Bifactor and Bifactor S-1 Model Estimations with Non-Reverse-Coded Data

Year 2022, Volume: 13 Issue: 3, 244 - 255, 30.09.2022

Fulya Baris Pekmezci

https://doi.org/10.21031/epod.1135567

Abstract

The bifactor model is an extension of Spearman’s two-factor theory. The bifactor model has a strict assumption, which is named orthogonality. The bifactor S-1 model was developed by stretching the orthogonality assumption of the bifactor model. The bifactor S-1 model, contrary to the bifactor model, allows correlation between specific factors and enables items that do not form a common specific factor to be loaded only on the general factor. In psychology, data are mostly multidimensional due to the nature of psychological constructs. The Positive and Negative Affect Schedule (PANAS) which is one of the psychological tests and has two dimensions named positive affect and negative affect. In the literature studies on PANAS, negative affect dimensions were not reverse coded while implementing the bifactor model. Therefore, negative path coefficients were revealed. The purpose of this study is to ascertain whether or not the items in the negative affect factor should be reverse coded in the PANAS. Within the scope of the current study, bifactor and bifactor S-1 model analyses were implemented for the two data sets, which were reverse coded and non-reverse coded. As a result of this study, with reverse-coded data, the bifactor S-1 model was seen as the better model for the PANAS. Additionally, in the modeling of unique variances of items with specific factors, the bifactor S-1 model performed well and also resolved the problem of negative loading on the general factor. The point to take into consideration, which should be noted by researchers who will study the PANAS, is that negative items should be reverse coded.

Keywords

PANAS, bifactor S-1, reverse coding

References

• Bernard, H. R. (2013). Social research methods: Qualitative and quantitative approaches. Sage.
• Brown, T. A. (2015). Confirmatory factor analysis for applied research. Guilford.
• Brown, J. D., & Marshall, M. A. (2001). Self-esteem and emotion: Some thoughts about feelings. Personality and Social Psychology Bulletin, 27(5), 575–584. https://doi.org/10.1177/0146167201275006
• Burns, G. L., Geiser, C., Servera, M., Becker, S. P., & Beauchaine, T. P. (2020). Application of the Bifactor S − 1 model to multisource ratings of ADHD/ODD symptoms: An appropriate bifactor model for symptom ratings. Journal of Abnormal Child Psychology, 48(7), 881-894. http://dx.doi.org/10.1007/s10802-019-00608-4
• Canivez, G. L. (2016). Bifactor modeling in construct validation of multifactored tests: Implications for understanding multidimensional constructs and test interpretation. Principles and Methods of Test Construction: Standards and Recent Advancements. Hogrefe Publishers.
• Comrey, A. L., & Lee, H. B. (1992). A first course in factor analysis (2nd ed.). Erlbaum and Associates.
• Cucina, J., & Byle, K. (2017). The bifactor model fits better than the higher-order model in more than 90% of comparisons for mental abilities test batteries. Journal of Intelligence, 5(3), 27. http://dx.doi.org/10.3390/jintelligence5030027
• DiStefano, C., & Hess, B. (2005). Using confirmatory factor analysis for construct validation: An empirical review. Journal of Psychoeducational Assessment, 23(3), 225-241. https://doi.org/10.1177/073428290502300303
• DiStefano, C., Zhu, M., & Mindrila, D. (2009). Understanding and using factor scores: Considerations for the applied researcher. Practical Assessment, Research, and Evaluation, 14(1), 20.
• Ebesutani C., Smith A., Bernstein A., Chorpita B. F., Higa-McMillan C., & Nakamura B. (2011). A bifactor model of negative affectivity: Fear and distress components among younger and older youth. Psychological Assessment, 23(3), 679–691. http://dx.doi.org/10.1037/a0023234
• Eid, M. (2020). Multi-faceted constructs in abnormal psychology: Implications of the bifactor S − 1 model for individual clinical assessment. Journal of Abnormal Child Psychology, 48(7), 895-900. http://dx.doi.org/10.1007/s10802-020-00624-9
• Flores-Kanter, P. E., Garrido, L. E., Moretti, L. S., & Medrano, L. A. (2021). A modern network approach to revisiting the Positive and Negative Affective Schedule (PANAS) construct validity. Journal of Clinical Psychology, 77(10), 2370-2404. http://dx.doi.org/10.1002/jclp.23191
• Gaudreau, P., Sanchez, X., & Blondin, J.-P. (2006). Positive and negative affective states in a performance related setting. European Journal of Psychological Assessment, 22(4), 240–249. https://doi.org/10.1027/1015-5759.22.4.240
• Gerbing, D. W., & Anderson, J. C. (1984). On the meaning of within-factor correlated measurement errors. Journal of Consumer Research, 11(1), 572-580. http://dx.doi.org/10.1086/208993
• Greenberger, E., Chen, C., Dmitrieva, J., & Farruggia, S. P. (2003). Item-wording and the dimensionality of the Rosenberg Self-Esteem Scale: Do they matter? Personality and Individual Differences, 35(6), 1241–1254. https://doi.org/10.1016/S0191-8869(02)00331-8
• Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (2008). Multivariate data analysis. Prentice Hall Publisher. Hartley, J. (2013). Some thoughts on Likert-type scales. International Journal of Clinical and Health Psychology, 13(1), 83–86. https://doi.org/10.1080/13645570802648077
• Holzinger, K. J., & Swineford, F. (1937). The Bi-Factor method. Psychometrika, 2(1), 41-54.
• Huebner, E. S., & Dew, T. (1995). Preliminary validation of the positive and negative affect schedule with adolescents. Journal of Psychoeducational Assessment, 13(3), 286–293. https://doi.org/10.1177/073428299501300307
• Ibrahim, A. M. (2001). Differential responding to positive and negative items: The case of a negative item in a questionnaire for course and faculty evaluation. Psychological Reports, 88, 497–500. https://doi.org/10.2466/pr0.2001.88.2.497
• Jöreskog, K. G., & Sörbom, D. (1989). LISREL 7 user’s reference guide. Scientific Software.
• Kula Kartal, S., Aybek, E. C., & Yaşar, M. (2022). Investigating the wording effect in scales based on different dimension reduction techniques. Journal of Uludağ University Faculty of Education, 35(1), 44-67. https://doi.org/10.19171/uefad.1033284
• Killgore, W. D. S. (2000). Evidence for a third factor on the positive and negative affect schedule in a college student sample. Perceptual and Motor Skills, 90(1), 147–152. https://doi.org/10.2466/pms.2000.90.1.147
• Kline, T. (2005). Psychological testing: A practical approach to design and evaluation. Sage.
• Kline, R. B. (2011). Principles and practice of structural equation modelling. Guilford.
• Lacobucci, D. (2010). Structural equations modelling: Fit indices, sample size, and advanced topics. Journal of Consumer Psychology, 20(1), 90-98. https://doi.org/10.1016/j.jcps.2009.09.003
• Leue, A., & Beauducel, A. (2011). The PANAS structure revisited: On the validity of a bifactor model in community and forensic samples. Psychological Assessment, 23(1), 215-225. http://dx.doi.org/10.1037/a0021400
• Locker, D., Jokovic, A., & Allison, P. (2007). Direction of wording and responses to items in oral health-related quality of life questionnaires for children and their parents. Community Dentistry and Oral Epidemiology, 35(4), 255–262. https://doi.org/10.1111/j.1600-0528.2007.00320.x
• Magyar-Moe, J. L. (2009). Therapist's guide to positive psychological interventions. Academic press.
• Mihić, L., Novović, Z., Čolović, P., & Smederevac, S. (2014). Serbian adaptation of the Positive and Negative Affect Schedule (PANAS): Its facets and second-order structure. Psihologija, 47(4), 393–414. http://dx.doi.org/10.2298/PSI1404393M
• Molwus, J. J., Erdogan, B., & Ogunlana, S. O. (2013). Sample size and model fit indices for structural equation modelling (SEM): The case of construction management research. In ICCREM 2013: Construction and Operation in the Context of Sustainability (pp. 338-347). http://dx.doi.org/10.1061/9780784413135.032
• Ortuño-Sierra, J., Santarén‐Rosell, M., de Albéniz, A. P., & Fonseca‐Pedrero, E. (2015). Dimensional structure of the Spanish version of the positive and negative affect schedule (PANAS) in adolescents and young adults. Psychological Assessment, 27(3), e1–e9. https://doi.org/10.1037/pas0000107
• Pires, P., Filgueiras, A., Ribas, R., & Santana, C. (2013). Positive and negative affect schedule: Psychometric properties for the Brazilian Portuguese version. The Spanish Journal of Psychology, 16, e58. https://doi.org/10.1017/sjp.2013.60
• Reise, S. P., Moore, T. M., & Haviland, M. G. (2010). Bifactor models and rotations: Exploring the extent to which multidimensional data Yield Univocal Scale Scores. Journal of Personality Assessment, 92(6), 544-559. http://dx.doi.org/10.1080/00223891.2010.496477
• Rush, J., & Hofer, S. M. (2014). Differences in within and between person factor structure of positive and negative affect: Analysis of two intensive measurement studies using multilevel structural equation modeling. Psychological Assessment, 26(2), 462–473. https://doi.org/10.1037/a0035666.
• Salazar, M. S. (2015). The dilemma of combining positive and negative items in scales. Psicothema, 27(2), 192–199. https://doi.org/10.7334/psicothema2014.266
• Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research Online, 8(2), 23-74.
• Seib-Pfeifer, L.-E., Pugnaghi, G., Beauducel, A., & Leue, A. (2017). On the replication of factor structures of the Positive and Negative Affect Schedule (PANAS). Personality and Individual Differences, 107(1), 201–207. https://doi.org/10.1016/j. paid.2016.11.053
• Stucky, B. D., & Edelen, M. O. (2015). Using hierarchical IRT models to create unidimensional measures from multidimensional data. In S. P. Reise & D. A. Revicki (Eds.), Handbook of item response theory modeling: Applications to typical performance assessment (pp. 183–206). Routledge/Taylor & Francis Group.
• Stucky, B. D., Edelen, M. O., Vaughan, C. A., Tucker, J. S., & Butler, J. (2014). The psychometric development and initial validation of the DCI-A short form for adolescent therapeutic community treatment process. Journal of Substance Abuse Treatment, 46(4), 516-521. https://doi.org/10.1016/j.jsat.2013.12.005
• Vera-Villarroel, P., Urzúa, A., Jaime, D., Contreras, D., Zych, I., Celis-Atenas, K., Silva, J. R., & Lillo, S. (2017). Positive and Negative Affect Schedule (PANAS): Psychometric properties and discriminative capacity in several chilean samples. Evaluation & the Health Professions, 42(4), 473–497. https://doi.org/10.1177/0163278717745344
• Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54, 1063–1070. https://doi.org/10.1037/0022- 3514.54.6.1063
• Zampetakis L. A., Lerakis M., Kafetsios, K., & Moustakis, V. (2015). Using item response theory to investigate the structure of anticipated affect: Do self-reports about future affective reactions conform to typical or maximal models? Frontiers in Psychology, 6, 1438. https://doi.org/10.3389/fpsyg.2015.01438
• Zimmer, C., & Odum Institute (2019). Learn to perform confirmatory factor analysis in Stata with data from general social survey (2016). In SAGE research Methods Datasets Part 2. SAGE Publications. https://dx.doi.org/10.4135/9781529700091