کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
4930967 1363355 2017 7 صفحه PDF ندارد دانلود رایگان
عنوان انگلیسی مقاله
Use of multiple cluster analysis methods to explore the validity of a community outcomes concept map
ترجمه فارسی عنوان
استفاده از چند روش تجزیه و تحلیل خوشه برای کشف اعتبار از یک نقشه مفهوم نتایج جامعه
کلمات کلیدی
مفهوم نقشه برداری؛ مقیاس چند بعدی؛ آنالیز خوشه ای؛ R نرم افزار آماری؛ اعتبار؛
Concept mapping; Multidimensional scaling; Cluster analysis; R statistical software; Validity;
موضوعات مرتبط
علوم پزشکی و سلامت پزشکی و دندانپزشکی سیاست های بهداشت و سلامت عمومی
چکیده انگلیسی

•We use multiple clustering methods to increase validity of concept mapping results.•We conduct concept mapping in the R statistical software package.•We suggest statistics which can assist in selection of most valid cluster outcomes.

Concept mapping is now a commonly-used technique for articulating and evaluating programmatic outcomes. However, research regarding validity of knowledge and outcomes produced with concept mapping is sparse. The current study describes quantitative validity analyses using a concept mapping dataset. We sought to increase the validity of concept mapping evaluation results by running multiple cluster analysis methods and then using several metrics to choose from among solutions. We present four different clustering methods based on analyses using the R statistical software package: partitioning around medoids (PAM), fuzzy analysis (FANNY), agglomerative nesting (AGNES) and divisive analysis (DIANA). We then used the Dunn and Davies-Bouldin indices to assist in choosing a valid cluster solution for a concept mapping outcomes evaluation. We conclude that the validity of the outcomes map is high, based on the analyses described. Finally, we discuss areas for further concept mapping methods research.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Evaluation and Program Planning - Volume 60, February 2017, Pages 277-283
نویسندگان
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