# Response Surface Methodology Using Observational Data: A Systematic Literature Review
## 4 Descriptive and Bibliometric Analysis  
The descriptive analysis in this section explained the research trends associated with the topics in this paper, and the bibliometric analysis focused on the methods involved in the RSM-OD and research fields to which it has been applied. Since the early 2000s (Figure 5), the increased number of indexed publications in the SCOPUS database with search strings (Figure 4) shows that the application of the RSM-OD has occurred in various research fields. Table 2 shows some of the research fields where the method has been applied. The pharmacy/chemistry/chemical engineering fields commonly deal with laboratory-scope experiments. They can be improved with the use of standard RSM, but they use already provided data as the input for RSM. Meanwhile, manufacturing, petroleum, and similar engineering fields with modern equipment mostly have a data-acquiring system. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Paper Inclusion Criteria** & **Paper Exclusion Criteria** \\ \hline Application of observational or historical data as an alternative to the DoE in RSM & The RSM should not conduct a designed experiment to obtain data (however, some papers still referred to nondesigned experiments/non-DoE with a rationale of hard-to-control factors; the details are in Figure 8) \\ \hline Involving previous experimental data for RSM, some papers referred to combined datasets from previous experiments & The RSM entirely refers to the dataset without completing it, with new additional experiments. \\ \hline Involvement of the three stages of standard RSM analysis (DoE, modeling, and optimization) & One of the stages of standard RSM analysis is missing \\ \hline RSM analysis involves searching for influencing factors, similar to the original RSM concept & A direct prediction system with real-time data recording and modeling is not a part of this SLR because no such analysis of significant influencing factors exists. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Inclusion criteria for filtering papers. Figure 5: Paper trends for RSM-OD. Thus, using the provided dataset rather than experimental data is more reasonable. Furthermore, using a treemap (Figure 6) to categorize the journal quartile shows that the highly impacted journal (Q1/Q2) gives the highest percentage among other quartiles, which means that the RSM-OD has supported high-quality research. For Q1 journals, the research field of (pharmacy/chemistry/chemical) engineering (10.00%) and manufacturing processes (11.25%) still dominated regarding the application of RSM-OD, followed by other fields. A similar interpretation is also drawn for Q2 and the others. Thus, scholars have opportunities to develop RSM-OD procedures required by various research fields involving designed, experiment-based optimization processes at any level of the impacted journals. VOSviewer(r) v.1.6.15 software provided by Centre for Science and Technology Studies of Leiden University was used to obtain the graphical network in Figure 7. In the figure, the author's keywords represent various terms incorporated in the RSM-OD. The figure also gives insights into the development of the integrated RSM tools/methods to handle nonexperimental data, particularly for certain RSM-related methodological terms, although specific research field-related keywords were still included. The bibliometric analysis consisted of nodes and the links connecting them. Large nodes represent high keyword occurrences, and the links indicate co-occurrences in the same papers. Table 3 shows the results of the complete bibliometric analysis, including the total link strength, which exhibited a high number of co-occurrences between the keywords.