transcript 
 
Event ID: 1550933
Event Started: 6/9/2010 12:48:46 PM ET






Please stand by for realtime captions.


Good morning, everybody. This is Jay. We just have a few more minutes before we 
start. And know that you are on and we are in the right place, but we will get 
going in just a moment. Thanks. 


Good morning, everybody. This is Jay again. Welcome to our webinar this morning. 
We're having, I think, a few technical difficulties with people trying to log 
into the audio conference feature, so that we may have some delays as we move 
forward. Before we get started, I am going to turn this over to Randy who is 
going to walk us through some basic logistics, many of which you are already 
familiar with. 


Just a reminder, today's conference is being recorded. In a thank you. Logistics 
about using Adobe connect. 


Then we would move forward with a conference of the webinar. Randy. 


Good morning, everyone. When we do begin the presentation portion, we will mute 
everybody's phone from this end in order to keep the background noise down to a 
minimum. We would ask that you use the chat to ask questions. We will have a 
question and answer period at the end of the presentation as well. So, we can 
move through this quickly. Keep notes and ask questions and we will get started 
in just a minute. 


Let me know when it is ready. 


Thank you, Randy. Again, this is D. Jay Gense. I want to begin by thanking 
everybody for taking time today. Randy, are there similar logistics we need to 
worry about? 


Yeah, I just need to mute the phones and then get you going. 


I am going to give Randy a moment to do that. 


All participants had been muted, but you can un- mute your line by pressing star 
six. 


All right. Thanks. Sorry for that delay. Again, thank you for taking the time to 
join this webinar today. We absolutely understand that the end of the school 
year is a crazy, busy time for everybody. We will do everything we can to get 
through the content as quickly as we can without rushing so much that it becomes 
confusing. Note that we have a couple differences with this webinar compared to 
other webinar as we have done using Adobe connect. First of all, the closed 
captioning feature we have purposely set up for those who wish to have closed 
captioning text of the content of the webinar today. We have set it up so that 
it is in a separate window, a separate URL that you need to enter. We did that 
purposely because we wanted to maximize the real estate of the desktop so when 
we are showing the tools, the pivot tables and graphic charts, that there is -- 
they are as large as they can be and allow you to see what is going on with the 
data. Note that that is the reason that we have done that. I believe everybody 
who is choosing to use closed captioning has that URL address and can access it 
in a separate window that ought to work. Also, know that we purposely did not 
send out the PowerPoint that we are using today. Mostly, because there is really 
not any content in that PowerPoint for anybody who wants it, we can certainly 
make it available. All of the content that we are presenting today is within the 
maps and the charts and tables. The PowerPoint is simply serving as an anchor so 
that you know what it is we are moving to. That is the reason we didn't send the 
PowerPoint. All right. That said, let me move forward. 


I want to start by giving just a brief history and laying the foundation of our 
conversation today. As you all know, the deaf blind child count data has been 
collected for many, many years. These data are vital in assisting all of us to 
understand and address the needs of students and families and schools and yet we 
know that the collection of the child count data can be very time-consuming. So, 
we want to make sure that we are maximizing the use of these data to 
appropriately inform and guide your thinking about local and state and national 
allocation of resources and deaf-blind project resources. To that end, and 
Smith, who is the project officer of NCDB and is the leader of the deaf blind 
portfolio workgroup, which is comprised of all of the project officers that have 
oversight as possibilities of the 32060 state deaf-blind project has been 
encouraging NCDB to develop and make available a graphic representation of 
deaf-blind child count data with the assumption that having graphic 
representations will be an easier way to do some of the queries. So, we worked 
hard to do just that and made it available about a month ago. Honestly, the 
feedback we received has been -- it was swift and positive. It is obvious that 
people really want and appreciate this kind of tool to be able to better 
understand the child count Davie -- child count data. Concurrently, we have been 
working to make the raw child count data available through Excel pivot charts 
and pivot tables. I understand prior to make him into the project that pivot 
charts were unavailable with earlier iterations of the child count data. We are 
going to be making the 2000 and eight child count data available as well. The 
purpose of this webinar is to walk you through the use of those two tools, the 
web-based child count graphic maps, and the Excel pivot tables and charts. We 
hope that by the time we end this conversation he will have a good understanding 
of how you might be able to use those tools. Specifically, we have targeted 
three objectives with the content of this webinar. First of all, we want to 
ensure that you're increasing your understanding of how to use the maps to 
retrieve and display data. Secondly, that you are understanding how to use pivot 
tables and charts, the Excel pivot tables and charts, to run data queries and 
report the results. Lastly, and critically, we are focusing on using those tools 
to allow you to analyze data to make better informed or data informed decisions 
about deaf-blind project resources and services. So, relatively and simple 
objectives today. The format. 


Basically, we are chunking out the content in three sections. First of all, we 
are going to spend some time talking about using the new child count graphic 
maps. Robyn bold -- Robbin Bull will walk you through that. Next, [ 
indiscernible ] while walk through the use of the child count pivot charts and 
tables. This tool is a rich and powerful mechanism to be able to run data 
queries that you may not be using already. We want to be sure that if you choose 
to use those, you know how. Lastly, we will move into a conversation that I will 
be facilitating that is talking about using and applying these tools to inform 
your thinking. We will end by opening it up to questions and comments. We also 
want to make sure that you understand that much of this is a work in progress, 
so we really need to hear from you on what works, what doesn't, what kind of 
features you would like to see. We will end as we always do with an evaluation. 


All right. So, with that, I will turn this over to Robbin Bull who will walk us 
through the deaf-blind child count maps. 





Good morning. Or good afternoon as the case may be. As Jay mentioned, later he 
is going to go through the maps and the pivot tables and their use. I am going 
to give a brief orientation to the map. First, I am going to show you how to get 
to the table or the maps if you have not already found your way to them. There 
are a couple different ways on the website. We do have this graphic that does 
change periodically. We do have this icon on the map and you can click on that. 
It is up on the screen. You also can go to the technical assistance and go down 
to the national child count. All right. I am having navigation problems myself 
here. Okay. All right. I will admit, I have the mouse upside down. There we go. 
All right. You can go to the child count page, scroll down, and there is a link 
for getting to the map. It is going to open up another page. This is where the 
maps live. I will mention that I am in Internet Explorer. Firefox will be just 
slightly different from this. But that is the version that I am using right now 
is Internet Explorer. Across the top, you have your main categories. These tabs 
will take you to those particular categories of data. Going back to the 
breakdown, which is where you first come in, down the side, you have the 
categories within -- the subcategories within those categories. For example, we 
are looking right now at zero two two. These are percentages. Most of the maps 
are representing percentages. You will see on a scale that it does show 
percentages. On the age breakdown, this particular one going down underneath the 
scale, you have a link that shows. You can go to the maps by camp. This is the 
only one that has just can't. I clicked on that. Now it brings up the actual map 
representing the count. Go to total by state. It is going to change the map. And 
it is like a map type thing where the darker the color, the more than numbers. 
Now, this scale on account is a little different scale on the full count. When 
you go to a subcategory, for example, I am going from age six to 11. This scale 
will change to be more representative of those numbers. I am going to go back at 
this point, to the original percentage tables that we started out with. There is 
the link on this one to get back to that. On each category, in the navigation 
bars, we have we have a tables available for the percentage as well as by the 
count. That comes in when you are looking at the EI settings. I know that is a 
bit of delay, so I am trying to keep you up at my conversation there. For 
example, you can see here, Kansas is kind of a lighter state. I am going to go 
to community based settings. We are currently in the home settings. And you see 
Kansas changes to a very dark state. With that, you would be thinking, okay, 
what is going on here? 


If you go to the child count table, you can look down at Kansas and see they 
only have two children. So, those two children are in the community based 
settings. You can see the differences in the total. Now if you look at the total 
on this table, the numbers represent the full count. Those are how to get around 
within them. The one other thing that I am going to show you is how to print if 
you wanted to print a tables. For example, or if he wanted to print the maps. 
Now, with the map on the screen -- and this is a little bit tricky. I am showing 
you in Internet Explorer, because I have had more success in Internet explorer 
then C-17. If you go to file, and go to print preview, it comes up as portraits. 
I am going to change it to landscape. As you see, there is a bank -- there is a 
blank page. I believe because this is set up in frames, you have to go to page 
two to actually see the map. Right now, as you can tell, it is not sitting on 
the page. Even though it says shrink to fit, I have found that if you go to 80% 
-- now goes back to that first blank page. Going back to the second page, then 
everything shows. With this, then you can print that out. For example, if you 
have a PDF printer, you can print it to a PDF. Then you have a copy of it. The 
tables are the same way. I found you keep it on portrait and you do 70%, I 
believe. You might have to play around a little bit, but you can get the tables 
that way as well. One other thing to mention -- I am going to get out of the 
print preview screen and go back to the actual NCDB website where the map link 
is. I just want to -- out, as Jay mentioned, we really like feedback. We have a 
link where you can give us feedback, but also after the presentation, if you are 
on the map on this page here, there is a link for sharing feedback to the 
webmaster and that will get back to all of us so we can review those and keep 
those in consideration. 


All right. Now I am going to pass it on to Mark Schalock. 


Good morning and good afternoon, again. We are going to move now to looking at a 
very different kind of tool. This is a very interactive tool. It lets you really 
select the mouse the variables you a result, -- it is more complex. I will try 
to spend a little bit of how to manipulate the data. We will do a few more 
complex. Hopefully, this fall we will get of cleaning the data and getting it 
back to you and finalizing it 


A couple things charts. This is going to be significantly Thought that most 
people would have been -- would have made the conversion by now. We went with 
2007. It looks a little bit different. Some of the ways that we can manipulate 
the data are a little bit different. If you're still using 2003, we would 
suggest that you go to the NCDB website under products and then webinars, and 
there is the archived webinar from 2008 that guides you through how to use the 
2003 version of Excel. There is another really important difference between 2003 
and 2008. That is in 2007, you are really unlimited in terms of the number of 
cases you can analyze. That is important when you start doing longitudinal data, 
which we are presenting here. Once you get up to six or seven or eight years, it 
far exceeds the capacity of 2003. All right. So, this is what you would open up 
on a pivot table chart file. We will talk about how you can access these a 
little bit later. The first thing I am going to do is make this bigger. You can 
get rid of all of the menus on the top and really expand the size of this. So, I 
am going to do that. The first thing you do is go to the top and click on view, 
and then go over to full screen. Hopefully, this will really help your -- 
hopefully, this will be able to help you see. It makes it a little bit bigger. 
Well, there are three pieces -- well, for really, but three pieces to the pivot 
table file. You could access them through the tabs down here on the file. There 
is the data, and this is individual -- well, once you see it. It is coming up 
slowly. There it owes. That took a long time. There is a table of the analysis 
that you do, which is also very important. It provides the specific numbers that 
you are seeing in the chart. In fact, the chart is simply an Excel graph of that 
table. Now, you control the analysis, you would do either from the chart or the 
table page I find it is easier to do from the chart page just because of the 
visual. Conceptually, it is just easier for me, but it works exactly the same 
way from either page. I think Jay prefers the table page and will be working in 
that as well. The important part of manipulating the data is over here. The 
pivot table field list. If you look at the items within that list, those look 
familiar. They are, in fact, the data elements within the deaf blind child count 
or even a subsection of them. Because this is a multiyear database, we have 
limited the variables to those that, for the most part, go across all five 
years. So, you have a list of the variables there. Then you have these for pods 
down here. This legends Field is what is over here. The access feel down here is 
what you see along the bottom. Any time you drop something down here into the 
axis Field, it will show up along the bottom of the chart. Every time you drop 
something down into the legends Field, it will be displayed vertically. All 
right. So, how do you manipulate the data? How do you actually analyze the data? 
Stronach well, I am going to take you through some examples and try to explain 
it. Right now we have race, ethnicity breakdown by year. By the way, if you 
scroll over the bar, you have those values. You'll see that that value 
corresponds to the value in the table. You are simply grasping the values in 
this table. All right. I am going to show you a couple of pretty basic 
manipulations or analyses. I am going to go than through a couple more examples 
where we have multiple variables that we are looking at and we are filtering but 
variables. It looks at a subpopulation. Then I will quickly show you some real 
simple editing things you can do within the charts and the tables and how you 
can grab the data and just copy it and paste it onto a worksheet and then 
manipulate that data to compare state with region with national. 


The first thing I am going to do is go from ethnicity by year two looking at the 
number of children being served in early intervention and early childhood 
special education by year. The first thing I am going to do is get rid of this 
variable. Just take it up in here and drop it anywhere. It doesn't matter. It 
will find its way home. Then I am going to take age grouping, which is down here 
at the bottom, and drag it down into the legends Field. All right. We have re- 
analyzed the data. It was that quick. Now, I don't want to look at all of the 
age groups. I just want to look at birth through two and three through five. I 
will go through here and deselect and then select the ones I want. Hit okay. All 
right. So, what we have here are the accounts of kids for the past five years. 
Birth through two and three through five. I am going to get a little more 
complex here. I am going to add gender to this mix. So, I am going to go back 
and grab gender. Pull it down into the axis field, because I want to look at it 
by year. Now, because there are unknowns, I want to get rid of those. I just 
want to select male and female. All right. Now, I can look at this and female 
and female, but what I really want to look at is how female population changes 
across years and male population changes across years. To do that, you simply 
change the order of the variables down here. Now we have got females crustal -- 
clustered across tiers and males clustered across years. If you want to change 
the order, you simply change the order of the variable. Now, I can look at the 
table and you could do the same thing. Now it is male/female by year. Now it is 
gender across years. Okay. Now I am going to go to a little bit more complex 
analysis. Again, using the same process. I am going to get rid of gender. Now, 
remember we have deselected some of the options. I needed to deselect some of 
those options when it was down here. So, I need to click on the little down 
star. They are also elected. Get rid of gender. Just throw it up anywhere. 
Again, we are just looking at two age groups. I want to reselect all age groups. 
All right. I want to get rid of age groups. And I want to drag down etiology. 
Well, that is a lot to look at. I want to select just the single ideology. I am 
going to select charge association. You can see that is a pretty dramatic 
increase over time. It is almost a 40 percent increase over the past five years. 
All right. Now, what I want to do is get a little fancy here and look at this in 
a couple specific states. That compares -- compare those states with the region 
and then compare that to the national pictures. How do I do that? Well, I need 
to drag state down into the access feel. This will be an interesting picture 
here in a second. I want to go up and select just two states. So, I go back up, 
click on the little down arrow, deselect all, and then I am going to just for 
example purposes and Michigan. All right. Now we have the data just for Illinois 
and Michigan. I want to change the order of that. 


All right. Now let's go to the table. You've got the raw accounts for two states 
across five years in his table. I want to be able to compare this to all the 
states in that region, and I want to compare this to the national picture. How 
do I do that? Well, that is one of the limitations of the pivot tables and 
charts. The mapping does not have it. You cannot do it. What you've got to do 
instead is copied this or highlight it, copy it, and come down and open up one 
of these worksheets. They are there for a purpose. And then paste. Okay. This 
will catch up with me and a second. There we go. So, that is that table we just 
created. It has Michigan and Illinois. Now I want to go back and select all of 
the states in our region two. Okay. So, to do that, I go to the state, click the 
little down a row, select all, hit okay. Notice that we have the variables 
called NCDB area. I also want to get rid of state. That just makes it more 
complex. We will select area two. All right. Now we have account for all the 
states and area -- in area two. I am going to go back to the table. I am going 
to copy back. I am going to take it down to the worksheet. Paste it. All right. 
I now want to compare that to the national. So, I go back up to NCDB area, 
select all. Go to the table. Paste it. Okay. Now you have three sets of data you 
can play with. You can do anything you want to with this data, just like you can 
in an Excel file. But you have to go through the steps to be able to compare 
state or several states to a broader group. All right. I am going to show you a 
quick couple of other things here, and then I will turn it back over to Jay. A 
couple things you can do. You can change how this looks by putting the cursor 
somewhere in the grass. Format the plot area. Change the color. So, I just 
change it to white. Just like an Excel chart, you can change the axis. You could 
put numbers in here if you wanted to. All that kind of thing. Now, I need to 
show you one other example of this. To do that, I need to go back and re- select 
all of the etiologies. All right. What I am going to do now is change this from 
count 2 percent. So, right click. , chart type. Go from a stacked column to a 
100% stacked column. 


Click okay. Now, a couple things to notice. The squirrel over function has done 
80%. It gives you counts. As does the table. But it does allow you to graph 
percentage. In the chart you can simply right-click and copy and then paste into 
a Word document. It has that ability as well. You are able to take this 
information and include it in reports. So, that is a quick, I'm sorry, basic 
overview of the pivot tables and charts. We will keep adding to this. We would 
like to build up at least a ten-year longitudinal database. Of course, we now 
have, hopefully, some stability in the data elements in the child count. We 
would be able to look at specific things like placement to have consistent 
definitions across a longer period of time. It is a complex and powerful tool. 
You gain skill by using it. You cannot hurt it. I would suggest you always make 
a copy of it and then play with the copied version. If you get to the point 
where you don't know where you are and you want to get back to where you 
started, you simply close out and select do not save changes and it will go back 
to where you started. With that, I will pass it back to Jay to provide some 
specific examples on how you might use these two tools for specific questions. 


Thank you, Mark. As I am moving over to do that, can I ask you to reset the 
tables back to something standard so I can start from the beginning? 


Yes. 


As we move into this kind of third content area for this webinar, which is the 
kind of application and spinning scenarios about use, I forgot to mention, and I 
apologize, of how we are going to make this pivot table in the pivot charts 
available to you, because at this point, they aren't. We were striving to make 
these available on kind of a private website that we will send to all the 
project directors and coordinators under the advice, even though there is 
nothing when using the pivot charts and tables that is personally identifiable. 
Therefore, a potential breach of confidentiality. Because in the child count 
collection, we are not asking for child names or any personally identifiable 
information, even though you can quickly get to the point in your data query 
that you have an end of say one or two students. You still wouldn't know what 
those students are, the most discreet data that you could query down to is in 
which state that student happens to be. So, there is nothing that we believe is 
the confidentiality issue. That said, we still want to make sure that there are 
no concerns about that. So, we are not going to be posting these on the general 
publicly available website. The value of the tool is primarily targeting project 
directors 326 C. and OSEP project directors. So, we will be posting this in a 
week or two on a stickier URL on a website. We are also working to make it 
password protected. We will send that information, the URL and password, to 
project directors and coordinators under a separate email. That way we are doing 
what we believe we can to ensure that we have security of these data. 


Are we good, Mark? 


I think we are back where we need to be. 


All right. Let me take a minute and highlight the fact that we understand that 
the use of the graphic maps, and maybe even more so the use of the pivot charts 
and pivot tables, can be a little bit overwhelming. I want to highlight what 
both Robbin and Mark have already said. You are not going to hurt anything. I 
really do believe that the value in learning, particularly the queries that are 
available to you in the charts and the tables, you will learn by simply playing 
with this and seeing the power of the tool itself. So, don't be afraid of it. Do 
know that you will learn to use it and to control the queries the more you use 
it. We also are available to provide some support and technical assistance to 
help you through this if you want that. Okay. Let's move into the third content 
area for this webinar, which is wanting to delve into the application of the 
tools for your data analysis. Now, I am not going to go into great depth about 
the data analysis or the value thereof, but I do want to spend some time talking 
about some of the, I don't know, big picture perspectives about how you may 
approach your own analysis of the child count data. Specifically, how you might 
use these tools to help in that effort. You know, it is interesting. The 
perspective that I bring to this is in Part performed from my years when I was 
working as an SCA and was responsible for the general special education 
monitoring system. We were designing and implementing a system that was 
supporting school districts use of analyzing their own special education data to 
inform their thinking about their services and, ultimately, to inform their 
thinking about compliance. So, we were designing a system that allowed and 
provided districts with tools to support their analysis of the data. My 
experience was if people had one of two reactions when it came to data and data 
analysis, it was either scary or it was boring. What I think, as we are looking 
these tools as they are available to us in the field of deaf blindness is that 
you will find that the tools will help your data analysis to be less scary, if 
you happen to be one of those in the former category. And it will be 
inordinately less boring for those of you who maybe are in the latter. What I 
would like to do is highlight some of the values and the benefits that you may 
find in using these tools. We will then simply walk through some scenarios in 
showing how you might find them to be helpful. The intent in doing that is not 
to give, you know, detailed step-by-step instructions or directions for the 
analysis. As Mark mentioned, that really would be impossible to do because the 
possibilities truly are endless for how you may run these queries and access the 
data he would have subsequently. Instead, what we want to do is spend time 
discussing the application of the tools. That discussion is meant to stimulate 
your thinking and raise awareness. Maybe awareness of and enthusiasm for 
possibilities for use. Our hope is that you believe the webinar kind of eager to 
simply jump in and begin playing with the tools. Again, I really believe that is 
the way you are going to learn and apply the information. Before we jump into 
the specific examples, let me step back and highlight some of the values or the 
benefits that you might find in applying these tools. 


My experience has been that these values, if you take the time to articulate 
them, really can serve as an anchor to the data queries that you may run. They 
allow kind of a concrete way to articulate what you can hope to accomplish 
through the data analysis. They help you to identify the questions to which 
answers are being sought. And Mark already hit on some of these. I am 
reiterating, mostly, because redundancy is important when it comes to some of 
these tools. The value of allowing you to identify comparators is significant. 
Remember that the data alone is not going to tell you anything. Taking the data 
and comparing it to something else may, in fact, tell you something. There are a 
lot of comparators that you may decide you want to contemplate. Mark was 
primarily demonstrating comparisons with other states. That, certainly, is a 
valuable compared her. But there are a variety of comparisons you can make with 
states. We will highlight some of those based on these scenarios. Secondly, 
these data and these tools will help you to identify trends. Trends over time, 
trends across states, trends by age of students. Again, there are a lot of 
possibilities. Being cognizant of trends, recognizing where they are and what 
they are will prove invaluable as you plan projects services, as you contemplate 
allocation of resources of services. Lastly, and maybe even most importantly, 
these tools will allow you to analyze those trends and those circumstances. You 
know, providing you information to either understand or explain what the data 
seem to be -- what the data seems to be illustrating. You know, it is 
interesting. When you look at these values or these benefits, and maybe that is 
the wrong word, but there are a lot of kind of responses that one can consider 
as you are jumping into these data queries. One of the questions that I find 
helpful as I am thinking about these values and benefits and specifically as I 
am identifying the questions to which I am hoping there will be some answers 
through the data query are some critical points. First of all, I believe that 
these data at the data queries will allow you to better understand in 
differentiate reality from perception. Ultimately, asking the question of 
yourself whether or not the data proves to be in alignment with what I believe 
is happening or what is happening or whether the data are potentially 
highlighting some kind of disconnect between what either is or is perceived to 
be. I think it is an important point to step back and realize that these data 
analysis can help you to identify reality from perception. They also can help 
you to confirm or not confirm some believe that you might have. Ultimately, 
bringing some objectivity to the perspectives that you might have of services. I 
know, for example, when I was serving as a project director of the state, I had 
circumstances that I thought something might be happening when, in fact, the 
data analysis showed me something else. I had to recognize the fact that my 
perceptions weren't necessarily accurate. Some of these tools helped me to 
identify that. A critical point to me and in kind of building on this theme that 
I am spinning here is helping you to identify where resources need to be 
pointed. 


We all know that the deaf-blind program doesn't always have all of the resources 
that we wish we had our need to have in serving kids and families. It is 
critical that we are maximizing the resources that we do have and pointing them 
in the right direction. I really believe that these data analysis can help you 
to identify what those directions need to be in that we need to be cognizant of 
those directions and not afraid to move in the directions to which the data 
tells us we need to have more attention. For example, maybe your data analysis 
will highlight that you really need to bring attention to your early 
identification efforts or maybe the data analysis will highlight some of what 
Mark just identified in seeing the significant increase in the kids identified 
with charge. Maybe that tells you something about where you need to be pointing 
your resources. I know one that is real common these days are some trends that 
we are seeing relevant to publicity and the extent to which we do or don't need 
to be thinking about our project resources targeting particular ethnic groups 
and whether or not there are other partners and other organizations or the part 
B. and C. organizations within our states to which we need to be thinking about 
that. Lastly, and importantly for the deaf-blind projects, I would highlight 
that sometimes the data analysis will simply allow you to identify who you might 
want to talk to to learn about their efforts. That is where the compared her 
data can be really useful. Maybe you choose to look at another state that you 
identify is a light state. Maybe you see something going on with them that you 
are interested in or wondering how they got there. It is simply a matter of 
getting on the phone and calling them and learning what the efforts are so you 
can decide is whether or not -- decide whether or not it is something you want 
to replicate. With that, let me go into some somewhat random examples of some 
applications. I will start by going back to the pivot tables and charts. You'll 
have to excuse as we go back and forth between the maps. There is some delay in 
all of this. Again, we are providing some examples to stimulate your thinking. 
You may or may not want to run these particular queries. It really depends on 
your own circumstances. Again, just showing the power of this. Some caveats that 
I want to point out before we jump in. It is really important that you approach 
these tools and the data analysis that is available through their use with some 
caution and some common sense. Don't jump to conclusions too quickly. It you 
cannot assume that there is something wrong. The data allows you to have a 
perspective for which further thought may be required. Further data analysis may 
be required, probably will be required. I always think of an example that I had, 
again, when I was as an SCA. In the data analysis, it identified 100% of the 
kids were black were identified as being eligible for special education 
services. Eligible under IDEA. They were thinking there was potentially 
disproportionate representation within that ethnic groupings, when in reality 
and further a day minutes to five -- in his district there were only seven kids 
were black in this district. All of them were part of the family, the same 
family. This family had adopted all seven of these kids specifically with the 
understanding that these kids had very significant disabilities and they were 
adopting them and they had come from another country. In fact, they were 
eligible for four [ indiscernible ], thank goodness. The fact that 100% of the 
kids were black were eligible for special ad wasn't a wrong thing. It simply 
wasn't an anomaly you would want in. I use it as an example to highlight how 
careful you need to be with this. Don't jump to conclusions. Okay. 


Let me start by building on the theme that we first identified in identifying 
comparator is that you may want to explore. Let's say that we want to use the 
pivot charts and tables and the maps by identifying states that might be like 
mine based on the total number of kids who are deaf-blind in the state. Let's 
say that we go back to the maps that Robbin started with. We are looking at 
percentages by age. Maybe doing a quick perusal to see if anything is jumping 
out at us. We probably would, when looking for comparators by size, would want 
to link to what Robbin identified as the count by total. I wanted to do deeper 
digging relative to that. Now we go back to the pivot table, and using the 
skills that Mark just walked me through, I am going to display the state by size 
for just 2000 and eight. So, right now I've got the state query and it is 
selected to all states. But I also have every year listed. I don't want that. I 
just want to look at 2008. Notice, when I look at this chart, it really doesn't 
tell me much. When I swap the Y. axis and asked axis, and I put the year into 
the legend and the state into the axis, I now have a chart displayed with the 
total number of kids who are identified as deaf-blind and each one of the state. 
Obviously, the key here is not readable, because it is trying to identify every 
state that there is. So, this will give you as much information, but using the 
scroll over features that Mark talked about, I can squirrel away 20 days bars 
and see what that state is. If I am looking for, say the largest three or four 
states, I can simply scroll over the charts and see what those states are. We 
have California, New York, and Texas. I find, however, that if I really want to 
look at the data, the table is an easier way for me to do that. One of the nice 
things that is important to highlight his once you are in the table, all of the 
Excel features for sorting are available to you. For example, if I just wanted 
and alphabetized listing, I can go into highlight the California, go into -- 
where did this sort features go? Well, I won't take the time to do it now. But 
you have this sort capabilities for all of the table just as you do in Excel 
more broadly. But here we see, and if I am talking in the back of my mind the 
concept of one that has some State comparators. I can now see how many kids are 
in my state. If I am California, I can say, I maybe want to meet -- to be making 
some comparators. I would scroll down and say, if I am a state that has about 80 
students, there are a group of states that also have about that. For example, we 
have a grouping of Delaware, Arkansas, Kansas, in Oregon, all of them who have 
about 80 kids. I can top that in the back of my mind and know that those are 
comparators that I might want to make. Remember that comparisons by size aren't 
the only comparisons that you may feel are appropriate. For example, there may 
be states for whom comparability might be more appropriate based on things other 
than size. For example, ethnic distributions. If I'm a state who has a 
particularly high representation of a particular ethnic grouping, maybe part of 
my queries will want to compare to a state that has similar representations of 
that ethnic grouping. And I may want to, specifically, speak to them about their 
efforts to reach out to that particular ethnic group. 


Another compared her that I think is important to us in the field of deaf 
blindness are some comparisons to the states who maybe have similar service 
delivery models that I do. If you are a state who is deaf-blind and project 
services primarily are implemented through a regionalized system where you are 
tapping into some regionalized access to expertise -- and I know several states 
have that -- may be part of the data query that I want to do is comparing my 
state to those states, regardless of the number of kids that are served. So, my 
point being that comparisons by size aren't the only comparators that you want 
to make. All right. Let me move into another example. I am simply going to keep 
building on what we have done. Let's say now we want to build on the theme of 
some trends. Beginning with trends, just bide my state. Following trends is 
going to be really important. So, I am going to set up the pivot chart to 
provide me some trend data based on total number of students by an individual 
state. What I am going to specifically highlight is the trends of the total 
number of students in a state of Oregon. I did that only because I got prior 
permission from the project director for Oregon that it is okay to do this. So, 
let me go back to state and deselect all and just who is Oregon. -- and just 
choose Oregon. Let me highlight the trend, because I want to highlight the trend 
across years, I want to see 2004, five, six, seven, and eight. Again, one of the 
mechanisms that Mark spoke about is what is identified as a legend and what is 
identified in the access to vary how it is displayed. I am going to swap those 
and put state in the legend, year in the access. Now I see the trend for the 
state of Oregon from 2004 through 2008. Using the roll over ability, -- is that 
a verb? It should be. Anyway. I can see what that total N is. 2004 there were 
105 students in Oregon. 2005, 84, and so on through 2008. There was a total of 
80. If I wanted to see in more detail more of the raw data, I can link to the 
table tab and see the raw information. Then step back and ask myself some 
questions. One could wonder why there was this decrease in a matter of four 
years from 105 to 80 students. That is a drop of about 23, 24%, if my quick math 
in my mind is correct. Correct me if I am wrong. But there is a potentially 
significant drop in students. Why is that? One could jump to the conclusion that 
there is something wrong. Yet to highlight the point that I made earlier about 
not jumping to conclusions, there may be a lot of real rational explanations for 
this. Maybe there was potentially over representation in 2004. Maybe in Oregon 
we were talking kids that we shouldn't have. You know, God for bid. Maybe we are 
doing the better job of accuracy in counting kids today than we were then. Maybe 
the state population is actually decreased. You know, we have had some states in 
our country over the last several years who have actually seen a decrease in 
population. Maybe the trends that we are seeing relative to the deaf-blind child 
count align with the general population decrease that you are seeing. Being that 
you cannot jump to conclusions about the data. You need to use it to inform your 
thinking and mostly to identify additional questions that might be there. The 
other obvious thing that comes to my mind that I would want to look at is trends 
that I would want to look at using other databases. Most states, for example, 
post their part B. and part C. special education child count on their website. I 
would want to go in and look at those data and make some some -- make some 
comparisons to the deaf-blind data to see whether they are Corlett or not. Maybe 
they -- maybe there are. It would explain some of the reasons for the decrease. 
You also could use the charts and tables. For purposes of time -- I don't want 
to take any of more time. One of the things I would also do would be to go back 
to the maps that Robbin walked us through and look at these total counts by 
state and the displays that I am given specifically looking at the comparators 
that I identified by size. Remember earlier, I identified that Arkansas, Oregon 
and -- I am forgetting the state. Kansas and Delaware. They all had about 80 
students. I may want to look at what those maps told me or showed me about those 
students. Then remembering with the pivot tables I can specifically bring up 
those states to see what the comparisons are. Let me do that just to show how 
that would happen. 


Go back into state. I identified I want to look at Arkansas, look at Delaware 
and Kansas. I have already chosen Oregon. Now I see the display. I could go into 
the skill that Mark showed us in going to the display that is by percent to see 
the full screen. I find it easier or useful to go into the table and see each 
state listed by year to look at the trends. What I see is that Kansas in that 
four years has gone from 91 to 80 students. Arkansas has gone from 13281. Almost 
an exact match that Oregon has experienced. Delaware, interestingly, has gone up 
during that time from 63 all the way to 83. What does that tell me? It could 
tell me a lot of things. I look at these data and I have a lot of questions. You 
know, maybe I would want to, at this point, get on the phone and call Delaware, 
call Mark and pick his brain about what might have gone on between 2004 and 2008 
in his state relative to child find. I may want to run some queries i.e. all a 
G. to see whether there were particular etiologies for which there were 
increases or decreases. Again, the point is that there are endless possibilities 
of how you might want to run these queries. In my mind, to anchor it on 
comparators and trends and analysis of those trends, you will be able to use 
these tools to identify the questions that you want answered and potentially 
point you to where you might go to inform your thinking about that. Okay. The 
last thing that I want to do is, very quickly, show you how you might dig a 
little deeper within an individual state to highlight trends beyond something 
that is just total population. So, let me continue to spin on the Oregon 
example. Let me reset this just to Oregon. Now I've got Oregon displayed. I am 
going to go back to the table. But I want to think about more specificity 
relative to those kids. As Mark identified, I can click on any of the fields 
that are in the pivot table field list to bring up by distribution by year that 
particular field. So I went to break a new city and I now see the trends by 
ethnicity. I can also look at it in chart format to see whether there are some 
obvious trends. Again, I could move state to the X. axis in the year to the Y. 
axis and I would have a different kind of display. I would encourage you to try 
some of that. The same queries are available by etiology, which is really an 
important thing, I think. If there is a particular etiology that you are looking 
at -- for example, Marquette highlighted the increase we had seen. Let's look at 
the charge trends in Oregon over the years. Deselect all and then just select 
charge. I see what the trend is in Oregon. The point being that you can follow 
this same format of a comparison. You can follow the same format for identifying 
an individual state trend. Then what I might want to do, and I won't take the 
time now because I want to end our content and move this into questions, but you 
could then take the same steps that I had used to provide comparators by size in 
bringing up Arkansas and Delaware and Kansas. Do the same thing by etiology or 
by ethnicity. I would encourage people to look at their settings. If you are 
wondering about your preschool inclusive practices or your practices relative to 
inclusion or lack there of at a school setting, I would highlight that the way 
the pivot table is set up, the early childhood and school age educational 
settings are listed together. If you want to differentiate that, you need to 
first bring up the setting and then you can do another query by age. 


Again, far too many possibilities than we could possibly show you in this 
webinar. Our point is to highlight the value of these tools, both the maps and 
the pivot charts and the tables and to encourage you to simply jump in and try 
some of these. We will have the charts and tables available to you, as I 
mentioned, in about a week. We will send that you are all out to you under 
separate cover. At this point, I am going to stop our content. Before we go to 
any questions, I haven't been following the chat pod. I am going to ask Robbin 
or Mark if there are any closing comments or additional comments that you want 
to make to all of the content we've provided. 


I guess -- this is Mark. I guess the only thing I would add is the tools 
complement each other. They do different things. But they do complement each 
other. The maps provide just a wonderful entr&é;e into the big picture. It is 
very visual. I think they point you in the direction of further and deeper 
analysis that you may be able to find in the tables that are attached. But you 
might want to go into the pivot charts and tables. I really see the tools 
complement each other very nicely. 


This is Robbin. I will just mention that if you don't get your questions within 
this webinar, the link for this survey is in the comments -- excuse me, the chat 
box. It may have scrolled off your screen. When we finished, if you could make 
sure that you go back -- 


[ overlapping speakers ] 


And Randy will repost that as well. You can add additional questions there. 
Again, there is the link on the website. We are always available to help via 
phone and email as well. 


Also, it is important for us that you don't particularly with the graphic web 
based maps that we are considering the present postings very much to be a work 
in progress. There are a lot of tools that we are exploring how the software 
tools that we are exploring, to make it even more user-friendly. Before we 
identify which path we want to go down in displaying some of these data, we 
really need to hear from you on what it is you need from these tools. At what 
you it to be. So, please know that we want your feedback. The last questions in 
the evaluation survey to this webinar gives you opportunity for that. Also, just 
email us. You can email -- in fact, Robbin, I am going to ask if we can use you 
as a kid one -- use you as a point feedback. You can provide her with kind of 
the wish statements for these tools. You want to give them your email address so 
they know what that is and we can also post it on the chat box if we can? 


Randy will put it in the chat box as well. It is bullr@wou.edu. 


That is the website for Robbin Bull. She will serve as a point place for 
feedback that you might have. Okay. So, some questions. We want to end by 
addressing some of the specific questions that we might have. 


Let's respond to Cindy. Cindy, we will post the files in both 2003 and 2007 
format. In 2007, you could open either one. I think in 2003 you could open 
either one. We will post both versions when we post them on the website. That 
will give you access to the files in 2003. 


Cindy is asking for a query by Spanish speaking. We need to be cautious in 
highlighting that the data that we collect on the deaf-blind child account -- 
child account does not identify the language used in the home of students, so we 
cannot assume that students, for example, who are identified as being Hispanic 
are Spanish speaking. We don't know how many Spanish-speaking families we have. 
It might be important for the future. What we can do, however, is a search by 
race ethnicity. I am responding, specifically, to Cindy's request. Let's say we 
want to look at nationwide trends relative to the Hispanic population. I am 
identifying race ethnicity and deselecting all. I want to do all states. And 
then let's look at the chart. 


Get rid of state. That will clear things up. 


We definitely see an increase in total number of students identified as being 
Hispanic. You know, 1500 and now pushing 2000. It absolutely is BS that group 
for which we are seeing the most significant increase. I am reading Sandy's 
comments. It reiterates a point that we made about Spanish-speaking. Thank you 
for that. Okay. Additional questions, if there are any, can be posted in the 
chat box. Randy, is it safe to take those off mute and if there are any specific 
questions that people want to ask verbally, we could do that. 


Weekend. -- weekend -- we can. 


We have now on muted everybody on the audio portion. Feel free -- this may be a 
little awkward. If you want to ask verbally, please feel free to do that. 


Melody is asking if there is a way to look nationwide Java. 


[ music playing ] 


Can I go back to Star 97? 


It looks like melody got put on hold. 


Let me just put out a general announcement. Can anybody hear me right now? 


Somebody's got some music on their phone in the background. It is overlaying all 
lot. 


From Mississippi. I can hear everything. Music as well. 


We cannot hear the music, so I hope it is widely music. What can I say? 


Star [ inaudible ] and go back to the chat pod. 


Okay. We had to put everybody back on hold or on you again because someone was 
on hold. The hold music started playing to everyone. Now we are back to 
questions on the chat pod. 


Responding to melody, no, there isn't. We don't collect that information as part 
of the child count. No, there wouldn't be a way to do that. 


Okay. We have about four minutes left. We are going to turn this over to the 
evaluation. Please take the time to link to the URL that Randy just posted in 
the chat box. It will connect you to -- is it survey gizmo? 


Yes. 


Again, it is highlighting that there is -- there are questions at the end of 
that survey for which you can offer narrative feedback. We really are 
considering the maps particularly being at work in progress. We really want your 
feedback on what you wish you could have in those maps. Please offer us that 
feedback. With that, we will go ahead and close out this webinar. Again, I will 
highlight that this will be -- the webinar it self was being recorded and will 
be available on our archive very shortly. We will make sure that everybody has 
access to that. Again, highlight, as Mark did, that an earlier webinar on the 
use of the pivot charts and tables using Excel 2003 is available. You may want 
to use that as a resource. As always, if you have any questions or concerns or 
want for the direction, please don't hesitate to contact us. We will do what we 
can to help. 


With that, I will close out. Thank you, again, for your time. We appreciate the 
opportunity to spend about 90 minutes with you today. Thanks. 


[ event concluded ]Actions