The Positive and Negative Affect Schedule (PANAS) is the most widely used self-report instrument for assessing affect. However, there are inconsistent findings regarding the factor structure of the PANAS. In this study, we applied Bayesian structural equation modeling (BSEM) to investigate the structure of the PANAS using data from a sample of 893 Chinese middle and high school students. Four models, the orthogonal two-, the oblique two-, the three-, and the bi-factor models were tested with prior specifications including approximately zero cross-loadings and residual covariances. The results indicated that the orthogonal two-factor model specified with informative priors for both cross-loadings and residual correlations has the best model fit. Confirmatory factor analysis with the maximum likelihood estimator (ML-CFA) based on modifications from BSEM analysis showed improved model fit compared to ML-CFA based on frequentist analysis, which is the evidence for the merit of BSEM for addressing misspecifications.
AsparouhovT.MuthénB.MorinA. 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
4.
BeckJ. G.NovyD. M.DiefenbachG. J.StanleyM. A.AverillP. M.SwannA. 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
CanS.van de SchootR.HoxJ. (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
7.
ChenF. F.WestS. G.SousaK. 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
8.
ChungP. K.Dong LiuJ. (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
9.
ConsedineN. S.MoskowitzJ. T. (2007, November1). 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
10.
CowenA. S.KeltnerD. (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
11.
CrawfordJ. R.HenryJ. 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
12.
CrockerP. 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
Garnier-VillarrealM.JorgensenT. 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
15.
GaudreauP.SánchezX.BlondinJ. (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
16.
GelmanA.CarlinJ. B.SternH. S.DunsonD. B.VehtariA.RubinD. B. (2014). Bayesian data analysis (3rd ed.). CRC Press.
17.
GorsuchR. L. (1988). Exploratory factor analysis. In NesselroadeJ. R.CattellR. B. (Eds.), Handbook of multivariate experimental psychology (pp. 231–258). Springer US. https://doi.org/10.1007/978-1-4613-0893-5_6
HarringJ. R.WeissB. A.HsuJ.-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
20.
HoofsH.van de SchootR.JansenN. W. H.KantI. (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
21.
HuL. T.BentlerP. 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
22.
JovanovićV.Gavrilov-JerkovićV. (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
23.
KaplanD.DepaoliS. (2012). Bayesian structural equation modeling. In HoyleR. H. (Ed.), Handbook of structural equation modeling (pp. 650–673). The Guilford Press.
24.
KaplanD.DepaoliS. (2013). Bayesian statistical methods. In LittleT. D. (Ed.), The Oxford handbook of quantitative methods (Vol. 1): Foundations (pp. 407–437). Oxford University Press.
25.
KercherK. (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
26.
KillgoreW. 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
27.
KohliN.HughesJ.WangC.ZopluogluC.DavisonM. 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
28.
KrohneH. W.EgloffB.KohlmannC.-W.TauschA. (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.
29.
LaurentJ.CatanzaroS. J.JoinerT. E.JrRudolphK. D.PotterK. I.LambertS.OsborneL.GathrightT. (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
30.
LeeS.-Y. (2007). Structural equation modeling: A Bayesian approach. John Wiley & Sons.
31.
LeueA.BeauducelA. (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
32.
LüdtkeO.MarshH. W.RobitzschA.TrautweinU. (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
33.
MackinnonA.JormA. F.ChristensenH.KortenA. E.JacombP. A.RodgersB. (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
34.
MarshH. W.LudtkeO.MuthénB.AsparouhovT.MorinA. J.TrautweinU.NagengastB. (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
35.
MarshH. W.MorinA. J. S.ParkerP. D.KaurG. (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
36.
MarshH. W.MuthénB.AsparouhovT.LüdtkeO.RobitzschA.MorinA. J. S.TrautweinU. (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
37.
MehrabianA. (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
38.
MelvinG. A.MolloyG. 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
39.
MerzE. L.MalcarneV. L.RoeschS. C.KoC. M.EmersonM.RomaV. G.SadlerG. 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
40.
MuthénB.AsparouhovT. (2012). Bayesian structural equation modeling: A more flexible representation of substantive theory. Psychological Methods, 17(3), 313–335. https://doi.org/10.1037/a0026802
41.
MuthénL. K.MuthénB. O. (2017). Mplus User's Guide. (8th ed.). Los Angeles, CA: Muthén & Muthén.
42.
SchootR.KaplanD.DenissenJ.AsendorpfJ.NeyerF.AkenM. (2013). A Gentle introduction to Bayesian analysis: Applications to developmental research. Child Development, 85. https://doi.org/10.1111/cdev.12169
43.
Seib-PfeiferL.-E.PugnaghiG.BeauducelA.LeueA. (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
44.
SerafiniK.Malin-MayorB.NichC.HunkeleK.CarrollK. 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
45.
ShiZ.WangL.LiH. (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
46.
ThompsonE. 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
47.
TyngC. M.AminH. U.SaadM. N. M.MalikA. S. (2017, August24). The influences of emotion on learning and memory [Review]. Frontiers in Psychology, 8(1454). https://doi.org/10.3389/fpsyg.2017.01454
48.
Vera-VillarroelP.Celis-AtenasK.UrzúaA.JaimeD.ContrerasD.LilloS.ZychI.SilvaJ. 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
49.
VillodasF.VillodasM. T.RoeschS. (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
50.
WangZ.GuoY. Y. (2017). Deriving a structural model for the multidimensional self- concept construct: A case of middle school students in mainland China. In WilliamsM. (Ed.), Self-Concept perceptions cultural differences and gender differences (pp. 77–106). Nova Publishers.
51.
WatsonD.ClarkL. A. (1994). The PANAS-X: Manual for the positive and negative affect schedule—expanded form. University of Iowa.
52.
WatsonD.ClarkL. A.TellegenA. (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
53.
WeidongZ.JingD.SchickC. J. (2004). The cross-cultural measurement of positive and negative affect examining the dimensionality of PANAS. Psychological Science (China), 27(1), 77–79.
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