Just When You Thought that Quantitizing Merely Involved Counting: A Renewed Call for Expanding the Practice of Quantitizing in Mixed Methods Research With a Focus on Measurement-Based Quantitizing
Abstract views: 12
/ 
PDF downloads: 12
DOI:
https://doi.org/10.59455/jomes.54Abstract
In this article, I explore the concept of quantitizing in mixed methods research, categorizing it into four types: descriptive-based quantitizing (i.e., converting qualitative data into quantitative summaries; e.g., frequencies), inferential-based quantitizing (i.e., using statistical methods to draw inferences from quantitized data), exploratory-based quantitizing (i.e., identifying patterns/relationships within quantitized data, often leading to further quantitative analysis), and measurement-based quantitizing (i.e., applying psychometric models to quantitized data to assess and to measure latent traits). Among these, measurement-based quantitizing is the least prevalent. Therefore, I expand the concept of measurement-based quantitizing by demonstrating how modern test theory (MTT) approaches (e.g., Rasch analysis and item response theory [IRT] models) can be applied effectively to quantitized themes or finer data units like categories, codes, and sub-codes. Rasch analysis and foundational IRT models (1-parameter IRT, 2-parameter IRT, 3-parameter IRT) add significant value to descriptive-based quantitizing by providing deeper insights into theme difficulty and discrimination. Other IRT models (e.g., 4-parameter IRT, 5-parameter IRT, Bayesian IRT) offer further refinement. Also, I highlight the value of these models in inferential-based quantitizing, particularly via differential item functioning analysis. When applying IRT to quantitized themes, tools such as the test information function, item characteristic curves, and item fit analysis are essential for refining measurements. I underscore the importance of optimizing theme quantity and sample size, recommending minimum guidelines for reliable IRT analysis of quantitized themes. In conclusion, I call for the broader adoption of measurement-based quantitizing, integrating MTT approaches to enhance the rigor, precision, and interpretative power of mixed methods research.
References
Abt, K. (1987). Descriptive data analysis: A concept between confirmatory and exploratory data analysis. Methods of Information in Medicine, 26(2), 77-88. https://doi.org/10.1055/s-0038-1635488 DOI: https://doi.org/10.1055/s-0038-1635488
Andrich, D. (2004). Controversy and the Rasch model: A characteristic of incompatible paradigms?. Medical care, 42(1), I-7. https://doi.org/10.1097/01.mlr.0000103528.48582.7c DOI: https://doi.org/10.1097/01.mlr.0000103528.48582.7c
Bacci, S., Bartolucci, F., & Gnaldi, M. (2014). A class of multidimensional latent class IRT models for ordinal polytomous item responses. Communications in Statistics-Theory and Methods, 43(4), 787-800. https://doi.org/10.1080/03610926.2013.827718 DOI: https://doi.org/10.1080/03610926.2013.827718
Bacci, S., & Caviezel, V. (2011). Multilevel IRT models for the university teaching evaluation. Journal of Applied Statistics, 38(12), 2775-2791. https://doi.org/10.1080/02664763.2011.570316 DOI: https://doi.org/10.1080/02664763.2011.570316
Barton, M. A., & Lord, F. M. (1981). An upper asymptote model for the three-parameter logistic item-response curves. ETS Research Report Series, 1981(1), i-8. https://doi.org/10.1002/j.2333-8504.1981.tb01239.x DOI: https://doi.org/10.1002/j.2333-8504.1981.tb01255.x
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of machine Learning research, 3(Jan), 993-1022.
Bombatkar, A., & Parvat, T. (2015). Improvements in clustering using affinity propagation: A review. Journal of Multidisciplinary Engineering Science and Technology, 2(6), 3159-0040. https://doi.org/10.1016/j.eswa.2014.09.054
Bond, T. G., & Fox, C. M. (2020). Applying the Rasch model: Fundamental measurement in the human sciences (4th ed.). Psychology Press. DOI: https://doi.org/10.4324/9780429030499
Bouguettaya, A., Yu, Q., Liu, X., Zhou, X., & Song, A. (2015). Efficient agglomerative hierarchical clustering. Expert Systems with Applications, 42(5), 2785-2797. https://doi.org/10.1016/j.eswa.2014.09.054 DOI: https://doi.org/10.1016/j.eswa.2014.09.054
Caracelli, V. J., & Greene, J. C. (1993). Data analysis strategies for mixed-method evaluation designs. Educational evaluation and policy analysis, 15(2), 195-207. https://doi.org/10.3102/01623737015002195 DOI: https://doi.org/10.3102/01623737015002195
Celebi, M. E., Kingravi, H. A., & Vela, P. A. (2013). A comparative study of efficient initialization methods for the k-means clustering algorithm. Expert systems with applications, 40(1), 200-210. https://doi.org/10.1016/j.eswa.2012.07.021 DOI: https://doi.org/10.1016/j.eswa.2012.07.021
Chen, W. H., & Revicki, D. (2023). Differential item functioning (DIF). In F. Maggino (Ed.) Encyclopedia of quality of life and well-being research. Springer DOI: https://doi.org/10.1007/978-3-031-17299-1_728
Chen, F. F., West, S. G., & Sousa, K. H. (2006). A comparison of bi-factor and second-order models of quality of life. Multivariate Behavioral Research, 41(2), 189-225. https://doi.org/10.1207/s15327906mbr4102_5 DOI: https://doi.org/10.1207/s15327906mbr4102_5
Collins, L. M., & Lanza, S. T. (2009). Latent class and latent transition analysis: With applications in the social, behavioral, and health sciences (Vol. 718). John Wiley & Sons. DOI: https://doi.org/10.1002/9780470567333
Cottrell, M., Olteanu, M., Rossi, F., & Villa-Vialaneix, N. N. (2018). Self-organizing maps, theory and applications. Revista de Investigacion Operacional, 39(1), 1-22.
Cuhadar, I. (2022). Sample size requirements for parameter recovery in the 4-parameter logistic model. Measurement: Interdisciplinary Research and Perspectives, 20(2), 57-72. https://doi.org/10.1080/15366367.2021.1934805 DOI: https://doi.org/10.1080/15366367.2021.1934805
David, S. L., Hitchcock, J. H., Ragan, B., Brooks, G., & Starkey, C. (2018). Mixing interviews and Rasch modeling: Demonstrating a procedure used to develop an instrument that measures trust. Journal of Mixed Methods Research, 12(1), 75-94. https://doi.org/10.1177/1558689815624586 DOI: https://doi.org/10.1177/1558689815624586
Davier, M., & Yamamoto, K. (2003). Partially observed mixtures of IRT models: An extension of the generalized partial-credit model. Applied Psychological Measurement, 28(6), 389-406. https://doi.org/10.1177/0146621604268734 DOI: https://doi.org/10.1177/0146621604268734
De Ayala, R. J. (2013). The theory and practice of item response theory. Guilford.
De La Torre, J. (2009). DINA model and parameter estimation: A didactic. Journal of educational and behavioral statistics, 34(1), 115-130. https://doi.org/10.3102/1076998607309474 DOI: https://doi.org/10.3102/1076998607309474
Djidu, H., Retnawati, H., & Haryanto, H. (2023). Ensuring parameter estimation accuracy in 3PL IRT modeling: The role of test length and sample size. JP3I (Jurnal Pengukuran Psikologi dan Pendidikan Indonesia), 12(2), 177-190. https://doi.org/10.15408/jp3i.v12i2.34130 DOI: https://doi.org/10.15408/jp3i.v12i2.34130
Dodeen, H. (2004). The relationship between item parameters and item fit. Journal of Educational Measurement, 41(3), 261-270. https://doi.org/10.1111/J.1745-3984.2004.TB01165.X DOI: https://doi.org/10.1111/j.1745-3984.2004.tb01165.x
Fischer, G. H. (1995). Derivations of the Rasch model. In G. H. Fischer & I. W. Molenaar (Eds.), Rasch models: Foundations, recent developments, and applications (pp. 15-38). Springer. DOI: https://doi.org/10.1007/978-1-4612-4230-7_2
Fox, J. P. (2010). Bayesian item response modeling: Theory and applications. Springer. DOI: https://doi.org/10.1007/978-1-4419-0742-4
Fox, J. P., & Glas, C. A. (2001). Bayesian estimation of a multilevel IRT model using Gibbs sampling. Psychometrika, 66, 271-288. https://doi.org/10.1007/BF02294839 DOI: https://doi.org/10.1007/BF02294839
Galdin, M., & Laurencelle, L. (2010). Assessing parameter invariance in item response theory’s logistic two item parameter model: A Monte Carlo investigation. Tutorials in Quantitative Methods for Psychology, 6(2), 39-51. https://doi.org/10.20982/TQMP.06.2.P039 DOI: https://doi.org/10.20982/tqmp.06.2.p039
Glaser, B. G. (1965). The constant comparative method of qualitative analysis. Social Problems, 12, 436-445. https://doi.org/10.1525/sp.1965.12.4.03a00070 DOI: https://doi.org/10.1525/sp.1965.12.4.03a00070
Gottschalk, P. G., & Dunn, J. R. (2005). The five-parameter logistic: A characterization and comparison with the four-parameter logistic. Analytical biochemistry, 343(1), 54-65. https://doi.org/10.1016/j.ab.2005.04.035 DOI: https://doi.org/10.1016/j.ab.2005.04.035
Hahn, L. W., Ritchie, M. D., & Moore, J. H. (2003). Multifactor dimensionality reduction software for detecting gene–gene and gene–environment interactions. Bioinformatics, 19(3), 376-382. https://doi.org/10.1093/bioinformatics/btf869 DOI: https://doi.org/10.1093/bioinformatics/btf869
Hambleton, R. K., Swaminathan, H., & Rogers, H. J. (1991). Fundamentals of item response theory (Vol. 2). Sage.
Harwell, M. R., & Janosky, J. E. (1991). An empirical study of the effects of small datasets and varying prior variances on item parameter estimation in BILOG. Applied Psychological Measurement, 15(3), 279-291. https://doi.org/10.1177/014662169101500308 DOI: https://doi.org/10.1177/014662169101500308
Hayat, B., Putra, M. D. K., & Suryadi, B. (2020). Comparing item parameter estimates and fit statistics of the Rasch model from three different traditions. Jurnal Penelitian dan Evaluasi Pendidikan, 24(1), 39-50. https://doi.org/10.21831/pep.v24i1.29871 DOI: https://doi.org/10.21831/pep.v24i1.29871
He, Q., & Wheadon, C. (2013). The effect of sample size on item parameter estimation for the partial credit model. International Journal of Quantitative Research in Education, 1(3), 297-315. https://doi.org/10.1504/IJQRE.2013.057692 DOI: https://doi.org/10.1504/IJQRE.2013.057692
Henson, R. A. (2009). Diagnostic classification models: Thoughts and future directions. Measurement: Interdisciplinary Research and Perspectives, 7(1), 34-36. https://doi.org/10.1080/15366360802715395 DOI: https://doi.org/10.1080/15366360802715395
Hitchcock, J. H., & Onwuegbuzie, A. J. (2022). The Routledge handbook for advancing integration in mixed methods research: An introduction. In J. H. Hitchcock & A. J. Onwuegbuzie (Eds.), Routledge handbook for advancing integration in mixed methods research (pp. 3-27). Routledge. DOI: https://doi.org/10.4324/9780429432828-2
Hout, M. C., Papesh, M. H., & Goldinger, S. D. (2013). Multidimensional scaling. Wiley Interdisciplinary Reviews: Cognitive Science, 4(1), 93-103. https://doi.org/10.1002/wcs.1203 DOI: https://doi.org/10.1002/wcs.1203
Huang, H. Y., Wang, W. C., Chen, P. H., & Su, C. M. (2013). Higher-order item response models for hierarchical latent traits. Applied Psychological Measurement, 37(8), 619-637. https://doi.org/10.1177/0146621613488819 DOI: https://doi.org/10.1177/0146621613488819
Huo, Y., de la Torre, J., Mun, E. Y., Kim, S. Y., Ray, A. E., Jiao, Y., & White, H. R. (2015). A hierarchical multi-unidimensional IRT approach for analyzing sparse, multi-group data for integrative data analysis. Psychometrika, 80, 834-855. https://doi.org/10.1007/s11336-014-9420-2 DOI: https://doi.org/10.1007/s11336-014-9420-2
Kalkan, Ö. K. (2022). The comparison of estimation methods for the four-parameter logistic item response theory model. Measurement: Interdisciplinary Research and Perspectives, 20(2), 73-90. https://doi.org/10.1080/15366367.2021.1897398 DOI: https://doi.org/10.1080/15366367.2021.1897398
Kamata, A., & Cheong, Y. F. (2007). Multilevel IRT models. In S. Sinharay & B. W. Wollack (Eds.), Handbook of statistics: Vol. 26. Psychometrics (pp. 543-567). Elsevier.
Kamata, A., & Vaughn, B. K. (2011). Multilevel IRT modeling. In A. Kamata & B. K. Vaughn (Eds.) Handbook of advanced multilevel analysis (pp. 41-57). Routledge.
Karabatsos, G. (2015). A Bayesian nonparametric IRT model. arXiv: Methodology.
König, C., Spoden, C., & Frey, A. (2020). An optimized Bayesian hierarchical two-parameter logistic model for small-sample item calibration. Applied Psychological Measurement, 44(4), 311-326. https://doi.org/10.1177/0146621619893786 DOI: https://doi.org/10.1177/0146621619893786
Koskey, K. L. K., Sondergeld, T. A., Stewart, V. C., & Pugh, K. J. (2018). Applying the mixed methods instrument development and construct validation process: The Transformative Experience Questionnaire. Journal of Mixed Methods Research, 12(1), 95-122. https://doi.org/10.1177/1558689816633310 DOI: https://doi.org/10.1177/1558689816633310
Koskey, K. L. K., & Stewart, V. C. (2014). A concurrent mixed methods approach to examining the quantitative and qualitative meaningfulness of absolute magnitude estimation scales in survey research. Journal of Mixed Methods Research, 8(2), 180-202. https://doi.org/10.1177/1558689813496905 DOI: https://doi.org/10.1177/1558689813496905
Kramer, J. M. (2011). Using mixed methods to establish the social validity of a self-report assessment: An illustration using the Child Occupational Self-Assessment (COSA). Journal of Mixed Methods Research, 5(1), 52-76. https://doi.org/10.1177/1558689810386376 DOI: https://doi.org/10.1177/1558689810386376
Kutscher, T., Eid, M., & Crayen, C. (2019). Sample size requirements for applying mixed polytomous item response models: Results of a Monte Carlo simulation study. Frontiers in Psychology, 10, 2494. https://doi.org/10.3389/fpsyg.2019.02494 DOI: https://doi.org/10.3389/fpsyg.2019.02494
Leach Sankofa, N. (2022). Transformativist measurement development methodology: A mixed methods approach to scale construction. Journal of Mixed Methods Research, 16(3), 307-327. https://doi.org/10.1177/15586898211033698 DOI: https://doi.org/10.1177/15586898211033698
Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. In T. Leen, T. Dietterich, & V. Tresp (Eds.), Advances in neural information processing systems, 13, 556-562. MIT Press.
Leighton, J., & Gierl, M. (Eds.). (2007). Cognitive diagnostic assessment for education: Theory and applications. Cambridge University Press. DOI: https://doi.org/10.1017/CBO9780511611186
Le Roux, B., & Rouanet, H. (2010). Multiple correspondence analysis (Vol. 163). Sage. DOI: https://doi.org/10.4135/9781412993906
Levy, R., & Mislevy, R. J. (2017). Bayesian psychometric modeling. Chapman and Hall/CRC. DOI: https://doi.org/10.1201/9781315374604
Lord, F. M. (1980). Applications of item response theory to practical testing problems. Routledge.
Lubke, G. H., & Muthén, B. (2005). Investigating population heterogeneity with factor mixture models. Psychological Methods, 10(1), 21-39. https://doi.org/10.1037/1082-989X.10.1.21 DOI: https://doi.org/10.1037/1082-989X.10.1.21
Madeira, S. C., & Oliveira, A. L. (2004). Biclustering algorithms for biological data analysis: a survey. IEEE/ACM transactions on computational biology and bioinformatics, 1(1), 24-45. https://doi.org/10.1109/TCBB.2004.2 DOI: https://doi.org/10.1109/TCBB.2004.2
Mair, P., & Hatzinger, R. (2007). Extended Rasch modeling: The eRm package for the application of IRT models in R. Journal of Statistical Software, 20, 1-20. https://doi.org/10.18637/JSS.V020.I09 DOI: https://doi.org/10.18637/jss.v020.i09
Masters, G. N. (1982). A Rasch model for partial credit scoring. Psychometrika, 47(2), 149-174. https://doi.org/10.1007/BF02296272 DOI: https://doi.org/10.1007/BF02296272
McClure, D. R., Ojo, E. O., Schaefer, M B., Bell, D., Abrams, S. S., & Onwuegbuzie, A. J. (2021). Online learning challenges experienced by university students in the New York City area during the COVID-19 pandemic: A mixed methods study. International Journal of Multiple Research Approaches, 13(2), 150-167. https://doi.org/10.29034/ijmra.v13n2editorial4 DOI: https://doi.org/10.29034/ijmra.v13n2editorial4
McDonald, R. P. (2013). Modern test theory. In T. D. Little (Ed.), The Oxford handbook of quantitative methods in psychology (Vol. 1, pp. 118-143). Oxford University Press. DOI: https://doi.org/10.1093/oxfordhb/9780199934874.013.0007
Michailidis, G. (2007). Correspondence analysis. In N. J. Salkind (Ed.), Encyclopedia of
measurement and statistics (pp. 191-194). Sage.
Miles, M., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook (2nd ed.). Sage.
Morell, L., & Tan, R. J. B. (2009). Validating for use and interpretation: A mixed methods contribution illustrated. Journal of Mixed Methods Research, 3(3), 242-264. https://doi.org/10.1177/1558689809335079 DOI: https://doi.org/10.1177/1558689809335079
Muraki, E. (1992). A generalized partial credit model: Application of an EM algorithm. Applied Psychological Measurement, 16(2), 159-176. https://doi.org/10.1177/014662169201600206 DOI: https://doi.org/10.1177/014662169201600206
Murtagh, F. (1983). A survey of recent advances in hierarchical clustering algorithms. The computer journal, 26(4), 354-359. https://doi.org/10.1093/comjnl/26.4.354 DOI: https://doi.org/10.1093/comjnl/26.4.354
Murtagh, F., & Legendre, P. (2014). Ward’s hierarchical agglomerative clustering method: which algorithms implement Ward’s criterion? Journal of classification, 31, 274-295. https://doi.org/10.1007/s00357-014-9161-z DOI: https://doi.org/10.1007/s00357-014-9161-z
Natesan, P., Onwuegbuzie, A. J., Hitchcock, J., & Newman, I. (2019). Fully Integrated Bayesian thinking: A mixed methods approach to the 1 + 1 = 1 formula. AERA Division D Newsletter, 10-12. http://www.aera.net/Portals/38/docs/DivD/DNews_current/DivDNewsletter_Spring19.pdf
Newman, I., Onwuegbuzie, A. J., & Hitchcock, J. H. (2015). Using the general
linear model to facilitate the full integration of qualitative and quantitative analysis: The potential to improve prediction and theory building and testing. General Linear Model Journal, 41(1), 12-28. http://www.glmj.org/archives/articles/Newman_v41n1.pdf
Onwuegbuzie, A. J. (2003). Effect sizes in qualitative research: A prolegomenon. Quality & Quantity: International Journal of Methodology, 37, 393-409. https://doi.org/10.1023/A:1027379223537 DOI: https://doi.org/10.1023/A:1027379223537
Onwuegbuzie, A. J. (2017, March). Mixed methods is dead! Long live mixed methods! Invited keynote address presented at the Mixed Methods International Research Association Caribbean Conference at Montego Bay, Jamaica.
Onwuegbuzie, A. J. (2021). Beyond identifying emergent themes in mixed methods research studies: The role of economic indices: The Thematic Herfindahl-Hirschman Index and the Thematic Concentration Ratio. International Journal of Multiple Research Approaches, 13(2), 137-149. https://doi.org/10.29034/ijmra.v13n2editorial3 DOI: https://doi.org/10.29034/ijmra.v13n2editorial3
Onwuegbuzie, A. J. (2022). Towards full(er) integration in mixed methods research: The role of canonical correlation analysis for integrating quantitative and qualitative data. Publicaciones, 52(2), 11-34. https://doi.org/10.30827/publicaciones.v52i2.27664 DOI: https://doi.org/10.30827/publicaciones.v52i2.27664
Onwuegbuzie, A. J. (2023). The 1 + 1 = 1 and 1 + 1 = 3 Integration formulas in mixed methods research: A poem promoting peaceful and productive co-existence. Journal of Mixed Method Studies, 8, 17-22. https://doi.org/10.14689/jomes.2022.7.X DOI: https://doi.org/10.59455/jomes.2023.8.3
Onwuegbuzie, A. J. (2024). On quantitizing revisited. Frontiers in Psychology, 15, 1421525. https://doi.org/10.3389/fpsyg.2024.1421525
Onwuegbuzie, A. J., Abrams, S. S., & Forzani, E. (2024). Critical dialectical pluralism 2.0: A multidimensional metaphilosophy addressing social justice, inclusion, diversity, equity, and social responsibility. International Journal of Multiple Research Approaches, 16(3).
Onwuegbuzie, A. J., Bustamante, R. M., & Nelson, J. A. (2010). Mixed research as a tool for developing quantitative instruments. Journal of Mixed Methods Research, 4, 56-78. https://doi.org/10.1177/1558689809355805 DOI: https://doi.org/10.1177/1558689809355805
Onwuegbuzie, A. J., & Combs, J. P. (2010). Emergent data analysis techniques in mixed methods research: A synthesis. In A. Tashakkori & C. Teddlie (Eds.), Handbook of mixed methods in social and behavioral research (2nd ed., pp. 397-430). Sage. DOI: https://doi.org/10.4135/9781506335193.n17
Onwuegbuzie, A. J., & Frels, R. K. (2013). Introduction: Toward a new research philosophy for addressing social justice issues: Critical dialectical pluralism 1.0. International Journal of Multiple Research Approaches, 7(1), 9-26. https://doi.org/10.5172/mra.2013.7.1.9 DOI: https://doi.org/10.5172/mra.2013.7.1.9
Onwuegbuzie, A. J., Frels, R. K., Leech, N. L., & Collins, K. M. T. (2011). A mixed research study of pedagogical approaches and student learning in doctoral-level mixed research courses. International Journal of Multiple Research Approaches, 5, 169-199. https://doi.org/10.5172/mra.2011.5.2.169 DOI: https://doi.org/10.5172/mra.2011.5.2.169
Onwuegbuzie, A. J., & Hitchcock, J. H. (2019). Toward a fully integrated approach to mixed methods research via the 1 + 1 = 1 integration approach: Mixed Research 2.0. International Journal of Multiple Research Approaches, 11(1), 7-28. https://doi.org/10.29034/ijmra.v11n1editorial1 DOI: https://doi.org/10.29034/ijmra.v11n1editorial2
Onwuegbuzie, A. J., & Hitchcock, J. H. (2022). Towards a comprehensive meta-framework for full integration in mixed methods research. In J. H. Hitchcock & A. J. Onwuegbuzie (Eds.), Routledge handbook for advancing integration in mixed methods research (pp. 565-606). Routledge. DOI: https://doi.org/10.4324/9780429432828-43
Onwuegbuzie, A. J., Hitchcock, J. H., Natesan, P., & Newman, I. (2018). Using fully integrated Bayesian thinking to address the 1 + 1 = 1 integration challenge. International Journal of Multiple Research Approaches, 10, 666-678. https://doi.org/10.29034/ijmra.v10n1a43 DOI: https://doi.org/10.29034/ijmra.v10n1a43
Onwuegbuzie, A. J., & Leech, N. L. (2019). On qualitizing. International Journal of Multiple Research Approaches, 11(2), 98-131. https://doi.org/10.29034/ijmra.v11n2editorial2 DOI: https://doi.org/10.29034/ijmra.v11n2editorial2
Onwuegbuzie, A. J., & Leech, N. L. (2021). Qualitizing data. In A. J. Onwuegbuzie & R. B. Johnson (Eds.), The Routledge reviewer’s guide to mixed analysis (pp. 239-258). Routledge. DOI: https://doi.org/10.4324/9780203729434-22
Onwuegbuzie, A. J., Ojo, E. O., Burger, A., Crowley, T., Adams, S. P., & Bergsteedt, B. T. (2020). Challenges experienced by students at Stellenbosch University that hinder their ability successfully to learn online during the COVID-19 era: A demographic and spatial analysis. International Journal of Multiple Research Approaches, 12(3), 240-281. https://doi.org/10.29034/ijmra.v12n3editorial2 DOI: https://doi.org/10.29034/ijmra.v12n3editorial2
Onwuegbuzie, A. J., & Teddlie, C. (2003). A framework for analyzing data in mixed methods research. In A. Tashakkori, & C. Teddlie (Eds.), Handbook of mixed methods in social and behavioral research (pp. 351-383). Sage.
Onwuegbuzie, A. J., Witcher, A. E., Collins, K. M. T., Filer, J. D., Wiedmaier, C. D., & Moore, C. W. (2007). Students’ perceptions of characteristics of effective college teachers: A validity study of a teaching evaluation form using a mixed methods analysis. American Educational Research Journal, 44, 113-160. https://doi.org/10.3102/0002831206298169 DOI: https://doi.org/10.3102/0002831206298169
Paek, I., & Cai, L. (2014). A comparison of item parameter standard error estimation procedures for unidimensional and multidimensional item response theory modeling. Educational and Psychological Measurement, 74(1), 58-76. https://doi.org/10.1177/0013164413500277 DOI: https://doi.org/10.1177/0013164413500277
Partchev, I. (2009). 3PL: A useful model with a mild estimation problem. Measurement: Interdisciplinary Research and Perspectives, 7, 94 - 96. https://doi.org/10.1080/15366360903117046 DOI: https://doi.org/10.1080/15366360903117046
Provalis Research. (2020). WordStat (Version 8.0.28) [Computer software]. Montreal, Quebec, Canada: Author.
Ravand, H., Baghaei, P., & Doebler, P. (2020). Examining parameter invariance in a general diagnostic classification model. Frontiers in psychology, 10, 2930. https://doi.org/10.3389/fpsyg.2019.02930 DOI: https://doi.org/10.3389/fpsyg.2019.02930
Reckase, M. D. (2009). The past and future of multidimensional item response theory. Applied Psychological Measurement, 21(1), 25-36. https://doi.org/10.1177/0146621697211002 DOI: https://doi.org/10.1177/0146621697211002
Reidy, P. (2009). An introduction to latent semantic analysis. Discourse Processes, 25, 259-284. https://doi.org/10.1080/01638539809545028 DOI: https://doi.org/10.1080/01638539809545028
Rijmen, F., Tuerlinckx, F., De Boeck, P., & Kuppens, P. (2003). A nonlinear mixed model framework for item response theory. Psychological Methods, 8(2), 185-205. https://doi.org/10.1037/1082-989X.8.2.185 DOI: https://doi.org/10.1037/1082-989X.8.2.185
Ross, A., & Onwuegbuzie, A. J. (2014). Complexity of quantitative analyses used in mixed research articles published in a flagship mathematics education journal. International Journal of Multiple Research Approaches, 8, 63-73. https://doi.org/10.5172/mra.2014.8.1.63 DOI: https://doi.org/10.5172/mra.2014.8.1.63
Rost, J. (1990). Rasch models in latent classes: An integration of two approaches to item analysis. Applied Psychological Measurement, 14(3), 271-282. https://doi.org/10.1177/014662169001400305 DOI: https://doi.org/10.1177/014662169001400305
Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores. Psychometrika Monograph Supplement, 17(4), 1-100. https://doi.org/10.1007/BF03372160 DOI: https://doi.org/10.1007/BF03372160
Sandelowski, M., Voils, C. I., & Knafl, G. (2009). On quantitizing. Journal of Mixed Methods Research, 3(3), 208-222. https://doi.org/10.1177/1558689809334210 DOI: https://doi.org/10.1177/1558689809334210
Schulz, W., & Fraillon, J. (2011). The analysis of measurement equivalence in international studies using the Rasch model. Educational Research and Evaluation, 17(6), 447-464. https://doi.org/10.1080/13803611.2011.630559 DOI: https://doi.org/10.1080/13803611.2011.630559
Sébille, V., Hardouin, J. B., Le Néel, T., Kubis, G., Boyer, F., Guillemin, F., & Falissard, B. (2010). Methodological issues regarding power of classical test theory (CTT) and item response theory (IRT)-based approaches for the comparison of patient-reported outcomes in two groups of patients-a simulation study. BMC medical research methodology, 10, 1-10. https://doi.org/10.1186/1471-2288-10-24 DOI: https://doi.org/10.1186/1471-2288-10-24
Şen, S., & Cohen, A. S. (2023). The impact of sample size and various other factors on estimation of dichotomous mixture IRT models. Educational and Psychological Measurement, 83(3), 520-555. https://doi.org/10.1177/00131644221094325 DOI: https://doi.org/10.1177/00131644221094325
Seo, D. G., & Kim, J. K. (2021). The accuracy and consistency of mastery for each content domain using the Rasch and deterministic inputs, noisy “and” gate diagnostic classification models: a simulation study and a real-world analysis using data from the Korean Medical Licensing Examination. Journal of Educational Evaluation for Health Professions, 18. https://doi.org/10.3352/jeehp.2021.18.15 DOI: https://doi.org/10.3352/jeehp.2021.18.15
Sessoms, J., & Henson, R. A. (2018). Applications of diagnostic classification models: A literature review and critical commentary. Measurement: Interdisciplinary Research and Perspectives, 16(1), 1-17. https://doi.org/10.1080/15366367.2018.1435104 DOI: https://doi.org/10.1080/15366367.2018.1435104
Smith, A. B., Rush, R., Fallowfield, L. J., Velikova, G., & Sharpe, M. (2008). Rasch fit statistics and sample size considerations for polytomous data. BMC Medical Research Methodology, 8, 1-11. https://doi.org/10.1186/1471-2288-8-33 DOI: https://doi.org/10.1186/1471-2288-8-33
Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & van der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(4), 583-639. https://doi.org/10.1111/1467-9868.00353 DOI: https://doi.org/10.1111/1467-9868.00353
Spurk, D., Hirschi, A., Wang, M., Valero, D., & Kauffeld, S. (2020). Latent profile analysis: A review and “how to” guide of its application within vocational behavior research. Journal of Vocational Behavior, 120, 103445. https://doi.org/10.1016/j.jvb.2020.103445 DOI: https://doi.org/10.1016/j.jvb.2020.103445
Svetina Valdivia, D., & Dai, S. (2024). Number of response categories and sample size requirements in polytomous IRT models. The Journal of Experimental Education, 92(1), 154-185. https://doi.org/10.1080/00220973.2022.2153783 DOI: https://doi.org/10.1080/00220973.2022.2153783
Tashakkori, A., & Teddlie, C. (1998). Mixed methodology: Combining qualitative and
quantitative approaches. Applied Social Research Methods Series (Vol. 46). Sage.
Teddlie, C., & Tashakkori, A. (2003). Major issues and controversies in the use of mixed methods in the social and behavioral sciences. In A. Tashakkori & C. Teddlie (Eds.), Handbook of mixed methods in social and behavioral research (pp. 3-50). Sage.
Teddlie, C., & Tashakkori, A. (2009). Foundations of mixed methods research: Integrating quantitative and qualitative techniques in the social and behavioral sciences. Sage.
Tharwat, A. (2021). Independent component analysis: An introduction. Applied Computing and Informatics, 17(2), 222-249. https://doi.org/10.1016/j.aci.2018.08.006 DOI: https://doi.org/10.1016/j.aci.2018.08.006
Thissen, D., Cai, L., & Bock, R. D. (2011). The nominal categories item response model. In M. W. Van der Linden & C. A. W. Glas (Eds.), Handbook of polytomous item response theory models (pp. 43-75). Routledge.
Ultsch, A. (1990). Kohonen's self organizing feature maps for exploratory data analysis. INNC'90.
Uyigue, A. V., & Orheruata, M. U. (2019). Test length and sample size for item-difficulty parameter estimation in item response theory. Journal of Education and Practice, 10(30), 72-75. https://doi.org/10.7176/jep/10-30-08 DOI: https://doi.org/10.7176/JEP/10-30-08
Vehtari, A., & Ojanen, J. (2012). A survey of Bayesian predictive methods for model assessment, selection, and comparison. Statistics Surveys, 6(1), 142-228. https://doi.org/10.1214/12-SS102 DOI: https://doi.org/10.1214/12-SS102
Viroli, C., & McLachlan, G. J. (2019). Deep Gaussian mixture models. Statistics and Computing, 29, 43-51. https://doi.org/10.1007/s11222-017-9793-z DOI: https://doi.org/10.1007/s11222-017-9793-z
Waller, N. G., & Feuerstahler, L. (2017). Bayesian modal estimation of the four-parameter item response model in real, realistic, and idealized data sets. Multivariate Behavioral Research, 52(3), 350-370. https://doi.org/10.1080/00273171.2017.1292893 DOI: https://doi.org/10.1080/00273171.2017.1292893
Wattenberg, M., Viégas, F., & Johnson, I. (2016). How to use t-SNE effectively. Distill, 1(10), e2. https://doi.org/10.23915/distill.00002 DOI: https://doi.org/10.23915/distill.00002
Wright, B. D., & Masters, G. N. (1982). Rating scale analysis. MESA Press.
Zumbo, B. D. (2007). Three generations of DIF analyses: Considering where it has been, where it is now, and where it is going. Language Assessment Quarterly, 4(2), 223-233. https://doi.org/10.1080/15434300701375832 DOI: https://doi.org/10.1080/15434300701375832
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Journal of Mixed Methods Studies
This work is licensed under a Creative Commons Attribution 4.0 International License.
