However, when specific studies focusing on one variable are reviewed, it is seen that the number of studies reporting the mean values is relatively low (e.g., Rakap et al., 2016 Shadish et al., 2009). The original mean values presented in articles could be useful for the researchers who will perform the effect-size calculations using the methods based on mean values (e.g., performance criteria-based effect size, standardized mean difference, and mean baseline reduction). Instead, some researchers that conduct interventions prefer to report the mean values of baseline and intervention phases. It is rare to present raw data along with graphical data in the research articles of SCEDs due to the page restrictions of journals or disrupting the reader-friendly flow. Therefore, regardless of whether the effect size is reported in the single-case studies, the need arises to reanalyze the studies’ data for the researchers performing studies such as meta-analysis (Shadish et al., 2009). Besides, researchers conducting meta-analysis studies may use other than the effect-size methods presented in articles to combine and interpret effect sizes of interventions. This leads to the absence of a common method used in the studies of SCEDs to report the effect size. Furthermore, there are numerous methods for calculating the effect size on SCEDs. On the other hand, it is not yet a common practice to report the effect sizes of interventions in single-case research (Busse et al., 2015 Olive & Franco, 2008). However, it is argued that visual analysis is invalid and unreliable owing to its subjectivity, and therefore, it is recommended to calculate effect sizes for SCEDs studies (Kratochwill et al., 2014 Manolov & Vannest, 2019 Shadish et al., 2015). In the studies conducted on SCEDs, repeated measurements are performed under standard conditions, and the changes between the baseline and intervention phases are visually analyzed using graphical data (Kennedy, 2005 Riley-Tillman & Burns, 2020 Tekin-Iftar, 2018). The research on SCEDs can be conducted with a single participant or through the individual evaluation of multiple participants’ performances, and these performances are shown with graphical data. Single-case experimental designs (SCEDs), which are increasingly used in the fields of education and psychology, are unique methods that allow questioning the causal and functional relation between dependent and independent variables among the participants or between conditions (Kratochwill et al., 2010 Maggin et al., 2017 Maggin & Odom, 2014 Tekin-Iftar, 2018). Based on the obtained results and considering the data extraction process, we presented various recommendations for the researchers that will use the PlotDigitizer program for the quantitative analysis of single-case graphs.
#Datathief online free#
The results showed that PlotDigitizer could be an alternative to other programs as it is free and can run on many current and outdated systems, and it is valid and reliable as it is nearly perfect. Based on our sampling, we calculated intercoder and intracoder Pearson correlation coefficients. Besides, using the values we digitized, we recalculated the 23 effect sizes presented in the original articles for validity analysis.
#Datathief online series#
We performed the digitization of 6.846 data points on three different computers using 15 hypothetical graphs with 20 data series and 186 graphs with 242 data series from 29 published articles to accomplish the goal.
#Datathief online software#
In this study, we aimed to analyze the validity and reliability of the PlotDigitizer software program, which is widely used in literature and an alternative to other data extraction programs, on computers with different operating systems. Researchers typically use data extraction software programs to extract raw data from the graphs in articles. Access to raw data of graphs presented in original articles to calculate the effect size of single-case research is a challenge for researchers conducting studies such as meta-analysis.
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