As additive manufacturing (AM) processes become ubiquitous in engineering and design, there has emerged the need for a workforce skilled in designing for AM (DfAM). Researchers have proposed educational interventions to train students in DfAM; however, few measures with sufficient validity evidence have been proposed to assess the effects of these educational interventions on student designers’ learning. In this paper, we present the development of a ten-item DfAM self-efficacy scale spanning the opportunistic and restrictive DfAM domains, as they relate to conceptual design (i.e., preliminary concept generation and selection). We tested the criterion-related validity of the scale by comparing students’ self-efficacy to their prior AM and DfAM experience. Additionally, we tested the construct validity of the scale through exploratory and confirmatory factor analyses. Students’ responses to the scale positively correlated with their prior experience in AM and DfAM, thereby lending criterion-related validity. Additionally, factor analyses reveal that students’ responses are composed of two dimensions: (1) opportunistic DfAM and (2) restrictive DfAM, reflecting the categorization observed in the literature. This finding lends construct validity evidence and demonstrates that the scale captures students’ self-efficacies in the two DfAM domains with sufficient separation. This work supports the use of the DfAM self-efficacy scale for assessing the effects of DfAM education on students’ DfAM learning in conceptual design. Moreover, the DfAM self-efficacy scale can support future research attempting to enhance the effectiveness of AM and DfAM educational interventions by measuring their effects on students’ self-perceived abilities.

Original languageEnglish (US)
Pages (from-to)437-453
Number of pages17
JournalResearch in Engineering Design
Issue number4
StatePublished - Oct 2022

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Architecture
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering


Dive into the research topics of 'Development and validity evidence investigation of a design for additive manufacturing self-efficacy scale'. Together they form a unique fingerprint.

Cite this