Development and evaluation of novel science and chemistry identity measures

Identity has been proposed as a mechanism to increase persistence within Science, Technology, Engineering and Mathematics (STEM) education programs. To assess the impact of identity on STEM persistence, measures that produce valid and reliable data within a given STEM discipline need to be employed. Therefore, this study developed and evaluated the functioning of science and chemistry identity measures in the context of university-level chemistry courses. The developed measures were administered to students enrolled in general and organic chemistry courses at four universities across the United States. Validity and reliability evidence for the data provided by the novel measures was supported using confirmatory factor analysis and McDonald's omega. Additionally, two competing structural equation models (SEMs), designed to explore the relations between mastery experiences, verbal persuasion, situational interest, and science or chemistry identity, were tested and compared to previously reported results. Both SEMs produced acceptable data-model fit, therefore a superior model was chosen based on theoretical support. Within both SEMs, the direct pathway (relation) between mastery experiences and identity was nonsignificant. The more supported model proposed that the relation was indirect and facilitated through verbal persuasion and situational interest. While the indirect relation was supported in both courses, the predominate pathway varied by course. Limitations of the science identity measure, recommendations for future use of the Measure of Chemistry Identity (MoChI), and suggestions for the facilitation of positive identity formation within chemistry classrooms are discussed.

[1]  Regis Komperda,et al.  Evaluation of the influence of wording changes and course type on motivation instrument functioning in chemistry , 2018 .

[2]  Conor V. Dolan,et al.  Factor analysis of variables with 2, 3, 5, and 7 response categories: A comparison of categorical variable estimators using simulated data , 1994 .

[3]  S. Hidi,et al.  The Four-Phase Model of Interest Development , 2006 .

[4]  Henk G. Schmidt,et al.  Interest development: Arousing situational interest affects the growth trajectory of individual interest , 2017 .

[5]  Ellen L. Usher,et al.  Sources of self-efficacy in mathematics: A validation study , 2009 .

[6]  Geoff Potvin,et al.  Understanding engineering identity through structural equation modeling , 2013, 2013 IEEE Frontiers in Education Conference (FIE).

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

[8]  H. Schmidt,et al.  The role of teachers in facilitating situational interest in an active-learning classroom , 2011 .

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

[10]  Frederick G. Lopez,et al.  Latent Structure of the Sources of Mathematics Self-Efficacy , 1996, Journal of vocational behavior.

[11]  D. Betsy McCoach,et al.  The Performance of RMSEA in Models With Small Degrees of Freedom , 2015 .

[12]  Peter M. Bentler,et al.  Ensuring Positiveness of the Scaled Difference Chi-square Test Statistic , 2008, Psychometrika.

[13]  Rex B. Kline,et al.  Principles and Practice of Structural Equation Modeling , 1998 .

[14]  K. Quinlan What triggers students’ interest during higher education lectures? personal and situational variables associated with situational interest , 2019, Studies in Higher Education.

[15]  M. J. Chang,et al.  Considering the Impact of Racial Stigmas and Science Identity: Persistence among Biomedical and Behavioral Science Aspirants , 2011, The Journal of higher education.

[16]  Xiaoying Xu,et al.  Understanding the State of the Art for Measurement in Chemistry Education Research: Examining the Psychometric Evidence , 2013 .

[17]  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).

[18]  Gordon W. Cheung,et al.  Evaluating Goodness-of-Fit Indexes for Testing Measurement Invariance , 2002 .

[19]  Zahra Hazari,et al.  Establishing an Explanatory Model for Mathematics Identity. , 2015, Child development.

[20]  H. Toyoda,et al.  Structural Equation Modeling : Present and Future. Festschrift in honor of Karl Joreskog , 2001 .

[21]  J. Osborne Best Practices in Quantitative Methods , 2009 .

[22]  Geoff Potvin,et al.  Identity, Critical Agency, and Engineering: An Affective Model for Predicting Engineering as a Career Choice , 2016 .

[23]  S. Hidi,et al.  Revisiting the Conceptualization, Measurement, and Generation of Interest , 2011 .

[24]  Jo Handelsman,et al.  Increasing Persistence of College Students in STEM , 2013, Science.

[25]  James D. Wright,et al.  Handbook of Survey Research. , 1985 .

[26]  Thomas C. Pentecost,et al.  Moving beyond Alpha: A Primer on Alternative Sources of Single-Administration Reliability Evidence for Quantitative Chemistry Education Research , 2018, Journal of Chemical Education.

[27]  B. Ferrell,et al.  Analysis of students' self-efficacy, interest, and effort beliefs in general chemistry , 2015 .

[28]  K. R. Scheel,et al.  Pedagogical approaches, contextual variables, and the development of student self-efficacy in undergraduate physics courses , 2004 .

[29]  R. MacCallum,et al.  Model modifications in covariance structure analysis: the problem of capitalization on chance. , 1992, Psychological bulletin.

[30]  A. Bandura Self-Efficacy: The Exercise of Control , 1997, Journal of Cognitive Psychotherapy.

[31]  Geoff Potvin,et al.  Understanding How Engineering Identity and Belongingness Predict Grit for First-Generation College Students , 2018, 2018 CoNECD - The Collaborative Network for Engineering and Computing Diversity Conference Proceedings.

[32]  P. Burke,et al.  The science identity and entering a science occupation. , 2017, Social science research.

[33]  Barbara K. Goza,et al.  The role of efficacy and identity in science career commitment among underrepresented minority students , 2011 .

[34]  Ulrich Schiefele,et al.  Interest, Learning, and Motivation , 1991 .

[35]  Jessica M. Fautch,et al.  Who Leaves, Who Stays? Psychological Predictors of Undergraduate Chemistry Students' Persistence. , 2015 .

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

[37]  K. E. Barron,et al.  The Role of Achievement Goals in the Development of Interest: Reciprocal Relations Between Achievement Goals, Interest, and Performance , 2008 .

[38]  Alonzo M. Flowers,et al.  Cultivating science identity through sources of self-efficacy , 2016 .

[39]  Christian D Schunn,et al.  The nature of science identity and its role as the driver of student choices , 2018, International journal of STEM education.

[40]  Jason T. Newsom,et al.  Longitudinal Structural Equation Modeling , 2015 .

[41]  Kathryn N. Hosbein,et al.  Alignment of theoretically grounded constructs for the measurement of science and chemistry identity , 2020 .

[42]  Gita Taasoobshirazi,et al.  Science motivation questionnaire II: Validation with science majors and nonscience majors , 2011 .

[43]  Edwin A. Locke,et al.  Studies of the relationship between satisfaction, goal-setting, and performance , 1970 .

[44]  A. Satorra,et al.  Corrections to test statistics and standard errors in covariance structure analysis. , 1994 .

[45]  Nathaniel J. Hunsu,et al.  Engendering situational interest through innovative instruction in an engineering classroom: what really mattered? , 2017 .

[46]  B. Ferrell Evaluation of Students' Interest, Effort Beliefs, and Self-Efficacy in General Chemistry , 2016 .

[47]  Paul R. Hernandez,et al.  Toward a Model of Social Influence that Explains Minority Student Integration into the Scientific Community. , 2011, Journal of educational psychology.

[48]  John T. Willse,et al.  The Search for "Optimal" Cutoff Properties: Fit Index Criteria in Structural Equation Modeling , 2006 .

[49]  Katerina Salta,et al.  Assessing motivation to learn chemistry: adaptation and validation of Science Motivation Questionnaire II with Greek secondary school students , 2015 .