@article{5e863378e52a46c7bbaae9837c8ba069,
title = "Modeling the Use of Mixed Methods–Grounded Theory: Developing Scales for a New Measurement Model",
abstract = "Mixed methods–grounded theory (MM–GT) has emerged as a promising methodology that intersects the value of mixed methods with rigorous qualitative design. However, recent reviews have found that MM–GT empirical studies tend to lack procedural details. The purpose of this article is to apply the “best practices” for conducting MM–GT in a study designed to develop and then test a theoretical model for how undergraduate engineering students develop interest in the engineering PhD. This study contributes to the field of mixed methods research by (a) illustrating best practices for MM–GT, (b) providing an MM–GT scale development example, (c) demonstrating how an MM-GT scale could potentially bypass exploratory factor analysis and proceed directly to confirmatory factor analysis for testing psychometric properties, and showing how a joint display for data collection planning can be used to strengthen integration in an instrument development study.",
keywords = "engineering education, grounded theory, mixed methods, qualitative, research design",
author = "{Howell Smith}, {Michelle C.} and Babchuk, {Wayne A.} and Jared Stevens and Garrett, {Amanda L.} and Wang, {Sherry C.} and Guetterman, {Timothy C.}",
note = "Funding Information: We would like to thank Vicki Plano Clark, Department of Educational Psychology, University of Nebraska-Lincoln, now at School of Education, University of Cincinnati, for her thoughtful advice and peer debriefing during this study and Chaorong Wu, Department of Educational Psychology, University of Nebraska-Lincoln, now at Institute for Clinical and Translational Science, University of Iowa, for his assistance in programming the quantitative analysis. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported in part by the National Science Foundation (award EEC-0935108). The opinions, views, and conclusions expressed in this article may not reflect those of the funding agency. Funding Information: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported in part by the National Science Foundation (award EEC-0935108). The opinions, views, and conclusions expressed in this article may not reflect those of the funding agency. Publisher Copyright: {\textcopyright} The Author(s) 2019.",
year = "2020",
month = apr,
day = "1",
doi = "10.1177/1558689819872599",
language = "English (US)",
volume = "14",
pages = "184--206",
journal = "Journal of Mixed Methods Research",
issn = "1558-6898",
publisher = "SAGE Publications Ltd",
number = "2",
}