Further considerations regarding PANAS: Contributions from four studies with different Argentinean samples

Abstract

In this research we analyzed the psychometric properties of the Positive and Negative Affect Schedule (PANAS) in samples coming from four independent studies: university students (Study 1, n = 392; Study 2, n = 395), general adult population (Study 3, n = 316), and athletes (Study 4, n = 533). Through confirmatory analyses we evaluated the following models: (a) two-factor model (Watson, Clark, & Tellegen, 1988), (b) three-factor model by Mehrabian (1997), (c) three-factor model by Gaudreau, Sánchez and Blondin (2006) and (d) bi-factor model (Leue & Beauducel, 2011), all of them in their oblique and orthogonal variants. Several models presented an acceptable fit, but only after allowing correlated errors and excluding the items alert and excited. While orthogonal solutions of the three models showed the best fit in Studies 2, 3 and 4, oblique solutions presented the best fit in Study 1. Cronbach's alpha, composite reliability and omega indexes oscillated between .55 and .89. Considering the original theoretical model and the practical utility of the PANAS, we favor the model of two orthogonal factors, excluding the aforementioned items.

A modern network approach to revisiting the Positive and Negative Affective Schedule (PANAS) construct validity

“…In addition, it has been shown in the previous PANAS history of applying CFAs (n = 27) that some items present a complex behavior (i.e., cross-load). This situation has been evidenced in six of the previous studies (22.22%), involving the items Alert, Excited, Strong, Nervous, Jittery, Hostile, and Active (Caicedo Cavagnis et al, 2018;Flores Kanter & Medrano, 2016;Heubeck & Boulter, 2020;Graudeau et al, 2006;Nunes et al, 2019). It is also relevant to note that in the remaining 11 studies that have used CFA (39.28%), only the fit of the two-factor model has been ascertained.…”

“…Within the ESEM models (n = 2), one of the antecedents has found better fit rates for the two-factor orthogonal solution (RMR = 0.04, RMSEA = 0.08; Carvalho et al, 2013), whereas another study found better fit for the three-factor solution (i.e., hierarchical model suggested by Mehrabian, 1997 1 ; over the 0.95 CFI criterion; Ortuño-Sierra et al, 2019). In the case of the CFA studies that have compared the fit of a two-factor model with other factor solutions (e.g., tree factor or bifactor models; n = 17), the same inconsistencies are observed, with six (35.29%) finding better fit for the threefactor model (i.e., suggested by Mehrabian, 1997or Gaudreau et al, 20062 Allan et al, 2015;Caicedo Cavagnis et al, 2018;Flores Kanter & Medrano, 2016;Merz et al, 2013;Ortuño-Sierra et al, 2015;Graudeau et al, 2006) and four (23.52%) reporting better fit for alternative hierarchical two-factor or bifactor models 3 (Leue & Beauducel, 2011;Mihić et al, 2014;Ortuño-Sierra et al, 2015;Seib-Pfeifer et al, 2017). It is important to mention that in the case of bifactor models, although they obtain higher fit indexes in most of the cases when they are applied, analysis of complementary rates has shown that these types of models present poor fit for the PANAS (see .…”

A Modern Network Approach to Revisiting the Positive and Negative Affective Schedule (PANAS) Construct Validity

“…In addition, it has been shown in the previous history of applying CFA (n = 27) that some items present a complex behavior (i.e., cross-load). This situation has been evidenced in 6 of the previous studies (22.22%), involving the items Alert, Excited, Strong, Nervous, Jittery, Hostile and Active (Caicedo-Cavagnis et al, 2018;Flores-Kanter & Medrano, 2016;Heubeck & Boulter, 2020;Graudeau et al, 2006;Nunes et al, 2019). It is also relevant to note that in the remaining 11 studies that have used CFA (39.28%), just the fit of the two-factor model has been verified.…”

“…In the case of the CFAs who have compared the fit of a two-factor model considering other factor solutions (e.g., tree factor or bifactor models; n = 17), the same inconsistencies are observed, in which six (35.29%) find better fit for the three-factor model (i.e. suggested by Mehrabian, 1997or Gaudreau, Sanchez, & Blondin, 2006Allan et al, 2015;Caicedo-Cavagnis et al, 2018;Flores-Kanter & Medrano, 2016;Merz et al, 2013;Ortuño-Sierra et al, 2015;Graudeau et al, 2006); four (23.52%) report better fit in alternative hierarchical 2-factor or bifactor models 3 (Leue & Beauducel, 2011;Mihíc, Novović, Čolović, & Smederevac, 2014;Ortuño-Sierra et al, 2015;Seib-Pfeifer et al, 2017). It is important to mention that in the case of bifactor models, although they obtain higher fit indexes in most of the cases when they are applied, analysis of complementary rates has shown that these types of models present poor fit for the PANAS (see Flores-Kanter, Dominguez-Lara, Trógolo, & Medrano, 2018).…”

“…In addition to ML, other methods more in line with the distribution of PANAS scores and the categorical nature of its indicators have been used. Among these, a method widely used in previous PANAS CFAs is the Weighted least squares means and variance adjusted (WLSMV) estimator, applied in 6 of the twenty-six previous studies (23.07%; Caicedo-Cavagnis et al, 2018;Díaz-García et al, 2020;Heubeck & Wikinson, 2019;Heubeck & Boulter, 2020;Ortuño-Sierra, Santarén-Rosell, Pérez de Albéniz, & Fonseca-Pedrero, 2015;Vera-Villarroel et al, 2017). Another alternative applied has been the Robust Maximum likelihood (RML) applied in 6 out of 26 previous studies (23.07%; Jovanovic´& Gavrilov-Jerkovic´, 2015;Lim, Yu, Kim, & Kim, 2010;Merz & Roesch, 2011;Merz et al, 2013;Narayanan, Abdelrasheed, Nasser, & Menon, 2019;Seib-Pfeifer, Pugnaghi, Beauducel, & Leue, 2017); and the Diagonal weighted least squares (DWLS) estimator, used by 4 of the 26 previous studies (15.38%; Buz, Pérez-Arechaederra, Fernández-Pulido, & Urchaga, 2015;Leue & Bauduquel, 2011;Santángalo et al, 2019;Tuccitto, Giacobbi, & Leite, 2009).…”