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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