Background
Role of subjective measurement
Pittsburgh Sleep Quality Index
Validation and reliability measures of the Pittsburgh Sleep Quality Index
Factor analysis
Dimensionality of the PSQI
Practical implication of the heterogeneity of the Pittsburgh Sleep Quality Index
Material and methods
Literature search scheme
Selection criteria
Data extraction
Author and year of publication | Sample description, age | Sample size | KMO & Bartlett’s Test of Sphericity | Determinant Score & Anti-image/Diagonal element of anti-correlation matrix | Inter-component co-relations |
---|---|---|---|---|---|
Aloba et al. 2007 [31] | Nigerian College students | 520 | – | – | – |
Anandakumar et al. 2016 [67] | outpatients at a hospital in Srilanka, 50.02 ± 13.5 | 205 | 0.83 & < 0.001 | – | 0.42–0.81 |
Babson et al. 2012 [30] | Military veterans with PTSD | 226 | – | – | − 0.02–0.53 |
Becker & Jesus 2017 [53] | Community dwelling Portugese adults, 70.05 ± 7.15 | 204 | 0.731 & < 0.001 | – | 0.12–0.52 |
Benhayon et al. 2013 [61] | Pediatric patients with Crohn disease (14.4 ± 2.3) and healthy controls (14.8 ± 2.0) | CD, n = 96 Healthy controls. N = 19 | 0.7 & < 0.001 | – | – |
Burkhalter et al. 2010 [29] | American renal transplant recipients | 135 | – | – | 0.14–0.73 |
Buysse et al. 2008 [28] | Senior African-American & Caucasian adults | 187 | – | – | – |
Casement et al. 2012 [35] | Women with PTSD | 319 | – | – | – |
Chong & Cheung 2012 [34] | Cantonese Chinese, age > 45 years | 794 | – | – | 0.07–0.76 |
Qiu et al. 2016 [58] | Pregnant women, 33.4 ± 4.2 | 1488 | 0.72 & < 0.001 | – | – |
Cole et al. 2006 [22] | American older adults | 207, 210 | – | – | 0.04–0.60, 0.11–0.66 |
De la Vega et al. 2015 [59] | Adolescents and young adults, 17.12 ± 3.05 | 216 | .77 & < 0.001 | – | – |
DeGutis et al. 2016 [62] | Trauma exposed veterans, 31.51 ± 8.16 | 283 | – | – | – |
Dudysova et al. 2017 [66] | Outpatients of the sleep laboratory at Prague psychiatric center, 44.5 ± 14.24 | 105 | – | – | 0.05–0.73 |
Gelaye et al. 2014 [44] | Chilean, Ethiopian, Peruvian and Thai college students | (N = 830), (N = 2230), (N = 2581), (N = 2840) | 0.724 to 0.801 & < 0.001 | – | – |
Hita-Contreras et al. 2014 [43] | Spanish fibromyalgia patients | 138 | 0.784 & < 0.001 | – | – |
Ho et al. 2014 [42] | Chinese breast cancer patients | 197 | – | – | 0.15–0.78 |
Jiménez-Genchi et al. 2008 [27] | Spanish healthy controls and psychiatric patients | 135 | – | – | 0.06–0.77 |
Jomeen& Martin 2007 [26] | Pregnant women with depression, 28.86 ± 55.19 | 180 | – | – | – |
Koh et al. 2015 [41] | Multi-ethnic Asians in Singapore | 489, 1976 | – | – | 0.02–0.48, 0.05–0.36 |
Kotronoulas et al. 2011 [25] | Cancer patients on chemotherapy | 209 | .79 & < 0.001 | – | .16–.70 |
Lequerica et al. 2014 [40] | Traumatic brain injury patients | 243 | – | – | – |
Magee et al. 2008 [24] | Australian adults | 364 | – | – | < 0.43 |
Manzar et al. 2016a [17] | Indian university students | 209, 209 | 0.754 & < 0.001 | > 0.00001 & all values > 0.5 | – |
Manzar et al. 2016b [15] | Indian university students | 418 | 0.758 & < 0.001 | > 0.00001 & all values > 0.5 | – |
Mariman et al. 2012 [33] | Belgians with CFS | 413 | – | – | 0.02–0.72 |
Nazifi et al. 2014 [39] | Iranian health professionals | 415 | 0.58 & < 0.05 | – | – |
Nicassio et al. 2014 [38] | American rheumatoid arthritis patients | 107 | – | – | – |
Otte et al. 2013 [32] | Breast cancer survivors | 1172 | – | – | 0.18–0.51 |
Otte et al. 2015 [37] | Perimenopausal and postmenopausal women with hot flashes | 849 | – | – | 0.01–0.46 |
Rener-Sitar et al. 2014 [46] | TMD, 37.1 ± 13.1 | TMD with pain (496) & TMD without pain (113) | – | – | − 0.18 to 0.74 |
Salahuddin et al. 2017 [16] | Commmunity dwelling Ethiopian Adults, 25.5 ± 6.0 | 311 | 0.51, 0.52 & < 0.001, < 0.001 | 0.08, 0.09 & 0.39–0.67 0.48–0.64 | – |
Skouteris et al. 2009 [23] | Pregnant women with depression, 31.67 ± 4.55 | 252 | – | – | – |
Tomfohr et al. 2013 [36] | Community-dwelling English speaking Spanish, English and Non-hispanic white | 792, 654, 1698 | – | – | 0.05–0.48, 0.18–0.59, 0.07–0.52 |
Yunus et al. 2016 [48] | Community dwelling older Malaysians | Phase 1, n = 183 Phase 2, n = 2118 | – | – | – |
Zheng et al. 2016 [50] | Chinese medical students, 20.2 ± 1.3 | 603 | – | – | 0.45–0.57 |
Zhong et al. 2015 [45] | Pregnant Peruvian women | 642 | 0.65 & < 0.001 | – | 0.10 to 0.40 |
João et al. 2017 [57] | Portuguese community-dwelling adults, 35.93 ± 11.01 | 347 | 0.59 & < 0.001 | – | 0.00–0.54 |
Chen et al. 2017 [63] | Taiwanese insomniacs, 43.15 | 114 | – | – | – |
Khosravifar et al. 2015 [51] | Depressed and healthy Iranians, 32.3 ± 7.1 | 193 | 0.598 & < 0.001 | – | – |
Fontes et al. 2017 [49] | Portuguese breast cancer patients, 57.9 ± 10.8 | 474 | – | – | – |
Guo et al. 2016 [60] | Chinese undergraduate students,20.86 ± 1.33 | 631 | – | – | – |
Morris et al. 2017 [65] | Diabetic Americans male and females, 55.3 ± 11.1, 58.5 ± 10.0 | 198 | 0.60 & < 0.001 | – | < 0.80 |
Passos et al. 2016 [52] | Brazilian adolescents, 10–19 | 309 | 0.59 & < 0.001 | – | – |
Zhu et al. 2018 [64] | Chinese adults with type 2 diabetes, 55.18 ± 12.65 | 240 | 0.82 & < 0.01 | – | 0.05–0.65 |
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 | – | – | – | – | – |
Author and year of publication | Software | Extraction method | Types of Modification index used | Correlation between factors | Standardized Factor loadings | Factors in final model; same/different from EFA | Number of models used in comparative CFA | Reason for the selection of models in comparative CFA | Model fit indices |
---|---|---|---|---|---|---|---|---|---|
Aloba et al. 2007 [31] | NO CFA | – | – | – | – | – | – | – | – |
Babson et al. 2012 [30] | NO CFA | – | – | – | – | – | – | – | – |
Burkhalter et al. 2010 [29] | Mplus version 5.21 | Not reported | Path diagram change | 0.532, 0.773, 0.801 | F1 DURAT = 0.85, HSE = 0.98, SLPQUAL = − 0.51 F2 SLPQUAL = 1.09, LATEN = 0.68, MEDS = 0.92 F3 DISTB = 0.93, DAYDYS = 0.56 | 3, No EFA | 3; 1F-1 3F-2 | Not explained | Non-significant p value of χ2; RMSEA< 0.08–0.05; CFI > 0.95; WRMR < 0.90. |
Buysse et al. 2008 [28] | NO CFA | – | – | – | – | – | – | – | – |
Casement et al. 2012 [35] | Mplus version 5.1 | Mean and variance-adjusted weighted least squares (WLSMV) estimator | Not reported | 0.46, 0.77, 0.81 | F1 DURAT = 0.87, HSE = 0.75 F2 SLPQUAL = 0.75, LATEN = 0.56, MEDS = 0.45 F3 DISTB = 0.74, DAYDYS = 0.43 | 3, No EFA | 4; 1F-1 2F-2 3F-1 | Not explained, some of the documented models not used, no reasons given for selection and/or inclusion | χ2/ df < 3, RMSEA < 0.06, WRMR < 0.90, CFI ≥ 0.95, and TLI ≥ 0.96 |
Chong & Cheung 2012 [34] | Mplus version 5 | Not reported | Not reported | 0.522, 0.567, 0.641 | F1 DURAT = 0.73/0.85/0.95, HSE = 0.76/0.84/0.78 F2 SLPQUAL = 0.81/0.59/0.63, LATEN = 0.64/0.64/0.70, DISTB = 0.59/0.40/0.47, DAYDYS = 0.44/0.21/49, MEDS = 0.33/0.35/0.17 | 2, No EFA | 9; 1F-1 2F-6 3F-2 | Partially explained, some of the documented models not used, no reasons given for their omission | SRMR< 0.05; RMSEA < 0.07; CFI > 0.95 |
Cole et al. 2006 [22] | Not reported | Maximum likelihood extraction on the covariance matrix, & multivariate non-normality smoothed by bootstrapping | Lagrange Modification index with change in path diagram | 0.42, 0.82, 0.75 | F1 DURAT = 0.76, HSE = 0.91 F2 SLPQUAL = 0.89, LATEN = 0.67, MEDS = 0.43 F3 DISTB = 0.67, DAYDYS = 0.52 | 2, 3 | 2; 2F-1 3F-1 | Comparison between originally proposed 1F model & outcome of EFA Fit indices for 1F model not reported | RMSEA≤0.06; CFI ≥ 0.90; GFI ≥ 0.90; AGFI≥0.90; LOWER χ2, BIC (difference of at least 10 between two models) |
Gelaye et al. 2014 [44] | Stata version 12.0 software | Maximum likelihood estimation | Not reported | 0.46, 0.26, 0.36, (0.53, 0.40, 0.10) | F1 DURAT = 0.79/0.73/1.0/0.6, HSE = 0.43/0.78/0.21/0.57 F2 SLPQUAL = 0.81/0.58/0.61/0.67, LATEN = 0.47/0.35/0.34/0.53, DISTB = 0.47/0.51/0.54/0.38, DAYDYS = 0.49/0.51/0.5/0.39, MEDS = 0.25/0.25/0.14/0.28 | 2, 2, 2, 3, same | Not performed | Not explained | SRMR ≤0.08; RMSEA ≤0.06; CFI ≥0.95 |
Hita-Contreras et al. 2014 [43] | NO CFA | – | – | – | – | – | – | – | – |
Ho et al. 2014 [42] | Mplus version 7.11 | Robust maximum likelihood estimator | Error-term correlation | Not applicable | F1 DURAT = 0.59, HSE = 0.60, SLPQUAL = 0.84, LATEN = 0.61, DISTB = 0.61, DAYDYS = 0.56, MEDS = 0.36 | 1, same | 4; 1F-2 2F-1 3F-1 | Partially explained, some of the documented models not used, no reasons given for their omission | Insignificant χ2-test; CFI & TLI ≥0.95; RMSEA≤0.06; SRMR≤0.08; Lower BIC |
Jiménez-Genchi et al. 2008 [27] | NO CFA | – | – | – | – | – | – | – | – |
Jomeen & Martin 2007 [26] | Mplus version 3 | Weighted least-square with mean and variance correction estimator (WLSMV) | Not reported | Not reported | not reported | 2, No EFA | 7; 1F-1, 2F-6 | Not clear | CFI & TLI > 0.90, RMSEA< 0.08–0.05, WRMR< 0.90 & Insignificant χ2 |
Koh et al. 2015 [41] | FactoMineR in R | Not reported | Not reported | (0.27, 0.64, 0.89); (0.39, 0.72, 0.90) in 2 sample groups | F1 DURAT = 0.68/0.60, HSE = 0.72/0.67 F2 SLPQUAL = 0.72/0.63, LATEN = 0.63/0.60 F3 DISTB = 0.37/0.52, DAYDYS = 0.51/0.42, MEDS = 0.40/0.26 | 3/3, 3/3, same | 4; 1F-1 2F-1 3F-2 | Not explained | GFI > 0.90; AGFI> 0.90; CFI ≥ 0.95 RMSEA < 0.08–0.05; LOWER χ2, BIC (difference of at least 10 between two models), CAIC |
Kotronoulas et al. 2011 [25] | NO CFA | – | – | – | – | – | – | – | – |
Lequerica et al. 2014 [40] | SPSS Statistics 21 with AMOS | Not reported | Not reported | 0.87, 0.85 | F1 DURAT = 0.68, HSE = 0.51, LATEN = 0.68 F2 DISTB = 0.73, DAYDYS = 0.66, MEDS = 0.25 | 2, same | 5; 1F-1 2F-3 3F-1 | Not explained, some of the documented models not used, no reasons given for selection and/or inclusion | Non-significant p value of χ2; CFI ≥ 0.95; NNFI≥0.95 RMSEA < 0.06 |
Magee et al. 2008 [24] | SPSS version 15 with AMOS version-7 | Not reported | Not reported | 0.73 | F1 DURAT = 0.68, HSE = 0.62 F2 SLPQUAL = 0.76, LATEN = 0.61, DISTB = 0.46, DAYDYS = 0.52, MEDS = 0.23 | 2, different | 6; 1F-2 2F-2 3F-2 | Partially explained, some of the documented models not used, no reasons given for their omission | χ2-test lower, non-significant values; RMSEA ≤0.05; CFI, GFI, & AGFI > 0.90 |
Manzar et al. 2016a [17] | SPSS 16.0 with amos | Maximum likelihood extraction with bootstrapping to smooth non-normality | Not reported | Not applicable | F1 DURAT = 0.74, HSE = 0.32, SLPQUAL = 0.74, LATEN = 0.63, DISTB = 0.43, DAYDYS = 0.41, MEDS = 0.40 | 1, 2 different | 2; 1F-1 2F-1 | Comparison between outcome(s) of EFA | Non-significant Bollen–Stine bootstrap χ2 p values, Non-significant p value of χ2; χ2/df < 2; RMR ≤ 0.05; CFI ≥ 0.95; RMSEA < 0.05; GFI & AGFI> 0.9; AIC = lesser value indicated a better fit |
Manzar et al. 2016b [15] | SPSS 16.0 with amos | Maximum likelihood extraction | Co-variance, Variance and regression weights | Not applicable | F1 DURAT = 0.363, HSE = 0.374, SLPQUAL = 0.705, LATEN = 0.633, DISTB = 0.501, DAYDYS = 0.406, MEDS = 0.30 | 1, No EFA | 17; 1F-3 2F-8 3F-6 | Most of models of the PSQI reported till 15–02-2015 | Non-significant p value of χ2; χ2/df < 2; RMR ≤ 0.05; CFI ≥ 0.95; RMSEA < 0.05; GFI & AGFI> 0.9; AIC = lesser value indicated a better fit |
Mariman et al. 2012 [33] | SPSS (PASW 17.0) with AMOS module (5.0) | Maximum Likelihood Algorithm | Not reported | 0.64, 0.53, 1.00 | F1 DURAT = 0.9, HSE = 0.78 F2 SLPQUAL = 0.85, LATEN = 0.57, MEDS = 0.18 F3 DISTB = 0.79, DAYDYS = 0.29 But, 3 latent factors shown to load on 1 factor | Second order model, No EFA | 3; 1F-1 2F-1 3F-1 Results for the 2F model not shown | Not explained, some of the documented models not used, no reasons given for selection and/or inclusion | Non-significant p value of χ2 (d.f.); GFI > 0.90; AGFI> 0.85; CFI > 0.90; RMSEA< 0.08–0.05; Lower CAIC |
Nazifi et al. 2014 [39] | NO CFA | – | – | – | – | – | – | – | – |
Nicassio et al. 2014 [38] | EQS 6.1 | Maximum likelihood (ML) method | Not reported | 0.65 | F1 DURAT = 0.85, HSE = 0.64 F2 SLPQUAL = 0.89, LATEN = 0.48, DISTB = 0.57, DAYDYS = 0.56 | 2, No EFA | 3; 1F-1 2F-1 3F-1 | Not explained, some of the documented models not used, no reasons given for selection and/or inclusion | S-Bχ2; an S-Bχ2/df < 2.0; robust CFI ≥ 0.95; RMSEA≤0.05; Lower & negative AIC |
Otte et al. 2013 [32] | LISREL 8.8 | Weighted least squares | Error term correlation | 0.37, 0.71 in 2 sample groups | F1 DURAT = 0.64, HSE = 0.97 F2 SLPQUAL = 0.86, LATEN = 0.82/0.66, DISTB = 0.66, DAYDYS = 0.5, MEDS = 0.46 | 2, No EFA | 4; 1F-1 2F-1 3F-2 Two 3F models differed with respect to use/non-use of error terms only | Not explained | Non-significant p value of χ2; SRMR ≤0.08; RMSEA< 0.06; CFI ≥ 0.95 |
Otte et al. 2015 [37] | LISREL version 8.8 | Weighted least-squares, none of the indicators showed excessive skew or kurtosis | Not reported | 0.40, 0.73, 0.68 | F1 DURAT = 0.92, HSE = 0.68 F2 SLPQUAL = 0.82, LATEN = 0.57, MEDS = 0.15 F3 DISTB = 0.61, DAYDYS = 0.61 | 3, No EFA | 7; 1F-1 2F-2 3F-3 4F-1 | Not explained | Non-significant p value of χ2; RMSEA< 0.06; CFI ≥ 0.95; |
Rener-Sitar et al. 2014 [46] | STATA version 12 | Diagonally weighted least squares (DWLS) and a “robust” method using the Huber-White sandwich estimator | Not reported | Not applicable | not reported | 1; same in both | Not applicable | Not applicable | SRMR: ≤0.08; RMSEA: ≤0.06; and CFI, TLI: ≥0.95 |
Skouteris et al. 2009 [23] | Structural equation modeling (SEM) | Not reported | Path diagram change | 0.44, 0.59 | F1 DURAT = 0.73/0.85, HSE = 0.91/0.94, LATEN = 0.36/0.39 F2 DISTB = 0.62/0.60, DAYDYS = 0.49/0.62 | Second order model, No EFA | 2; 2F-2 | Compared with model reported in similar population, i.e., pregnant women | CFI & GFI > 0.90–1.0; RMSEA< 0.10 - < 0.05; χ2/df of 2 to 3 (lower is better); lower ECVI |
Tomfohr et al. 2013 [36] | Mplus version 5.21 | Maximum likelihood estimation | Reported but detail is not clear | Not reported, distinct model with age & gender as co-variates | F1 DURAT = 0.71/0.82, HSE = 0.70/0.72 F2 SLPQUAL = 0.77/0.76, LATEN = 0.64/0.63 F3 DISTB = 0.64/0.70, DAYDYS = 0.56/0.61 | 3, No EFA | 3; 1F-1 3F-2 | Not explained | CFI ≥ 0.90; SRMR ≤0.05; χ2 test of difference (P ≤ 0.01) |
Zhong et al. 2015 [45] | SAS 9.4 | Weighted least squares (WLS) estimation | Not reported | 0.07, 0.36 | F1 DURAT = 0.66, HSE = 0.52 F2 SLPQUAL = 0.47, LATEN = 0.46, DISTB = 0.45, DAYDYS = 0.64 F3 MEDS = 0.48 SLPQUAL = 0.22, LATEN = 0.26 | 3, same | 5; 1F-1 2F-3 3F-1 | Not explained, some of the documented models not used, no reasons given for selection and/or inclusion | CFI ≥ 0.90; SRMR< 0.08; RMSEA < 0.06 |
De la Vega et al. 2015 [59] | Not reported | maximum likelihood mean adjusted | Not reported | Not applicable | SLPQUAL = 0.421 LATEN = 0.620 DURAT = 0.656 HSE = 0.567 DISTB = 0.606 DAYDYS = 0.485 | 1, No EFA | 2; 1F-1 2F-1 | Compared with model reported in similar population, i.e., adolescents | S-Bχ2, CFI, RMSEA; cut-off for the indices not reported |
Anandakumar et al. 2016 [67] | No CFA | – | – | – | – | – | – | – | |
Zheng et al. 2016 [51] | Not reported | Not reported | Not reported | 0.34 | F1 DURAT = 0.69 HSE = 0.65 MEDS = 0.15 F2 DISTB = 0.43 DAYDYS = 0.51 SLPQUAL = 0.721 LATEN = 0.63 | 2, No EFA | 4; 1F-1 2F-2 3F-1 | explained, some of the documented models not used, no reasons given for selection and/or inclusion | χ2, GFI, AGFI, RMR, RMSEA, CFI, NFI, NNFI, AIC, CAIC, SBC |
Becker & Jesus 2017 [53] | SPSS 21 and AMOS-29 | Not reported | – | – | F1 SLPQUAL = 0.59 LATEN = 0.76 F2 DURAT = 0.76 HSE = 0.69 F3 DISTB = 0.52 DAYDYS = 0.57 | 3, 2 different | 6; 1F-2 2F-2 3F-2 | Not explained, some of the documented models not used, no reasons given for selection and/or inclusion | non-significant χ2, RMSEA ≤0.08, CFi, GFI & AGFI > 0.97 |
Benhayon et al. 2013 [61] | No CFA | – | – | – | – | – | – | – | – |
DeGutis et al. 2016 [62] | R | maximum likelihood estimation | Not reported | 0.76, 0.75, 0.45 | F1 HSE = 0.68 DURAT = 0.78 F2 LATEN = 0.70 SLPQUAL = 0.52 MEDS = 0.77 F3 DISTB = 0.56 DAYDYS = 0.78 | No EFA | 4; 1F-1 2F-2 3F-1 | Not explained, some of the documented models not used, no reasons given for selection and/or inclusion | χ2/df < 3, SRMR & RMSEA≤0.06, CFI & TLI > 0 .95 |
Yunus et al. 2016 [48] | SPSS 20 | weighted least squares method | Not reported | Not applicable | LATEN = 0.65 SLPQUAL = 0.65 DISTB = 0.49 | 1, No EFA | 4; 1F-2 2F-1 3F-1 | Not explained, some of the documented models not used, no reasons given for selection and/or inclusion | CFI, TLI, RMSEA, SRMR cut-off for the indices not reported |
Qiu et al. 2016 [58] | SAS 9.4 | weighted least squares (WLS) estimation | Error term correlation | 0.68 | F1 HSE = 0.48 DURAT = 0.45 LATEN = 0.44 SLPQUAL = 0.83 F2 DISTB = 0.62 DAYDYS = 0.49 | 2, same | 6; 2F-6 | None | CFI ≥ 0.90, SRMR≤0.08, RMSEA ≤0.06 |
Dudysova et al. 2017 [66] | Not reported | diagonally weighted least squares (DWLS) estimator | Not reported | 0.80, 0.30, 0.16 | F1 HSE = 0.68 DURAT = 0.88 F2 LATEN = 0.70 SLPQUAL = 0.79 MEDS = 0.89 F3 DISTB = 0.32 DAYDYS = − 0.29 | No EFA | 11; 1F-1 2F-6 3F-4 | Not explained, some of the documented models not used, no reasons given for selection and/or inclusion | non-significant & lower, GFI > 0.90, CFI & TLI ≥0.95, RMSEA ≤0.05 (≤0.08 adequate fit), SRMR ≤0.08 |
Salahuddin et al. 2017 [16] | SPSS -16.0 | maximum likelihood | Error term correlation | Not applicable | Not reported | 1, 1–3 | 5; 1F-4 2F-1 | All based on EFA | RMR & RMSEA ≤0.05, GFI, AGFI ≥0.90, Lesser ECVI, CFI ≥ 0.95, χ2/df ≤ 3 |
João et al. 2017 [57] | SPSS-21.0 | No CFA | – | – | – | – | – | – | – |
Chen et al. 2017 [63] | R 3.1.1 and its package lavaan | Not reported | Used modification indices but details not mentioned | Not reported | Unstandardized loadings Reported | None, No EFA | 1; 3F-1 | Not applicable | CFI & TLI > 0.90, RMSEA < 0.08 |
Khosravifar et al. 2015 [51] | Not reported | Not reported | Not reported | Not reported | Not reported | 2 | 3; 1F-1 2F-1 3F-1 | Based on EFA | Not reported |
Fontes et al. 2017 [49] | STATA version, R, version 3.0.1 | Not reported | Correlation between the PSQI components | Not applicable | HSE = 0.44 DURAT = 0.53 LATEN = 0.54 SLPQUAL = 0.88 MEDS = 0.22 DISTB = 0.42 DAYDYS = − 0.37 | 1, 2 | 2; 1F-1 2F-1 | Based on EFA | non-significant χ2, χ2/df = 2–3, SRMR ≤0.08, RMSEA≤0.07, CFI & TLI ≥ 0.95 |
Guo et al. 2016 [60] | SPSS-22.0 with AMOS18.0 | Not reported | Error term correlation | Not reported | HSE = 0.47 DURAT = 0.52 LATEN = 0.41 SLPQUAL = 0.83 DISTB = 0.35 DAYDYS = − 0.60 | 2, No EFA | 6; 1F-2 2F-2 3F-2 | Not explained, some of the documented models not used, no reasons given for selection and/or inclusion | χ2/df = 2–5, 0.05 < RMSEA < 0.08, CFI > 0.95, SRMR< 0.05 |
Morris et al. 2017 [65] | SPSS-22.0 | No CFA | – | – | – | – | – | – | – |
Passos et al. 2016 [52] | SPSS-20.0 with AMOS 23.0 | Not reported | Error term correlation | 0.17 | Unstandardized loadings Reported | 2–3, 2 | 3; 2F-2 3F-1 | Based on EFA | SRMR≤0.08, CFI > 0.95, 0.5 < RMSEA> 0.8 |
Zhu et al. 2018 [64] | Stata 13.1 | Maximum Likelihood Algorithm | Not reported | Not applicable | HSE = 0.81 DURAT = 0.75 LATEN = 0.61 SLPQUAL = 0.63 DISTB = 0.46 DAYDYS = − 0.43 | 1, No EFA | 3; 1F-2 3F-1 | Not explained, some of the documented models not used, no reasons given for selection and/or inclusion | non-significant χ2, RMSEA < 0.05, CFI > 0.95, lower BIC, SRMR< 0.06 |
Results
Sample description, sample size, and measures of the suitability of the data for factor analysis
Exploratory factor analysis
Confirmatory factor analysis
Discussion
Sample description, sample size, and measures of the suitability of the data for factor analysis
Exploratory factor analysis
Confirmatory factor analysis
Practice points for future
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The studies investigating factor analysis of a questionnaire should employ both EFA and CFA.
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The reporting of details of sample suitability for factor analysis is preferable. This gives supporting evidence about distribution, levels of multicollinearity, singularity, and shared variance among measured variables.
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The details of EFA like extraction methods, rotation and factor retention should be reported along with their justification.
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The reporting of CFA like extraction methods and modification indices is preferable along with their justification.
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It is preferable to employ multiple goodness of fit indices from different categories.