دانلود رایگان مقاله R-CMAP-نرم افزار منبع باز برای نقشه مفهومی

Abstract

Planning and evaluating projects often involves input from many stakeholders. Fusing and organizing many different ideas, opinions, and interpretations into a coherent and acceptable plan or project evaluation is challenging. This is especially true when seeking contributions from a large number of participants, especially when not all can participate in group discussions, or when some prefer to contribute their perspectives anonymously. One of the major breakthroughs in the area of evaluation and program planning has been the use of graphical tools to represent the brainstorming process. This provides a quantitative framework for organizing ideas and general concepts into simple-to-interpret graphs. We developed a new, open-source concept mapping software called R-CMap, which is implemented in R. This software provides a graphical user interface to guide users through the analytical process of concept mapping. The R-CMap software allows users to generate a variety of plots, including cluster maps, point rating and cluster rating maps, as well as pattern matching and go-zone plots. Additionally, R-CMap is capable of generating detailed reports that contain useful statistical summaries of the data. The plots and reports can be embedded in Microsoft Office tools such as Word and PowerPoint, where users may manually adjust various plot and table features to achieve the best visual results in their presentations and official reports. The graphical user interface of R-CMap allows users to define cluster names, change the number of clusters, select rating variables for relevant plots, and importantly, select subsets of respondents by demographic criteria. The latter is particularly useful to project managers in order to identify different patterns of preferences by subpopulations. R-CMap is user-friendly, and does not require any programming experience. However, proficient R users can add to its functionality by directly accessing built-in functions in R and sharing new features with the concept mapping community.

9. Conclusion

This paper introduced new software for performing concept mapping analysis. We demonstrated new features and capabilities including the ability to perform statistical tests to compare ratings across clusters and alternative graphical representations. This extends easily to more sophisticated linear models, where the user can control for other covariates, such as demographic variables. We also explained how our software can be used to generalize known tools, such as the go-zones and discussed how the software can help the user reach more informed decisions about the selected number of clusters, and more generally how to choose a model which provides the best fit for the data. We plan to introduce other features and capabilities which may be useful for project planning and evaluation, beyond those that were mentioned in this article. For example, one may consider performing network analysis on the statements. This will include introducing notions such as “centrality” or “connectivity” of nodes (statements, but also possibly stakeholders). This can be seen as a generalization of the clustering step. When statements are clustered, we simply assume that all statements in one cluster are connected (via a common concept), and we do not attempt to assign strengths to connections between pairs of statements. In reality, some statements are more closely related than others, and they may also be related (perhaps to a lesser degree) to statement in other clusters.