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Open Access

Dimensionality of the Pittsburgh Sleep Quality Index: a systematic review

Health and Quality of Life Outcomes201816:89

https://doi.org/10.1186/s12955-018-0915-x

Received: 25 October 2017

Accepted: 29 April 2018

Published: 9 May 2018

Abstract

Background

The Pittsburgh Sleep Quality Index (PSQI) dimensionality is much debated, with the greatest number of reported factor structures. Therefore, this review appraised the methodologies of studies investigating the factor structure of the PSQI.

Material and methods

MEDLINE, PsycInfo, AJOL, BASE, Cochrane Library, Directory of Open Access Journals (Lund University), CINAHL, and Embase were searched systematically to include articles published till 23rd March, 2018. The articles with the objective of factor analysis of the PSQI (20 articles) or with a major section on the same subject (25 articles) were included. There was no limitation about participant characteristics. Descriptive analysis of articles for measures of the suitability of the data for factor analysis, details of the exploratory factor analysis (EFA) and details of the confirmatory factor analysis (CFA) was performed.

Results

The analysis used by the majority did not employ the simplest scheme for interpreting the observed data: the parsimony principle. Other shortcomings included under- or non-reporting of sample adequacy measures (11 out of 45 articles), non-use of EFA (20 out of 45 articles), use of EFA without relevant details, non-use of CFA (11 out of 45 articles), and use of CFA without relevant details. Overall, 31 out of 45 articles did not use either EFA or CFA.

Conclusion

We conclude that the various PSQI factor structures for standard sleep assessment in research and clinical settings may need further validation.

Trial registration

Not applicable because this was a review of existing literature.

Keywords

Confirmatory factor analysisDimensionalityExploratory factor analysisModel fitSystematic reviewSleep

Background

Population-based epidemiological studies have confirmed that sleep disorders occur frequently in almost every country [13]. Complaints of disturbed or poor quality sleep are also exceedingly common among patients presenting to all specialties of medicine [46]. The most common sleep disorders are insomnia, circadian rhythm sleep disorders, obstructive sleep apnea, sleep-disordered breathing, hypersomnia, daytime sleepiness, parasomnias, and restless legs syndrome [47]. Untreated sleep disorders may lead to potentially life-threatening symptoms. It is now recognized that far from being only a consequence of medical illnesses, sleep disorders are often primary drivers of other illnesses. Sleep disturbance is linked to neurocognitive dysfunctions, including attention deficits, impaired cognitive performance, depression, anxiety, stress, and poor impulse control. These disturbances are in turn linked to sympathetic activity changes and an increased risk of cardiovascular and cerebrovascular diseases [4, 5, 8]. These impairments have wider consequences in patients’ lives. Poor sleep severely impairs daytime performance, both socially and at work, and increases the risk of occupational and automobile accidents, poor quality of life, and poor overall health [4, 5, 911].

Role of subjective measurement

The ever-increasing list of problems known to be caused by sleep dysfunction has led to recognition that poor sleep has a complex relationship with overall health. It is now appreciated that disturbed sleep interacts bi-directionally with numerous neurological, physiological, psychological, and behavioral factors [4, 1214]. The central role of sleep in overall health has thus underscored the need for both reliable, validated subjective tools and objective polysomnographic (PSG) assessment in modern medical practice. While these represent very different diagnostic approaches, they are nevertheless complementary in as much as subjective tools account for psychological and behavioral manifestations not assessed by PSG. Self-rating questionnaires such as the Pittsburgh Sleep Quality Index (PSQI) have an important role in sleep health assessment in both clinical and research settings [4, 15, 16]. These questionnaires have the advantages of cost effectiveness, high patient compliance, and ease of administration. Perhaps more importantly, since such questionnaires are self-explanatory and do not require supervision, they reduce demand on medical specialists’ time [5]. Given the important diagnostic role of rating scale questionnaires, it is essential that their reliability and validity be established beyond doubt. A key element of this quality assurance is psychometric confirmation of the questionnaires’ dimensionality, i.e., whether the questionnaire’s items are all correlated and representative of factors affecting sleep quality [4, 15]. This review critically appraises the evidence for dimensionality of one of the most widely used self-rating instruments of sleep quality, the PSQI [4, 15, 17].

Pittsburgh Sleep Quality Index

The PSQI is the most widely used sleep health assessment tool in both clinical and non-clinical populations. The original 1989 article describing the Index has, since 26-06-2015, had 1545, 7863, 4962, and 4554 citations on PMC, Google Scholar, ResearchGate, and Web of Science, respectively. It is also possibly the most widely translated sleep questionnaire. The PSQI consists of 24 questions or items to be rated (0–3 for 20 items while 4 items are open-ended), 19 of which are self-reported and 5 of which require secondary feedback from a room or bed partner. Only the self-reported items (15 rated as 0–3 while 4 open-ended) are used for quantitative evaluation of sleep quality as perceived by the patient. The open-ended items are also finally scored as structured categorical values (rated at 0–3) as per the range of values reported for them by the patient. These 19 self-reported items are used to generate categorical scores representing the PSQI’s 7 components. The individual component scores each assess a specific feature of sleep. Finally, the scores for each component are summed to get a total score, also termed the global score (range: 0 to 21). This score provides an efficient summary of the respondent’s sleep experience and quality for the past month [12].

Validation and reliability measures of the Pittsburgh Sleep Quality Index

The PSQI is possibly the most rigorously validated tool used in sleep diagnostics [4, 5, 1517]. Of the many psychometric studies carried out on the PSQI, 75% have reported an internal consistency in the ideal range for within- and between-group comparisons but not for comparisons made between questionnaires for individual patients [4]. Mollayeva et al. [4] performed a meta-analysis and found strong evidence for the PSQI’s reliability and validity. Further, the meta-analysis revealed a moderately positive evidence for the questionnaire’s structural validity across a variety of samples. The PSQI was found to have known-group validity, and, while some studies showed methodological weaknesses in this regard, its convergent and divergent validity were generally confirmed.

Factor analysis

A tool’s dimensionality is evaluated by factor analysis. Factor analysis attempts to discover patterns in a set of variables based on shared variance [18]. A key goal of this analysis is identifying the simplest and most parsimonious means for interpreting and representing observed data [19]. More specifically, the procedure seeks to use measured variables to infer the smallest number of unobserved or latent variables that can still account for the observable variables [20]. The mathematical operations are broadly categorized into 2 sub-groups: exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). EFA aims to find the smallest number of common latent factors that can account for the correlations [21]. CFA is then employed to test the relationship between the observed variables and their underlying latent factors [15]. Factor analysis is useful for studies involving many variables that can be reduced to a smaller set, such as questionnaire items or a battery of tests. The goal of this process is identifying the concepts underlying a phenomenon and thus facilitating interpretations.

Dimensionality of the PSQI

The PSQI’s dimensionality is much debated, with many studies supporting multiple factors and some supporting unidimensionality [4, 1517]. Among sleep diagnostic tools, the PSQI has the greatest number of reported factor structures [15]. The intensity of discussion around the topic of the dimensionality of the PSQI is reflected in the publication of 45 articles on the subject since 2006 [15]. As the PSQI components are structured categorical variables scored 0–3, therefore the factor analysis should ideally begin with a polychoric correlation matrix. However, most of the programs do use a Pearson correlation matrix. It may be one of the reasons for the discrepancy among studies. Some evidence suggests that some studies may have over-factored the PSQI [15]. Several reviews have concluded that many previous efforts to investigate the PSQI’s factor structure have suffered from non-parsimonious methodologies [4, 15, 17, 2246]. Given a choice between close fit and parsimony (i.e., model with fewer latent factors), the latter may be preferred [47]. Manzar et al. [15] used an innovative strategy of performing comparative CFA of all the documented PSQI models on a discrete sample to disprove the questionnaire’ soft-reported multidimensionality and heterogeneity. However, the study had the important limitation of being unable to address inter-software, inter-sample, and inter-model differences [15]. Mollayeva et al. [4] mentioned procedural discrepancies in the studies investigating the PSQI factor structures without providing further details. Approximately 30 distinct PSQI models have been proposed in the literature. Of these models, 7 were 1-factor, 17 were 2-factor, 4 were 3-factor, 1 was 4-factor and 2 were second order models [1517, 2246, 4853]. The current state of the literature, with its broad range of suggested factor structure models, represents an impediment to an efficient consideration of the PSQI’s use. There evidently exists a need for a thorough appraisal of the procedural details and application of standard practices in the previous methodological studies of the PSQI. Such an investigation is indispensable for streamlining the debate about the PSQI’s heterogeneity.

Practical implication of the heterogeneity of the Pittsburgh Sleep Quality Index

One consequence of the PSQI’s presumed heterogeneity is the possible attenuation of its practical application in clinical diagnostics [15]. A questionnaire’s dimensionality directly affects the reporting of its intended measures. Currently, however, very few efforts have been undertaken to validate the PSQI’s disparate models in either research or clinical settings. This is possibly related to the choice of the appropriate PSQI model for a particular sample. Previous attempts by Hancock and Larner [54] and Yurcheshen et al. [55] to test the disparate PSQI models did not adequately address the reason(s) for the specific model’s selection. In fact, both studies used a 3-factor PSQI model initially reported to be valid in a different population [22, 5456]. Such reports using unrelated PSQI models will complicate inter-study comparisons for the PSQI-based measures. The goal of the present systematic review is to help develop strategies for managing the methodological discrepancies in the PSQI factor analysis and reporting of the PSQI-based sleep assessment. An additional goal is to provide possible guidelines for factor analyses of questionnaires in general and sleep inventories in particular.

Material and methods

Literature search scheme

All articles available online on 23-03-2018 were included. The comprehensive search strategy was planned in consultation with epidemiological experts, information technologists, and sleep scientists. We searched 8 electronic databases: CINAHL, Bielefeld University (BASE: Bielefeld Academic Search Engine), Cochrane Library, Directory of Open Access Journals (Lund university), Embase, Medline, PsycInfo, and African Journals On Line (AJOL).

To minimize inclusion of irrelevant articles, we searched for a combination of 2 keywords (Pittsburgh Sleep Quality Index/PSQI with dimensionality/dimension/factor model/factor analysis/factor structure/domain/Exploratory factor analysis/EFA/Confirmatory factor analysis/CFA). Seventy-eight articles were initially identified (Fig. 1). Thirty four articles; 30 duplicates, 3 with reasons (Factor analysis details were missing) and 1 for unavailability of full-length article were removed.
Figure 1
Fig. 1

Schematic of the article selection process

Selection criteria

Forty five full-length peer-reviewed articles were used. Forty three articles were in English, and 1 each was in Spanish and Chinese. We e-mailed the lead author of the Spanish article an English translation of the section covering factor analysis. It was included after gaining the author’s approval. The lead author of the Chinese article provided translation of the factor analysis section, therefore it was also included. The articles’ reference lists were thoroughly reviewed for other relevant publications. There were no restrictions on the type or age range of the population covered. We only included articles that had a primary objective of exploring and/or confirming dimensionality (20 studies) and articles that reported multiple indices of psychometric properties with a substantial section devoted to factor analysis (25 studies) (Fig. 1).

Data extraction

The measures used to present the factor analysis findings were grouped in three broad categories; measures of the suitability of the data for factor analysis (Table 1), summary of the exploratory factor analysis conditions (Table 2) and summary of the confirmatory factor analysis conditions (Table 3). Descriptive analysis of articles for measures of these three categories was performed. Meta-analysis was not conducted as the included studies were heterogeneous in methods and statistical analyses used.
Table 1

Sample description, sample size, and measures of the suitability of the data for factor analysis in the studies reporting factor structure of the Pittsburgh Sleep Quality Index

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

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

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

Table 3

Summary of the confirmatory factor analysis conditions used and reported by the studies investigating the factor structure of the Pittsburgh Sleep Quality Index

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

The factor analysis of the PSQI has been reported on diverse samples including university/college students in Nigeria, India, Chili, Chinese, Ethiopia, Peru and Thailand, pregnant women, community dwelling adults and older adults in America, Australia, China, Ethiopia, Spain, Portugal, Iranian health professionals, adolescents and young adults [1517, 23, 24, 2628, 31, 34, 36, 39, 41, 44, 45, 52, 53, 5760]. Moreover, it has been reported for patient population with breast cancer, Crohn’s disease, depression, diabetes, women with hot flashes, Taiwanese insomniacs, post-traumatic stress disorder, Trauma exposed veterans, temporo-mandibular disorder, traumatic brain injury, fibromyalgia, arthritis, chronic fatigue syndrome, psychiatric disorders and renal transplant patients [25, 27, 29, 30, 32, 33, 35, 37, 38, 40, 42, 43, 46, 49, 51, 6165].

Few of the studies reviewed had large sample sizes (Table 1). The sample sizes of studies reporting the PSQI factor structures differed widely, ranging from 105 to 2840 (Table 1) [38, 44, 66]. Only 3 studies reported determinant scores and anti-image values (Table 1) [1517]. We counted the number of studies reporting any of the following 5 indices: Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy, Bartlett’s sphericity test, determinant score, anti-image, and component correlations (Table 1). Eighteen studies reported both the KMO and Bartlett’s sphericity test [1517, 25, 39, 4345, 5153, 5759, 61, 64, 65, 67], reported inter-item correlations [16, 22, 24, 25, 27, 29, 30, 3234, 36, 37, 41, 42, 45, 46, 50, 53, 57, 6467], and 11 studies did not report any index [23, 26, 28, 31, 35, 38, 40, 49, 60, 62, 63].

Exploratory factor analysis

Twenty articles did not report having carried out EFA [15, 23, 26, 29, 3238, 42, 48, 50, 59, 60, 6264, 66]. The types of EFA tests used were principal component analysis (PCA) in 18 studies [17, 22, 24, 25, 27, 28, 31, 39, 41, 4345, 49, 51, 57, 58, 65, 67, 68], maximum likelihood estimation (MLE) in 6 [16, 17, 22, 24, 40, 41, 53] and unreported in 2 [30, 52] (Table 2). The types of rotation used for EFA were orthogonal varimax in 8 studies [28, 39, 41, 43, 44, 46, 49, 52, 65], oblique direct oblimin in 8 [16, 17, 22, 24, 25, 51, 53, 61], oblique promax in 5 [40, 45, 46, 58, 65], standardized geomin 1 [30], and unreported in 4 [27, 31, 57, 67]. Few of the studies justified the type of extraction or rotation used. Seven studies reported using a Cattell’s scree test to determine the number of factors to retain [16, 17, 44, 46, 58, 61, 65, 69]. Fifteen studies employed the Kaiser criterion of eigenvalue greater than one [16, 17, 25, 27, 28, 40, 4346, 49, 51, 58, 61, 65].

There is increasing consensus among statisticians that, parallel analysis and Velicer’s minimum average partial (MAP) test, are better than to other procedures. This is because these methods usually give optimal solutions for the number of factors to retain [70]. However, only 4 studies used parallel analysis [16, 17, 49, 65, 71]. Fifteen studies used multiple criteria for factor retention [16, 17, 25, 27, 40, 4346, 49, 51, 52, 58, 61, 65]. The number of factors retained after EFA varied across articles. Five studies reported a 1-dimensional PSQI structure [16, 17, 46, 52, 67], 17 reported a 2-dimensional structure [16, 17, 22, 24, 25, 27, 28, 30, 40, 43, 44, 49, 5153, 58, 61], and 8 reported a 3-dimensional structure [16, 31, 39, 41, 44, 45, 52, 65]. Twenty studies reported the cumulative percent of variance extracted [16, 17, 22, 25, 27, 39, 40, 43, 45, 49, 5153, 57, 58, 65, 67]. Only 4 studies reported the value of communality retention criteria of items for factor analysis [17, 25, 43, 59].

Confirmatory factor analysis

As in the investigation of EFA, the overall finding for CFA was that a broad range of analytic techniques was used, all yielding a variety of inferred factor models. Eleven studies did not use CFA [25, 27, 28, 30, 31, 39, 43, 57, 61, 65, 67] (Table 3). Several different software programs were used for CFA: 6 studies used Mplus [26, 29, 3436, 42], 12 used SPSS with Amos [1517, 24, 33, 40, 48, 52, 53, 57, 60, 65], 4 used STATA [44, 46, 49, 64] and 2 each used LISREL [32, 37], and SAS [45 [58]], 1 each used FactoMineR [41], EQS [38], and SEM [23]. Five studies did not report which software program was used [22, 50, 51, 59, 66]. Thirteen studies did not report the method of extraction [23, 24, 29, 34, 40, 41, 4953, 60, 63]. Nine studies used weighted least squares methods [26, 32, 35, 37, 45, 46, 48, 58, 66], 8 employed MLE [15, 16, 33, 36, 38, 44, 59, 62], 2 used MLE with bootstrapping for smoothing multivariate non-normality [17, 22], and 1 used robust MLE [42] (Table 3). Most of the studies did not use a modification index [17, 24, 26, 3335, 37, 38, 40, 41, 4446]. Twelve studies reported the result of the suggestions of the modification indices, 2 used path diagram change [23, 29], 6 used correlations between error terms [15, 16, 32, 42, 58, 60], 1 used path diagram change with Lagrange modifiers [22], and 2 did not provide details [36, 63] (Table 3). The reported correlation values between the factors of the final CFA models varied from 0.07 to 1.0. Some studies failed to report correlation values between the factors [26, 36] (Table 3).

Eight studies found that a 3-factor model best explained the data [29, 3537, 41, 45, 52, 53], Thirteen reported a 2-factor model [22, 24, 26, 32, 34, 38, 40, 5052, 58, 60], and 9 reported a 1-factor model [1517, 42, 46, 48, 49, 59, 64]. One study reported both 2-factor and 3-factor models, but in separate sample populations [44] (Table 3). Two studies reported second-order models; i.e., 2 or 3 first-order latent factors loaded on a higher-order factor [23, 33] (Table 3). Seven studies found the same PSQI structure with both EFA and CFA [4042, 4446, 58], while 3 derived different models from EFA and CFA [17, 22, 24] (Table 3). The medicine component of the PSQI was removed from the final models in some studies [23, 36, 38], while sleep quality component of the PSQI was not reported in the final model by 2 studies [23, 40] (Table 3). Two studies reported finding a 2-factor model with just 5 PSQI components [16, 23], while 1 study reported a model with only three PSQI components [48] (Table 3). Three studies reported final models with cross-loads [29, 45, 63]. Two studies reported non-standardized factor loadings, while 2 studies did not report the factor loadings (Table 3) [26, 29, 46, 63]. The studies showed little variation in number, types, and limit values of the fit indices used.

Discussion

Sample description, sample size, and measures of the suitability of the data for factor analysis

The gradual development of a heterogeneous multiple factor structure of the PSQI has often been defended by the complexities of sleep problems among diverse samples. However, there is no consensus about this assertion that complexities of sleep problems in diverse samples must result in multiple factor structure [15]. Moreover, this speculative presumption conveniently ignores to explain why the measured variables, i.e. individual items of the PSQI and the PSQI component scores cannot account for this complexity.

The appropriate sample size for factor analysis is a frequently debated topic among statisticians. There are disparate guidelines [7274]. There are also different opinions on such issues as sample to variable ratio (N:p ratio) criteria [72, 75], the factorability of the correlation matrix [76, 77], use of the KMO/Bartlett’s Test of Sphericity [76, 78], and use of the determinant of the matrix and anti-image or diagonal element of the anti-correlation matrix [72]. The suitable data for factor analysis and replicable factor extraction may require large samples and the satisfaction of a number of conditions as determined by such measures as the KMO, Bartlett’s test, the determinant of the matrix, the anti-image of the anti-correlation matrix, and inter-component correlations [79]. A non-zero determinant of the matrix indicates the absence of multi-collinearity, meaning that linear combinations of items can form factors [80, 81].

The non-reporting of these conditions by the majority (29 out of 45) of studies may create doubt about the applicability of the reported factor structures, even in the study populations. Three studies reported all but inter-component correlations) [1517], while 7 reported KMO, Bartlett’s test and inter-component correlations (Table 1) [25, 39, 4345, 53, 57, 64, 65, 67]. The conclusions about dimensionality of the PSQI by some of these studies are limited by non-reporting of CFA (Table 3) [25, 39, 43], or non-reporting of EFA [15]. However, reporting of multiple sample size suitability indices by these studies indicate suitability of their data for factor analysis [15, 17, 25, 39, 4345].

Exploratory factor analysis

The non-reporting of EFA results is fundamentally contrary to recommended norms for factor analysis, a deficiency that is particularly important considering the debate about the number and patterns of common factors for the PSQI [4, 15, 17, 82]. Although the choice of extraction types for performing EFA is much-debated, though some prefer the use of principal axes for initial solutions [72]. The choice of the extraction method (principal axis or principal factors) may depend on the underlying data and the assumptions [60]. Many studies failed to report the final extraction method used in the EFA (Table 2). Four studies reported using MLE for the final extraction [17, 22, 24, 41], but 3 of these did not report the normality and/or skewness of the distribution of data being analyzed [22, 24, 41]. The extracted factors’ applicability seems unclear because MLE entails multivariate normality [83]. Two studies reported using the principal factors method and principal component factor analysis, the authors might have meant principal axis method and PCA, respectively [43, 46]. Under these circumstances, it is unsurprising that most of the studies did not explain the types of extraction used, plus most of the studies did not explain the choice of rotation.

Factor rotation increases interpretability by optimizing a simple structure with a distinct cluster of interrelated variables loading on the least number of latent variables [80]. Oblique rotations are better suited to accounting for the inter-relationships in the clinical data. They can be used even when the factors are not significantly correlated [81]. However, the use of rotation methods in the PSQI factor analysis studies is inconsistent. Of the studies reporting rotation methods, similar numbers used orthogonal and oblique rotations (Table 2). Some of the studies using orthogonal rotation did report the correlation value of the extracted factors [28, 30, 41, 43, 44]. The reported factor correlations were in the range of 0.1–0.9 [39, 41, 44]. Therefore, the factor correlation values of the various PSQI models do not seem to support the choice of orthogonal rotation methods.

There are many criteria for determining the number of factors to be retained from EFA. These include the Cattell’s Scree test, Kaiser Criterion of Eigenvalue greater than one, the percentage of cumulative variance explained, and robust measures such as Horn’s Parallel analysis, the Broken-Stick (B-S) criterion, and the minimum average partial (MAP) test [72, 84]. These tests have many limitations, and more so for the first three tests mentioned earlier. Therefore, the consensus opinion is to employ multiple criteria [72, 84]. It is perhaps concerning that only approximately one-third of the PSQI factor analysis studies used multiple criteria, and none used multiple robust measures (Table 2) [84]. The B-S criterion and MAP test were not used by any of the studies exploring the PSQI’s factor structure. The communality accounts for the variance of the common factors. Factor analysis aims to explain variance through common factors. Therefore communalities less than 0.2 are removed [80]. However, communality criteria were frequently under-reported in the studies investigating the PSQI’s factor structure (Table 2). These inconsistencies and discrepancies might explain the variation in the number of factors retained after EFA (Table 2) [4, 15, 17].

Confirmatory factor analysis

For finding prospective models and validation of the dimensionality of a questionnaire tool in discrete populations, it is recommended that factor analysis studies use both EFA and CFA [80]. More than 68% of the studies investigating the PSQI’s factor structure employed either EFA or CFA. Some of the PSQI models are based only on EFA [25, 27, 28, 31, 39, 43], while some are based only on CFA [15, 23, 26, 29, 32, 3436, 38, 42], neither of which is the recommended practice for performing factor analysis [85].

Another issue is the influence of user software. The software packages used to perform CFA (LISREL, Mplus, SAS, STATA, Amos, and EQS) differ with regard to estimation; path diagrams; availability of standard errors for standardized estimates, factor covariance, and factor correlations; availability of modification indices; and ability to handle different types (i.e., continuous and categorical) of measured and latent variables [68]. However, the fact that studies investigating the PSQI’s factor structure used different software for CFA should not affect the results, as there are only slight differences in the statistics reported by the various programs, but the solutions are comparable [15]. LISREL, Mplus, SAS and STATA can handle the PSQI component scores, which are ordered as categorical variables, using diagonally weighted least squares estimation methods. Amos cannot accurately estimate models because it treats the PSQI component scores as measured variables. This is especially true if the PSQI component scores’ distributions are characterized by skewness and kurtosis [17, 68]. However, Amos allows model estimation using MLE with bootstrapping to smooth non-normality with standardized estimates of factor loading [86]. Non-reporting of distribution characteristics is a common problem with the PSQI factor analysis studies. Further, some studies using SPSS with Amos did not describe their extraction and bootstrapping methods [24, 40]. More than a quarter of the studies (i.e.13 out of 34 studies that used CFA) failed to report their extraction methods (Table 3). It is therefore difficult to reach a conclusion about the applicability of these studies’ results. Modification indices should be used discretely to avoid over-capitalization on sample specific variations. It may be better to validate the modification index incorporated models on unrelated samples [87]. Few studies reported using the modification index, and they did not explain the choice of the type of modification index [23, 29, 32, 36, 42].

Inter-factor correlation of 0.85 and above arises from multicollinearity and indicate poor discriminant validity [88]. The reported correlation coefficients between CFA model factors were as high as 0.89, 0.9, and 1.0 [33, 41]. This is technically undesirable because correlation coefficients greater than 0.9 suggest that the 2 correlated factors might not be practically distinct. Instead, the items loading on them might load on a common factor [17]. Jomeen and Martin [26] did not report inter-factor correlations in their final model. Moreover, they failed to report the factor loadings (Table 3). It is therefore difficult to reach a conclusion about their model’s parsimony.

Low loadings for some of the PSQI components’ scores (i.e., medicine component and sleep quality component) in some studies might reflect a reduced sensitivity of the questionnaire items measuring them [23, 36, 38, 40]. Tomfohr et al. [36] reported only the inter-component correlations as a sample size adequacy measure, did not use EFA, and did not provide details regarding the modification index. Among all the studies, Dudysova et al. [66] had the smallest sample size at 105. They did not report their EFA findings, nor did they provide information regarding suitability of the data for factor analysis, such as the KMO test, Bartlett’s test, determinant score, nor anti-image matrix. Similarly, Skouteris et al. [23] did not report their findings regarding EFA or sample size adequacy measures. They also did not report the CFA extraction method. Lequerica et al. [40] did not report any sample size measures or the CFA extraction method. The study used Amos without reporting normality conditions or bootstrapping.

Methodological discrepancies between these studies might have affected their results and the reliability of their findings. The model fit indices were streamlined with regard to number, types, and limit values (Table 3). Almost all the reviewed studies used multiclass model fits, which is consistent with generally accepted guidelines for factor analysis [89]. Gelaye et al. reported using 4 model fit indices in their study but mentioned the cut-off criteria of only 3. A model fit was presented for a 2-factor solution, though the EFA supported a 3-factor model [17, 44]. It is also concerning that in almost all the studies, the basic parsimony requirements for factor analysis were not upheld [15, 17]. It is worth noting that the recommended practice for factor analysis gives preference to parsimonious models over multidimensional models if differences are irreconcilable [47]. Therefore, the non-application of parsimony, together withother procedural discrepancies, has made it difficult to endorse the applicability of the various PSQI factor structures, even in similar samples.

Practice points for future

  • The studies investigating factor analysis of a questionnaire should employ both EFA and CFA.

  • 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.

  • The details of EFA like extraction methods, rotation and factor retention should be reported along with their justification.

  • The reporting of CFA like extraction methods and modification indices is preferable along with their justification.

  • It is preferable to employ multiple goodness of fit indices from different categories.

Limitations

This review has some limitations. We did not perform a meta-analysis, but the discrepancies made that almost impractical. The studies’ methodological qualities were not assessed, but such approaches have their own demerits [1]. We mostly reviewed articles published in the English language; with only 2 non-English articles included after their authors approved/provided a translation of the factor analysis sections [27]. Some authors did not respond to the queries regarding details of the factor analysis in their study. The authors of the other included articles were not contacted. Model fit indices were not discussed in detail because the studies were methodologically sound in this regard. Interested readers are referred Cheung and Rensvold [90].

Conclusion

The results of this review do not permit an optimistic conclusion regarding the applicability of factor analysis studies on this widely used questionnaire. The generalizations from the majority are severely limited by issues including non-application of parsimony, non-use of EFA or non-reporting of relevant details, and non-use of CFA or non-reporting of relevant details. The generalizations from studies using small size may be difficult. Furthermore, under- or non-reporting of sample adequacy measures “and” non-reporting of relevant details make understanding the diversity of factor structures difficult to interpret. In summary, the factor analysis may not be replicable across different methodologies. The structured categorical data of the PSQI may be sensitive to the specific model (method of extraction) being applied.

Therefore, the applicability of the various PSQI factor structures even in related samples seems doubtful.

Declarations

Acknowledgements

We are grateful to the Mizan-Tepi University for providing the running cost of the project.

Authors’ contributions

MDM: Concept development and study design, analysis and interpretation, manuscript preparation, critical revision of the manuscript, and approved the final version of the manuscript. AS: Interpretation, manuscript preparation, critical revision of the manuscript, and approved the final version of the manuscript. UAH: Interpretation, manuscript preparation, critical revision of the manuscript, and approved the final version of the manuscript. DWS: Interpretation, manuscript preparation, critical revision of the manuscript, and approved the final version of the manuscript. SRP: Interpretation, manuscript preparation, critical revision of the manuscript, and approved the final version of the manuscript. AM: Interpretation, manuscript preparation, critical revision of the manuscript, and approved the final version of the manuscript. DLS: Interpretation, manuscript preparation, critical revision of the manuscript, and approved the final version of the manuscript. All authors read and approved the final manuscript.

Ethics approval and consent to participate

The study was approved by the Human Institutional Ethics Committee of Mizan-Tepi University.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

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Authors’ Affiliations

(1)
Department of Nursing, College of Applied Medical Sciences, Majmaah University, Al Majmaah, Kingdom of Saudi Arabia
(2)
Department of Biomedical Sciences, College of Medicine and Health Sciences, Mizan-Tepi University (Mizan Campus), Mizan-Aman, Ethiopia
(3)
The University Sleep Disorders Center, Department of Medicine, College of Medicine, King Saud University, Riyadh, Saudi Arabia
(4)
The National Plan for Science and Technology, King Saud University, Riyadh, Saudi Arabia
(5)
Department of Physiotherapy, Fatima College of Health Sciences, Abu Dhabi City, United Arab Emirates
(6)
, Toronto, Canada
(7)
Sleep and Fatigue Institute, University of Calgary, Calgary, Canada
(8)
Department of Psychiatry and Behavioral Neurosciences, McMaster University, Hamilton, Canada
(9)
Department of Psychiatry, University of Toronto, Toronto, Canada

References

  1. Ohayon MM. Epidemiological overview of sleep disorders in the general population. Sleep Med Res. 2011;2:1–9.View ArticleGoogle Scholar
  2. Attarian HP. Epidemiology of sleep disorders in women. In: Attarian HP, Viola-Saltzman M, editors. Sleep disorders in women: a guide to practical management. New York: Springer Science+Business Media; 2013.View ArticleGoogle Scholar
  3. de Souza Lopes C, Robaina JR, Rotenberg L. Epidemiology of insomnia: prevalence and risk factors. In: Sahoo S, editor. In Can’t sleep? Issues of being an insomniac: In Tech; Shanghai. 2012. p. 1–21.Google Scholar
  4. Mollayeva T, Thurairajah P, Burton K, Mollayeva S, Shapiro CM, Colantonio A. The Pittsburgh sleep quality index as a screening tool for sleep dysfunction in clinical and non-clinical samples: a systematic review and meta-analysis. Sleep Med Rev. 2016;25:52–73.View ArticlePubMedGoogle Scholar
  5. Manzar MD, Moiz JA, Zannat W, Spence DW, Pandi-Perumal SR, Hussain ME. Validity of the Pittsburgh sleep quality index in Indian university students. Oman Med J. 2015;30:193–202.View ArticlePubMedPubMed CentralGoogle Scholar
  6. Udwadia ZF, Doshi AV, Lonkar SG, Singh CI. Prevalence of sleep-disordered breathing and sleep apnea in middle-aged urban Indian men. Am J Respir Crit Care Med. 2004;169:168–73.View ArticlePubMedGoogle Scholar
  7. Sweileh WM, Ali IA, Sawalha AF, Abu-Taha AS, Zyoud SH, Al-Jabi SW. Sleep habits and sleep problems among Palestinian students. Child Adolesc Psychiatry Ment Health. 2011;5:25.View ArticlePubMedPubMed CentralGoogle Scholar
  8. Leger D, Bayon V, Ohayon MM, Philip P, Ement P, Metlaine A, Chennaoui M, Faraut B. Insomnia and accidents: cross-sectional study (EQUINOX) on sleep-related home, work and car accidents in 5293 subjects with insomnia from 10 countries. J Sleep Res. 2014;23:143–52.View ArticlePubMedGoogle Scholar
  9. Shekleton JA, Flynn-Evans EE, Miller B, Epstein LJ, Kirsch D, Brogna LA, Burke LM, Bremer E, Murray JM, Gehrman P, et al. Neurobehavioral performance impairment in insomnia: relationships with self-reported sleep and daytime functioning. Sleep. 2014;37:107–16.PubMedPubMed CentralGoogle Scholar
  10. Fortier-Brochu E, Beaulieu-Bonneau S, Ivers H, Morin CM. Insomnia and daytime cognitive performance: a meta-analysis. Sleep Med Rev. 2012;16:83–94.View ArticlePubMedGoogle Scholar
  11. Guallar-Castillon P, Bayan-Bravo A, Leon-Munoz LM, Balboa-Castillo T, Lopez-Garcia E, Gutierrez-Fisac JL, Rodriguez-Artalejo F. The association of major patterns of physical activity, sedentary behavior and sleep with health-related quality of life: a cohort study. Prev Med. 2014;67:248–54.View ArticlePubMedGoogle Scholar
  12. Buysse DJ, Reynolds CF 3rd, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh sleep quality index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989;28:193–213.View ArticlePubMedGoogle Scholar
  13. Bassetti C, Dijk D, Dogas Z, Levy P. The future of sleep research and sleep medicine in Europe: a need for academic multidisciplinary sleep Centres. In: European Sleep Research Society 1972–2012. 2012.Google Scholar
  14. Manzar MD, Zannat W, Hussain ME. Sleep and physiological systems: a functional perspective. Biol Rhythm Res. 2015;46:195–206.View ArticleGoogle Scholar
  15. Manzar MD, Zannat W, Moiz JA, Spence DW, Pandi-Perumal SR, Bahammam AS. Factor scoring models of the Pittsburgh sleep quality index: a comparative confirmatory factor analysis. Biol Rhythm Res. 2016; 47:851-64.Google Scholar
  16. Salahuddin M, Maru TT, Kumalo A, Pandi-Perumal SR, Bahammam AS, Manzar MD. Validation of the Pittsburgh sleep quality index in community dwelling Ethiopian adults. Health Qual Life Outcomes. 2017;15:58.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Manzar MD, Zannat W, Hussain ME, Pandi-Perumal SR, Bahammam AS, Barakat D, Ojike NI, Olaish A, Spence DW. Dimensionality of the Pittsburgh sleep quality index in the collegiate young adults. Spring. 2016;5:1550.View ArticleGoogle Scholar
  18. Child D. The essentials of factor analysis. New York: Bloomsbury Academic; 2006.Google Scholar
  19. Harman HH. Modern factor analysis. Chicago: University of Chicago Press; 1976.Google Scholar
  20. Bartholomew DJ, Knott M, Moustaki I. Latent variable models and factor analysis: a unified approach. West Sussex: Wiley; 2011.Google Scholar
  21. McDonald RP. Factor analysis and related methods. New York: Psychology press; 2014.Google Scholar
  22. Cole JC, Motivala SJ, Buysse DJ, Oxman MN, Levin MJ, Irwin MR. Validation of a 3-factor scoring model for the Pittsburgh sleep quality index in older adults. Sleep. 2006;29:112–6.View ArticlePubMedGoogle Scholar
  23. Skouteris H, Wertheim EH, Germano C, Paxton SJ, Milgrom J. Assessing sleep during pregnancy: a study across two time points examining the Pittsburgh sleep quality index and associations with depressive symptoms. Womens Health Issues. 2009;19:45–51.View ArticlePubMedGoogle Scholar
  24. Magee CA, Caputi P, Iverson DC, Huang XF. An investigation of the dimensionality of the Pittsburgh sleep quality index in Australian adults. Sleep Biol Rhythms. 2008;6:222-27.Google Scholar
  25. Kotronoulas GC, Papadopoulou CN, Papapetrou A, Patiraki E. Psychometric evaluation and feasibility of the Greek Pittsburgh sleep quality index (GR-PSQI) in patients with cancer receiving chemotherapy. Support Care Cancer. 2011;19:1831–40.View ArticlePubMedGoogle Scholar
  26. Jomeen J, Martin CR. Assessment and relationship of sleep quality to depression in early pregnancy. J Reprod Infant Psychol. 2007;25:87–99.Google Scholar
  27. Jimenez-Genchi A, Monteverde-Maldonado E, Nenclares-Portocarrero A, Esquivel-Adame G, de la Vega-Pacheco A. Reliability and factorial analysis of the Spanish version of the Pittsburg sleep quality index among psychiatric patients. Gac Med Mex. 2008;144:491–6.PubMedGoogle Scholar
  28. Buysse DJ, Hall ML, Strollo PJ, Kamarck TW, Owens J, Lee L, Reis SE, Matthews KA. Relationships between the Pittsburgh sleep quality index (PSQI), Epworth sleepiness scale (ESS), and clinical/polysomnographic measures in a community sample. J Clin Sleep Med. 2008;4:563–71.PubMedPubMed CentralGoogle Scholar
  29. Burkhalter H, Sereika SM, Engberg S, Wirz-Justice A, Steiger J, De Geest S. Structure validity of the Pittsburgh sleep quality index in renal transplant recipients: a confirmatory factor analysis. Sleep Biol Rhythms. 2010;8:274–81.View ArticleGoogle Scholar
  30. Babson KA, Blonigen DM, Boden MT, Drescher KD, Bonn-Miller MO. Sleep quality among U.S. military veterans with PTSD: a factor analysis and structural model of symptoms. J Trauma Stress. 2012;25:665–74.View ArticlePubMedGoogle Scholar
  31. Aloba OO, Adewuya AO, Ola BA, Mapayi BM. Validity of the Pittsburgh sleep quality index (PSQI) among Nigerian university students. Sleep Med. 2007;8:266–70.View ArticlePubMedGoogle Scholar
  32. Otte JL, Rand KL, Carpenter JS, Russell KM, Champion VL. Factor analysis of the Pittsburgh sleep quality index in breast cancer survivors. J Pain Symptom Manag. 2013;45:620–7.View ArticleGoogle Scholar
  33. Mariman A, Vogelaers D, Hanoulle I, Delesie L, Tobback E, Pevernagie D. Validation of the three-factor model of the PSQI in a large sample of chronic fatigue syndrome (CFS) patients. J Psychosom Res. 2012;72:111–3.View ArticlePubMedGoogle Scholar
  34. Chong AML, Cheung CK. Factor structure of a Cantonese-version Pittsburgh sleep quality index. Sleep Biol Rhythms. 2012;10:118–25.View ArticleGoogle Scholar
  35. Casement MD, Harrington KM, Miller MW, Resick PA. Associations between Pittsburgh sleep quality index factors and health outcomes in women with posttraumatic stress disorder. Sleep Med. 2012;13:752–8.View ArticlePubMedPubMed CentralGoogle Scholar
  36. Tomfohr LM, Schweizer CA, Dimsdale JE, Loredo JS. Psychometric characteristics of the Pittsburgh sleep quality index in English speaking non-Hispanic whites and English and Spanish speaking Hispanics of Mexican descent. J Clin Sleep Med. 2013;9:61–6.PubMedPubMed CentralGoogle Scholar
  37. Otte JL, Rand KL, Landis CA, Paudel ML, Newton KM, Woods N, Carpenter JS. Confirmatory factor analysis of the Pittsburgh sleep quality index in women with hot flashes. Menopause. 2015;22:1190–6.View ArticlePubMedPubMed CentralGoogle Scholar
  38. Nicassio PM, Ormseth SR, Custodio MK, Olmstead R, Weisman MH, Irwin MR. Confirmatory factor analysis of the Pittsburgh sleep quality index in rheumatoid arthritis patients. Behav Sleep Med. 2014;12:1–12.View ArticlePubMedGoogle Scholar
  39. Nazifi M, Mokarami H. Psychometric properties of the Persian translation of Pittsburgh sleep quality. Health Scope. 2014;3:e15547.Google Scholar
  40. Lequerica A, Chiaravalloti N, Cantor J, Dijkers M, Wright J, Kolakowsky-Hayner SA, Bushnick T, Hammond F, Bell K. The factor structure of the Pittsburgh sleep quality index in persons with traumatic brain injury. A NIDRR TBI model systems module study. NeuroRehabilitation. 2014;35:485–92.PubMedGoogle Scholar
  41. Koh HW, Lim RB, Chia KS, Lim WY. The Pittsburgh sleep quality index in a multi-ethnic Asian population contains a three-factor structure. Sleep Breath. 2015;19:1147–54.View ArticlePubMedGoogle Scholar
  42. Ho RT, Fong TC. Factor structure of the Chinese version of the Pittsburgh sleep quality index in breast cancer patients. Sleep Med. 2014;15:565–9.View ArticlePubMedGoogle Scholar
  43. Hita-Contreras F, Martinez-Lopez E, Latorre-Roman PA, Garrido F, Santos MA, Martinez-Amat A. Reliability and validity of the Spanish version of the Pittsburgh sleep quality index (PSQI) in patients with fibromyalgia. Rheumatol Int. 2014;34:929–36.View ArticlePubMedGoogle Scholar
  44. Gelaye B, Lohsoonthorn V, Lertmeharit S, Pensuksan WC, Sanchez SE, Lemma S, Berhane Y, Zhu X, Velez JC, Barbosa C, et al. Construct validity and factor structure of the Pittsburgh sleep quality index and Epworth sleepiness scale in a multi-national study of African, south east Asian and south American college students. PLoS One. 2014;9:e116383.View ArticlePubMedPubMed CentralGoogle Scholar
  45. Zhong QY, Gelaye B, Sanchez SE, Williams MA. Psychometric properties of the Pittsburgh sleep quality index (PSQI) in a cohort of Peruvian pregnant women. J Clin Sleep Med. 2015;11:869–77.PubMedPubMed CentralGoogle Scholar
  46. Rener-Sitar K, John MT, Bandyopadhyay D, Howell MJ, Schiffman EL. Exploration of dimensionality and psychometric properties of the Pittsburgh sleep quality index in cases with temporomandibular disorders. Health Qual Life Outcomes. 2014;12:10.View ArticlePubMedPubMed CentralGoogle Scholar
  47. Browne MW, Cudeck R. Alternative ways of assessing model fit. Sociol Methods Res. 1992;21:230-58.Google Scholar
  48. Yunus RM, Wazid SW, Hairi NN, Choo WY, Hairi FM, Sooryanarayana R, Ahmad SN, Razak IA, Peramalah D, Aziz SA. Association between elder abuse and poor sleep: a cross-sectional study among rural older Malaysians. PLoS One. 2017;12:e0180222.View ArticlePubMedPubMed CentralGoogle Scholar
  49. Fontes F, Gonçalves M, Maia S, Pereira S, Severo M, Lunet N. Reliability and validity of the Pittsburgh sleep quality index in breast cancer patients. Support Care Cancer. 2017;25:3059–66.View ArticlePubMedGoogle Scholar
  50. Zheng B, Li M, Wang K, Lv J. Analysis of the reliability and validity of the Chinese version of Pittsburgh sleep quality index among medical college students. Beijing Da Xue Xue Bao Yi Xue Ban. 2016;48:424–8.PubMedGoogle Scholar
  51. Khosravifar S, Bandi MG, Alavi K, Javadi PHS. Evaluation of the psychometric properties of the Persian version of the Pittsburgh sleep quality index in depressed patients. Electron Physician. 2015;7:1644.View ArticlePubMedPubMed CentralGoogle Scholar
  52. Passos MH, Silva HA, Pitangui AC, Oliveira VM, Lima AS, Araújo RC. Reliability and validity of the Brazilian version of the Pittsburgh sleep quality index in adolescents. J Pediatr. 2017;93:200–6.View ArticleGoogle Scholar
  53. Becker NB, de Neves Jesus S. Adaptation of a 3-factor model for the Pittsburgh sleep quality index in Portuguese older adults. Psychiatry Res. 2017;251:298–303.View ArticlePubMedGoogle Scholar
  54. Hancock P, Larner AJ. Diagnostic utility of the Pittsburgh sleep quality index in memory clinics. Int J Geriatr Psychiatry. 2009;24:1237–41.View ArticlePubMedGoogle Scholar
  55. Yurcheshen ME, Guttuso T Jr, McDermott M, Holloway RG, Perlis M. Effects of gabapentin on sleep in menopausal women with hot flashes as measured by a Pittsburgh sleep quality index factor scoring model. J Women’s Health (Larchmt). 2009;18:1355–60.View ArticleGoogle Scholar
  56. Curcio G, Tempesta D, Scarlata S, Marzano C, Moroni F, Rossini PM, Ferrara M, De Gennaro L. Validity of the Italian version of the Pittsburgh sleep quality index (PSQI). Neurol Sci. 2013;34:511–9.View ArticlePubMedGoogle Scholar
  57. João KADR, Becker NB, de Neves Jesus S, Martins RIS. Validation of the Portuguese version of the Pittsburgh sleep quality index (PSQI-PT). Psychiatry Res. 2017;247:225–9.View ArticleGoogle Scholar
  58. Qiu C, Gelaye B, Zhong Q-Y, Enquobahrie DA, Frederick IO, Williams MA. Construct validity and factor structure of the Pittsburgh sleep quality index among pregnant women in a Pacific-northwest cohort. Sleep Breath. 2016;20:293–301.View ArticlePubMedPubMed CentralGoogle Scholar
  59. de la Vega R, Tomé-Pires C, Solé E, Racine M, Castarlenas E, Jensen MP, Miró J. The Pittsburgh sleep quality index: validity and factor structure in young people. Psychol Assess. 2015;27:e22.View ArticlePubMedGoogle Scholar
  60. Guo S, Sun W, Liu C, Wu S. Structural validity of the Pittsburgh sleep quality index in Chinese undergraduate students. Front Psychol. 2016;7:1126.View ArticlePubMedPubMed CentralGoogle Scholar
  61. Benhayon D, Youk A, McCarthy FN, Davis S, Keljo DJ, Bousvaros A, Fairclough D, Kupfer D, Buysse DJ, Szigethy EM. Characterization of relationships between sleep, inflammation, and psychiatric dysfunction in depressed youth with Crohn’s disease. J Pediatr Gastroenterol Nutr. 2013;57:335.View ArticlePubMedPubMed CentralGoogle Scholar
  62. DeGutis J, Chiu C, Thai M, Esterman M, Milberg W, McGlinchey R. Trauma sequelae are uniquely associated with components of self-reported sleep dysfunction in OEF/OIF/OND veterans. Behav Sleep Med. 2018;16:38–63.View ArticlePubMedGoogle Scholar
  63. Chen P-Y, Jan Y-W, Yang C-M. Are the insomnia severity index and Pittsburgh sleep quality index valid outcome measures for cognitive behavioral therapy for insomnia? Inquiry from the perspective of response shifts and longitudinal measurement invariance in their Chinese versions. Sleep Med. 2017;35:35–40.View ArticlePubMedGoogle Scholar
  64. Zhu B, Xie M, Park CG, Kapella MC. Adaptation of the Pittsburgh sleep quality index in Chinese adults with type 2 diabetes. J Chin Med Assoc. 2018;81:242–7.View ArticlePubMedGoogle Scholar
  65. Morris JL, Rohay J, Chasens ER. Sex differences in the psychometric properties of the Pittsburgh sleep quality index. J Women’s Health. 2017;27:278-82.Google Scholar
  66. Dudysova D, Malá I, Mlada K, Saifutdinova E, Koprivova J, Sos P. Structural and construct validity of the Czech version of the Pittsburgh sleep quality index in chronic insomnia. Neuro Endocrinol Lett. 2017;38:67–73.PubMedGoogle Scholar
  67. Anandakumar D, Dayabandara M, Ratnatunga S, Hanwella R, de Silva V. Validation of the Sinhala version of the Pittsburgh sleep quality index. Ceylon Med J. 2016;61;22-25.Google Scholar
  68. Albright JJ, Park HM. Confirmatory factor analysis using Amos, LISREL, Mplus, SAS/STAT CALIS. Bloomington: Indiana University; 2009.Google Scholar
  69. Cattell RB. The scree test for the number of factors. Multivar Behav Res. 1966;1:245–76.View ArticleGoogle Scholar
  70. O’connor BP. SPSS and SAS programs for determining the number of components using parallel analysis and Velicer’s MAP test. Behav Res Methods Instrum Comput. 2000;32:396–402.View ArticlePubMedGoogle Scholar
  71. Horn JL. A rationale and test for the number of factors in factor analysis. Psychometrika. 1965;30:179–85.View ArticlePubMedGoogle Scholar
  72. Williams B, Onsman A, Brown T. Exploratory factor analysis: a five-step guide for novices. J Emerg Prim Health Care. 2010;8:990399.Google Scholar
  73. Comrey AL. A first course in factor analysis. New York: Academic Press; 1973.Google Scholar
  74. Sapnas KG, Zeller RA. Minimizing sample size when using exploratory factor analysis for measurement. J Nurs Meas. 2002;10:135–54.View ArticlePubMedGoogle Scholar
  75. Bryant FB, Yarnold PR. Comparing five alternative factor-models of the student Jenkins activity survey: separating the wheat from the chaff. J Pers Assess. 1995;64:145–58.View ArticlePubMedGoogle Scholar
  76. Hair JF, Black WC, Babin BJ, Anderson RE: Multivariate Data Analysis. 2011.Google Scholar
  77. Henson RK, Roberts JK. Use of exploratory factor analysis in published research common errors and some comment on improved practice. Educ Psychol Meas. 2006;66:393-416.Google Scholar
  78. Tabachnick BG, Fidell LS. Using multivariate statistics: Pearson Education Limited; Harlow, 2013.Google Scholar
  79. Costello AB, Osborne JW. Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis. Pract Assess Res Eval. 2005;10:1-9.Google Scholar
  80. Gie Yong A, Pearce S. Beginner’s guide to factor analysis: focusing on exploratory factor analysis. Tutor Quant Methods Psychol. 2013;9:79-94.Google Scholar
  81. Beavers AS, Lounsbury JW, Richards JK, Huck SW, Skolits GJ, Esquivel SL. Practical considerations for using exploratory factor analysis in educational research; 2013. p. 18.Google Scholar
  82. Cudeck R, MacCallum RC. Factor analysis at 100: historical developments and future directions. Mahwah: Lawrence Erlbaum Associates; 2007.Google Scholar
  83. Pett MA, Lackey NR, Sullivan JJ. Making sense of factor analysis: the use of factor analysis for instrument development in health care research. California: SAGE Publications; 2003.Google Scholar
  84. Courtney MGR. Determining the number of factors to retain in EFA: using the SPSS R-menu v2.0 to make more judicious estimations. ERIC. 2013;18:14.Google Scholar
  85. Thompson B. Exploratory and confirmatory factor analysis: understanding concepts and applications. Washington, DC: American Psychological Association; 2004.Google Scholar
  86. Bollen K, Stine RA. Bootstrapping goodness-of-fit measures in structural equation models. Sociol Methods Res. 1992;21:205–29.View ArticleGoogle Scholar
  87. Jackson DL, Gillaspy JA, Purc-Stephenson R. Reporting practices in confirmatory factor analysis: an overview and some recommendations. Psychol Methods. 2009;14:6–23.View ArticlePubMedGoogle Scholar
  88. Awang Z. Validating the measurement model: CFA. In: A Handbook on SEM. 2nd editiion ed: Kuala Lumpur: Universiti Sultan Zainal Abidin; 2015. p. 54–73.Google Scholar
  89. Jaccard J, Wan CK. LISREL approaches to interaction effects in multiple regression. California: SAGE Publications; 1996.Google Scholar
  90. Cheung GW, Rensvold RB. Evaluating goodness-of-fit indexes for testing measurement invariance. J Struct Equ Model. 2009;9:233-55.Google Scholar

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