Understanding engineering identity through structural equation modeling

Understanding students' self-ascribed engineering identity may be one way to understand engineering choices and to help recruit new students to the engineering pipeline. In our framework, identity is composed of students' perceptions of their performance/competence, recognition, and interest in a domain. This paper outlines the creation of a model of engineering choice based on this framework. The data utilized in this analysis come from the nationally-representative Sustainability and Gender in Engineering (SaGE) survey. Distributed during the fall of 2011, the survey was completed by 6,772 college students across the United States enrolled in first-year English courses. A structural equation model was built using previously validated constructs of mathematics, physics, and general science identities. These three constructs predict an engineering identity which, in turn, influences the choice of engineering in college. The model is a step towards a better understanding of the choice of an engineering major in college.

[1]  Hariharan Swaminathan,et al.  Development of a Classification System for Engineering Student Characteristics Affecting College Enrollment and Retention , 2009 .

[2]  W. Marsden.  I and J , 2012 .

[3]  Gary King,et al.  Amelia II: A Program for Missing Data , 2011 .

[4]  H. Marsh,et al.  In Search of Golden Rules: Comment on Hypothesis-Testing Approaches to Setting Cutoff Values for Fit Indexes and Dangers in Overgeneralizing Hu and Bentler's (1999) Findings , 2004 .

[5]  Philip M. Sadler,et al.  Examining the impact of mathematics identity on the choice of engineering careers for male and female students , 2011, 2011 Frontiers in Education Conference (FIE).

[6]  James Paul Gee,et al.  Chapter 3 : Identity as an Analytic Lens for Research in Education , 2000 .

[7]  Aditya Johri,et al.  On the development of a professional identity: engineering persisters vs engineering switchers , 2009, 2009 39th IEEE Frontiers in Education Conference.

[8]  Ruth A. Streveler,et al.  Why Do Students Choose Engineering? A Qualitative, Longitudinal Investigation of Students' Motivational Values , 2010 .

[9]  Matthew W. Ohland,et al.  Demographic Factors And Academic Performance: How Do Chemical Engineering Students Compare With Others? , 2003 .

[10]  Richard G. Lomax,et al.  A Beginner's Guide to Structural Equation Modeling , 2022 .

[11]  E. Seymour,et al.  Talking About Leaving: Why Undergraduates Leave The Sciences , 1997 .

[12]  Helen L. Chen,et al.  Being and Becoming: Gender and Identity Formation of Engineering Students. Research Brief. , 2008 .

[13]  N. Brickhouse,et al.  What Kind of a Girl Does Science? The Construction of School Science Identities , 2000 .

[14]  Т В Горбунова.  2001. 02. 011. Показатели (индикаторы) науки и техники США. Us science and engineering indicators / science and engineering indicators – 2000 rep. / nat. Science boards. – Wash. , 2000. – P. 2-1, 2-40 , 2001 .

[15]  Philip M. Sadler,et al.  The Difference Between Engineering And Science Students: Comparing Backgrounds And High School Experiences , 2009 .

[16]  A. Satorra,et al.  A scaled difference chi-square test statistic for moment structure analysis , 1999 .

[17]  R. Layton,et al.  Persistence, Engagement, and Migration in Engineering Programs , 2008 .

[18]  Brenda Capobianco,et al.  Engineering Identity Development Among Pre‐Adolescent Learners , 2012 .

[19]  Yves Rosseel,et al.  lavaan: An R Package for Structural Equation Modeling , 2012 .

[20]  W. Mau,et al.  Factors that Influence Persistence in Science and Engineering Career Aspirations. , 2003 .

[21]  P. Bentler,et al.  Significance Tests and Goodness of Fit in the Analysis of Covariance Structures , 1980 .

[22]  Philip M. Sadler,et al.  Connecting High School Physics Experiences, Outcome Expectations, Physics Identity, and Physics Career Choice: A Gender Study. , 2010 .

[23]  Elizabeth Godfrey,et al.  Mapping the Cultural Landscape in Engineering Education , 2010 .

[24]  P. Bentler,et al.  Cutoff criteria for fit indexes in covariance structure analysis : Conventional criteria versus new alternatives , 1999 .

[25]  Amy M. Hightower,et al.  Science and Engineering Indicators , 1993 .

[26]  Heidi B. Carlone,et al.  Understanding the Science Experiences of Successful Women of Color: Science Identity as an Analytic Lens. , 2007 .

[27]  D. Shen,et al.  Women Engineering Students and Self‐Efficacy: A Multi‐Year, Multi‐Institution Study of Women Engineering Student Self‐Efficacy , 2009 .

[28]  R. MacCallum,et al.  Power analysis and determination of sample size for covariance structure modeling. , 1996 .

[29]  Philip M. Sadler,et al.  Development of an Explanatory Framework for Mathematics Identity , 2012 .

[30]  Karen L. Tonso,et al.  Student Engineers and Engineer Identity: Campus Engineer Identities as Figured World , 2006 .

[31]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[32]  Geoff Potvin,et al.  The Development of Critical Engineering Agency, Identity, and the Impact on Engineering Career Choices , 2013 .

[33]  Robert W. Lent,et al.  Relation of Contextual Supports and Barriers to Choice Behavior in Engineering Majors: Test of Alternative Social Cognitive Models. , 2003 .

[34]  Heidi B. Carlone,et al.  Authoring identity amidst the treacherous terrain of science: A multiracial feminist examination of the journeys of three women of color in science , 2011 .