Asparouhov, T., Muthén, B. (2009). Exploratory structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 16(3), 397–438. https://doi.org/10.1080/10705510903008204
Google Scholar Asparouhov, T., Muthén, B. (2010). Bayesian analysis of latent variable models using Mplus. Version 4. Retrieved from http://www.statmodel.com/download/BayesAdvantages18.pdf
Google Scholar Asparouhov, T., Muthén, B., Morin, A. J. S. (2015). Bayesian structural equation modeling with cross-loadings and residual covariances: Comments on Stromeyer et al. Journal of Management, 41(6), 1561–1577. https://doi.org/10.1177/0149206315591075
Google Scholar Beck, J. G., Novy, D. M., Diefenbach, G. J., Stanley, M. A., Averill, P. M., Swann, A. C. (2003). Differentiating anxiety and depression in older adults with generalized anxiety disorder. Psychological Assessment, 15(2), 184–192. https://doi.org/10.1037/1040-3590.15.2.184
Google Scholar Berger, J. (2006). The case for objective Bayesian analysis. Bayesian Analysis, 1(3), 385–402. https://doi.org/10.1214/06-BA115
Google Scholar Can, S., van de Schoot, R., Hox, J. (2015). Collinear latent variables in multilevel confirmatory factor analysis: A comparison of maximum likelihood and Bayesian estimation. Educational and Psychological Measurement, 75, 406–427. https://doi.org/10.1177/0013164414547959
Google Scholar Chen, F. F., West, S. G., Sousa, K. H. (2006). A Comparison of Bifactor and Second-Order Models of Quality of Life. Multivariate Behavioral Research, 41(2), 189–225. https://doi.org/10.1207/s15327906mbr4102_5
Google Scholar Chung, P. K., Dong Liu, J. (2012). Examination of the psychometric properties of the Chinese translated behavioral regulation in exercise questionnaire-2. Measurement in Physical Education and Exercise Science, 16(4), 300–315. https://doi.org/10.1080/1091367X.2012.693364
Google Scholar Consedine, N. S., Moskowitz, J. T. (2007, November 1). The role of discrete emotions in health outcomes: A critical review. Applied and Preventive Psychology, 12(2), 59–75. https://doi.org/10.1016/j.appsy.2007.09.001
Google Scholar Cowen, A. S., Keltner, D. (2017). Self-report captures 27 distinct categories of emotion bridged by continuous gradients. Proceedings of the National Academy of Sciences, 114(38), E7900–E7909. https://doi.org/10.1073/pnas.1702247114
Google Scholar | Crossref Crawford, J. R., Henry, J. D. (2004). The positive and negative affect schedule (PANAS): Construct validity, measurement properties and normative data in a large non-clinical sample. British Journal of Clinical Psychology, 43, 245–265. https://doi.org/10.1348/0144665031752934
Google Scholar Crocker, P. R. E. (1997). A confirmatory factor analysis of the positive affect negative affect schedule (PANAS) with a youth sport sample. Journal of Sport and Exercise Psychology, 19(1), 91–97. https://doi.org/10.1123/jsep.19.1.91
Google Scholar De Beer, L. T., Van Zyl, L. E. (2019). ESEM code generator for Mplus. https://www.surveyhost.co.za/esem/ https://doi:10.6084/m9.figshare.8320250
Google Scholar Garnier-Villarreal, M., Jorgensen, T. D. (2020). Adapting fit indices for Bayesian structural equation modeling: Comparison to maximum likelihood. Psychological Methods, 25(1), 46–70. https://doi.org/10.1037/met0000224
Google Scholar Gaudreau, P., Sánchez, X., Blondin, J. (2006). Positive and negative affective states in a performance-related setting: Testing the factorial structure of the PANAS across two samples of French-Canadian participants. European Journal of Psychological Assessment, 22, 240–249. https://doi.org/10.1027/1015-5759.22.4.240
Google Scholar Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., Rubin, D. B. (2014). Bayesian data analysis (3rd ed.). CRC Press.
Google Scholar Gorsuch, R. L. (1988). Exploratory factor analysis. In Nesselroade, J. R., Cattell, R. B. (Eds.), Handbook of multivariate experimental psychology (pp. 231–258). Springer US. https://doi.org/10.1007/978-1-4613-0893-5_6
Google Scholar Grice, J. W ., (2001). Computing and evaluating factor scores. Psychological Methods, 6, 430–450. https://doi.org/10.1037//1082-989X.6.4.430-450
Google Scholar Harring, J. R., Weiss, B. A., Hsu, J.-C. (2012). A comparison of methods for estimating quadratic effects in nonlinear structural equation models. Psychological Methods, 17(2), 193–214. https://doi.org/10.1037/a0027539
Google Scholar Hoofs, H., van de Schoot, R., Jansen, N. W. H., Kant, I. (2018). Evaluating model fit in Bayesian confirmatory factor analysis with large samples: Simulation study introducing the BRMSEA. Educational Psychological Measurement, 78(4), 537–568. https://doi.org/10.1177/0013164417709314
Google Scholar Hu, L. T., 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
Google Scholar Jovanović, V., Gavrilov-Jerković, V. (2016). The structure of adolescent affective well-being: The case of the PANAS among Serbian adolescents. Journal of Happiness Studies, 17(5), 2097–2117. https://doi.org/10.1007/s10902-015-9687-8
Google Scholar Kaplan, D., Depaoli, S. (2012). Bayesian structural equation modeling. In Hoyle, R. H. (Ed.), Handbook of structural equation modeling (pp. 650–673). The Guilford Press.
Google Scholar Kaplan, D., Depaoli, S. (2013). Bayesian statistical methods. In Little, T. D. (Ed.), The Oxford handbook of quantitative methods (Vol. 1): Foundations (pp. 407–437). Oxford University Press.
Google Scholar | Crossref Kercher, K. (1992). Assessing subjective well-being in the old-old: The PANAS as a measure of orthogonal dimensions of positive and negative affect. Research on Aging, 14(2), 131–168. https://doi.org/10.1177/0164027592142001
Google Scholar 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
Google Scholar Kohli, N., Hughes, J., Wang, C., Zopluoglu, C., Davison, M. L. (2015). Fitting a linear–linear piecewise growth mixture model with unknown knots: A comparison of two common approaches to inference. Psychological Methods, 20(2), 259–275. https://doi.org/10.1037/met0000034
Google Scholar Krohne, H. W., Egloff, B., Kohlmann, C.-W., Tausch, A. (1996). Untersuchungen mit einer deutschen Version der “Positive and Negative Affect Schedule” (PANAS) [Investigations with a German version of the Positive and Negative Affect Schedule (PANAS)]. Diagnostica, 42(2), 139–156.
Google Scholar Laurent, J., Catanzaro, S. J., Joiner, T. E., Rudolph, K. D., Potter, K. I., Lambert, S., Osborne, L., Gathright, T. (1999). A measure of positive and negative affect for children: Scale development and preliminary validation. Psychological Assessment, 11(3), 326–338. https://doi.org/10.1037/1040-3590.11.3.326
Google Scholar Lee, S.-Y. (2007). Structural equation modeling: A Bayesian approach. John Wiley & Sons.
Google Scholar | Crossref 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. https://doi.org/10.1037/a0021400
Google Scholar Lüdtke, O., Marsh, H. W., Robitzsch, A., Trautwein, U. (2011). A 2 × 2 taxonomy of multilevel latent contextual models: Accuracy–bias trade-offs in full and partial error correction models. Psychological Methods, 16(4), 444–467. https://doi.org/10.1037/a0024376
Google Scholar Mackinnon, A., Jorm, A. F., Christensen, H., Korten, A. E., Jacomb, P. A., Rodgers, B. (1999). A short form of the positive and negative affect schedule: Evaluation of factorial validity and invariance across demographic variables in a community sample. Personality and Individual Differences, 27(3), 405–416. https://doi.org/10.1016/S0191-8869(98)00251-7
Google Scholar Marsh, H. W., Ludtke, O., Muthén, B., Asparouhov, T., Morin, A. J., Trautwein, U., Nagengast, B. (2010). A new look at the big five factor structure through exploratory structural equation modeling. Psychological Assessment, 22(3), 471–491. https://doi.org/10.1037/a0019227
Google Scholar Marsh, H. W., Morin, A. J. S., Parker, P. D., Kaur, G. (2014). Exploratory structural equation modeling: an integration of the best features of exploratory and confirmatory factor analysis. Annual Review of Clinical Psychology, 10(1), 85–110. https://doi.org/10.1146/annurev-clinpsy-032813-153700
Google Scholar Marsh, H. W., Muthén, B., Asparouhov, T., Lüdtke, O., Robitzsch, A., Morin, A. J. S., Trautwein, U. (2009). Exploratory structural equation modeling, integrating CFA and EFA: Application to students’ evaluations of university teaching. Structural Equation Modeling: A Multidisciplinary Journal, 16(3), 439–476. https://doi.org/10.1080/10705510903008220
Google Scholar Mehrabian, A. (1997). Comparison of the PAD and PANAS as models for describing emotions and for differentiating anxiety from depression. Journal of Psychopathology and Behavioral Assessment, 19(4), 331–357. https://doi.org/10.1007/BF02229025
Google Scholar Melvin, G. A., Molloy, G. N. (2000). Some psychometric properties of the positive and negative affect schedule among Australian youth. Psychological Reports, 86(3). https://doi.org/10.2466/pr0.2000.86.3c.1209
Google Scholar Merz, E. L., Malcarne, V. L., Roesch, S. C., Ko, C. M., Emerson, M., Roma, V. G., Sadler, G. R. (2013). Psychometric properties of positive and negative affect schedule (PANAS) original and short forms in an African American community sample. Journal of Affective Disorders, 151(3), 942–949. https://doi.org/10.1016/j.jad.2013.08.011
Google Scholar Muthén, B., Asparouhov, T. (2012). Bayesian structural equation modeling: A more flexible representation of substantive theory. Psychological Methods, 17(3), 313–335. https://doi.org/10.1037/a0026802
Google Scholar Muthén, L. K., Muthén, B. O. (2017). Mplus User's Guide. (8th ed.). Los Angeles, CA: Muthén & Muthén.
Google Scholar Schoot, R., Kaplan, D., Denissen, J., Asendorpf, J., Neyer, F., Aken, M. (2013). A Gentle introduction to Bayesian analysis: Applications to developmental research. Child Development, 85. https://doi.org/10.1111/cdev.12169
Google Scholar 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, 201–207. https://doi.org/10.1016/j.paid.2016.11.053
Google Scholar Serafini, K., Malin-Mayor, B., Nich, C., Hunkele, K., Carroll, K. M. (2016). Psychometric properties of the positive and negative affect schedule (PANAS) in a heterogeneous sample of substance users. The American Journal of Drug and Alcohol Abuse, 42(2), 203–212. https://doi.org/10.3109/00952990.2015.1133632
Google Scholar Shi, Z., Wang, L., Li, H. (2009). Age-related change in emotional experience in a sample of Chinese adults: A preliminary study. Psychological Reports, 105(1), 37–42. https://doi.org/10.2466/pr0.105.1.37-42
Google Scholar Thompson, E. R. (2007). Development and validation of an internationally reliable short-form of the positive and negative affect schedule (PANAS). Journal of Cross-Cultural Psychology, 38(2), 227–242. https://doi.org/10.1177/0022022106297301
Google Scholar Tyng, C. M., Amin, H. U., Saad, M. N. M., Malik, A. S. (2017, August 24). The influences of emotion on learning and memory [Review]. Frontiers in Psychology, 8(1454). https://doi.org/10.3389/fpsyg.2017.01454
Google Scholar Vera-Villarroel, P., Celis-Atenas, K., Urzúa, A., Jaime, D., Contreras, D., Lillo, S., Zych, I., Silva, J. R. (2019). Positive and negative affect schedule (PANAS): Psychometric properties and discriminative capacity in several Chilean samples. Evaluation and the Health Professions, 42(4), 473–497. https://doi.org/10.1177/0163278717745344
Google Scholar Villodas, F., Villodas, M. T., Roesch, S. (2017). Examining the factor structure of the positive and negative affect schedule (PANAS) in a multiethnic sample of adolescents. Measurement and Evaluation in Counseling and Development, 44(4), 193–203. https://doi.org/10.1177/0748175611414721
Google Scholar Wang, Z., Guo, Y. Y. (2017). Deriving a structural model for the multidimensional self- concept construct: A case of middle school students in mainland China. In Williams, M. (Ed.), Self-Concept perceptions cultural differences and gender differences (pp. 77–106). Nova Publishers.
Google Scholar Watson, D., Clark, L. A. (1994). The PANAS-X: Manual for the positive and negative affect schedule—expanded form. University of Iowa.
Google Scholar 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(6), 1063–1070. https://doi.org/10.1037//0022-3514.54.6.1063
Google Scholar Weidong, Z., Jing, D., Schick, C. J. (2004). The cross-cultural measurement of positive and negative affect examining the dimensionality of PANAS. Psychological Science (China), 27(1), 77–79.
Google Scholar