now, we’re gonna have some fun. All right… so let’s say we have those two variables: we have insured and we have
whether or not you’re in the marketplace…in the plant. Alright, so
let’s look at the first one let’s take…I don’t know We’re gonna go “Race/Ethnicity”
and then we’re gonna go here and figure out well how many people are using the
plan by race. We’re gonna add row percentages that was right here. By
default, you see column but I added row. All right…now I’m gonna build the table. And this is what we’re going to see. All
right so, what we see now…we’re looking at race over here: Race & Ethnicity and
then we’re looking at the questions… Whether or not someone is enrolled in
the health insurance plan through the marketplace or they’re not enrolled in a
plan through the marketplace. Right so, we can look across for white non-hispanics
right and see that 11 percent of the White Non-Hispanics in here are rolled
in it. 22 percent of the Black respondents. 15 percent of the other, I know. 24% of the Hispanics and then about what
14 percent of multiple people who identify as multiple races. And so, you’re
like okay, so now you know if we were going to you know mark that out. We would see that, right? So what you’re
seeing is this graph is the overall graph. Where I talk to you
about the more within the category numbers so if we go back to the overall graph we’ll actually see that still the majority of the people in who
say “Yes, they’re enrolled” are white. All right, so another thing we might want to
look at, you know, just for ease I’m just going to go backwards. So let’s choose
another row. Let’s look at sexual orientation and see what that looks like and look at the numbers. Alright and we
can decide, “Oh, is there any discernible difference?” And you would have to do…
write the you know…your due diligence and checking of course, you know, your
significant scores and all of that. But this is just a starting point to get you
through there. And another place to look would be…let
me make sure. Marital status…will that matter? All right…do we see any differences
within that? So within married people, do we see any
big differences versus widowed people, versus divorced people, versus separated
people, versus never married people, versus living with a partner and how we
might make assessments and figure out well who is using just based on some of
these data? Who’s using the…who’s using the marketplace? Who isn’t using the
marketplace? We could also pull these numbers by insured and look at them that
way and just again like start getting that information to start thinking about…ok so what are these data telling us? How can we compare them to the data we might
be getting from the government? And what can we say coming out of that..about what
well what’s happening and what effects we might assume. I just want to go back
to an idea. I lost my train of thought…I’m sorry. Go
back to an idea about okay so this study this particular single study is part of a larger series of studies called the Health Reform Monitoring Surveys. And as the series you could take those by the different quarters and go… “Oh, okay. well let me look at it for this quarter, that quarter, that quarter, that quarter, that quarter.” And then pull up an aggregate and start looking at the numbers across the different surveys. And say like, “Do we see any changes over time, in the short periods of time. Do we see any movement?” “What’s going on? What could be the effect?” You know…if…if we know there was a special decision made or a particular decision made at a point in time we could look after that point in time or
before that point in time and then we can say to people like, “Oh, well. You said
this…what about this?” And…and in that way we can talk about and be more informed and say, “Yes. This is how you might do it.” And if you’re thinking about…you know…if
you noticed that I went to that online analysis piece and the simple crosstab
query and all of that, that again says like this is something that is accessible to people. You don’t have to have all the statistical skills. You just have to have the thought process behind it. And then let the machines…[in robotic voice] let the machines… [Normal Voice] let the machine kind of…you know…do the work for you. And that way and you can get these numbers out. Another thing I wanted to look at,
because well I’m the one driving this, right? Is to look at what about those
with specialized conditions. We have this question here: “Do you have a physical and mental condition impairment or disability that that is affected?” And then it’s by “Is your current coverage a health insurance plan through the
marketplace?” Right? And so what we’re looking at here is you know the people who said yes versus no as far as the condition. And then yes, they’re in the
plan or no, they’re not in the plan…you know. So…do you have a list? Do you have an
impairment? I believe that one’s “Yes” and that one’s “No”. And then this one over
here is “Yes, I’m enrolled”…12 percent, 11 percent. So there really doesn’t seem to be a difference. And so, again, as we’re thinking this through and kind of going “Okay. Well let’s kind of set this through and then say…you know…you can can speak.” I’m just thinking of whether…because it’s been on the news, right? So we can pick on Feinstein and Grassley. I remembered them sitting next to each other. And so you could write to either one of them or you can hear something from them. And you can say you can do your own work. You can also as an academic or as a local policy person look at things and say like, “Oh, okay. Well
how is this affecting our community?” Now that might be limited by the
geographical variables that we have in the study. And there is a restricted data
version of these…of these particular data. So that you know you can go through
the process to get more information that way. We use public…some public use
variables in this case just for ease. But then looking at and saying okay well how do I pull this out and now what are the next steps. Are you willing then to take
some of your data or some of the information you have, if you’re in the….if you’re in the…if you’re in academia and go maybe talk on this at your local council? Or at your…or talk to your local politicians? Or go to your State Capitol and be involved in that way? What are the different ways in that
can shape policy? Even if you’re maybe not an academic and you’re outside of
that whole sphere, you can go to these data and look at them and say like, “Oh, okay. Well this is how I might write…you know…” “Based on these data, I might write this
particular policy this way or write this informed letter, write this opinion piece
all that in these different ways to support and to be involved and to be
more aware and informed. Some other relevant archives that have come up or that I can think of are like the NAHDAP archive which is Addiction and HIV Data Archive Program. So if you’re looking at…you know…health policy around like prep, maybe. Or you’re looking at health policy…looking at policies around Child Care Research Connections… another archive here or project at ICPSR. That’s another thing you can do. These particular archives, again, are ones that
already are looking in that direction as far as taking data, bringing data and
policy together. So like Research Connections has a bunch of additional resources besides data where you can look at things and see reports and see all that and see the effects and be informed. I talk kind of fast I’m going to get through this. I was a little worried about like, “Oh, will we have enough time?” So, here we are, we’re at our wrap up phase. I like to put in a plug,
always, for…let us know what data we should pursue to remit our holdings. Again, this
was not an exhaustive list of archives. If you go to the main ICPSR page, you can
access all the data we have under the umbrella of ICPSR. I can’t say enough
so I’m saying it again within the last minute, right? It is
really vital because at this point there’s so much data out there, we can’t
keep track of all of it. So it’s super vital and incredibly helpful if people
let us know and if they have ideas about data we should pursue let us know and we
can go focus on those types of things and we will. We’ll try. Now we can’t get everything…we might not get everything, but we will try. So thank you for joining me on this journey This is my stuff. I’m turning it back over to Dory. We’ll see what questions happen. [Dory] Okay. So, we’re gonna look and see what questions have come in. If you have questions this
would be the time to send them. We just wait a moment Okay. While we are waiting, just wanted to
give you a heads up about what’s happening later on today. Directly after
this session, we actually have a Facebook video premiere of one of…one of our new
ICPSR 101 videos. This one is titled, “Why should I cite data?” And if you…if you are using data, if you’re creating data you get to hear about how you know just getting this one thing right can have ripple effects throughout their research community. And then later today at 2:00 p.m., we’re going to be presenting Beyond the Academy: Data for the People by the People That’s at 2:00 p.m. and then after that Beyond the Academy: Building Data Coalitions That’s at 3:00 p.m. and
then we will round out today with one of our more popular sessions, attendee wise. At 4:00 p.m., Diversity, Equity, and Inclusion: How Data Affects a Diverse Community. Hey, and I do see some questions are coming in. So, okay. So that was…this was a question about I guess someone who wanted to see the video
premiere and you’re not on Facebook. It will also be on YouTube, after today. So
you’ll still be able to see it and share it. Thanks for that question. [David] But I can address something. And so, this is David again. Going back to it, one of the things that comes up a lot and I think about is thinking about like, alright so, this was an overview and it was kind of
quick. And maybe you’re still not comfortable of figuring out how you
might fit into this and what you could do. These data…these ICPSR 101 courses is exactly the kind of thing to get your feet wet to
get you interested and to help you along no matter where you’re
starting, to figure out how to keep going, and how to use ICPSR data effectively and in a comfortable way. And so I just wanted to plug that as well you know I also wanted to plug that the
Resource Center for minority data, which I’m a part, and Open Data Flint are
always building their collections. And for example, some of the stuff in the
Open Data Flint collection are things like…you know…water levels…not water levels. Lead levels and copper levels in the water. So you can see that as comparisons
to maybe other places and stuff like that. And so, yeah. So again, please go check those out. We will be sharing these slides. [Dory] Okay. So it looks like that is it for questions. So we just wanted to thank you for attending this ICPSR data fair session. There’s much more coming up and we just look forward to seeing you throughout this week. Check us out on social media as well and thank you. [David] Great!

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