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In the field of SCEDs, individualized longitudinal data is obtained and traditionally graphically displayed. Repeated measures are obtained during control conditions preceding treatment conditions. Changes in level, slope, and variability between the treatment condition data and baseline condition data can be visually analyzed. Traditionally, SCEDs follow a response-guided approach, where design decisions during the study are based on participants’ observed patterns of behavior. In order to generalize the effectiveness of the treatment across participants, the experiment is repeated across multiple individuals using a multiple-baseline design across participants (MBD). In order to enhance the internal validity of the MBD, the introduction of the treatment is staggered across participants and participants are randomly assigned to staggered intervention start points. As a method that can increase the rigor of SCED studies while preserving the benefits of a response-guided approach, visual analysis and randomization, masked visual analysis (MVA) has been recommended and warrants expanded attention (Ferron & Jones, 2006; Byun, Hitchcock, & Ferron, 2006).
In order to conduct a MVA, the research team needs to be split in two groups: (1) an intervention team and (2) an analysis team. The procedure is described in detail in Ferron and Jones (2006). In order to facilitate, enhance and help implementing MVA, we developed a mobile application, called MVA (i.e., Masked Visual Analysis, to be downloaded for free), which allows the user to create a profile and share a study with other research teams. Users can invite, or join other peer’s research as an interventionist or analyst by using a unique research code (PIN) provided by the application when the research project is created. Once the PIN number is entered in the application, the research project details pops up on the user’s screen. Further, the user will be able to choose his/her role as either an interventionist or analyst. This application is designed with the capacity to give its users the ability to specify their research parameters, namely, number of the participants in the MBD, minimum number of baseline and intervention data, the minimum number of observations in the stagger, the criteria to evaluate data stability, outliers and identify a treatment effect. Moreover, the analysis team will be able to see time series graphs in real time created by the data send by the interventionist team. Depending on the criteria defined, the analyst will be able to request more data, or ask the interventionist to start the intervention for the next participant (based on the random assigned that the application generated.
The MVA application has the potential to enhance the methodology to design, gather and analyze time series by (1) creating in real time graphs, (2) avoiding errors in data transfer from intervention team to analysis team (3) making the process standardized and ensuring the masked component (4) helping applied researchers implementing a randomized experiment, (5) assisting in making inferences and understanding the masked visual procedure and (6) helping applied researchers making causal inferences and calculating p-values, (7) data sharing and communication with other research teams.