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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