Harmonizing perspectives to understand attitudes: A mixed methods approach to crafting an assessment literacy attitude scale
Year 2024,
Volume: 11 Issue: 3, 424 - 444, 09.09.2024
Beyza Aksu
,
Stefanie Wind
,
Mehmet Can Demir
Abstract
Assessment literacy's vital role in faculty effectiveness within higher education lacks sufficient tools for measuring faculty attitudes on this matter. Employing a sequential mixed-methods approach, this study utilized the theory of planned behavior to develop the Assessment Literacy Attitude Scale (ALAS) and evaluate its psychometric properties within the U.S. higher education context. The qualitative phase involved a literature review of relevant studies and existing self-report measures, interviews with stakeholders, and panel reviews to shape initial item development. Following the establishment of a conceptual foundation and a comprehensive overview of the scale's construction, our study advanced to the quantitative stage that involves factor analytical and item response theory approaches using data from 260 faculty across three public universities in the U.S. Exploratory factor analysis (EFA) was employed initially to obtain preliminary insights into the scale's factorial structure and dimensionality. Confirmatory factor analysis (CFA) was subsequently applied with separate data and the findings largely supported the conclusions from the EFA. Exploratory and confirmatory factor analyses resulted in 15 items loading across two factors in a good model fit range. Finally, we used nonparametric item response theory (IRT) techniques based on Mokken Scale Analysis (MSA) to evaluate individual items for evidence of effective psychometric properties to support the interpretation of ALAS scores, including monotonicity, scalability, and invariant item ordering. The newly-developed scale shows promise in assessing faculty attitudes toward enhancing their assessment literacy.
References
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- Howard, M.C. (2016). A review of exploratory factor analysis decisions and overview of current practices: What we are doing and how can we improve?. International Journal of Human Computer Interaction, 32(1), 51 62. https://doi.org/10.1080/10447318.2015.1087664
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Harmonizing perspectives to understand attitudes: A mixed methods approach to crafting an assessment literacy attitude scale
Year 2024,
Volume: 11 Issue: 3, 424 - 444, 09.09.2024
Beyza Aksu
,
Stefanie Wind
,
Mehmet Can Demir
Abstract
Assessment literacy's vital role in faculty effectiveness within higher education lacks sufficient tools for measuring faculty attitudes on this matter. Employing a sequential mixed-methods approach, this study utilized the theory of planned behavior to develop the Assessment Literacy Attitude Scale (ALAS) and evaluate its psychometric properties within the U.S. higher education context. The qualitative phase involved a literature review of relevant studies and existing self-report measures, interviews with stakeholders, and panel reviews to shape initial item development. Following the establishment of a conceptual foundation and a comprehensive overview of the scale's construction, our study advanced to the quantitative stage that involves factor analytical and item response theory approaches using data from 260 faculty across three public universities in the U.S. Exploratory factor analysis (EFA) was employed initially to obtain preliminary insights into the scale's factorial structure and dimensionality. Confirmatory factor analysis (CFA) was subsequently applied with separate data and the findings largely supported the conclusions from the EFA. Exploratory and confirmatory factor analyses resulted in 15 items loading across two factors in a good model fit range. Finally, we used nonparametric item response theory (IRT) techniques based on Mokken Scale Analysis (MSA) to evaluate individual items for evidence of effective psychometric properties to support the interpretation of ALAS scores, including monotonicity, scalability, and invariant item ordering. The newly-developed scale shows promise in assessing faculty attitudes toward enhancing their assessment literacy.
References
- Adam, S. (2004). Using learning outcomes: A consideration of the nature, role, application and implications for European education of employing “learning outcomes” at the local, national and international levels. Paper presented at the Bologna Seminar, Heriot-Watt University, Edinburgh United Kingdom. http://www.aic.lv/ace/ace_disk/Bologna/Bol_semin/Edinburgh/S_Adam_Bacgrerep_presentation.pdf Accessed on 16 November 2023.
- Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
- Ajzen, I. (2001). Nature and operation of attitudes. Annual Review of Psychology, 52(1), 27-58. https://doi.org/10.1146/annurev.psych.52.1.27
- Ajzen, I., & Timko, C. (1986). Correspondence between health attitudes and behavior. Basic and Applied Social Psychology, 7(4), 259 276. https://doi.org/10.1207/s15324834basp0704_2
- Archie, T., Hayward, C.N., Yoshinobu, S., & Laursen, S.L. (2022). Investigating the linkage between professional development and mathematics instructors’ use of teaching practices using the theory of planned behavior. Plos One, 17(4), e0267097. https://doi.org/10.1371/journal.pone.0267097
- Balloo, K., Norman, M., & Winstone, N.E. (2018, January). Evaluation of a large-scale inclusive assessment intervention: a novel approach to quantifying perceptions about assessment literacy. In The Changing Shape of Higher Education-Can Excellence and Inclusion Cohabit?: Conference Programmme and Book of Abstracts. University of Southern Queensland. https://srhe.ac.uk/arc/conference2018/downloads/SRHE_Conf_2018_Programme_Papers.pdf
- Biggs, J., & Tang, C. (2011). Train-the-trainers: Implementing outcomes-based teaching and learning in Malaysian higher education. Malaysian Journal of Learning and Instruction, 8, 1-19.
- Caspersen, J., & Smeby, J.C. (2018). The relationship among learning outcome measures used in higher education. Quality in Higher Education, 24(2), 117 135. https://doi.org/10.1080/13538322.2018.1484411
- Chang, L. (1995). Connotatively consistent and reversed connotatively inconsistent items are not fully equivalent: Generalizability study. Educational and Psychological Measurement, 55(6), 991-997. https://doi.org/10.1177/0013164495055006007
- Coates, H. (2016). Assessing student learning outcomes internationally: Insights and frontiers. Assessment & Evaluation in Higher Education, 41(5), 662 676. https://doi.org/10.1080/02602938.2016.1160273
- Cochran, W.G. (1977). Sampling techniques. John Wiley & Sons.
- Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37 46. https://doi.org/10.1177/001316446002000104
- Cole, K.L., Turner, R.C., & Gitchel, W.D. (2019). A study of polytomous IRT methods and item wording directionality effects on perceived stress items. Personality and Individual Differences, 147(6), 63-72. https://doi.org/10.1016/j.paid.2019.03.046
- Conner, M., & Armitage, C.J. (1998). Extending the theory of planned behavior: A review and avenues for further research. Journal of Applied Social Psychology, 28(15), 1429-1464. https://doi.org/10.1111/j.1559-1816.1998.tb01685.x
- Creswell, J.W., & Clark, V.P. (2011). Mixed methods research. SAGE Publications.
- Crick, R.D., Broadfoot, P., & Claxton, G. (2004). Developing an effective lifelong learning inventory: The ELLI project. Assessment in Education: Principles, Policy & Practice, 11(3), 247-272. https://doi.org/10.1080/0969594042000304582
- Cronbach, L.J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297-334. https://doi.org/10.1007/BF02310555
- Dann, R. (2014). Assessment as learning: blurring the boundaries of assessment and learning for theory, policy and practice. Assessment in Education: Principles, Policy & Practice, 21(2), 149-166. https://doi.org/10.1080/0969594X.2014.898128
- Dilek, H., & Akbaş, U. (2022). Investigation of education value perception scale's psychometric properties according to CTT and IRT. International Journal of Assessment Tools in Education, 9(3), 548-564. https://doi.org/10.21449/ijate.986530
- Dill, D. (2007). Quality assurance in higher education: Practices and issues. The 3rd International Encyclopedia of Education.
- Dunn, R., Hattie, J., & Bowles, T. (2018). Using the Theory of Planned Behavior to explore teachers’ intentions to engage in ongoing teacher professional learning. Studies in Educational Evaluation, 59, 288-294. https://doi.org/10.1016/j.stueduc.2018.10.001
- Eubanks, D. (2019). Reassessing the elephant, part 1. Assessment Update, 31(2), 6-7. https://doi.org/10.1002/au.30166
- Evans, C. (2016). Enhancing assessment feedback practice in higher education: The EAT framework. University of Southampton. https://www.southampton.ac.uk/assets/imported/transforms/content block/UsefulDownloads_Download/A0999D3AF2AF4C5AA24B5BEA08C61D8E/EAT%20Guide%20April%20FINAL1%20ALL.pdf
- Field, A. (2003). Discovering Statistics using IBM SPSS statistics. Sage Publications.
- Fokkema, M., & Greiff, S. (2017). How performing PCA and CFA on the same data equals trouble: Overfitting in the assessment of internal structure and some editorial thoughts on it [Editorial]. European Journal of Psychological Assessment, 33(6), 399–402. https://doi.org/10.1027/1015-5759/a000460
- Fornell, C., & David, F.L. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39 50. https://doi.org/10.2307/3151312
- Henseler, J., Ringle, C.M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy Marketing Science, 43, 115–135. https://doi.org/10.1007/s11747-014-0403-8
- Hines, S.R. (2009). Investigating faculty development program assessment practices: What's being done and how can it be improved?. The Journal of Faculty Development, 23(3), 5.
- Holmboe, E.S., Ward, D.S., Reznick, R.K., Katsufrakis, P.J., Leslie, K.M., Patel, V.L., ... & Nelson, E.A. (2011). Faculty development in assessment: the missing link in competency-based medical education. Academic Medicine, 86(4), 460 467. https://doi.org/10.1097/acm.0b013e31820cb2a7
- Hora, M.T., & Anderson, C. (2012). Perceived norms for interactive teaching and their relationship to instructional decision-making: A mixed methods study. Higher Education, 64, 573-592. https://doi.org/10.1007/s10734-012-9513-8
- Howard, M.C. (2016). A review of exploratory factor analysis decisions and overview of current practices: What we are doing and how can we improve?. International Journal of Human Computer Interaction, 32(1), 51 62. https://doi.org/10.1080/10447318.2015.1087664
- Hu, L.T., & Bentler, P.M. (1999). Cutof criteria for fit indexes in covariance structural analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
- Jankowski, N.A., & Marshall, D.W. (2017). Degrees that matter: Moving higher education to a learning systems paradigm. Routledge. https://doi.org/10.4324/9781003444015
- Kao, C.P., Lin, K.Y., & Chien, H.M. (2018). Predicting teachers’ behavioral intentions regarding web-based professional development by the theory of planned behavior. EURASIA Journal of Mathematics, Science and Technology Education, 14(5), 1887-1897. https://doi.org/10.29333/ejmste/85425
- Kline, P. (1994). An easy guide to factor analysis. Routledge.
- Knauder, H., & Koschmieder, C. (2019). Individualized student support in primary school teaching: A review of influencing factors using the Theory of Planned Behavior (TPB). Teaching and Teacher Education, 77, 66-76. https://doi.org/10.1016/j.tate.2018.09.012
- Kremmel, B., & Harding, L. (2020). Towards a comprehensive, empirical model of language assessment literacy across stakeholder groups: Developing the language assessment literacy survey. Language Assessment Quarterly, 17(1), 100 120. https://doi.org/10.1080/15434303.2019.1674855
- Landis, J.R., & Koch, G.G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159-174. https://doi.org/10.2307/2529310
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