Table 2 Summary of the exploratory factor analysis conditions used and reported by the studies investigating the factor structure of the Pittsburgh Sleep Quality Index
From: Dimensionality of the Pittsburgh Sleep Quality Index: a systematic review
Author and year of publication | Extraction test | Rotation | Scree plot reported (Y/N), Total variance reported (Y/N), Eigen value rule (Y/N), Robust measure of factor retention (Y/N) | Number of factors | Pattern matrix reported (Y/N) | Communality reported (Y/N) |
|---|---|---|---|---|---|---|
Aloba et al. 2007 [31] | Principal component analysis | Not reported | N, N, N, N | 3 | Y | – |
Anandakumar et al. 2016 [67] | principal components analysis | Not reported | N, Y, N, N | 1 | Y | – |
Babson et al. 2012 [30] | Not reported | Standardized geomin rotation | N, N, N, N | 2 | Y | – |
Becker & Jesus 2017 [53] | maximum likelihood estimation | direct oblimin rotation | N, Y (40.56%), N, N | 2 | Y | N |
Benhayon et al. 2013 [61] | principal axis factoring method | direct oblimin rotation | Y, N, Y, N | 2 | Y | N |
Burkhalter et al. 2010 [29] | NO EFA | – | – | – | – | – |
Buysse et al. 2008 [28] | Principal components analysis | Varimax rotation | N, N, Y, N | 2 | Y | – |
Casement et al. 2012 [35] | NO EFA | – | – | – | – | – |
Chen et al. 2017 [63] | No EFA | – | – | – | – | – |
Chong & Cheung 2012 [34] | NO EFA | – | – | – | – | – |
Cole et al. 2006 [22] | Principal components analysis & maximum likelihood estimation | Direct oblimin rotation | N, Y (57.3%), N, N | 2 | Y | N |
De la Vega et al. 2015 [59] | No EFA | – | – | – | – | Y, 0.42–0.66 |
DeGutis et al. 2016 [62] | No EFA | – | N, N, N, N | – | – | N |
Dudysova et al. 2017 [66] | No EFA | – | – | – | – | – |
Fontes et al. 2017 [49] | Principal component analysis | Varimax with Kaiser Normalization rotation | N, Y (38, 57%), Y, N | 1, 2 | Y | – |
Gelaye et al. 2014 [44] | Principal component analysis | Orthogonal rotation | Y, Y, Y, N | 2 & 3 | Y | N |
Guo et al. 2016 [60] | No EFA | – | – | – | – | – |
Hita-Contreras et al. 2014 [43] | Principal component factor analysis | Varimax rotation | N, Y (54.96%), Y, N | 2 | Y | Y, 0.21 to 0.71 |
Ho et al. 2014 [42] | NO EFA | – | – | – | – | – |
Jiménez-Genchi et al. 2008 [27] | Principal components analysis | Not reported | N, Y (63.2%), Y, N | 2 | Y | N |
João et al. 2017 [57] | Principal components analysis | Not reported | N, Y (26.47%), N, N | 1 | Y | – |
Jomeen& Martin 2007 [26] | NO EFA | – | – | – | – | – |
Khosravifar et al. 2015 [51] | principal component | Oblimin rotation | N, Y (58.3%), Y, N | 2 | Y | – |
Koh et al. 2015 [41] | Principal component analysis & maximum likelihood estimation | Varimax rotation | N, N, N, N | 3 | – | – |
Kotronoulas et al. 2011 [25] | Principal component analysis | Direct oblimin rotation | N, Y (59.2%), Y, N | 2 | Y | Y, 0.38 to 0.75 |
Lequerica et al. 2014 [40] | Maximum likelihood estimation | Promax rotation | N, Y (62.4%), Y, N | 2 | Y | N |
Magee et al. 2008 [24] | Principal component analysis with maximum likelihood estimate extraction | Direct oblimin rotation | N, Y, N, N | 2 | Y | N |
Manzar et al. 2016a [17] | Principal component analysis & maximum likelihood estimation | Direct oblimin rotation | Y, Y (51.27%), Y, Parallel analysis | 2& 1 | Y | Y, 0.39–0.64 |
Manzar et al. 2016b [15] | NO EFA | – | – | – | – | – |
Mariman et al. 2012 [33] | NO EFA | – | – | – | – | – |
Morris et al. 2017 [65] | Principal components analysis | varimax & Promax rotation | Y, Y (68.08%, 74.11), Y, Y, Parallel analysis | 3 | Y | – |
Nazifi et al. 2014 [39] | Principal components analysis | Varimax rotation | N, Y (63.485%), N, N | 3 | N | N |
Nicassio et al. 2014 [38] | NO EFA | – | – | – | – | – |
Otte et al. 2013 [32] | NO EFA | – | – | – | – | – |
Otte et al. 2015 [37] | NO EFA | – | – | – | – | – |
Passos et al. 2016 [52] | Not reported | varimax orthogonal | N, Y (66.57, 52.07, 60.41%), N, N | 3, 2, 2 | Y | – |
Qiu et al. 2016 [58] | principal component analysis | oblique promax rotation | Y, Y (52.8%), Y, N | 2 | N | N |
Rener-Sitar et al. 2014 [46] | Principal factors method | Orthogonal varimax or oblique promax | Y, Y, Y, N | 1 | Y | – |
Salahuddin et al. 2017 [16] | maximum likelihood estimation | direct oblimin | Y, Y, Y, Y | 1, 2, 3 | Y | – |
Skouteris et al. 2009 [23] | NO EFA | – | – | – | – | – |
Tomfohr et al. 2013 [36] | NO EFA | – | – | – | – | – |
Yunus et al. 2016 [48] | No EFA | – | – | – | – | – |
Zheng et al. 2016 [50] | No EFA | – | – | – | – | – |
Zhong et al. 2015 [45] | principal component analysis | promax rotation | N, Y (60.10%), Y, N | 3 | Y | N |
Zhu et al. 2018 [64] | No EFA | – | – | – | – | – |