Social Science

Time Series Design

Time Series Design Program Transcript

RICHARD BALKIN: Time series design simply refers to study done over time, as opposed to time to collect data at one particular instant. Often, time series design is really the single subject design. But you can have multiple participants in time series design. In the article that we’ll even discuss for this week, we see a time series design occur as an element of looking at changes across a program over time, and perceptions that participants have about that program over time.

So time series design can be used to look at how data is predictable, how information can become verifiable, and can this information be replicated over time? In other words, in a good time series design, I should be able to conduct a study like this again, and get the same result. So in a time series design, instead of looking at how changes may occur between groups, we may see how change occurs with a single subject, or even within a group, or for a program over particular period of time.

And that period of time can even be more of a longitudinal nature. We can look at changes across a few months, but we can look at changes across years. Additionally, if multiple subjects are used in a time series design, and if the research is longitudinal in nature, you need to take into consideration attrition rates. Are the participants who began the study the same participants at the end of the study? Was there attrition? And maybe consider why attrition might occur. For example, is the researcher able to keep up with all of the participants at the beginning, intermediate, and latter stages of the study. Attrition is normal in any research study, but it also needs to be accounted for.

An example of some time series research that I’ve conducted in the past, has been when I worked as a therapist at a psychiatric hospital. At that time, we were very interested in seeing what happened to our clients once they leave the hospital. We knew how they were when they were admitted. They were either a danger to self or a danger to others. And we had an idea of how stable they were when they discharged. But how are they doing one month, three months, six months, and 12 months after treatment?

So we had an after care program. And through the active aftercare program, we were able to do some post-care follow up with each of the clients once they left the hospital. One of our experiences was that after six months it was very difficult to continue to get feedback from the participants. One of the reasons simply was that working with this population, they were highly transient. Phone numbers would change. Addresses would change. And we just weren’t able to get a lot of one-year follow up. Or, perhaps a child had relapsed and the parents were maybe angry at the treatment center, didn’t want to respond to our queries. So those elements can play a role to. As I said before, attrition occurs.

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Time Series Design

But that process of getting data from each client at one month, and the three months, and the six months, and the 12 months interval, was essential in terms of doing a time series design, and finding out did kids relapse or regress to their previous high risk behavior after receiving treatment at the hospital. And what were the influencing factors? We also would want to know information as, did they continue an outpatient counseling, for example?

In examining an article that uses time series design, we’ve selected an article that’s quite multi-faceted. So in this particular article, they use a four-phase design to conduct the time series research. The 12-month baseline pre-exposure phase assessed program and patient outcomes. In Phase II, which occurs after six months of training, MDFT experts train Adolescent Day Treatment Program staff and administrators. and then in Phase III they have an implementation stage. And this is at 14 months. And then at Phase IV, they have a Durability Practice Phase, which is around 18 months.

So let’s take a look at how the program dimensions changed over time through this time series design. So these program dimensions included aspects like autonomy, and clarity, and program organization, and control. And what they notice is that as a result of implementing this MDFT program, that participants, patients within the program, noticed positive differences among these program dimensions. So here what we end up with is a statistically significant difference in the way a program is perceived by the primary stakeholders, in this case, the patients who are experiencing treatment in the day program.

So imagine being able to implement an intervention that across time improves your program and improves receptiveness to treatment. And that was the importance of the study. Hopefully, when practitioners see this, they can see a treatment model that affects the quality of care. And they may be more apt to use such a model in their programs.

In terms of multicultural ethical and legal considerations, we might want to once again review, who was a sample? Who are the participants in this study? So that we make sure that the participants in the study are truly generalizable to the population of interest.

Additionally, whenever doing a time series design, you want to think about and consider, what occurs during the study? What is the intervention? What is the change that we’re looking at? Is this change positive or not? For example, what would happen if the study was being conducted and immediately a negative consequence as a result of the intervention is occurring? Well, of course the ethical thing to do would be to stop the study.

And then it would be important to note that maybe this is not a good intervention to use. The study was cut short. And none of the phases were completed, because an unforeseen event or negative consequence was occurring. So that’s

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Time Series Design

another element of time series design, particularly when the study is longitudinal in nature.

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