توجيهات نمذجة القياس في الأبحاث السلوكية والاجتماعية: مقارنة بين النماذج العاكسة والمركبة والتكوينية

Zakariya  BELKHAMZA

doi:10.34118/sej.v6i4.4102

Zakariya BELKHAMZA Ahmed Bin Mohammed Military college (Qatar) https://orcid.org/0000-0002-5195-3173

DOI: https://doi.org/10.34118/sej.v6i4.4102

Keywords: Measurement Model, Reflective Model, Composite Model, formative model

Abstract

Statistics plays an essential role in the realization of many scientific researches and university theses given the rapid development of information technologies, computing and statistical methods, such as Excel tables and the statistical program SPSS, which made it easier to process data that was previously difficult to process.

And because of the apparent lack of use of graphs and tables of all kinds, and of the errors made in the field studies which are carried out as part of the end of studies, and with the aim of dispelling fear or mental heaviness in those who avoid statistics and these representations in particular, despite its graphic importance, its explanatory effectiveness, and the pleasure and suspense it gives to figures, percentages and averages, in this work, using the descriptive approach, we decided to highlight the most important problems related to the representation of data on social phenomena previously organized in frequency tables and statistics, and from there, to transfer the modern statistical flavor to students and researchers in general by presenting the concept and characteristics of graphical representation and its most important formal components, while examining its types and various common usage errors.

References

Al-Ghamdi, M. N., Abdelfattah, E. H., & Ezz, A. (2023). The Impact of Weighting Data in Some Fit Indices of Structural Equation Models: A Simulation Study. Advances and Applications in Statistics, 73–106. https://doi.org/10.17654/0972361723052

Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423. https://doi.org/10.1037/0033-2909.103.3.411

Bagozzi, R. P. (1979). The Role of Measurement in Theory Construction and Hypothesis Testing: Toward a Holistic Model. Conceptual and Theoretical Developments in Marketing, 15–33.

Bagozzi, R. P. (1980). Causal Models in Marketing. In Journal of Marketing (Vol. 44, Issue 4). John Wiley & Sons Inc. https://doi.org/10.2307/1251239

Bagozzi, R. P. (1984). A Prospectus for Theory Construction in Marketing. Journal of Marketing, 48(1), 11. https://doi.org/10.2307/1251307

Bollen, K. A., & Diamantopoulos, A. (2017). In defense of causal-formative indicators: A minority report. Psychological Methods, 22(3), 581–596. https://doi.org/10.1037/met0000056

Burnham, K. P., & Anderson, D. R. (2004). Multimodel Inference: Understanding AIC and BIC in Model Selection. Sociological Methods & Research, 33(2), 261–304. https://doi.org/10.1177/0049124104268644

Chin, W. W. (1998). Modern Methods for Business Research. In Modern Methods for Business Research (Issue July). https://doi.org/10.4324/9781410604385

DeLone, W. H., McLean, E. R. (1992). The DeLone and McLean Model of Information Systems A Ten-Year Update. J. Manag. Inf. Syst., Vol Succes (4), 9-30.

Diamantopoulos, A., & Winklhofer, H. M. (2001). Index construction with formative indicators: An alternative to scale development. Journal of Marketing Research, 38(2), 269–277. https://doi.org/10.1509/jmkr.38.2.269.18845

Dijkstra, T. K., & Henseler, J. (2011). Linear indices in nonlinear structural equation models: Best fitting proper indices and other composites. Quality and Quantity, 45(6), 1505–1518. https://doi.org/10.1007/s11135-010-9359-z

Dijkstra, T. K., & Henseler, J. (2015a). Consistent and asymptotically normal PLS estimators for linear structural equations. Computational Statistics and Data Analysis, 81, 10–23. https://doi.org/10.1016/j.csda.2014.07.008

Dijkstra, T. K., & Henseler, J. (2015b). Consistent partial least squares path modeling. MIS Quarterly: Management Information Systems, 39(2), 297–316. https://doi.org/10.25300/MISQ/2015/39.2.02

Edwards, J. R., & Bagozzi, R. P. (2000). On the nature and direction of relationships between constructs and measures. Psychological Methods, 5(2), 155–174. https://doi.org/10.1037/1082-989X.5.2.155

Fassott, Georg, and J. H. (2015). Formative (Measurement). In Wiley Encyclopedia of Management.

Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39. https://doi.org/10.2307/3151312

Franke, G. R., Sarstedt, M., & Danks, N. P. (2021). Assessing measure congruence in nomological networks. Journal of Business Research, 130, 318–334. https://doi.org/10.1016/j.jbusres.2021.03.003

Gable, G. G., Sedera, D., & Chan, T. (2008). Re-conceptualizing information system success: The IS-impact measurement model. Journal of the Association for Information Systems, 9(7), 377–408. https://doi.org/10.17705/1jais.00164

Gefen, D., Rigdon, E. E., & Straub, D. (2011). An update and extension to SEM guidelines for administrative and social science research. MIS Quarterly: Management Information Systems, 35(2). https://doi.org/10.2307/23044042

Hair, J. F. (2019). Multivariate data analysis (Eighth edition). Cengage.

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A Primer on Partial Least Squares Structural Equation Modeling. Sage. https://uk.sagepub.com/en-gb/asi/a-primer-on-partial-least-squares-structural-equation-modeling-pls-sem/book244583

Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., & Thiele, K. O. (2017). Mirror, mirror on the wall: A comparative evaluation of composite-based structural equation modeling methods. Journal of the Academy of Marketing Science, 45(5), 616–632. https://doi.org/10.1007/s11747-017-0517-x

Hair, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2017). Advanced issues in partial least squares structural equation modeling.

Harris, C.R., Millman, K.J., van der Walt, S.J. et al. Array programming with NumPy. Nature 585, 357–362 (2020). https://doi.org/10.1038/s41586-020-2649-2

Henseler, J. (2012). Why generalized structured component analysis is not universally preferable to structural equation modeling. Journal of the Academy of Marketing Science, 40(3), 402–413. https://doi.org/10.1007/s11747-011-0298-6

Henseler, J. (2017). Bridging Design and Behavioral Research with Variance-Based Structural Equation Modeling. Journal of Advertising, 46(1), 178–192. https://doi.org/10.1080/00913367.2017.1281780

Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., Ketchen, D. J., Hair, J. F., Hult, G. T. M., & Calantone, R. J. (2014). Common Beliefs and Reality About PLS: Comments on Ronkko¨ and Evermann. Organizational Research Methods, 17(2), 182–209.

Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management and Data Systems, 116(1), 2–20. https://doi.org/10.1108/IMDS-09-2015-0382

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 of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8

Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009a). The use of partial least squares path modeling in international marketing. In Advances in International Marketing (Vol. 20, pp. 277–319). https://doi.org/10.1108/S1474-7979(2009)0000020014

Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009b). The use of partial least squares path modeling in international marketing. In R. R. Sinkovics & P. N. Ghauri (Eds.), Advances in International Marketing (Vol. 20, pp. 277–319). Emerald Group Publishing Limited. https://doi.org/10.1108/S1474-7979(2009)0000020014

Henseler, J., & Sarstedt, M. (2013). Goodness-of-fit indices for partial least squares path modeling. Computational Statistics, 28(2), 565–580. https://doi.org/10.1007/s00180-012-0317-1

Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

Hwang, H., Malhotra, N. K., Kim, Y., Tomiuk, M. A., & Hong, S. (2010). A comparative study on parameter recovery of three approaches to structural equation modeling. Journal of Marketing Research, 47(4), 699–712. https://doi.org/10.1509/jmkr.47.4.699

Hwang, H., Takane, Y., & Jung, K. (2017). Generalized structured component analysis with uniqueness terms for accommodating measurement error. Frontiers in Psychology, 8(DEC), 26–27. https://doi.org/10.3389/fpsyg.2017.02137

Jarvis, C. B., Mackenzie, S. B., Podsakoff, P. M., Giliatt, N., & Mee, J. F. (2003). A Critical Review of Construct Indicators and Measurement Model Misspecification in Marketing and Consumer Research. Journal of Consumer Research, 30(2), 199–218. https://doi.org/10.1086/376806

Joreskog, K. G. (1978). Structural Analysis of Covariance and Correlation Matrices. Psychometrika, 43(4).

Jr., J. F. H., Matthews, L. M., Matthews, R. L., & Sarstedt, M. (2017). PLS-SEM or CB-SEM: Updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107. https://doi.org/10.1504/IJMDA.2017.087624

Kline, R. B. (2023). Principles and Practice of Structural Equation Modeling. In Canadian Graduate Journal of Sociology and Criminology (Vol. 1, Issue 1). The Guilford Press. https://doi.org/10.15353/cgjsc.v1i1.3787

Liu, Y., Schuberth, F., Liu, Y., & Henseler, J. (2022). Modeling and assessing forged concepts in tourism and hospitality using confirmatory composite analysis. Journal of Business Research, 152, 221–230. https://doi.org/10.1016/j.jbusres.2022.07.040

Lu, I. R. R., Kwan, E., Thomas, D. R., & Cedzynski, M. (2011). Two new methods for estimating structural equation models: An illustration and a comparison with two established methods. International Journal of Research in Marketing, 28(3), 258–268. https://doi.org/10.1016/j.ijresmar.2011.03.006

Markus, K. A., & Borsboom, D. (2013). Frontiers of test validity theory: Measurement, causation and meaning. Routledge / Taylor & Francis Group.

McDonald, R. P. (1996). Path analysis with composite variables. Multivariate Behavioral Research, 31(2), 239–270. https://doi.org/10.1207/s15327906mbr3102_5

Meyer, J. P., & Alien, N. J. (1991). A three-component conceptualization of organizational commitment. Human Resource Management Review, 1(1), 61–89. https://doi.org/10.1016/1053-4822(91)90011-Z

Mooi, E., Sarstedt, M., & Mooi-Reci, I. (2018). Market Research. The Process, Data, and Methods Using Stata. In [Book]. http://link.springer.com/10.1007/978-981-10-5218-7

Mosier, C. I. (1943). On the reliability of a weighted composite. Psychometrika, 8(3), 161–168. https://doi.org/10.1007/BF02288700

Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed). McGraw-Hill.

Preacher, K. J., & Yaremych, H. E. (2023). Model selection in structural equation modeling. Handbook of structural equation modeling, 206-222.

Rademaker, M. E., Schuberth, F., & Dijkstra, T. K. (2019). Measurement error correlation within blocks of indicators in consistent partial least squares: Issues and remedies. Internet Research, 29(3), 448–463. https://doi.org/10.1108/IntR-12-2017-0525

Raykov, T. (1997). Estimation of composite reliability for congeneric measures. Applied Psychological Measurement, 21(2), 173–184. https://doi.org/10.1177/01466216970212006

Rigdon, E. E. (1998). Structural equation modeling. In G. A. Marcoulides (Ed.), Modern Methods for Business Research (pp. 51–94). Erlbaum. https://doi.org/10.4337/9781781001295.00011

Rigdon, E. E. (2014). Rethinking Partial Least Squares Path Modeling: Breaking Chains and Forging Ahead. Long Range Planning, 47(3), 161–167. https://doi.org/10.1016/j.lrp.2014.02.003

Rigdon, E. E., Sarstedt, M., & Becker, J. M. (2020). Quantify uncertainty in behavioral research. Nature Human Behaviour, 4(4), 329–331. https://doi.org/10.1038/s41562-019-0806-0

Ringle, C. M., Sarstedt, M., Sinkovics, N., & Sinkovics, R. R. (2023). A perspective on using partial least squares structural equation modelling in data articles. Data in Brief, 48, 109074. https://doi.org/10.1016/j.dib.2023.109074

Schuberth, F., Rademaker, M. E., & Henseler, J. (2023). Assessing the overall fit of composite models estimated by partial least squares path modeling. European Journal of Marketing, 57(6), 1678–1702. https://doi.org/10.1108/EJM-08-2020-0586

Steenkamp, J. B. E. M., & Baumgartner, H. (2000). On the use of structural equation models for marketing modeling. International Journal of Research in Marketing, 17(2–3), 195–202. https://doi.org/10.1016/s0167-8116(00)00016-1

van Riel, A. C. R., Henseler, J., Kemény, I., & Sasovova, Z. (2017). Estimating hierarchical constructs using consistent partial least squares. Industrial Management & Data Systems, 117(3), 459–477. https://doi.org/10.1108/imds-07-2016-0286

Virtanen, P., Gommers, R., Oliphant, T.E. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods 17, 261–272 (2020). https://doi.org/10.1038/s41592-019-0686-2

Voorhees, C. M., Brady, M. K., Calantone, R., & Ramirez, E. (2016). Discriminant validity testing in marketing: An analysis, causes for concern, and proposed remedies. Journal of the Academy of Marketing Science, 44(1), 119–134. https://doi.org/10.1007/s11747-015-0455-4

Whitley, B. E., Kite, M. E., & Adams, H. L. (2012). Principles of research in behavioral science: Third edition. In Principles of Research in Behavioral Science: Third Edition (Issue July). Routledge. https://doi.org/10.4324/9780203085219

Zhang, X., & Savalei, V. (2016). Bootstrapping Confidence Intervals for Fit Indexes in Structural Equation Modeling. Structural Equation Modeling: A Multidisciplinary Journal, 23(3), 392–408. https://doi.org/10.1080/10705511.2015.1118692

Guidelines for Measurement Modeling in Behavioral and Social Research : A Comparison Between Reflective, Composite, and Formative Models