FREE client record keeping with The Clinical Record

Last month, we, along with our colleague, Dr. Susan Mason, conducted a workshop about free client software at the Council on Social Work Education's Annual Program Meeting in Denver.  There was some real interest and enthusiasm for this and Making Your Case at that meeting, so we thought we would share with you some of what was presented and tell you how you can also get this free software.  If this piques your interest, please send us an email and we can send you the software, slides, scripts, etc.

We created The Clinical Record to help small and mid-sized agencies/practices collect client data.  Why did we do this?  Because we wanted to help agencies create a repository for storing client records in a way that can be useful for program evaluation later on.  In this blog post, we will give you a quick tour of The Clinical Record and in our next post, we will show you how to download data so you can analyze it.

When you first enter The Clinical Record, you will be on the Client tab:

Here, you will be able to view, enter and store basic demographic client data.  You can define ALL drop-down menus so you can do some tailoring to your own agency/practice need using the Modify Codes tab!

Because The Clinical Record assumes that the agency in which you work is not in a vacuum, we allow you to identify resources in the community and relate them to your clients on the Resources tab.

On the Notes tab, you can put an UNLIMITED amount of qualitative information.  In our example, we show you dated session notes.

The Interventions tab allows you to associate defined client interventions with a particular client problem or diagnosis.  In this example, we show you that this client is receiving group counseling for academic problems.

We also let you define desired outcomes, which can be assigned to various clients regardless of whether or not they have achieved them yet. This is done on the Outcomes tab.

The Disposition tab allows you to terminate with a client, provide a final diagnosis and then add some last notes.  If the client returns to your agency, you can simply look up the client and add another admission.

What would you do with all this client information?  We have a few ideas:
1)  You can use the Reports tab to produce some basic reports.  We provide 3 with The Clinical Record, but we can create customized reports to fit specific agency needs.  Just let us know!

2)  You can download data, import it into R and analyze it for FREE!  You can have a facility to collect and analyze agency/program data for FREE!  And how wonderful is that?

Let us know what you think!  We appreciate your comments and emails, as always!

Using R in the classroom - after a hiatus

It's been a super long time since we wrote on our blog, but WE HAVE NOT ABANDONED YOU!  We have been busy, busy, busy with a few things.  Our second book finally came out, and that has taken up some of our time with the extensive book tour and all....

Seriously, as soon as the book came out, we were up and running with it during our summer semester in our MSW program.  We thought you might be interested in how we are using it and R in our Master's classes.

To set the stage properly, this book is not about any particular R package at all.  It is really a guide on how to do program evaluations using R instead of other proprietary software.  The majority of the book steps through different types of evaluations with case studies.  They range from very simple evaluations where you might want to simply describe the characteristics of clients to much more complex outcome evaluations with multivariate regression models and, of course, everything in between.

In our MSW classes, we used R to do a class project where the students evaluated our educational program with the outcome being overall student satisfaction.  We could go on and on describing what this project was like, but a picture is worth 1,000 words, and we thought it might be more interesting to show you some slides from one of our groups' final presentations. But first....  a shout-out to our students who agreed to share their work with us, so thank you Avallon Leopold, Ashley Moses and Alyssa Tanz!

The overall research question was what: factors contributed to student satisfaction in our MSW program.  Avallon, Ashley and Alyssa, after conducting a literature review, hypothesized that fieldwork placement, supervision, and academic instruction would all be positively related to student satisfaction.  They also thought there would be no significant relationship between marital status and overall student satisfaction.  Let's tune in to see what they found....

With regard to fieldwork supervision:

The students found a weak, but non-significant between students' perceptions of fieldwork supervision and overall satisfaction with the MSW program.

About fieldwork satisfaction: 

Nothing! Nada!  No relationship at all!

When testing their hypothesis about marital status, this is what they found:

Still no significant relationships, but what what about students' satisfaction with academic instruction?

Whoa!  We finally found something!  A moderate and significant relationship between students' satisfaction with their instructors and with the overall program.  You know what that means to us as professors?  PRESSURE!

On a more serious note, this is how our students produced such fabulous work in 7 short weeks - hard work!  To do those really cool scatterplots with the regression line superimposed, they used the scatterplot() function from the car package.  The correlations were done using the rcorr() function from the Hmisc package because it helped the students deal with missing values and calculate the chances of making a Type I error in one easy step.  The snazzy box plots and one way ANOVA were put together by using functions from base R.  No fancy packages.

We asked the students at the end of the summer about their experiences with R anecdotally and this is what we heard:

• easier to use than that really, really expensive software package because all the commands were on one-line and they didn't have to go through menu after menu.
• they found the book really helpful because everything, including all the commands needed to do their analysis were in there.
• R just wasn't nearly as scary as they thought it would be!

All-in-all, a good first experience for us using Making Your Case with our MSW students. Oh, and by the way, Making Your Case is available at all the usual places you might think of:  Amazon, Barnes and Noble and Oxford University Press.  We asked Oxford to produce the book in paperback to keep the cost down and they did!

Happy Social Work Month!

While it's nearly over, March IS Social Work Month, so in honor of this auspicious occasion, we were asked by our publisher, Oxford University Press, to share a few thoughts.

Well, if you actually know us or have seen us speak, that is a very dangerous request!  We tend to go on and on and on.....  especially when it's a topic we really care about.

It wasn't hard to figure out what to write about, though, because The New York Times, of all places, published an Op-Ed piece titled Social Programs That Work.  That article was written by Ron Haskins, co-director of the Center on Children and Families at the Brookings Institution and a former Senior Advisor to the President for Welfare Policy during George W. Bush’s administration.  His article looked at the need to fund social programs that are effective from a policy perspective.

Besides writing books and teaching classes, we ACTUALLY work with agencies to do (what else?) program evaluation.  So we often get a real snapshot of what is going on in the trenches with regard to the very topic of Mr. Haskin's article.  In our article, which you can find on the Oxford University Press Blog, we look at some of the real-life situations facing agencies today and suggest one solution for identifying effective social programs within agency settings.

While not specifically about R, you can probably see where our heads are, and how we think all this R stuff that we have been working on fits into the larger picture of social work practice!

We would love to hear what YOU think!  Be sure to leave a comment on the bottom of the blog post on Oxford's site!  We will be checking in frequently to see what others think about our ideas.

How do we love thee, R? Let us count the ways....

We have been talking about R (well, actually pushing R) as a good replacement for proprietary statistical software for some time, but we thought we’d take this opportunity to share with you WHY we think this is.

By the way, we have developed this thinking more clearly recently because of conversations we’ve had with our esteemed editor, Dana Bliss, at Oxford University Press, and our colleagues, Dr. John Orme and Matthew Cuellar, down in the relative warmth of Knoxville, Tennessee.

1)  As educators, we should be teaching our students to use software that they have access to once they go out into the field.  From our own little anecdotal survey, very few social service agencies have SPSS, which is what most schools teach, so what good is it to teach students skills that they can’t apply later?

Can we convince agencies to use SPSS?  Wouldn’t that be easier?  The answer to that is most likely not.  SPSS is expensive.  Really, really expensive.  SPSS is sold in different versions and with different modules.  If you’d like to purchase the high-end networked version, it will set you back tens of thousands of dollars.  Per year.  You’ve got it – licenses are only good for one year.

2)  R is cutting edge.  We have only talked about using R for statistical analysis, but it can be used for much, much more. 

Think social service agency again.  You may be producing similar reports regularly.  You might, for instance, have to report to your board how many clients you served in a quarter, how many terminated, etc.  You may have to show graphs.  Or you may need to do more sophisticated analysis, such as determining how successful your programs are.

You can actually create dynamic documents in R with some user-contributed packages that will run your analysis and insert your findings, tables and graphics AUTOMATICALLY in your documents. Way cool.

3)  R has statistical capabilities that that really expensive software can’t do.  For all that money you just spent, you still can’t do it all! How disheartening is that?

4)  We are pretty certain that the steep learning curve that people write about is not quite as steep as you may think.  Sure, if you want to run diagnostics on your logistic regression, you may have some learning to do, but you can easily solve that problem by purchasing a REALLY GOOD BOOK THAT IS GOING TO BE AVAILABLE FROM OXFORD UNIVERSITY PRESS IN JUNE FOR A GREAT PRICE.  Or you can spend thousands and thousand per year for SPSS.  But then you’ll still have some learning to do.

Bottom line – we have been teaching R to our students for a couple of years now (yes, we converted from SPSS) and we have seen NO difference in our students’ abilities to learn statistical methods.  If anything, our students are learning more now because they install R on their home computers and can practice skills we teach in class at home.

5)  All the cool kids are doing it!  R is growing in popularity across all sectors.  This includes academia, science, and business.  Microsoft, for example, just purchased Revolution Analytics, a firm that focuses on using R for analyzing Big Data.  R is powerful and everyone knows it, including the “big boys.”

6)  Resources abound.  You can learn about R, in large part, for FREE!  A good starting point is http://cran.r-project.org/, the home of all things R.  Here, you can find, downloadable manuals, a refereed journal, and a list of recommended books.  Or go to YouTube and enter “R + statistics.”  The last time we did that, we got 381,000 results.  And in the spirit of things being freely available, top-notch universities including Harvard, UC Berkley, and Johns Hopkins are offering classes on R via massive open online courses such as Coursera and edX.

R is just good for the planet.

And did we mention R and all of its packages are FREE?

Yup.  We love R.

Catching the wave

Recently, we were down in New Orleans for The Society for Social Work and Research annual meeting.  While we enjoyed all that that city has to offer, we also enjoyed something else.  We've noticed that R seems to be growing in popularity among social work researchers!

Two years ago, we did a workshop at SSWR on analyzing single-system data using our package, SSDforR.  We believe it may have been the first workshop at SSWR ever focusing on the use of R. Last year, we discovered that Joe Mienko, Richard Smith and Gregor Passolt had a workshop in addition to another one that we did.  And then there were two.

THIS year, there were three R-based workshops - we did one on effect sizes, Joe and his colleagues, David Rothwell, Richard Smith and Gregor Thomas, did one on visualizing data, and Kaipeng Wang, from Boston College, did a third on basic statistics using R.  And we jointly hosted a special interest group - which is basically a collection of like-minded people who sit down and chat about a particular topic.

So our question is - are we catching a wave?

We think so!

As we arrived back in New York, we got inspired to see how popular R really is becoming, and this is what we found (purely anecdotal folks) -

LinkedIn has a pretty active group for social science researchers called "Research, Methodology, and Statistics in the Social Sciences."  One recent poster wanted recommendations for basic books about R.  He got 42 replies in less than a week.  Another wanted volunteers to beta test an R package he wrote for educational purposes.  He got 76 replies!

This is sort of the buzz we've been hearing on the academic and popular fronts:

1)  R is free.  As in no cost.  Ever.  Big highlight.

2)  SPSS, probably the most widely used statistical package in the social sciences, is getting more expensive.  So expensive that they have basically priced themselves out in many cases.

3)  Most social service agencies have little to no budget for statistical software so....  why teach students to use something that they will likely never have access to once they leave school.  Why not give them tools that they can bring into the field and build research capacity in the agencies in which they work?

4)  The learning curve for learning R is less steep than rumors might suggest.  We are successfully using it in all our research classes and, it seems, other schools are picking it up, too.  Including some of the biggies.

5)  This week, Microsoft announced its purchase of Revolution Analytics, a firm that leverages the power of R to analyze "big data."

So.....

We come back from New Orleans INVIGORATED and ready to do more work with R!

The new book is officially IN PRESS!

It really has been a while since we have written, but it's not because we have forgotten you or moved on to something else - quite the contrary!  We finished the manuscript to our next book and completed the revisions.

We thought we would take this opportunity to tell you a little bit about it because WE ARE SO EXCITED!  Like our first book, we provide readers with a software freebie.  This time it is a database that practitioners and smaller agencies can use to track clients.

The book is called Making Your Case:  Using R for Program Evaluation.

There have been lots of books written about research methodology and data analysis, and many books have been written about using R to analyze and present data; however, this book specifically addresses using R to evaluate programs in organizational settings.  

The book is divided into three sections.  The first section addresses background information that is helpful in conducting practice-based research.  The second section provides  background to begin working with R.  Topics include how to download R and RStudio, navigation, R packages, basic R functions, and importing data.  This section also introduces The Clinical Record, the software we mentioned earlier.  The remainder of the book uses different case studies to illustrate how to use R to conduct program evaluations.  Techniques include visualizing and describing data, bivariate analysis, simple and multiple regression, and logistic regression.  The final chapter illustrates a comprehensive summary of the skills demonstrated throughout the book using The Clinical Record as a data repository. 

We will also be providing readers with the sample files and scripts so you will be able to work through all the examples in our book.

We're not quite sure when the book will be available, but we are working closely with our editor at Oxford University Press to move it along as quickly as possible.

HELP!!!! files

We know, we know....  We said that we were going to talk about using R in the classroom for a while, but we just couldn't resist sharing something small, but exciting in SSDforR.

First, if you have already installed SSDforR, be sure to download the latest version, 1.4.7, from CRAN.  We have updated the help files in a small, but useful way.  Let us show you what we mean.....

To get to the help files, you really have three choices once you require the package by clicking on the box next to SSDforR in the Packages tab:

1)  In the Console, type ?SSDforR at the prompt.  You will be taken here:

If you click on the Index hyperlink (see the arrow above), you will be taken to a page showing all the help files.  It looks like this:

Now click on the hyperlink for the function you want. 

2)  You can also simply click on the SSDforR hyperlink in the Packages tab:

 You will then be taken to the help file screen, shown above.

3)  If you know the function you want specific help for, simply type "?" followed by the function in the Console.  For example, if you want help with the ABrf2 function, simply enter:  ?ABrf2.

 ******

Now, here's our help file reveal that will, hopefully, make your lives easier.....

In the references section, for each function, we inserted ALL the pages from our book that refer to that particular function. 

What does that mean and how will that help you?

Now, you will have in-depth descriptions of each function, an explanation of when you would want to use the function,  how you would interpret your findings, and a contextual example.

As an example, let's look at the ABrf2 function.  Notice that the listed pages in the help file are clustered around pages 37-41 and 65-66.  When you refer to those pages in our text, you find a more comprehensive discussion of autocorrelation and related functions.  We also provide, in these pages, multiple examples of how we would interpret findings from this function.  When is autocorrelation problematic?  Why is it problematic?  Could I somehow rid my data of this dreadful condition?

You get the point.....

Check it out, and let us know how well this works for you!  Feel free to email us, comment on this post, and, of course, continue to check out our website:  www.ssdanalysis.com

Starting the Semester With A Little R

In our last blog post, we told you that in our Applied Methods in Social Work Research classes that we taught during the summer,  we introduced R on the first day of class EVEN though we don't get into data analysis until much further along.  

We thought that introducing students to R early would build confidence as we taught skills incrementally. Comments from two students from our last post provided support that this may be the way to go in the future, too.

In this post, we thought we would share a little bit about how we approached this.

First, the Applied Methods courses in our school are taught in computer labs, so each student had a computer available during class time. This made our lives infinitely easier when we did in-class activities.  

During the first class we did some really simple stuff:

1)  We showed the students how to open RStudio and gave them a tour of the panes.  We showed them that they would be most interested in looking at datasets, called data frames in R, in the Environment tab.  We showed them that they would enter commands and view output in the Console.  We also showed them that they would view graphs in the Plots tab in the lower right pane.

2)  We opened an existing dataset and showed the students the tabular view by clicking on the spreadsheet icon in the Environment tab.  This was a great way to first introduce students to the idea of multiple observations/cases/subjects and variable names.

3)  Finally, we encouraged students to download R and RStudio at home since in the following classes there was going to be homework that needed to be completed outside of class.  To provide additional support, we pointed them to two videos on our YouTube channel, SSDforR.  The first was on installing R and RStudio in a Windows environment and the other was on installing on a Mac.  These videos are also accessible from our website at:  https://www.ssdanalysis.com/Videos.html

We would love to hear how this worked for our students so feel free to post a comment or send us an email! Also, if you teach or train people to use R, we would also love to hear your ideas! 



 

Using R in the Classroom (or How We Tried to Inspire Reluctant Students)

The summer is nearly over and we are preparing for our fall classes.  As we are readying ourselves for a busy semester, we thought we would reflect back over the summer semester and let you know how we used R with our students taking Applied Methods in Social Work Research, a general course in  research methods.

This was the first semester where ALL of our Applied Methods sections used R instead of proprietary software, so we took this opportunity to make some small, but substantial changes to our teaching methods.

For the first time:

• We introduced R on the first day of the semester EVEN though we don't get into data analysis until much further along.  Why did we do this?  We thought that introducing students to R early would build confidence as we taught skills incrementally.
• We gave brief and simple homework assignments after most classes.  This is something we had been hesitant to do in the past, as not all students had access to statistical software off-campus (i.e., we never required students to purchase expensive propriety software previously).  Since R and RStudio are free, safe downloads, everyone could access it from their home computers.  Some authors have suggested that requiring students to do independent research work outside of the classroom  is related to more positive attitudes towards research and increased abilities (see Elliott, Choi, & Friedline, 2013).
• We had an open lab at least once a week where students could join us and ask questions about R or their homework assignments.  This gave students the opportunity to get a little one-on-one attention in areas where they thought they might need some individual help.
• The students completed a semester-long research class evaluating factors related to satisfaction with social work education.  They got involved in all aspects of the project from identifying research questions, creating hypotheses, constructing a survey, collecting data, to analyzing data and reporting findings.

By the way, if you are reading this blog and you were one of our students this summer, we welcome comments and constructive criticism, particularly on how R worked for you!

If you find this interesting, we invite you to join us at The Council on Social Work Education Annual Program Meeting in Tampa.  We are presenting a skills workshop on Saturday, October 25th at 11 am.  The title of the workshop is Using R to Build Research Skills and Research Capacity in Master's-Level Students.  As we get closer to APM, we will share with you a bit of what we will be presenting, but, as usual, we will be able to provide you with much more content and many more resources than makes sense in a blog.

Feel free to check out our website at www.ssdanalysis.com and, as always, we welcome your comments!

R, R and more R!

When we first set out to write this blog, we were really only thinking about using R to analyze data with single-subject designs - and SSD for R was born and our book was written!

But, as we worked on that software and book, we did a lot of reading and spoke to a lot of people.  We piloted SSD for R with students, and we talked to each other about it a lot.  We came to the same  (anecdotal) conclusion - using R is just as easy for students to use as any other statistical software package.  Even those really slick menu-driven packages.  Even those really expensive proprietary packages that EVERYONE uses.

In fact, R seemed to have a distinct advantage over those  for our students - it is free so students could easily download it at home at no cost and mess with it.  And with a little prodding, they have!  And so a new idea was born - what if we taught ALL our Master's-level courses with R?

This is just what we did this summer at Wurzweiler - all Master's-level research courses were taught using R with great success!  Over the next few blog posts, we will share a bit of what we have done with R in our classes.  We hope to inspire YOU to check out R more, too!

What do you want to know about using R in the classroom?  Let us know!

A Single-Subject Study of the Impact of Exercise on Pain and Fatigue in an Ostomy Patient

This is one last project from one of our Spring 2014 semester students. 

Our student did a really interesting project focusing on a patient with ulcerative colitis.  The patient, Beth, had had an emergency ileostomy.  Beth’s presenting problems were fatigue and pain.  It was thought that introducing an exercise regimen would address these issues. 

To measure fatigue, Beth responded to the following prompt three days each week:  “How tired do you feel today?”  This was measured with a self-anchoring scale with 0 indicating that she didn’t feel tired at all during the day and 5 indicating that she felt tired most of the time.

Beth’s pain was measured on an 11-point self-anchoring scale on the same days and times as she measured pain.  Zero indicated no pain at all and 10 indicated unbearable pain.  As Beth was primarily concerned with back pain, the prompt she responded to was a rating of her back pain, specifically.

Like our other class projects, this was an A-B design.

In a general analysis of her data, our student noted no significant trend in the baseline data for the fatigue or pain variables.  Autocorrelation was high and significant in the baseline for fatigue; however, there was not a problem with data being autocorrelated for the pain variable.

In the intervention phase, the trend in the fatigue variable was significant as was the level and magnitude of autocorrelation.  There was not a significant trend for the pain variable; however, there was a high and significant level of autocorrelation.

Therefore, to statistically compare phases, our student used the conservative dual criteria (CDC) for both of her variables.  The graph of the CDC for fatigue is shown below:

 Output from SSD for R indicated that eleven data points would be required below both the mean and regression lines in the intervention, but only two were present. 

To determine the effect size, our student used the g-index, as there was a significant trend in one of the phases.  A small effect size was observed and was in concert with Beth’s reported level of fatigue during the intervention.

To examine the pain variable, our student also used the CDC, the graph of which is shown below:

In this case, eleven data points were needed below both lines, and thirteen were obtained, illustrating a statistically significant improvement from baseline to intervention.

Effect size was measured using the d-index, and a 45.1% improvement in pain was noted since the introduction of the intervention.

This study had interesting results.  Introduction of the exercise program coincided with a decrease in back pain, but not a decrease in fatigue.  Therefore, it was suggested that Beth examine other options to relieve her fatigue.

With regard to implications for practice, our student noted that ostomy surgery often takes a toll on patients as body images are altered.  She concluded her study by saying that “more research studies are needed to break down the stigma against ostomies, to show how effective mind-body therapies can be, and to determine the types of exercise that can keep an ostomate in healthy remission.”

Nice work and interesting study!