
==== Front
BMC Public Health
BMC Public Health
BMC Public Health
1471-2458
BioMed Central London

19990
10.1186/s12889-024-19990-w
Research
Psychometric properties of the social determinants of health questionnaire (SDH-Q): development and validation
Sabo Abdulwali 12
Kuan Garry 3
Abdullah Sarimah 1
Kuay Hue San 4
Goni Mohammed Dauda 56
Kueh Yee Cheng yckueh@usm.my

1
1 https://ror.org/02rgb2k63 grid.11875.3a 0000 0001 2294 3534 Biostatistics and Research Methodology Unit, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Kelantan Malaysia
2 https://ror.org/0278jft56 0000 0004 4660 0618 Department of Public and Environmental Health, Faculty of Basic Medical Sciences, Federal University Dutse, Dutse, Jigawa State Nigeria
3 https://ror.org/02rgb2k63 grid.11875.3a 0000 0001 2294 3534 Exercise and Sports Science Programme, School of Health Sciences, Universiti Sains Malaysia, Kubang Kerian, Kelantan Malaysia
4 https://ror.org/02rgb2k63 grid.11875.3a 0000 0001 2294 3534 Department of Psychiatry, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Kelantan Malaysia
5 https://ror.org/0463y2v87 grid.444465.3 0000 0004 1757 0587 Faculty of Veterinary Medicine, Universiti Malaysia Kelantan, Pengkalan Chepa, Kelantan Malaysia
6 https://ror.org/0463y2v87 grid.444465.3 0000 0004 1757 0587 Institute for Artificial Intelligence and Big Data, Universiti Malaysia Kelantan, Kota Bharu, Kelantan Malaysia
16 9 2024
16 9 2024
2024
24 250721 2 2024
4 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Background

The influence of social determinants of health (SDH) on sustainable development goals (SDG) has gained attention in recent years. However, there is a scarcity in the availability of valid and reliable instruments to assess the multiple aspects of SDH. Hence, this study was conducted to develop a brief self-reported measure for assessing SDH.

Method

A cross-sectional survey was conducted among university undergraduate students in Nigeria. The study consisted of 300 participants in the EFA (males 55.7%, females 44.3%) and 430 participants in the CFA (males 54.0%, females 46.0%). Participants were selected using a convenience sampling approach to assess their perceptions regarding SDH. Content Validity Index (CVI), Face Validity Index (FVI), Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), Composite Reliability (CR), Average Variance Extracted (AVE), Cronbach’s alpha, and Intraclass Correlation Coefficient (ICC) were computed to determine the psychometric properties of the newly developed SDH scale.

Results

In the EFA, two factors were extracted (structural determinants of SDH and intermediary determinants of SDH), with all 20 items retained. The total variance explained by the EFA model was 61.8%, and the factor correlation was 0.178. The Cronbach’s alpha values of the two factors were 0.917 and 0.939. In the CFA, the initial model did not fit the data well based on fit indices. After several re-specification of the model, the final re-specified measurement model demonstrated adequate fit factor structure of the SDH scale with two factors and 20 items (CFI = 0.943, TLI = 0.930, SRMR = 0.056, RMSEA = 0.053, RMSEA p-value = 0.220). The CR was 0.797 for structural determinants of SDH and 0.794 for intermediary determinants of SDH. The ICC was 0.938 for structural determinants of SDH and 0.941 for intermediary determinants of SDH.

Conclusion

The findings indicate that the SDH scale has adequate psychometric properties and can be used to assess the perceived level of SDH. We recommended that this tool be tested in other populations with diverse age groups and other demographic characteristics.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-024-19990-w.

Keywords

Social determinants of health
Questionnaire
Validity
Reliability
Construct
This research was supported by the Universiti Sains Malaysia Short-Term GrantR501-LR-RND002-0000000147-0000. R501-LR-RND002-0000000147-0000. issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
==== Body
pmcBackground

Health inequities caused by the influence of social determinants of health (SDH) have become critical issues globally that necessitate immediate action to improve the quality of life and well-being for all [1]. SDH was recognized as an essential component for the Sustainable Development Goals (SDG), which are a set of 17 global goals established by the United Nations in 2015 as part of the 2030 Agenda for Sustainable Development [2]. SDH has generated a great deal of interest in the field of public health in recent decades [3]. The term “social determinants of health” generates a lot of research studies and documents, most of which have just recently been published [4]. The necessity for a multifaceted approach to the SDH, the rapid introduction of theoretical frameworks and models, and the rise in the volume of research in a very short period of time have led to significant ambiguity surrounding this concept [5].

The term “social determinants of health” has taken on a dual meaning, referring to both the social factors that affect an individual’s or population’s health as well as the social processes that result in an unequal distribution of these factors among groups with unequal status in society [5–7]. As a result, SDH refers to both the factors that influence health and the factors that influence health inequities [6]. Essentially, this idea involves improving social factors that affect health and distributing them fairly. Therefore, researchers suggested changing the phrase to “social determinants of health and related inequalities” to encompass both the factors influencing health and those influencing health inequalities [4, 8, 9].

The wide and expanding number of societal factors that affect SDH serves as another source of complexity. Initially, researchers frequently reported a small number of factors—such as diet, education, employment, and living conditions—but in peer-reviewed literature and academic textbooks, the list has significantly expanded recently [1, 10, 11]. In fact, the list has become so lengthy that if someone wishes for a comprehensive list of SDH, their enthusiasm may immediately decrease upon learning how extensive it is [4]. Some of the most prominent SDH factors that were common in the previous research include education [12], housing and/or living conditions [13], wealth and its distribution [14], stress, young life, social isolation, career, unemployment, social protection, addiction, food, and transportation [15]. Moreover, recent studies have identified the healthcare system, sexual identity, and gender preference as components of SDH [14, 16, 17].

The World Health Organization Commission on Social Determinants of Health (CSDH) work has played a crucial role in consolidating the SDH framework into two essential components: (1) structural determinants of SDH and (2) intermediary determinants of SDH [18–20]. According to the CSDH [18], the most critical indicators of structural determinants include income, education, occupation, social class, gender, and race or ethnicity. These factors, in conjunction with surrounding circumstances, help establish and reinforce class divisions, determining a person’s socioeconomic position within hierarchies of authority, status, and access to resources. Similarly, the key components of intermediary determinants include material conditions, psychological factors, behavioral and/or biological factors, and the health system, all of which influence differences in exposure to and susceptibility to poor health outcomes [18, 20].

The need for available questionnaires to assess and address SDH at the outpatient, inpatient, and community-based levels is becoming increasingly apparent to health care providers in various settings [1, 21]. Most of the available screening tools only look at one aspect of SDH, such as food insecurity and shelter [22], health literacy [23, 24], social support [25], sexual abuse [26], and social support for exercise [27]. Additionally, a recent systematic review by O’Brien [1] reported that four screening tools look at multiple aspects of SDH, including the Income, Housing, Education, Legal Status, Literacy, and Personal Safety (IHELLP) Questionnaire; the Questionnaire Literacy Screen; Well, Child Care, Evaluation, Community Resources, Advocacy, Referral, and Education (We Care); and the Child Poverty Tool and Resource Guide. However, none of these scales account for factors like social support, childhood, access to healthcare, and other resources and take time to complete, which may limit their applicability during many healthcare screenings where a brief summary is usually required [28–30].

A brief self-report measure of SDH called the social determinants of health, the Steps to Better Health Questionnaire (STBH-Q), was recently developed and validated among the Australian adult population [28]. The STBH-Q, which consists of 16 items and five underlying constructs, was created to assess multiple factors that influence SDH at the individual level. These factors include: access (six items); employment, finances, and education (three items); safety at home and in the community (two items); physical and mental health (three items); and family and childhood (two items) [28]. However, the main limitation of their study is the many cross-loadings of the items in the EFA, and some factors had only two items. Therefore, in this study, we aim to develop a new and brief SDH questionnaire (SDH-Q) based on the CSDH framework [18] with two factors: (1) structural determinants of SDH (10 items) and (2) intermediary determinants of SDH (10 items) that can overcome the previous limitations.

Materials and methods

Study design

The current study was a cross-sectional survey, and a total of 730 undergraduate students from the College of Medicine and Allied Medical Sciences at Federal University Dutse, Nigeria (FUD), were recruited using a convenience sampling approach between 3rd April 2023 and 30th June 2023. We started by distributing the Online Google Survey Form link to the students through emails, WhatsApp groups, and Telegrams. The link included a brief introduction to the study and outlined the inclusion criteria. We asked those who agreed to participate and met the inclusion criteria to click on the link and submit their responses. In exploratory or preliminary studies aimed at generating hypotheses or insights, convenience sampling may be considered appropriate, although random sampling is widely recognized as a more rigorous method for participant selection [31, 32]. This method enables researchers to efficiently collect data and lay the groundwork for future investigations [31]. We opted for a convenience sampling approach in participant selection for this study because of its accessibility, simplicity, and potential to establish a solid foundation for generalizability, ensuring that the sample data accurately represent the population from which the sample was drawn [33].

Data collection

The data collection process involved sharing the Online Google Survey Form link with students who met the study’s inclusion criteria. Online Google Survey Form are widely preferred for research and survey purposes because of their practical, adaptable, and cost-effective features, effectively reducing response bias [34]. The inclusion criteria for the study included being enrolled in the College of Medicine and Allied Medical Sciences at FUD, undergraduate students ranging from first to final year, being registered students during the data collection period, and expressing consent to participate. Additionally, we specifically targeted participants from the college of medicine and allied medical sciences due to their presumed familiarity and understanding of the concepts and constructs being assessed. Participants possessing a certain level of understanding of a specific scale contribute to the enhancement of the scale’s construct validity, ensuring that it accurately measures the intended concepts [35].

Ethical approval

The Human Research Ethics Committee, Ministry of Health, Jigawa State, Nigeria [JGHREC/2023/151], and Universiti Sains Malaysia’s Human Research Ethics Committee [USM/JEPeM/22110695], granted ethical approval for the study. The participants were informed about the research aim and methods before signing the informed consent form. The investigation conforms to the principles outlined in the Declaration of Helsinki.

Generation of items

The items were generated based on the conceptual framework of the World Health Organization Commission on Social Determinants of Health (CSDH) principles [18, 20], which we structured into two (2) factors: structural and intermediary determinants of SDH. We solicited input and recommendations from experts in public health, psychometrics, health psychology, and questionnaire validation regarding the generated items. Additionally, an in-depth literature search and interviews were conducted with 12 undergraduate students to gather further information. Initially, the SDH-Q consisted of 20 items, with 10 items assigned to each construct. For the structural determinants of the SDH factor, the responses were evaluated using a five-point Likert scale from 1 (totally unsatisfied) to 5 (totally satisfied), whereas the responses of the intermediary determinants of the SDH factor were evaluated using a five-point Likert scale from 1 (very poor) to 5 (very good).

Content validity, face validity, and pre-testing of the SDH-Q

Following the item generation, we conducted content validity by inviting six experts using the non-face-to-face approach from health psychology, psychometrics, public health, and questionnaire development to rate each item’s relevance to its respective factor. The item content validity index (I-CVI) and scale content validity index (S-CVI) were computed to determine the scale content validity based on the recommended guidelines [36–38]. These guidelines recommend having at least six experts for an acceptable CVI of 0.83. The rating of relevance was transformed into either 1 (the item is quite relevant or the item is highly relevant) or 0 (the item is not relevant or the item is somewhat relevant) for each item. The I-CVI was determined by computing the proportion of content experts who rated the items as 1 for relevance. Subsequently, the S-CVI was computed by averaging the I-CVIs for each construct on the SDH-Q. The I-CVI of the SDH scale ranged from 0.83 to 1, and the S-CVIs for the two SDH-Q factors were 0.93 and 0.95. These CVI values therefore satisfied the required cutoff of 0.83 (for six experts) [37].

Furthermore, the clarity and comprehension of the items were assessed using the face validity index (FVI). Ten undergraduate students from the targeted population evaluated each item for clarity and comprehension. The item face validity index (I-FVI) and scale face validity index (S-FVI) were computed to determine the scale face validity based on the recommended guidelines [39, 40]. These guidelines recommend having at least 10 raters for an acceptable FVI of 0.83. All the responses were received using a Google Form. The rating of relevance was transformed into either 1 (the item is clear and understandable, or the item is very clear and understandable) or 0 (the item is not clear and understandable, or the item is somewhat clear and understandable) for each item. The I-FVI was determined by computing the proportion of participants who assigned a relevance rating of 1 to the items. The S-FVIs were computed by averaging the I-FVIs for each construct on the SDH-Q. The I-FVI of the SDH scale ranged from 0.90 to 1, and the S-FVIs for the two SDH scale factors were 0.98 and 1. These FVI values therefore satisfied the required cutoff of 0.83 (for 10 raters) [40].

Sample size

In exploratory factor analysis (EFA), the recommended minimum sample size ranges from 100 to 250 [41]. Initially, for our study, we set the EFA minimum sample size at 200, and after factoring in a 30% adjustment for missing values, the adjusted sample size became 286. Consequently, we rounded up the sample size to 300 for the EFA, consistent with the recommendation by Tabachnick et al. [42], who advocate for an acceptable sample size of 300 for the EFA. For confirmatory factor analysis (CFA), the recommended minimum sample size for studies with seven or fewer constructs is 300 [43]. In our study, we adhered to this guideline and set the CFA sample size at 300. After incorporating a 30% adjustment for missing values (i.e., 300/(1-0.3)), the final adjusted sample size for CFA was 430. Therefore, the total sample size consisted of 730 undergraduate students, with 300 for the EFA and 430 for the CFA.

Data analysis

The data were pre-screened to check for wrong data entry and missing values, and only the items with complete responses were included in the analysis. The EFA was performed using the Statistical Package for Social Sciences (SPSS) version 27 (IBM, Armonk, NY, USA). The CFA was conducted using Mplus 8 to test the initial hypothesized model. In this study, the Maximum Likelihood Ratio (MLR) Estimator was used during the CFA because of its robustness to non-normal data distributions [44].

The EFA study had a total of 300 participants. The 20 completed items on the SDH scale were tested using principal axis factoring with Promax rotation to extract the main contributing factors. In EFA, researchers applied Promax rotation when they desired a more practical and comprehensible factor structure or expected and had a theoretical rationale for correlated factors [42]. Moreover, Promax rotation facilitates better alignment of the hypothesized model with established theories or expectations [42]. The factors with an eigenvalue greater than one were examined, and those with a factor loading greater than 0.40 were regarded as significantly relevant and were retained for subsequent analysis [43]. If there is a deletion of an item, the EFA model will be re-specified, and the factor loadings will be inspected following each re-specification. A Cronbach’s alpha value of 0.60 or higher was regarded as acceptable for each construct’s internal consistency [43].

The EFA model was further tested using the CFA with a total of 430 participants. The standardized factor loading of equal to or greater than 0.40 was recommended as a cut-off for acceptable factor loading, and as such, for the current study, it was applied as a criterion to retain or delete an item [44, 45]. According to Hair et al. [46], the acceptable fit indices for a sample size greater than 250 with 12 items and higher were: root mean square error of approximation (RMSEA) less than 0.07; standardized root mean square residual (SRMR) less than 0.08; and comparative fit index (CFI) or Tucker and Lewis index (TLI) greater than 0.92. Model re-specification was performed by referring to the CFA modification index to improve the model fit indices. The models were respecified after considering sufficient theoretical guidance.

To further investigate the convergent validity of the SDH scale, average variance extracted (AVE) and composite reliability (CR) were computed. The acceptable cut-off values were equal to or greater than 0.70 and 0.50 for CR and AVE, respectively [38, 45, 47]. However, convergent validity is still regarded as adequate if the AVE values are less than 0.50 but the CR values are greater than 0.60 [47]. The discriminant validity, or the degree to which a factor differs from another factor, was investigated by examining the correlation between the factors [45]. A correlation coefficient between two factors of 0.85 or less was deemed to have adequate discriminant validity [45]. Also, Fornell and Larcker [48] pointed out that the AVE of the constructs must be greater than the shared variance (i.e., the square of the correlation coefficient) between the factors in order for discriminant validity to be established. Furthermore, a sub-sample of 70 respondents completes the SDH questionnaire twice over the course of a 7-day period to determine test-retest reliability. There was sufficient reliability when the intra-class correlation coefficient (ICC) values were above 0.70 [49].

Results

Table 1 presents the general characteristics of study participants for both the EFA and CFA samples. A total of 730 students completed the study survey (100 response rate) over a period of 3 months. The researchers used the first 300 responses for the EFA and the remaining 430 responses for the CFA. Among the 300 samples used for EFA, 55.7% were males and 44.3% were females, with a mean age of 21.1 years (SD = 3.00). The mean frequency and duration of exercise per week were 4.1 (SD = 2.25) and 46.2 (SD = 37.42), respectively. The majority of the students were Hausa (70.7%) and studying Medicine (43.7%). Furthermore, most of the students were in Year 1 (43.7%). Whereas, Among the 430 samples used for CFA, 54.0% were males and 46.0% were females, with a mean age of 22.4 years (SD = 2.43). The mean frequency and duration of exercise per week were 3.4 (SD = 2.12) and 46.2 (SD = 52.01), respectively. The majority of the students were Hausa (70.9%) and studying Medicine (53.4%). Furthermore, most of the students were in Year 3 (70.0%).

Table 1 General characteristics of the participants in EFA and CFA (n = 730)

	EFA (300)	CFA (430)	
Variables	Mean (SD)	n (%)	Mean (SD)	n (%)	
Age	21.1 (3.00)		22.4 (2.43)		
Frequency of exercise/week	4.1 (2.25)		3.4 (2.12)		
Duration of exercise (min)	46.2 (37.42)		46.2 (52.01)		
Gender					
Male		167 (55.7)		232 (54.0)	
Female		133 (44.3)		198 (46.0)	
Ethnicity					
Hausa		212 (70.7)		305 (70.9)	
Yoruba		31 (10.3)		45 (10.5)	
Igbo		11 (3.7)		6 (1.4)	
Others		46 (15.3)		74 (17.2)	
Field of study					
Medicine		131 (43.7)		229 (53.4)	
Human anatomy		109 (36.3)		118 (27.5)	
Human physiology		60 (20.0)		82 (19.1)	
Study year					
Year 1		131 (43.7)		16 (3.7)	
Year 2		51 (17.0)		14 (3.3)	
Year 3		5 (1.7)		301 (70.0)	
Year 4		113 (37.7)		99 (23.0)	
Note: EFA = exploratory factor analysis; CFA = confirmatory factor analysis; SD = standard deviation; n = number; min = minutes

EFA results of the SDH-Q

The initial EFA model of the SDH with 20 items yielded good sampling adequacy with an estimated Kaiser-Meyer-Olkin (KMO) value of 0.899, and the Bartlett’s test of sphericity was significant (p < 0.001). Thus, the model is considered to have adequate validity. Three factors in the initial EFA model had eigenvalues greater than 1, but only two of the factors loaded well with all the items (Fig. 1). Hence, the number of factors was set at two in the subsequent stage, which conforms with the SDH-Q proposed structure. Using principal axis factoring with Promax rotation, two factors were extracted. The findings indicate that the two factors had factor loadings greater than 0.40 with no cross-loadings, a factor correlation of 0.178, and a cumulative percentage of 61.8%. As such, none of the items were deleted from the EFA (Table 2). The factor loading plot is shown in Fig. 1.

Fig. 1 Factor loading plot of exploratory factor analysis (EFA)

Table 2 Items descriptive statistics, exploratory factor analysis, and reliability analysis (n = 300)

Item content	Mean	SD	Factor loading	
			1	2	
How satisfied are you with your gender?	4.29	1.12	0.738		
In terms of all the opportunities in your community, how would you evaluate gender equality?	3.48	1.16	0.694		
How satisfied are you with your ethnic background?	4.20	1.14	0.745		
In terms of all the opportunities in your community, how would you evaluate ethnic equality?	3.50	1.18	0.746		
How satisfied are you with your present financial income?	3.24	1.29	0.684		
How do you rate your financial opportunities in the future?	3.87	1.10	0.784		
How satisfied are you with your present education?	3.74	1.09	0.789		
How do you rate your employment opportunity in the future?	3.61	1.13	0.781		
How satisfied are you with your present standard of living?	3.64	1.14	0.776		
How do you rate the government’s effort towards improving your standard of living?	2.60	1.23	0.557		
How do you rate the state of your current housing or accommodations?	3.41	1.23		0.757	
How do you rate the availability of healthy food or safe water in your neighbourhood?	3.29	1.18		0.789	
How do you rate the support you received from your family members?	4.00	1.27		0.793	
How do you rate the support you received from your friends?	3.56	1.20		0.784	
How do you rate the state of your mental health?	3.92	1.24		0.803	
How do you rate the state of your physical health?	3.89	1.22		0.869	
How would you rate your good lifestyle habits, such as healthy eating?	3.63	1.19		0.823	
How do you rate the quality of the health system services in your community?	3.13	1.08		0.751	
How do you rate your access to health services when needed?	3.19	1.10		0.776	
How do you rate the affordability of health services in your community?	3.03	1.11		0.757	
 Eigenvalue			7.20	5.16	
 Variance explained (%)			36.00	25.80	
 Cumulative variance (%)			36.00	61.80	
 Cronbach’s alpha			0.917	0.939	
SD = standard deviation; Factor correlation = 0.178, item-total correlation = 0.511–0.756 (structural SDH) and 0.627–0.837 (intermediary SDH)

CFA results of the SDH-Q

We further tested the EFA measurement model using CFA with an independent sample of 430 students, comprising 20 items and two factors: structural determinants of SDH (10 items) and intermediary determinants of SDH (10 items). The results of the initial specified measurement model (Model-1) show poor fit indices (Table 3). However, all the items had a standardized factor loading greater than 0.40 (Fig. 2). The model fit indices were improved after adding 15 pairs of error covariances between items within the same factor (Fig. 3). The fit indices of the respecified model (Model-2) were acceptable (Table 3), with all the items retained. The result of the final model (Model-2) showed standardized factor loading ranging from 0.435 to 0.780, which was considered moderate to very good (Fig. 3).

Table 3 Summary for SDH model fit indices (n = 430)

Path model	RMSEA (90% CI)	CFI	TLI	SRMR	RMSEA p-value	
Model-1	0.114 (0.108, 0.121)	0.715	0.680	0.083	< 0.001	
Model-2	0.053 (0.046, 0.061)	0.943	0.930	0.056	0.220	
Notes: RMSEA = Root Mean Square Error of Approximation, CI = Confidence Interval, CFI = Comparative Fit Index, TLI = Tucker-Lewis Index, SRMR = Standardised Root Mean square Residual, Model-2 with 15 correlated items residual: SDH16 with SDH15; SDH20 with SDH19; SDH3 with SDH1; SDH14 with SDH13; SDH8 with SDH6; SDH4 with SDH2; SDH19 with SDH18; SDH20 with SDH18; SDH8 with SDH7; SDH17 with SDH15; SDH17 with SDH16; SDH10 with SDH5; SDH6 with SDH1; SDH10 with SDH3; SDH9 with SDH5

Fig. 2 SDH measurement (Model-1)

Fig. 3 SDH measurement (Model-2)

Composite reliability (CR), average variance extracted (AVE), and discriminant validity

The CR was 0.797 for structural determinants of SDH and 0.794 for intermediary determinants of SDH. The AVE was 0.356 and 0.404 for structural determinants of SDH and intermediary determinants of SDH, respectively. Even though the AVE values were below the recommended cut-off of 0.50, all the CR values were above 0.60, so it was concluded that the SDH scale had sufficient convergent validity [47]. The correlation coefficient between the two factors is 0.149. Furthermore, the squared of the correlation coefficient between the two factors (0.022) is lower than all the AVE values. This indicates sufficient discriminant validity [48]. The final SDH model’s CR and AVE values, correlation coefficients, and square of the correlation coefficient are shown in Table 4.

Table 4 Composite reliability (CR), average variance extraction (AVE), factor correlation and squared correlation for SDH final model

Construct	CR (95% CI)	AVE	1	2	r 2	
Structural	0.797 (0.754, 0.840)	0.356	1	0.149	0.022	
Intermediary	0.794 (0.750, 0.839)	0.404		1		

Test-retest reliability

A total of 70 participants completed the SDH scale twice within the interval of 7 days. For the structural determinants of SDH, the mean score decreased from 38.8 (SD = 4.77) at day 1 to 37.4 (SD = 5.53) at day 7, with an ICC value of 0.938 (95% CI: 0.901, 0.961, p-value < 0.001). For the intermediary determinants of SDH, the mean score decreased from 37.5 (SD = 5.37) at day 1 to 37.2 (SD = 4.37) at day 7, with an ICC value of 0.941 (95% CI: 0.907, 0.963, p-value < 0.001).

The SDH questionnaire development process is presented in Fig. 4.

Fig. 4 Summary of questionnaire development process

Discussion

According to the WHO Commission on Social Determinants of Health (CSDH), we developed a brief measure to evaluate the perceived level of SDH among university undergraduate students. SDH are referred to as social factors that affect an individual’s or population’s health as well as the social processes that lead to an unequal distribution of these factors among groups with unequal status in society [5, 6, 10]. These factors include income, education, occupation, social class, gender, race, or ethnicity, material circumstances, psychological circumstances, behavioral, and/or biological factors, both of which were categorized as structural determinants of health and intermediary determinants of health [21]. Therefore, the items were developed to evaluate the individual’s perceived satisfaction with these factors and the equal opportunities they provide.

The SDH-Q contained 20 items measuring two underlying constructs, namely, the structural determinants of the SDH (10 items) and the intermediary determinants of the SDH (10 items). The items under SDH structural determinants assess a range of factors that create or reinforce social stratification in society and define individuals’ socioeconomic position using a Likert option ranging from 1 (totally unsatisfied) to 5 (totally satisfied). These factors are typically determined by government policies or inheritance [22]. The intermediary determinants of SDH evaluate various factors associated with psychosocial conditions, the individual’s environment, and the health care system, employing a Likert scale from 1 (extremely poor) to 5 (excellent). These intermediary determinants of SDH are also referred to as individual-level mediators of health inequities that shape health outcomes [22].

The content validity results show that the I-CVI of all 20 items ranged from 0.83 to 1, and the S-CVIs were 0.93 and 0.95. For face validity, the results reveal that the I-FVI values ranged from 0.90 to 1, and the S-FVIs were 0.90 and 1. These results indicate acceptable content validity and face validity [36–38]. Subsequently, the SDH scale was tested for EFA and CFA among two independent samples of undergraduate students, who were mostly adolescents. The relationship between health and health behaviors from adolescence to adulthood is significant; therefore, how these social determinants impact adolescent health is critical for both the general population’s health and the growth of nations’ economies [50]. Additionally, the transition from adolescence to adulthood affects how people develop in terms of their health and quality of life. Both social and economic factors within nations influence these changes, leading to inequalities [50].

A total of 730 students participated in the study, divided into two independent samples, with 300 used for EFA and 430 for CFA. The EFA sample includes a higher proportion of first-year students (43.7%), while the CFA sample has a higher proportion of third-year students. However, since the distribution of other characteristics (e.g., mean age, gender, ethnicity, and field of study) is relatively consistent between the two samples, they are considered homogenous. In addition, using undergraduate students as a sample has limitations, such as potential biases and limited generalizability, but their convenience, availability, homogeneity, and familiarity with academic settings and research procedures make them more likely to adhere to study protocols [51, 52]. Consequently, undergraduate students remain a valuable sample for exploring research questions or testing hypotheses [52].

In the EFA, two factors were extracted, comprising 20 items. All the items loaded satisfactorily on their respective constructs, with factor loading above 0.40 and no cross-loading. In a previous study [28], the EFA extracted the SDH with five underlying constructs comprising 16 items. The constructs were: Access; Employment, Finances and Education; Family and Childhood; Physical and Mental Health; and Safety at home and in the community. However, there was cross-loading of items throughout their EFA process [28]. We believe that this might happen because of the similarities between the constructs. Therefore, the current study resolves these issues by creating a similar scale with two constructs, namely, structural determinants of health and intermediary determinants of health, which is in line with the WHO’s CSDH work [18]. Furthermore, Patton et al. [50] emphasized that, while safe and supportive relationships with families, schools, and peers are critical to assisting young people in developing to their full potential, structural factors such as national wealth, financial inequality, and access to education are the strongest determinants of health worldwide.

The CFA results confirmed the final 20-item, 2-factor model of the new SDH scale, with all the items retained. The final model showed adequate fit indices, and all the items had acceptable factor loading on their respective constructs. Also, the two constructs had acceptable internal consistency, composite reliability, and discriminant validity. These demonstrate that the SDH scale has adequate psychometric properties and can be applied to assess individuals perceived social determinants of health [45, 47, 48, 53]. In addition, 15 pairs of error covariances were added between items within the same construct (8 for structural determinants of health and 7 for intermediary determinants of health). These residual covariances were added based on the MI values reported in Mplus output after taking into account sufficient theoretical backing. When residual covariances have important meaning in social psychological studies, they can be included in the model [54].

There are some limitations related to this study. Firstly, since the survey was conducted solely at one university, inferences regarding the study findings should be made with caution. However, the large sample size may give the study’s conclusions and results greater weight. Second, using a self-reported survey may lead to response bias and lower the accuracy of the information obtained. We assured all participants that their information would remain private, urged them to answer all questions accurately based on their real perceptions, and advised them not to discuss the results with their friends. We also used a convenience sample approach to recruit study participants, potentially explaining the differences in sociodemographic variables between the EFA and CFA samples.

Conclusions

The objective of this study was to create a novel self-report tool for evaluating perceived social determinants of health (SDH-Q). This was achieved through a thorough examination of existing literature, along with assessments of content validity and face validity. Subsequently, the measure’s construct validity and stability were evaluated among a sample of undergraduate students in Nigeria. The final results provide psychometric evidence of the underlying structure, consisting of structural determinants of SDH and intermediary determinants of SDH. We recommend that future research replicate this study and assess the psychometric properties of the SDH-Q in diverse populations with varying sociodemographic characteristics. This would enable researchers and healthcare professionals to assess individuals’ SDH, facilitate the provision of accessible social support services, and inform service development efforts.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Acknowledgements

We express our appreciation to all the students that participated in this study.

Author contributions

Conceptualization: Abdulwali Sabo, Garry Kuan, Sarimah Abdullah, Hue San Kuay, Mohammed Dauda Goni, and Yee Cheng KuehData curation: Abdulwali Sabo and Yee Cheng KuehFormal analysis: Abdulwali Sabo and Yee Cheng Kueh.Methodology: Abdulwali Sabo, Garry Kuan, Sarimah Abdullah, Hue San Kuay, and Yee Cheng Kueh.Visualization: Abdulwali Sabo, Garry Kuan, Sarimah Abdullah, Hue San Kuay, Mohammed Dauda Goni, and Yee Cheng KuehWriting: original draft: Abdulwali Sabo.Writing, review, and editing: Abdulwali Sabo, Garry Kuan, Sarimah Abdullah, Hue San Kuay, Mohammed Dauda Goni, and Yee Cheng Kueh.

Funding

This research was supported by the Universiti Sains Malaysia Short-Term Grant, Grant No. R501-LR-RND002-0000000147-0000.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

The Human Research Ethics Committee, Ministry of Health, Jigawa State, Nigeria [JGHREC/2023/151], and Universiti Sains Malaysia’s Human Research Ethics Committee [USM/JEPeM/22110695], granted ethical approval for the study. The participants were informed about the research aim and methods before signing the informed consent form. The investigation conforms to the principles outlined in the Declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Abbreviations

FUD Federal University Dutse

SDH Social determinants of health

CSDH Commission on social determinants of health, EFA: Exploratory factor analysis

CFA Confirmatory factor analysis

CVI Content validity index

FVI Face validity index

KMO Kaiser-Meyer-Olkin

CR Composite reliability

AVE Average variance extracted

ICC Intraclass correlation coefficient

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References

1. O’Brien KH Social determinants of health: the how, who, and where screenings are occurring; a systematic review Soc Work Health Care 2019 58 8 719 45 10.1080/00981389.2019.1645795 31431190
O’Brien KH. Social determinants of health: the how, who, and where screenings are occurring; a systematic review. Soc Work Health Care. 2019;58(8):719–45.31431190 10.1080/00981389.2019.1645795
2. Beisheim M. The G20 and the 2030 Agenda for Sustainable Development Sustainable Development: how to strengthen policy coherence and accountability. 2017.
3. Lucyk K McLaren L Taking stock of the social determinants of health: a scoping review PLoS ONE 2017 12 5 e0177306 10.1371/journal.pone.0177306 28493934
Lucyk K, McLaren L. Taking stock of the social determinants of health: a scoping review. PLoS ONE. 2017;12(5):e0177306.28493934 10.1371/journal.pone.0177306
4. Islam MM Social determinants of health and related inequalities: confusion and implications Front Public Health 2019 7 11 10.3389/fpubh.2019.00011 30800646
Islam MM. Social determinants of health and related inequalities: confusion and implications. Front Public Health. 2019;7:11.30800646 10.3389/fpubh.2019.00011
5. Marmot M Bell R Social inequalities in health: a proper concern of epidemiology Ann Epidemiol 2016 26 4 238 40 10.1016/j.annepidem.2016.02.003 27084546
Marmot M, Bell R. Social inequalities in health: a proper concern of epidemiology. Ann Epidemiol. 2016;26(4):238–40.27084546 10.1016/j.annepidem.2016.02.003
6. Marmot M Social Justice, epidemiology and health inequalities Eur J Epidemiol 2017 32 537 46 10.1007/s10654-017-0286-3 28776115
Marmot M. Social Justice, epidemiology and health inequalities. Eur J Epidemiol. 2017;32:537–46.28776115 10.1007/s10654-017-0286-3
7. Eckersley R Beyond inequality: acknowledging the complexity of social determinants of health Soc Sci Med 2015 147 121 5 10.1016/j.socscimed.2015.10.052 26560411
Eckersley R. Beyond inequality: acknowledging the complexity of social determinants of health. Soc Sci Med. 2015;147:121–5.26560411 10.1016/j.socscimed.2015.10.052
8. McCartney G Popham F McMaster R Cumbers A Defining health and health inequalities Public Health 2019 172 22 30 10.1016/j.puhe.2019.03.023 31154234
McCartney G, Popham F, McMaster R, Cumbers A. Defining health and health inequalities. Public Health. 2019;172:22–30.31154234 10.1016/j.puhe.2019.03.023
9. Kelly-Irving M Ball WP Bambra C Delpierre C Dundas R Lynch J Falling down the rabbit hole? Methodological, conceptual and policy issues in current health inequalities research Crit Public Health 2023 33 1 37 47 10.1080/09581596.2022.2036701
Kelly-Irving M, Ball WP, Bambra C, Delpierre C, Dundas R, Lynch J, et al. Falling down the rabbit hole? Methodological, conceptual and policy issues in current health inequalities research. Crit Public Health. 2023;33(1):37–47.10.1080/09581596.2022.2036701
10. Kostelanetz S Pettapiece-Phillips M Weems J Spalding T Roumie C Wilkins CH Health care professionals’ perspectives on universal screening of social determinants of health: a mixed-methods study Popul Health Manage 2022 25 3 367 74 10.1089/pop.2021.0176
Kostelanetz S, Pettapiece-Phillips M, Weems J, Spalding T, Roumie C, Wilkins CH, et al. Health care professionals’ perspectives on universal screening of social determinants of health: a mixed-methods study. Popul Health Manage. 2022;25(3):367–74.10.1089/pop.2021.0176
11. Cottrell EK Dambrun K Cowburn S Mossman N Bunce AE Marino M Variation in electronic health record documentation of social determinants of health across a national network of community health centers Am J Prev Med 2019 57 6 S65 73 10.1016/j.amepre.2019.07.014 31753281
Cottrell EK, Dambrun K, Cowburn S, Mossman N, Bunce AE, Marino M, et al. Variation in electronic health record documentation of social determinants of health across a national network of community health centers. Am J Prev Med. 2019;57(6):S65–73.31753281 10.1016/j.amepre.2019.07.014
12. Shankar J Ip E Khalema E Couture J Tan S Zulla RT Education as a social determinant of health: issues facing indigenous and visible minority students in postsecondary education in Western Canada Int J Environ Res Public Health 2013 10 9 3908 29 10.3390/ijerph10093908 23989527
Shankar J, Ip E, Khalema E, Couture J, Tan S, Zulla RT, et al. Education as a social determinant of health: issues facing indigenous and visible minority students in postsecondary education in Western Canada. Int J Environ Res Public Health. 2013;10(9):3908–29.23989527 10.3390/ijerph10093908
13. Bambra C Gibson M Sowden A Wright K Whitehead M Petticrew M Tackling the wider social determinants of health and health inequalities: evidence from systematic reviews J Epidemiol Community Health 2010 64 4 284 91 10.1136/jech.2008.082743 19692738
Bambra C, Gibson M, Sowden A, Wright K, Whitehead M, Petticrew M. Tackling the wider social determinants of health and health inequalities: evidence from systematic reviews. J Epidemiol Community Health. 2010;64(4):284–91.19692738 10.1136/jech.2008.082743
14. Raphael D. Social determinants of health: Canadian perspectives. Canadian Scholars’; 2016.
15. Wilkinson RG, Marmot M. Social determinants of health: the solid facts. World Health Organization; 2003.
16. Kim PJ Social determinants of health inequities in indigenous canadians through a life course approach to colonialism and the residential school system Health Equity 2019 3 1 378 81 10.1089/heq.2019.0041 31346558
Kim PJ. Social determinants of health inequities in indigenous canadians through a life course approach to colonialism and the residential school system. Health Equity. 2019;3(1):378–81.31346558 10.1089/heq.2019.0041
17. Artiga S, Hinton E. Beyond health care: the role of social determinants in promoting health and health equity. Kaiser Family Foundation. 2018;10.
18. WHO Commission on Social Determinants of Health. Closing the gap in a generation: health equity through action on the social determinants of health: Commission on Social Determinants of Health final report. World Health Organization; 2008.
19. Lundberg O Next steps in the development of the social determinants of health approach: the need for a new narrative Scand J Public Health 2020 48 5 473 9 10.1177/1403494819894789 32009587
Lundberg O. Next steps in the development of the social determinants of health approach: the need for a new narrative. Scand J Public Health. 2020;48(5):473–9.32009587 10.1177/1403494819894789
20. Solar O, Irwin A. A conceptual framework for action on the social determinants of health. WHO Document Production Services; 2010.
21. Pai N Kandasamy S Uleryk E Maguire JL Social risk screening for pediatric inpatients Clin Pediatr 2016 55 14 1289 94 10.1177/0009922815623498
Pai N, Kandasamy S, Uleryk E, Maguire JL. Social risk screening for pediatric inpatients. Clin Pediatr. 2016;55(14):1289–94.10.1177/0009922815623498
22. Baer TE Scherer EA Fleegler EW Hassan A Food insecurity and the burden of health-related social problems in an urban youth population J Adolesc Health 2015 57 6 601 7 10.1016/j.jadohealth.2015.08.013 26592328
Baer TE, Scherer EA, Fleegler EW, Hassan A. Food insecurity and the burden of health-related social problems in an urban youth population. J Adolesc Health. 2015;57(6):601–7.26592328 10.1016/j.jadohealth.2015.08.013
23. Chung S-Y Nahm E-S Testing reliability and validity of the eHealth literacy scale (eHEALS) for older adults recruited online Computers Inf Nursing: CIN 2015 33 4 150 10.1097/CIN.0000000000000146
Chung S-Y, Nahm E-S. Testing reliability and validity of the eHealth literacy scale (eHEALS) for older adults recruited online. Computers Inf Nursing: CIN. 2015;33(4):150.10.1097/CIN.0000000000000146
24. Cho M Lee H Lee Y-M Lee J-y Min H Kim Y Psychometric properties of the Korean version of the Health Literacy on Social Determinants of Health Questionnaire (K-HL-SDHQ) PLoS ONE 2019 14 11 e0224557 10.1371/journal.pone.0224557 31738802
Cho M, Lee H, Lee Y-M, Lee J-y, Min H, Kim Y, et al. Psychometric properties of the Korean version of the Health Literacy on Social Determinants of Health Questionnaire (K-HL-SDHQ). PLoS ONE. 2019;14(11):e0224557.31738802 10.1371/journal.pone.0224557
25. Littlewood K Cummings DM Lutes L Solar C Psychometric properties of the family support scale adapted for African American women with type 2 diabetes mellitus Ethn Dis 2015 25 2 193 9 26118148
Littlewood K, Cummings DM, Lutes L, Solar C. Psychometric properties of the family support scale adapted for African American women with type 2 diabetes mellitus. Ethn Dis. 2015;25(2):193–9.26118148
26. Usta J Farver J Child sexual abuse in Lebanon during war and peace Child Care Health Dev 2010 36 3 361 8 10.1111/j.1365-2214.2010.01082.x 20507328
Usta J, Farver J. Child sexual abuse in Lebanon during war and peace. Child Care Health Dev. 2010;36(3):361–8.20507328 10.1111/j.1365-2214.2010.01082.x
27. Sabo A Kueh YC Arifin WN Kim Y Kuan G The validity and reliability of the malay version of the social support for exercise and physical environment for physical activity scales PLoS ONE 2020 15 9 e0239725 10.1371/journal.pone.0239725 32986724
Sabo A, Kueh YC, Arifin WN, Kim Y, Kuan G. The validity and reliability of the malay version of the social support for exercise and physical environment for physical activity scales. PLoS ONE. 2020;15(9):e0239725.32986724 10.1371/journal.pone.0239725
28. Oster C Gransbury B Anderson D Martin V Skuza P Leibbrandt R Development and validation of a self-report social determinants of health questionnaire in Australia Health Promot Int 2023 38 3 daac029 10.1093/heapro/daac029 35325112
Oster C, Gransbury B, Anderson D, Martin V, Skuza P, Leibbrandt R. Development and validation of a self-report social determinants of health questionnaire in Australia. Health Promot Int. 2023;38(3):daac029.35325112 10.1093/heapro/daac029
29. Morgenlander MA Tyrrell H Garfunkel LC Serwint JR Steiner MJ Schilling S Screening for social determinants of health in pediatric resident continuity clinic Acad Pediatr 2019 19 8 868 74 10.1016/j.acap.2019.02.008 30862512
Morgenlander MA, Tyrrell H, Garfunkel LC, Serwint JR, Steiner MJ, Schilling S. Screening for social determinants of health in pediatric resident continuity clinic. Acad Pediatr. 2019;19(8):868–74.30862512 10.1016/j.acap.2019.02.008
30. Schickedanz A Hamity C Rogers A Sharp AL Jackson A Clinician experiences and attitudes regarding screening for social determinants of health in a large integrated health system Med Care 2019 57 Suppl 6 2 S197 10.1097/MLR.0000000000001051 31095061
Schickedanz A, Hamity C, Rogers A, Sharp AL, Jackson A. Clinician experiences and attitudes regarding screening for social determinants of health in a large integrated health system. Med Care. 2019;57(Suppl 6 2):S197.31095061 10.1097/MLR.0000000000001051
31. Etikan I Musa SA Alkassim RS Comparison of convenience sampling and purposive sampling Am J Theoretical Appl Stat 2016 5 1 1 4 10.11648/j.ajtas.20160501.11
Etikan I, Musa SA, Alkassim RS. Comparison of convenience sampling and purposive sampling. Am J Theoretical Appl Stat. 2016;5(1):1–4.10.11648/j.ajtas.20160501.11
32. Andrade C The inconvenient truth about convenience and purposive samples Indian J Psychol Med 2021 43 1 86 8 10.1177/0253717620977000 34349313
Andrade C. The inconvenient truth about convenience and purposive samples. Indian J Psychol Med. 2021;43(1):86–8.34349313 10.1177/0253717620977000
33. Winton BG Sabol MA A multi-group analysis of convenience samples: free, cheap, friendly, and fancy sources Int J Soc Res Methodol 2022 25 6 861 76 10.1080/13645579.2021.1961187
Winton BG, Sabol MA. A multi-group analysis of convenience samples: free, cheap, friendly, and fancy sources. Int J Soc Res Methodol. 2022;25(6):861–76.10.1080/13645579.2021.1961187
34. Nayak M Narayan K Strengths and weaknesses of online surveys Technology 2019 6 7 0837 2405053138
Nayak M, Narayan K. Strengths and weaknesses of online surveys. Technology. 2019;6(7):0837–2405053138.
35. Jones SM LaRusso M Kim J Yeon Kim H Selman R Uccelli P Experimental effects of Word Generation on vocabulary, academic language, perspective taking, and reading comprehension in high-poverty schools J Res Educational Eff 2019 12 3 448 83
Jones SM, LaRusso M, Kim J, Yeon Kim H, Selman R, Uccelli P, et al. Experimental effects of Word Generation on vocabulary, academic language, perspective taking, and reading comprehension in high-poverty schools. J Res Educational Eff. 2019;12(3):448–83.
36. Polit DF Beck CT The content validity index: are you sure you know what’s being reported? Critique and recommendations Res Nurs Health 2006 29 5 489 97 10.1002/nur.20147 16977646
Polit DF, Beck CT. The content validity index: are you sure you know what’s being reported? Critique and recommendations. Res Nurs Health. 2006;29(5):489–97.16977646 10.1002/nur.20147
37. Polit DF Beck CT Owen SV Is the CVI an acceptable indicator of content validity? Appraisal and recommendations Res Nurs Health 2007 30 4 459 67 10.1002/nur.20199 17654487
Polit DF, Beck CT, Owen SV. Is the CVI an acceptable indicator of content validity? Appraisal and recommendations. Res Nurs Health. 2007;30(4):459–67.17654487 10.1002/nur.20199
38. DeVon HA Block ME Moyle-Wright P Ernst DM Hayden SJ Lazzara DJ A psychometric toolbox for testing validity and reliability J Nurs Scholarsh 2007 39 2 155 64 10.1111/j.1547-5069.2007.00161.x 17535316
DeVon HA, Block ME, Moyle-Wright P, Ernst DM, Hayden SJ, Lazzara DJ, et al. A psychometric toolbox for testing validity and reliability. J Nurs Scholarsh. 2007;39(2):155–64.17535316 10.1111/j.1547-5069.2007.00161.x
39. Yusoff MSB. ABC of response process validation and face validity index calculation. Educ Med J. 2019;11(10.21315).
40. Marzuki MFM Yaacob NA Yaacob NM Translation, cross-cultural adaptation, and validation of the malay version of the system usability scale questionnaire for the assessment of mobile apps JMIR Hum Factors 2018 5 2 e10308 10.2196/10308 29759955
Marzuki MFM, Yaacob NA, Yaacob NM. Translation, cross-cultural adaptation, and validation of the malay version of the system usability scale questionnaire for the assessment of mobile apps. JMIR Hum Factors. 2018;5(2):e10308.29759955 10.2196/10308
41. Kyriazos TA Applied psychometrics: sample size and sample power considerations in factor analysis (EFA, CFA) and SEM in general Psychology 2018 9 08 2207 10.4236/psych.2018.98126
Kyriazos TA. Applied psychometrics: sample size and sample power considerations in factor analysis (EFA, CFA) and SEM in general. Psychology. 2018;9(08):2207.10.4236/psych.2018.98126
42. Tabachnick BG, Fidell LS, Ullman JB. Using multivariate statistics: Pearson Boston. MA; 2013.
43. Black W Babin BJ Multivariate data analysis: its approach, evolution, and impact. The great facilitator: reflections on the contributions of Joseph F Hair 2019 Jr to marketing and business research Springer 121 30
Black W, Babin BJ. Multivariate data analysis: its approach, evolution, and impact. The great facilitator: reflections on the contributions of Joseph F Hair. Jr to marketing and business research: Springer; 2019. pp. 121–30.
44. Muthén LK, Muthén BO. Mplus user’s guide (Version 7). Los Angeles, CA: Author1998.
45. Brown TA. Confirmatory factor analysis for applied research. Guilford; 2015.
46. Hair JF Anderson RE Babin BJ Black WC Multivariate data analysis: a global perspective 2010 USA Pearson Prentice Hall: Upper Saddle River, NJ
Hair JF, Anderson RE, Babin BJ, Black WC. Multivariate data analysis: a global perspective. USA: Pearson Prentice Hall: Upper Saddle River, NJ;; 2010.
47. Byrne BM. Structural equation modeling with Mplus: basic concepts, applications, and programming. routledge; 2013.
48. Fornell C Larcker DF Evaluating structural equation models with unobservable variables and measurement error J Mark Res 1981 18 1 39 50 10.1177/002224378101800104
Fornell C, Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. J Mark Res. 1981;18(1):39–50.10.1177/002224378101800104
49. Baumgartner TA, Jackson AS. Measurement for evaluation in physical education and exercise science. WCB/McGraw-Hill; 1998.
50. Patton GC Sawyer SM Santelli JS Ross DA Afifi R Allen NB Our future: a Lancet commission on adolescent health and wellbeing Lancet 2016 387 10036 2423 78 10.1016/S0140-6736(16)00579-1 27174304
Patton GC, Sawyer SM, Santelli JS, Ross DA, Afifi R, Allen NB, et al. Our future: a Lancet commission on adolescent health and wellbeing. Lancet. 2016;387(10036):2423–78.27174304 10.1016/S0140-6736(16)00579-1
51. Wheeler AR Shanine KK Leon MR Whitman MV Student-recruited samples in organizational research: a review, analysis, and guidelines for future research J Occup Organizational Psychol 2014 87 1 1 26 10.1111/joop.12042
Wheeler AR, Shanine KK, Leon MR, Whitman MV. Student-recruited samples in organizational research: a review, analysis, and guidelines for future research. J Occup Organizational Psychol. 2014;87(1):1–26.10.1111/joop.12042
52. Ashraf R Merunka D The use and misuse of student samples: an empirical investigation of European marketing research J Consumer Behav 2017 16 4 295 308 10.1002/cb.1590
Ashraf R, Merunka D. The use and misuse of student samples: an empirical investigation of European marketing research. J Consumer Behav. 2017;16(4):295–308.10.1002/cb.1590
53. Kline R. Exploratory and confirmatory factor analysis. Applied quantitative analysis in education and the social sciences. Routledge; 2013. pp. 171–207.
54. Enders CK Tofighi D Centering predictor variables in cross-sectional multilevel models: a new look at an old issue Psychol Methods 2007 12 2 121 10.1037/1082-989X.12.2.121 17563168
Enders CK, Tofighi D. Centering predictor variables in cross-sectional multilevel models: a new look at an old issue. Psychol Methods. 2007;12(2):121.17563168 10.1037/1082-989X.12.2.121
