Wednesday, December 11, 2013

Busy, busy, busy!

We haven't posted for a while, but it's not that we have forgotten about this blog - just the opposite! We've been incredibly busy, and we didn't want to post again until we had the time to fill you in adequately.

In late October, we were down in Dallas for the Council on Social Work Education's Annual Program Meeting.  While we were given a VERY early time slot on the last day of the conference (7:30 am!), our session was well-attended, and another interesting discussion ensued.  In addition to demonstrating SSD for R, Dr. Susan Mason joined us and spoke about the relationship between theory, evidence-based practice, single-subject research.

And breaking news - the manuscript is FINISHED!  We've spent the last few days working with Oxford on abstracts, approving the layout of the book, etc. Of course we knew this was all coming, but we are incredibly excited that the book is going to production next week.  SSD for R: An R Package for Analyzing Single-Subject Data will be available for our students to use this summer semester.  Stay tuned - we will be excited to let you know when the book is officially for sale.

Next up - we will be going back to Texas in January to demonstrate SSD for R at the Society for Social Work and Research in San Antonio.  We are just hoping that the weather is better in Texas then than it is right now.

If we don't post again until 2014, we want to wish you all a healthy and happy New Year!

Saturday, October 26, 2013

Charlie and Wendy Go To Washington

Not at all like the movie Mr. Smith Goes to Washington, but equally exciting - at least for us!



As you can imagine, things were a little touch and go as we prepared for our first presentation at the American Evaluation Association meeting.  After all, the Federal government had been shut down for 16 days and had just re-opened on Thursday.  We Amtracked it down to DC (is Amtracked a word?) on Friday and were scheduled to present on Saturday morning.

The conference, however, actually began prior to the re-opening of the Federal government, and we had already been warned that attendance would likely be low, sessions would be cancelled, etc.  We had already steeled ourselves for a less-than-stellar showing, especially since we were presenting on the last day of the conference, a less than ideal time slot.

We had never presented at AEA before so we have no idea how to gauge turnout at our session, but the presentation was exceptionally well received!  As was our goal, we were able to demonstrate SSD for R to a diverse audience.  We were lucky that Mansour Kazi, a colleague from The University at Albany who has written a seminal article in single-case research, was able to attend, but we also had evaluators from other disciplines show up, including medicine and education.

The presentation went very well, if we do say so ourselves.  Feel free to watch a version of our presentation here.  Lots of conversation ensued, even after the presentation ended, so we are hoping to start building a learning community where those of us interested in single-subject research have a virtual space to meet and talk.

That being said, if you have any suggestions for blog posts, feel free to contact us as ssdforr2013@gmail.com! Also, feel free to check out our website - it's growing regularly!






Saturday, October 12, 2013

SSD for R Workshop #1

Next week, we are on tour!  Well, not the wait-on-line all night for tix kind of tour or, in the modern era, pummeling Ticketmaster.com repeatedly until you get the seats you want kind-of-tour.  This is more like the software developer/statistical-folks type of tour.

Actually, this is the first in a series of three workshops we are giving at conferences this year.  Next Saturday, October 19, we will be at The American Evaluation Association's Annual Conference in the Morgan Room at the Washington Hilton from 10:45 - 11:30.

Two weeks later, we will be at the Council on Social Work Education's APM at the Hilton Anatole in Dallas, Texas.  Our presentation is bright and early:  7:30 - 8:30 on Sunday, November 3 in the Edelweiss Room.

Then, we won't be presenting again until January, where we will be doing another workshop at Society for Social Work and Research's Annual Conference in San Antonio, Texas.

Three conferences tailored to three different audiences!  Join us and let us know what you think.  In the mean time, we will be posting our presentation slides on our website after each conference.

Tuesday, September 3, 2013

Lots and lots of activity

Well, the summer session ended and you would think that we had a little bit of a break, but we have been busy, busy, busy!

First, we received acceptances to present SSD for R at two different conferences and we are waiting to hear about a third.  In mid-October, we will be taking what seems to be our annual trek to Washington, DC, to present at the American Evaluation Association meeting.  At the end of October, we will be flying down to Dallas to present at the Council on Social Work Education's Annual Program Meeting.  At both of these conferences, we will be doing long-ish skills presentations.

The goal of both of these is to show people how to use SSD for R in their work as evaluators, professors,  and researchers.  Once the presentations are completed, we are planning to upload the PowerPoints to our website.  As each of these meetings get closer, we will let you know when and where we are doing our talks.  We would love for you to join us!

The second VERY EXCITING thing that we did was complete the first draft of our book, which we have finally given a working title:  SSD for R:  An R Package For Analyzing Single-Subject Data.  We sent the entire manuscript off to the wonderful folks at Oxford today.  The reviewers we had for our proposal were really helpful at giving suggestions that, we think, really strengthened the book overall, so we are looking forward to more helpful feedback in the coming weeks.

As usual, we look forward to hearing from you with any comments you may have.  Feel free to e-mail us!


Sunday, August 4, 2013

Using SSD for R in the Classroom: Another final project

We hope you enjoyed Shmuel's final project in last week's post.  We thought that this week, we would share Arthur Zaczkiewicz's final project.  You may remember Arthur - he was our creative student that, in great British tradition, motivated his fellow students with his "Keep Calm and SSDforR On" meme.

Today, you will learn a little more about Arthur's work with one of his clients....

**********

At his agency, Arthur frequently works with clients who are in financial distress.  To help these clients, he has adapted a group financial literacy intervention, Making Ends Meet, for use with individuals.  One of these clients was JD.

Like many folks in recent years, JD has had a difficult time making ends meet.  JD was recently divorced.  He also reported sleep disruptions and anxiety, which he attributed to his financial troubles.  JD was $30,000 in debt, which was left over from his divorce.  JD had contemplated bankruptcy, but decided to attempt to address his financial difficulties first by reaching out to Arthur's agency.

Arthur decided to empirically measure his client's progress over time with two different indicators:  JD's sleep disruption and his level of anxiety.  Both of these were measured on a five-point self-anchoring scale with higher numbers indicating more dysfunction.

Below, you will see simple line graphs depicting JD's progress on both indicators prior to and after the introduction of the intervention:


Using visual analysis alone, it appears as if the intervention is improving both JD's sleep and level of anxiety, but we need to be careful in drawing conclusions!  For both these indicators, Arthur determined that autocorrelation was problematic and trending was a problem in some phases.  Therefore, strictly visual analysis could be misleading!

So.... WHAT IS AN INTERVENTION ANALYST TO DO?

Our recommendation is to look for both statistical significance AND clinical significance.

Because of data issues with trending and autocorrelation, Arthur chose to analyze his data statistically using the conservative dual criteria for both indicators.  Arthur's presentation of his findings for JD's sleep disruptions are shown below.  You will see both the graph and statistical output in his slide.


Well, while it looked like the intervention was making a difference, Arthur found NO statistical significance for JD's sleep disruption.  His findings were similar for JD's level of anxiety; however, Arthur examined clinical significance by looking at effect sizes for each indicator.  In both cases, the intervention produced moderate changes.

As a clinician, this analysis should help Arthur inform his work with JD.  Here are his conclusions:  


Arthur has made some very good points:
1)  Perhaps the intervention needs to continue for a longer period as the intervention seems to be moving the indicators in the right direction. We simply may not have enough data yet.
2)  In the future, it may be useful to examine indicators that are more closely associated with financial difficulties.
3)  Research support can help guide the use of this intervention for others in the future.

Our suggestions:
1)  Keep going, Arthur!  Now that you have learned how to analyze your client's progress, you can do this again and again!
2)  Share what you have learned with others.  You can share your findings with colleagues and others interested in financial literacy programs.  Present at conferences.  Try writing this up and submitting it to a journal.  We need to share what we learn with others.
3)  Continue collaborating with researchers and/or conduct your own research.  The more we look at what is happening in close interactions between clients and practitioners, the more effective we can be in our work.

For more information about SSD for R, check out our website.  And, as always, feel free to contact us!

Sunday, July 28, 2013

Using SSD for R in the classroom: FINAL projects

Well, there's nothing like summer in Washington Heights, but all good things must come to an end, and our summer semester is no exception.  We will not be teaching this class in the fall, but will be resuming in the Spring.  Before we move on to other topics, however, we thought we would share with you some highlights of presentations from student who shared their final projects with the class.

This week, we'd like to give a nod to an outstanding student who told me after his final presentation that he could not have imagined, at the beginning of the summer, understanding terms such as SSD for R, autocorrelation, and conservative dual criteria.  You will see below, however, that Rabbi Shmuel Maybruch did, in fact, not only learn those terms, but was able to apply these concepts to evaluating his own practice with great clarity....

The client that this student wanted to evaluate was Quentin, a young man who was looking to date in order to get married, but Quentin never got very far and he sought help.  The indicator that our student used to assess Quentin's progress over time was a self-anchoring scale measuring his despair with dating.  A score of 1 indicated the least amount of despair while a 10 indicated the most.

Five weeks of baseline data were collected before the intervention was initiated.  The intervention consisted of helping Quentin to be more appreciative of the women he dated by identifying and complimenting them on less-than-superficial traits and helping Quentin to identify the role that physical attraction played in his quest for a life partner.

The graph below shows a basic line graph comparing the baseline to the intervention.



Notice how Shmuel made a line graph labeling not only the phases, but added a mean line for each phase, clearly depicting a drop in means between phases.

When deciding how to evaluate his data further, Shmuel noted a problem with both autocorrelation AND trending in the intervention phase, so he decided to use the CDC (conservative dual criteria) to test statistically for a difference between the phases.


From the output in the Console of RStudio, Shmuel learned that he needed nine data points below both the mean and regression lines to achieve statistical significance, and he had twelve!  Looking good so far....

The students learned, however, that statistical significance is difficult to achieve with small samples, although Shmuel was able to detect this level of change in his project.  Effect sizes, however, are very important in intervention research because they can be indicative of clinical, or practical significance.  Shmuel noted a d-index of 1.822, which indicated nearly a 47% change between phases - a moderate change!

While it looks like the intervention worked well for Quentin, Shmuel shared with us what happened post-intervention....



Quentin and his bride are living happily ever after!

For more information about SSD for R, check out our website.  If you have any questions or comments about using SSD for R in your own practice or teaching using this software, feel free to contact us.

Saturday, July 20, 2013

Using SSD for R in the classroom: Using the manuscript

About a month and half ago, I told you that we would be road testing a version of our  manuscript for our book with our Master's students this summer.  We thought that, since the summer semester is nearly over (graduation is on Thursday), we'd share with you what we have learned so far this summer.


The manuscript for our book is not at all complete at this point, but we do have a decent draft of six core chapters done.  This is what we have shared with our students.  The first chapter discusses how to quickly and easily import data into R.  The next chapter is an overview of SSD for R and its functionality.  The third chapter discusses how to analyze baseline data with the goal of understanding characteristics of the data.  Chapters 4 and 5 talk about visual and statistical comparisons between phases, and Chapter 6 is about analyzing group data.


But first, a few words about how we have been using our manuscript in class this summer.  We figure this will give you some context about its usage.  Since the manuscript is not complete and in draft-mode, we used the book as a supplement to our main text, Bloom, Fischer, and Orme's EXCELLENT text, Evaluating Practice:  Guidelines for the Accountable Professional.  As we have proceeded through the course, we suggested, but didn't require, students to read various chapters after the material was taught in class in order to provide clarification.

While we have not asked for feedback, one student contacted us earlier this week.  With her permission, we decided to share with you a portion of  Shira Levitt's e-mail:

Hi Professor,

I wanted to work on the final paper at home and was reading the manuscript and it is so well written.  It is clear and concise and truly helpful....I wanted you and Professor Auerbach to know that, as a student who is not good with computers or numbers, this manuscript is phenomenal.

Thank you so much.
Sincerely,
Shira Levitt


Thank you, Shira!

To sum up our feelings about this in a few words - we think we are on to something because this is what we are aiming for!

Come check out our website and, as always, feel free to email us!  We would love to hear from you!


Saturday, July 13, 2013

Using SSD for R: Comparing the baseline to the intervention

With the summer semester beginning to wind down, we have reached what our students consider to the the most interesting part of the course - learning how to figure out whether the interventions they were doing with their clients are making a difference.

While we continued with some visual analysis, the students quickly learned why autocorrelated or trending data could not be analyzed as simply as one might think!  Therefore, we taught the students to use the ABbinomial() or ABttest() functions if neither phase has a trend or an issue with autocorrelation.  We also taught the students to use one of the chi-square functions if there was a trend in any phase and to use the critical dual criteria (CDC) if either has an issue of autocorrelation.

One thing that the students really liked was interpreting the statistical output with support from visual output.  

For example, in this example, we are comparing a client's level of enjoyment in the baseline to the level of enjoyment in the intervention.  The regabove() function informed us that there was a statistically significant improvement between phases with 40% of the baseline data being successful and 100% of the intervention data being successful.  This is really easy to visualize when you actually SEE that 2 out of the 5 baseline points are above the regression line in the baseline, while all the points are above it in the intervention phase.

Check out THIS cool output!


This really helps the student understand the notion of continually comparing the intervention to the baseline as they can visualize the baseline regression line being extended into the intervention.  

We only have three more classes to go, so we will be sure to keep you posted on how these final projects are shaping up!

For more information on SSD for R, please visit our website. As always, feel free to email us!


Saturday, July 6, 2013

Using SSD for R in the Classroom: Funny anecdotes

So, two amusing things happened this week that we thought we'd share with you before going into the details of how the class went this week.

The first includes a shout-out to one of our most excellent (and creative) students, Arthur Zaczkiewicz, who, besides getting up at 4 am to attend classes in NYC, has decided to entertain and inspire his classmates as they were working on their midterm papers with this:


Brilliant, Arthur, brilliant!

Second amusing story:  As you know, our students have been busily working on their mid-term papers so they have obviously been talking about their projects in the hallways.  One of our students from LAST semester came up to me and said, "I heard your students talking about SSD for R in the hall.  I liked this research class so much that I actually felt nostalgic!"

Well, we don't really hear the words "research" and "nostalgic" in the same sentence, so we really enjoyed our student relating her experience back to us!

And now for our class progress.....

This was a short week for us due to the July 4th holiday.  This week we began talking about visual comparisons between baseline and intervention phases and introduced the students to the binomial function in SSD for R so they can compare what would be considered success in the baseline  and compare it to what would be considered success in the intervention.  To do this, we showed them standard deviation band graphs that extend the standard deviation bands from the baseline through the intervention.  Then we discussed the desired direction of change and how we could consider that data points outside of these bands would be considered "successful" if they were in the desired direction.  Then, using the ABbinomial function, the students could enter the number of successes they observed in both the baseline and intervention to figure out if there were statistically significant differences between the phases.

We thought this was a great way to start our analysis since a) it is visually based, and b) most people readily understand the notion of increasing or decreasing proportions of success across phases.

Next up.... SPC charts.  We offered the students some GREAT beach reading - Orme and Cox's excellent 2001 article entitled, "Analyzing single-subject design data using statistical process control charts."  We wonder how many enthusiastic students will be reading THAT on the beach this holiday weekend!

Thursday, June 27, 2013

Using SSD for R in the Classroom: Analyzing Baseline Data

Well, the summer semester is officially more than halfway over here in steamy NYC!

What does that mean for students in our Practice Research and Evaluation courses?  It means that our students are hard at work on their midterm papers describing their baselines from their own projects. To continue learning about this, we taught our students how to use several functions in SSD for R to visually and statistically analyze their data.

We showed the students how to produce and interpret one and two standard deviation band graphs in order to assess the stability of their baseline data and look for outliers.  Then we taught the students how to evaluate their baselines for two problems that could impact how they compare their baseline and intervention phases, which we will begin focusing on next week.  The first problem we discussed was trending while the second was autocorrelation.

Here's an example of a graph that our students were able to produce for testing for a trend by using the Aregres() function:



Autocorrelation is, perhaps, the most difficult concept we teach in this course.  Most of our students have never heard of the notion of serial dependence before, and it is a fairly complicated concept to teach.  The other issue with teaching students about autocorrelation is that it is not detectable visually.  Our students MUST learn to interpret some statistical output!

To help our students, we have several videos posted on our website and YouTube on all of these topics.

Their mid-term papers are due next week, and we're pretty sure that our students are going to do really well with these!





Thursday, June 20, 2013

Using SSD for R in the Classroom: Analyzing the Baseline

We just finished Week 3 of our 7 week summer semester here in sunny (finally) NYC, and we are spending more and more time in Practice Research and Evaluation course using SSD for R.

Now that our students know how to navigate SSD for R, build input files, and create basic graphs, we have moved on to teaching the students how to analyze their baseline data.  We have taught them how to create one- and two-standard deviation band graphs to help them identify "typical" behavior and to look for outliers.  We have shown them how to retrieve a host of descriptive statistics (sample size, mean, trimmed mean, standard deviation, coefficient of variation, quantiles) and produce a box plot depicting the variation in their data.

Today, we began talking about analyzing baseline data visually for trends and then to calculate a regression line to determine if any trend they may see could be statistically significant.  One of the classes also began to talk about that niggling little problem of autocorrelation, which needs to be evaluated statistically since there is no way to visually determine if autocorrelation is problematic.

All this, of course, is a precursor to teaching the students how compare their baseline and intervention phases for their final projects.

As we are working through these lessons, we are referring the students to drafted chapters of the manuscript for our upcoming book.  We are hoping that student feedback will help us make this book as useful and user-friendly as possible.

For more information on SSD for R, visit our website or email us!

Thursday, June 13, 2013

Using SSD for R in the Classroom: Unintentional Visual Analysis

Well, our Social Work Practice and Evaluation Research class just completed its second week.  So far, we have been devoting the first part of each class to teaching the students the fundamentals of SSD for R which we think has been extremely helpful.  The students clearly seem more comfortable navigating RStudio than they did last week, and they seem a bit more computer savvy, in general.

So far, the goals for using SSD for R have simply been pragmatic - learning how to use the various functions.  During our first class this week, we entered intervention data into our Excel spreadsheets and then imported all of this into SSD for R.  The lesson then focused on creating a simple line graph that showed multiple phases (both baseline and intervention).  We showed the students how to add a vertical line between phases and then label each phase. We did this for each of the two behaviors that we were tracking for our hypothetical client.  We also showed them a handy-dandy searchable PDF that is located on the ssdanalysis.com website  and is useful for identifying each of the functions based on what is trying to be created.

Today, the second class for the week, we continued the lesson based upon a student's request - he wanted to know how to view two graphs simultaneously.  This was a great jumping off point for a discussion about the impact of an intervention on multiple indicators, so this was our hands-on lesson for today.  We showed the students how to use the plotnum() and ABplotm() functions to build multiple graphs in one window.  We then practiced using data from our hypothetical client.

Here's what the students produced:
This was SUPPOSED to be the end of the lesson for today; however, it wasn't.  One student observed how the behaviors moved in tandem, particularly during the baseline.  Then, another student noted how variable the data was during the baseline and how could you make comparisons between the baseline and the intervention, if the baseline data weren't stable - what was it actually representing?  Good point!

While we couldn't address this issue entirely today, this provides a great entrée into subsequent lessons on the baseline.

For more information on SSD for R, visit our website or e-mail us.

Thursday, June 6, 2013

Using SSD for R in the classroom

Here at Wurzweiler School of Social Work, we have a summer program designed for long-distance students.  Some live in the US, but many come from other countries.  Our students spend seven weeks living and studying for their MSW degree in New York City over the course of three consecutive summers.

During their third summer, our students are required to take a course in Social Work Practice and Evaluation Research.  This class teaches students to empirically evaluate their clients' progress so the students' can adapt their work according to the needs of each client.  During the course of the semester, students are taught how to do this regardless of whether their clients are individuals, couples, families, groups, or communities.This semester, we will be using SSD for R in this class for the students' projects, which will be a semester-long evaluation of one of their clients.

We thought it would be interesting to use this blog to share with you how we are using SSD for R in this course and what our experiences are.

We just finished the second of fourteen classes of the semester and we are already using SSD for R! During the first class, we had the students become oriented to the package by opening RStudio and giving them a "tour" of the interface.  We explained how to install SSD for R (only once) and how to require it (every session).  We also encouraged all the students to follow the instructions on our website to download R, RStudio, and SSD for R on their own computers at home.  We showed them how to view our tutorial videos on our website, in case they needed extra help.

I can't tell you how delighted we were this morning when we found out that all the students who attempted to install everything, did so successfully!  This is great because, like many social workers, many of our students are a little reticent about computer usage, in general.  I think this bodes well for the semester, and we are hoping that the students feel that they can use the skills we teach them in class at their own agencies.

Today's lesson was also interesting. We started out by creating a fictional client whose problems we plan to measure.  We showed the students how to create an Excel file to track client data and save it as a .csv file.  We then opened the file in SSD for R and created simple line graphs for each of the behaviors we were tracking. A couple of students had minor difficulties.  These included people forgetting to attach the file they had opened or in mistakes in entering the command for the simple line graphs.  However, between the instructor and our trusty teaching assistant, each student was able to create two different line graphs.  We concluded this lesson by showing the students how to cut and paste their graphs into Word.

We are already hearing sighs of relief.

For more information about SSD for R, visit our website.  You can also reach us by e-mail.

Wednesday, May 29, 2013

The BOOK - it's coming!

I don't think I mentioned in our last post that we are working on a book on how to use SSD for R. We are hoping to have the manuscript for this finished by the fall, as is Oxford University Press, who will be publishing our book!

We thought we would share with you a little bit here about what the book is going to be like because it is going to be UNIQUE!  First, what our book is NOT going to be:  it's not going to be a primer on single-subject research designs because there are several excellent texts currently available that address this - and we point you to those texts in our book.  It is, however, going to teach you how to use SSD for R to collect and analyze your own single-subject research data for FREE!  We show you how to use the robust set of visual analytic tools in the package (we have graphics galore), but also when and how to give consideration to statistical techniques that are unique to single-subject research data.  For example, we give suggestions for dealing with that niggling little problem of autocorrelation that can impede any type of analysis - both visual and statistical - and can't be detected through visual analysis alone.

The book is also structured a bit differently than statistics textbooks.  We don't have, for instance, chapters about specific tests, but we focus more on the process of how to analyze this type of data.  Visual and statistical analysis is brought in throughout the text in the context of the analysis process.  We think that this is a user-friendly way to approach the topic, and it is the same method we use to teach and conduct our own research.

Also good news - we are road testing the manuscript.  We have used portions of the manuscript with students in our classes last semester and we continue to do so as we keep writing and refining.  Our next group of students will be starting classes next week so we hope to share with you how we use SSD for R in the classroom throughout the summer.

In the meantime, please visit our website frequently.  We are always adding documents and videos to make using SSD for R accessible for everyone!  And, as always, feel free to e-mail us.  We love to hear from you!

Sunday, May 19, 2013

Welcome to SSD for R!


Welcome to the SSD for R Blog!  

Through our blog, we hope to create an on-line community in which we, the developers of SSD for R, and you, folks interested in single-subject research, can have dialogues that will be interesting and helpful to all of us.  We will be writing on this blog on a regular basis on topics of interest to single-subject researchers.  We invite you to react to our postings and to each other. 

We are asking for your contributions to this blog!  If there is a topic you would like us to address, please let us know.  If you have an idea for a posting and/or would like to be a guest author, we would welcome that, too.  We are hoping to create an open dialogue on the design and analysis of single-subject research across areas of study - so please join us if you are a social worker, educator, physician, occupational therapist, psychologist, student, or just someone who is interested in learning more about this type of research.

As you may already know, SSD for R is FREELY available on CRAN (The Comprehensive R Archive Network).  Learn more about SSD for R and how to quickly and easily get started using it by visiting our website.

Also, feel free to contact us here with any comments or questions.