Psychometric Properties of MOS Social Support Survey among Malaysian Community-dwelling Older Adults
Hazwan Mat Din1*, Raja Nurzatul Efah Raja Adnan1, Halimatus Sakdiah Minhat1,2
1 Malaysian Research Institute on Ageing, Universiti Putra Malaysia, Serdang, Malaysia
2 Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Malaysia
*Corresponding Author: [email protected]
Accepted: 15 April 2020 | Published: 30 April 2020
_________________________________________________________________________________________
Abstract: The Medical Outcome Study – Social Support Survey (MOS-SSS) is widely used to measure social support among different population. This study aimed to examine psychometric properties of Malay version MOS-SSS among Malaysian community-dwelling older adults. A cross sectional, validation study was conducted among 295 respondents.
Confirmatory factor analysis revealed first-order four-factor model showed a good fit (χ2 = 379.92, P <0.001; χ2/df = 2.62; CFI = 0.926; TLI = 0.913; RMSEA = 0.074; SRMR = 0.064) and strong construct validity. The second-order four-factor model empirically demonstrated that social support was predominantly influenced by affective support and positive social interaction. High correlation with other social support questionnaire indicated for concurrent validity. All factors showed an excellent reliability (Cronbach’s alpha ≥ 0.79). The Malay version MOS-SSS demonstrates a good validity and reliability to measure social support among older adults living in community in Malaysia.
Keywords: validation, structural validity, construct validity, reliability, older persons, elderly ___________________________________________________________________________
1. Introduction
The lack of social support has become a risk factor among older adults, especially in mental related disorders. Studies showed that older adults whose receiving social support improved their cognitive status (Yeh & Liu, 2003). In addition, social support reduces the risk of health problems among older adults including depression, cognitive impairment and anxiety (Eskin, 2003). These findings indicate the impact and importance of social support on older adults’
health.
In Malaysia, rapid demographic changes has result in accelerated population ageing. As the life expectancy continue to rise and decreasing fertility rate, Malaysia is to become an aged nation by 2030 (Samad & Mansor, 2017). Given the positive impact of social support on older adults and increasing older adult population in Malaysia, there is a dire need of valid and reliable instrument for the measurement.
In general, social support can be measured in terms of whether the support is perceived, which refers to one’s beliefs about availability of support during times of need, or received, which refers to specific types of support one actually received (Haber, Cohen, Lucas, &
Baltes, 2007). There are many instruments available for assessing social support namely Medical Outcomes Study Social Support Survey (MOS-SSS) (Sherbourne & Stewart, 1991), the Multidimensional Scale of Perceived Social support (MSPSS) (Zimet, Dahlem, Zimet, &
Farley, 1988), the Duke-UNC Functional Social Support Questionnaire (DUFSS)
(Broadhead, Gehlbach, de Gruy, & Kaplan, 1988) and the Perceived Social Support Scale (PSSS) (Procidano & Heller, 1983). Among all of mentioned instruments, MOS-SSS has been widely used across different populations (Margolis, Bellin, Sacco, Harrington, & Butz, 2018; Saddki et al., 2017).
The MOS-SSS was originally developed by Sherbourne and Stewart (1991) in English and has been translated into several languages. The instrument also showed excellent validity and reliability among different populations including students, cardiovascular patients, Hodgkin’s lymphoma survivors, neoplastic patients, postpartum women and people living with HIV/AIDS (Alonso Fachado, Montes Martinez, Menendez Villalva, & Pereira, 2007; Costa Requena, Salamero, & Gil, 2007; Dafaalla et al., 2016; Giangrasso & Casale, 2014; Mahmud, Awang, & Mohamed, 2004; Robitaille, Orpana, & McIntosh, 2011; Saddki et al., 2017;
Soares et al., 2012; Wang, Zheng, He, & Thompson, 2013; Yu, Lee, & Woo, 2004).
However, to our knowledge, validity and reliability of MOS-SSS has never been explored among older adults population. Thus, with the potential uses of MOS-SSS in older adults’
population, there are needs of psychometric study of the instrument.
Psychometric properties of an instrument were assessed through validity and reliability (Fletcher, Fletcher, & Fletcher, 2012). Validity assessment of an instrument particularly questionnaire is examined through content validity, construct validity and criterion validity (Hair, Anderson, Babin, & Black, 2010). Content validity commonly assessed by the extent of an instrument cover the entire range of relevant behaviors, concepts or construct. Construct validity is assessed through the level of an instrument measure the construct. Most common statistical methods to assess construct validity are exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) (Brown, 2014). Criterion validity is assessed through how good an instrument correlates with other similar measure or construct. Concurrent validity is subtype of criterion validity examined by conducting correlational analysis between two similar measures. Lastly, internal consistency namely Cronbach’s alpha is among several types of reliability tests (Brown, 2014).
The original MOS-SSS has been translated in Malay version in previous studies (Mahmud et al., 2004; Saddki et al., 2017). The translated version has been validated in postpartum women and HIV patients using EFA. The recent study by Saddki et al. (2017) demonstrated that Malay version MOS-SSS contained four factors, consistent with the original version. As the Malay version MOS-SSS has be validated using EFA, there is a need to conduct a CFA to examine construct validity of the instrument.
In summary, the needs of valid and reliable instrument to measure social support among older adult is crucial as Malaysia is experiencing population ageing. The translated version MOS- SSS, a widely used instrument to measure social support, has been adapted in Malaysia in few populations. The psychometric properties has been examined only at EFA level.
Compared to EFA, CFA provide superior analysis, where it can actually confirms which item loads on which factor (Brown, 2014). In addition, MOS-SSS content validity is a theoretically established by its original authors and previous studies (Dafaalla et al., 2016;
Giangrasso & Casale, 2014; Saddki et al., 2017; Sherbourne & Stewart, 1991). Then, CFA which is theory driven analysis, is highly advisable as researchers are able to specify the model or small subset of models that is plausible (Brown, 2014; Fabrigar, Wegener, MacCallum, & Strahan, 1999). Thus, the primary aim of this study was to examine psychometric properties of Malay version MOS-SSS namely construct validity, concurrent validity and reliability among community-dwelling older adults at CFA level. The secondary aim was to examine the potential presence of second-order factors in the instrument.
2. Methodology
Study Respondents
This was a cross sectional, validation study. Data were collected among community dwelling older adults in Selangor, Malaysia. A total of 296 respondents participated in this study which were recruited from four activity center for older persons (PAWE). Inclusion criteria for eligible respondents were able to read and answer items in Malay, aged 60 years old and above, and willing to participate in this study. Sufficiently impaired mental functions, vision or hearing respondents were excluded. The ethical approval was obtained from the Ethics Committee for Research Involving Human Subjects Universiti Putra Malaysia (JKEUPM).
Samples Size
A minimum sample size of 250 was determined based on recommendation for Confirmatory Factor Analysis (CFA) with non-normal data by Nevitt and Hancock (2001). With expected 30% non-response rate, the study sample size was inflated to 321.
Measurement Tools
The Malay version Medical Outcome Study-Social Support Survey (MOS-SSS) is a self- administered instrument to measure social support. The Malay version MOS-SSS is a translated version of original MOS-SSS (Sherbourne & Stewart, 1991) and permission to use the Malay version MOS-SSS was obtained from its original authors (Saddki et al., 2017).Malay version MOS-SSS contained 19 items and each item was rated under 5-point Likert scale (Strongly disagree to strongly agree). Malay version MOS-SSS composed of four underlying factors; Informational Support (8 items), Tangible Support (4 items), Affective Support (3 items) and Positive Social Interaction (4 items). The Malay version MOS-SSS was reported to have strong construct validity and reliability (Mahmud et al., 2004; Saddki et al., 2017).
The Malay version Multidimensional Scale of Perceived Social Support (MSPSS) is a self- administered instrument to measure social support (Zimet et al., 1988). Malay version MSPSS is a translated version of original MSPSS (Ng, Amer Siddiq, Aida, Zainal, & Koh, 2010). It contained 12 items rated under 7-point Likert scale. In the current study, to improve ease of use for the study sample, 5-point Likert scale was used (Strongly disagree to strongly agree). The scoring method was based on average mean score whereby total score of the items was divided with number of the items. Higher score indicated higher social support and treated as continuous variable. The Malay version MSPSS was reported to have excellent validity and reliability (Ng et al., 2010).
The Malays version General Health Questionaire-12 items (GHQ-12) is a self-administered instruments derived from two sources, namely original version GHQ-12 (Goldberg, 1978) and the validated Malay version 30-items GHQ (Abdul Hamid & Hatta, 1996). The Malay version GHQ-12 contained 12 items rated under categories of response. The scoring method was based on binary scoring method whereby the two least symptomatic response score 0 and the two most symptomatic response score 1 (Yusoff, 2009). The maximum score was 12 while the minimum score was 0. The GHQ-12 score in the present study was treated as continuous variable. The Malay version GHQ-12 was reported to be valid and reliable (Yusoff, 2009).
The Malay version Geriatric Depression Scale-15 items (GDS-15) is a self-rated translated version of original short form GDS-15 (Sheikh & Yesavage, 1986), used as a screening tool
for depression. Short form GDS-15 consisted of 15 items and specifically developed for uses among older adults. The items are rated on binary response (Yes/No). The instrument was reported to have satisfactory construct validity and reliability (Teh & Hasanah, 2004). GDS- 15 score in this study was calculated based on total score of the items. The score was treated as continuous variable.
The Malay version Beck Depression Inventory (BDI) is a self-rated translated version of original BDI (Beck, Ward, Mendelson, Mock, & Eebaugh, 1961) contained 20 items and is used for indication of depression. The respondents responded to items in relation to how they felt over that past week, with higher scores indicating more severe depression. The score ranged from 0 to 3. The scoring method used was based on total scores of the items and treated as continuous variable. The instrument was reported to have strong validity and reliability (Beck, Steer, & Carbin, 1988).
Statistical Analysis
Data entry and data analysis were conducted using SPSS version 21. Sociodemographic variables were analyzed using descriptive statistics, in which numerical variables were reported in the form of mean and standard deviations, while categorical variables were reported in the form of frequency and percentages.
CFA was done in Amos. For model fit evaluation three categories of model fit indices were used; absolute fit (chi-square goodness-of-fit [χ2] and Standardized Root Mean Square Residual [SRMR]), parsimony fit (Root Mean Square Error of Approximation [RMSEA]) and comparative fit (Tucker-Lewis Fit Index [TLI] and Comparative Fit Index [CFI]) (Brown, 2014). Non-significant model chi-square goodness-of-fit was taken as indicative of model fit, with significance level set at 0.05. For RMSEA a cut-off value of 0.06 and less was acceptable, with upper limit of 90 percent confidence intervals (CI) also below the cut-off value and Clfit of more than 0.05 (Brown, 2014). For TLI and CFI, a cut-off point of 0.95 and above was taken to indicate model fit (Brown, 2014; Schreiber, Nora, Stage, Barlow, &
King, 2006). For SRMR a cut-off point of 0.08 and less was used to indicate model fit (Brown, 2014). TLI, CFI, RMSEA and SRMR were given priority to determine the model fit.
For model comparison, Aikaike Information Criterion (AIC) and Expected Cross Validation Index (ECVI) were used. Smaller values for AIC and ECVI indicate for better model fit (Brown, 2006; Schreiber et al., 2006). Factor loadings and modification indices were criteria used for item removal to improve model fit. Item with factor loading less than 0.5 was considered for removal (Kline, 2015). Construct validity was assessed in form of convergent and discriminant validity. High item factor loading (≥ 0.5) in its factor and factor Average Variance Extracted (AVE) ≥ 0.5 were indication for convergent validity (Brown, 2014).
Discriminant validity was assessed by comparing the factor AVE and its Share Variance (SV). Value of AVE less than SV was an indication of discriminant validity. Concurrent validity between instruments was assessed using Spearman’s correlation with significant positive correlation as indication of concurrent validity (Kline, 2015). Reliability was assessed in the form of internal consistency through Cronbach’s alpha and consistency by item-to-total score correlations (Cronbach & Meehl, 1955).
3. Results
Preliminary Data Analysis
From a total of 356 distributed questionnaires, 296 were returned. The response rate of this study was 83.1%. One respondent had eight missing values (42.1%) and was removed from
the dataset. In these remaining respondents there were four respondents had three missing values (MOS-SSS = 2, MSPSS =1 and GDS = 1) and 38 respondents had one missing value (MOS-SSS = 13, MSPSS = 9, BDI = 11 and GDS = 5). The items had percentage of missing values from 0.3 to 3.1% with average of 1.25% for each item. The missing values were handled using single imputation average method. There was no item affected by multivariate collinearity as there was no item with VIF > 10. The data skewness ranged from -1.00 to 0.02 while kurtosis ranged from -0.86 to 0.78. As recommended by Byrne (2010), one can assume multivariate normality if data normality for skewness and kurtosis are met. Thus, CFA with ML was then conducted.
Of the 295 respondents, 113 (38.3%) were men and 182 (61.7%) were women. The mean age of the respondents was 66.98 (standard deviation = 7.05) ranged from 60 to 109 years old.
62.7% of the respondents were married, 30.2% were widowed, 4.7% were divorced or separated and 2.4% were never married. 25.1% of the respondents had tertiary education, 36.6% had secondary education, 28.1% had primary education and 10.1% never received formal education. Majority of the respondents were Malay (92.5%) while 7.5% of them were other races (Chinese and Indian).
First-order Four-factor Model and Three-factor Model
Malay version MOS-SSS CFA first-order four-factor model was specified based on model used by its original authors (Sherbourne & Stewart, 1991), named as Model A. The model showed acceptable model fit except for RMSEA which showing borderline value, marginally lower than the cut-off value (χ2 = 411.80, P <0.001; χ2/df = 2.82; CFI = 0.917; TLI = 0.902;
RMSEA = 0.079; SRMR = 0.066). All item factor loadings were significant. To improve model, MI was then examined and revealed that there was potential error term covariance between e1 and e2 in the model (MI = 29.60) (Figure 1). After this modification, the modified model, named as Model B showed a goof fit based on all fit index (χ2 = 379.92, P
<0.001; χ2/df = 2.62; CFI = 0.926; TLI = 0.913; RMSEA = 0.074; SRMR = 0.064) (Figure 1). The model also showed improvement based on reduced value of chi-square, AIC and ECVI. All item factor loadings were significantly loaded on their proposed factor with values ranged from 0.65 to 0.87. Inspection of MI revealed there was no potential covariance and cross loading that might cause model poor fit. However, the factor correlation between affective support and positive social interaction was relatively high (0.85). For a factor to achieve discriminant validity, its AVE must exceed its SV. The AVE values for affective support and positive social interaction were 0.56 and 0.63, respectively. The SV of the factors was 0.72 (0.852). The greater value of SV and the factors’ AVEs was an indication of poor discriminant validity, and so the factors were combined into single factor, as a three-factor first-order model named as Model C. Model C showed an acceptable but poorer fit compared to the previous model, Model B. The model showed model fit only for χ2/df, CFI and SRMR (χ2 = 434.06, P <0.001; χ2/df = 2.93; CFI = 0.910; TLI = 0.896; RMSEA = 0.081; SRMR = 0.065). This indicated that Model C did not show an improvement to Model B.
Table 1: Summary of model fit indices
Fit index Model
Model A Model B Model C Model D
χ2(df) 411.79 (146) 379.92 (145) 434.06 (148) 408.74 (147)
P <0.001 <0.001 <0.001 <0.001
χ2/df 2.82 2.62 2.93 2.78
CFI 0.917 0.926 0.910 0.918
TLI 0.902 0.913 0.896 0.904
RMSEA 0.079 0.074 0.081 0.078
(90% CI) (0.070, 0.088) (0.065, 0.083) (0.072, 0.090) (0.069, 0.087)
SRMR 0.066 0.064 0.065 0.082
AIC 499.79 469.92 518.06 494.74
ECVI 1.69 1.59 1.76 1.68
Abbreviation: Model A = Initial first-order four-factor model; Model B = first-order four-factor model with modification; Model C = first-order three-factor model with modification; Model D = second-order four-factor model with modification; χ2(df) = Chi-square (degree of freedom); P = P-value; CFI = comparative fit index;
TLI = Tucker-Lewis index; RMSEA = root mean square error of approximation; 90% CI = 90% confidence interval; SRMR = standardized root mean residual; AIC = Aikake information criterion; ECVI = expected cross- validation index.
Figure 1: First-order four-factor model (Model B) of MOS social support survey with modification
Second-order Four-factor Model
Model B factor correlations ranged from 0.54 to 0.84. The moderate to high correlations among the factors implied the potential present of a second-order factor; social support. The second-order four-factor measurement model was specified and named as Model D (Figure 2). Model fit indices showed that Model D fit the data well except for SRMR (χ2 = 408.74, P
<0.001; χ2/df = 2.78; CFI = 0.918; TLI = 0.904; RMSEA = 0.078; SRMR = 0.082). The regression coefficients of the social support were highest on affective support (0.95), followed by positive social interaction (0.86), emotional/informational support (0.72) and tangible support (0.71).
Figure 2: Second-order four-factor model (Model D) of MOS social support survey
Construct Validity
Construct validity through the assessment of convergent and discriminant validity of most fitted model, Model B; first-order four-factor model. Convergent validity shown by significant and high item standardized factor loadings in their proposed factor and AVE >0.5.
All factors had AVE >0.5 except for emotional/informational support which marginally lower than the cut-off value (0.49). The model displayed an adequate discriminant validity for all factors except for affective support and positive social interaction. The SV for both factors (0.72) exceeded the AVEs of the factors; 0.56 and 0.63 for affective support and positive social interaction respectively. The summary of AVEs, SVs and factor correlations are presented in Table 2.
Table 2: Summary of AVEs, SVs and factor correlations
Factor EIS TS AS PSI
EIS 0.49 0.66 0.62 0.59
TS 0.44 0.64 0.66 0.55
AS 0.39 0.43 0.56 0.85
PSI 0.35 0.30 0.72* 0.63
Abbreviation: EIS = emotional/informational support; TS = tangible support; AS = affective support; PSI = positive social interaction.
Note: AVEs are presented on the diagonal (bold); Factor correlations are presented above the diagonal; SVs are presented below the diagonal (italic).
Concurrent Validity
The Spearman’s correlation between MOS-SSS factors and other measurement tools are presented in Table 3. All MOS-SSS factors were significantly correlated with MSPSS scores.
However, only affective support was significantly correlated with GHQ-12.
Reliability
Internal consistency through evaluation Cronbach’s alpha showed all factors were reliable.
The Cronbach’s alpha ranged from 0.79 to 0.89 among the factors (Table 3).
Table 3: Spearman’s correlation between MOS-SSS scores and other tools
Total EIS TS AS PSI
EIS 0.86** - 0.60** 0.50** 0.53**
TS 0.81** 0.60** - 0.51** 0.48**
AS 0.74** 0.50** 0.51** - 0.69
PSI 0.78** 0.53** 0.48** 0.69** -
MSPSS 0.43** 0.34** 0.27** 0.39** 0.48**
GHQ-12 -0.02 -0.10 -0.06 0.15* 0.09
BDI 0.03 0.08 0.12 -0.02 -0.13*
GDS 0.07 0.09 0.15 -0.04 -0.06
Cronbach’s α 0.93 0.89 0.88 0.79 0.86
Abbreviation: Total = MOS social support survey total score; EIS = emotional/informational support; TS = tangible support; AS = affective support; PSI = positive social interaction; MSPSS = multidimensional scale of perceived social support; GHQ-12 = general health questionnaire 12 items; BDI = Beck depression scale; GDS
= geriatric depression scale; Cronbach’s α = Cronbach’s alpha.
Note: **P <0.001; *P <0.05 4. Discussion
The factor structure evaluated using CFA in this community-dwelling older adults sample, revealed the confirmation of Malay version MOS-SSS as four-factor model as proposed by its original authors (Sherbourne & Stewart, 1991). The first-order four-factor model demonstrated a good fit. Recent study conducted among local HIV patients also reported similar results (Saddki et al., 2017). As other studies conducted among different context and sample (Alonso Fachado et al., 2007; Robitaille et al., 2011; Wang et al., 2013; Yu et al., 2004; Zimet, Powell, Farley, Werkman, & Berkoff, 1990), there was no any requirements to refine items for the Malay version MOS-SSS model. However, opposite findings from the present study has been reported. Different version of MOS-SSS reported to have one factor (Margolis et al., 2018), two factors (Shyu, Tang, Liang, & Weng, 2006; Westaway, Seager,
Rheeder, & Van Zyl, 2005) and three factors (Mahmud et al., 2004; Soares et al., 2012).
Result from four-factor model showed that a strong correlation between affective support and positive social interaction. This was also reported by Sherbourne and Stewart (1991), and Giangrasso and Casale (2014). However, when the two factors were combined into one factor, the model resulted in poor fit. Thus, for the present study sample, three-factor model was not appropriate. Additionally, the result of four-factor model strengthen the theory that MOS-SSS composed of emotional support, tangible support, affective support and positive social interaction.
Present study also examined the potential presence of second-order four-factor model.
Sherbourne and Stewart (1991) stated high variance shared between the factors was an indication of higher order factor, social support. However, when comparing both model fit indices, the second-order four-factor model showed poorer fit to data. This finding was also reported by Margolis et al. (2018) among low-income caregivers of African American children with poorly controlled asthma. However, results from second-order four-factor model in the present study provided insight on how the four-first-order factors contribute to social support. It showed that social support of community-dwelling older adults was largely influenced by affective support and positive social interaction.
The convergent validity presented satisfied values for all the factors, similar with other studies (Dafaalla et al., 2016; Giangrasso & Casale, 2014; Saddki et al., 2017; Soares et al., 2012; Wang et al., 2013). On the assessment of discriminant validity, only affective support and positive social interaction did not discriminate well between themselves. This was due to high correlation between the factors (Giangrasso & Casale, 2014; Sherbourne & Stewart, 1991). The original authors of MOS-SSS confirmed this finding while also supported by other study. They also stated that this was expected as the items represented dimension of a common higher order factor. Moreover, a clear discriminant between the factors were indicated by their item-to-other factors correlations. In this study, the item-other factor correlation were significant, by most of them were lower than their item-own factor correlations. It is noteworthy to mention that convergent validity does not affect the fitness of the model. One of the ways to improve discriminant validity is to increase number of items in the factor. Some recommend that minimum three items for each factor (Fabrigar et al., 1999).
However, factors with at least five items with factor loadings of 0.5 and above are more solid (Osborne, Costello, & Kellow, 2008).
Similar with other studies, the reliability evaluation for overall and each factor showed a satisfied values (Dafaalla et al., 2016; Giangrasso & Casale, 2014; Saddki et al., 2017; Wang et al., 2013). The high value of internal consistently measured through evaluation of Cronbach’s alpha showed that Malay version MOS-SSS was a reliable measure. It indicated that the items were consistent and presence of high agreement between themselves. It also means that the items were measuring the same factor or construct (Hair et al., 2010).
The concurrent validity of the Malay version MOS-SSS was satisfied in this study, as predicted. The correlational analysis showed that Malay version MOS-SSS was highly correlated with other similar instrument to measure social support. Strong positive correlation between similar instrument indication of concurrent validity and indicate that both instruments measure the same thing. On assessment of Malay version MOS-SSS with other health related measurement, the findings showed Malay version MOS-SSS total score did not correlate with any of the health measurement. Affective support and positive social interaction were weakly correlated with GHQ-12 and BDI, respectively. These findings were
opposite to other findings from previous study among different samples (Giangrasso &
Casale, 2014; Mahmud et al., 2004; Sherbourne & Stewart, 1991; Wang et al., 2013).
Like any study, the current study also presents with some limitations. Firstly, this study design unable to accommodate assessment of predictive validity. Secondly, this study relied mostly on self-reported data. Thus, there were subjected to respondents’ preference and other response bias. Next, this study utilized data composed mostly of Malay ethnic (92.5%). This is due to low Malays language literacy among other races which resulted the potential participants to be excluded from the study. Thus, future study among more balanced ethnicity is recommended. Finally, the reliability of Malay version MOS-SSS in this study was only evaluated through internal consistency. It is recommended for that test-retest reliability to be done to check for items stability over time.
5. Conclusion
This study showed that the structural validity of the Malay version MOS-SSS was better for a first-order four-factor model. However, the second-order four-factor model revealed that social support among community-dwelling older adults was predominantly influenced by affective support and positive social interaction. Furthermore, Malay version MOS-SSS displayed a good construct validity, concurrent validity and reliability. Therefore, it is recommended that Malay version MOS-SSS to be used to measure social support among community-dwelling older adults in Malaysia.
Acknowledgement
The authors are thankful to all respondents and local authorities involved in this study. This study was funded by Putra Grant-Initiative for Young Researcher grant, Universiti Putra Malaysia (Grant No: 9759600).
References
Abdul Hamid, A., & Hatta, S. (1996). Validation study of the Malay language translation of General Health Questionnaire (GHQ-30). Malaysian Psychiatry, 2(4), 34-37.
Alonso Fachado, A., Montes Martinez, A., Menendez Villalva, C., & Pereira, M. G. (2007).
Cultural adaptation and validation of the Medical Outcomes Study Social Support Survey questionnaire (MOS-SSS). Acta medica portuguesa, 20(6), 525-534.
Beck, A. T., Steer, R. A., & Carbin, M. G. (1988). Psychometric properties of the Beck Depression Inventory: Twenty-five years of evaluation. Clinical Psychology Review, 8(1), 77-100. doi: 10.1016/0272-7358(88)90050-5
Beck, A. T., Ward, C. H., Mendelson, M., Mock, J., & Eebaugh, J. (1961). An Inventory for Measuring Depression. Archives of General Psychiatry, 4(6), 561-571. doi:
10.1001/archpsyc.1961.01710120031004
Broadhead, W. E., Gehlbach, S. H., de Gruy, F. V., & Kaplan, B. H. (1988). The Duke–UNC Functional Social Support Questionnaire: Measurement of social support in family medicine patients. Medical Care, 26(7), 709-723. doi: 10.1097/00005650-198807000- 00006
Brown, T. A. (2014). Confirmatory factor analysis for applied research: Guilford Publications.
Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming. Abingdon: Routledge Academic.
Costa Requena, G., Salamero, M., & Gil, F. (2007). Validity of the questionnaire MOS-SSS of social support in neoplastic patients. Medicina Clinica, 128(18), 687-691. doi:
10.1157/13102357
Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests.
Psychological Bulletin, 52(4), 281-302. doi: 10.1037/h0040957
Dafaalla, M., Farah, A., Bashir, S., Khalil, A., Abdulhamid, R., Mokhtar, M., . . . Abdalrahman, I. (2016). Validity and reliability of Arabic MOS social support survey.
SpringerPlus, 5(1), 1306. doi: 10.1186/s40064-016-2960-4
Eskin, M. (2003). Self-reported assertiveness in Swedish and Turkish adolescents: A cross- cultural comparison. Scandinavian Journal of Psychology, 44(1), 7-12. doi:
10.1111/1467-9450.t01-1-00315
Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272.
Fletcher, R. H., Fletcher, S. W., & Fletcher, G. S. (2012). Clinical epidemiology: The essentials: Lippincott Williams & Wilkins.
Giangrasso, B., & Casale, S. (2014). Psychometric properties of the Medical Outcome Study Social Support Survey with a general population sample of undergraduate students.
Social Indicators Research, 116(1), 185-197. doi: 10.1007/s11205-013-0277-z Goldberg, D. (1978). Manual of the general health questionnaire: NFER Nelson.
Haber, M. G., Cohen, J. L., Lucas, T., & Baltes, B. B. (2007). The relationship between self- reported received and perceived social support: A meta-analytic review. American Journal of Community Psychology, 39(1), 133-144. doi: 10.1007/s10464-007-9100-9 Hair, J. F., Anderson, R. E., Babin, B. J., & Black, W. C. (2010). Multivariate data analysis:
A global perspective (Vol. 7): Pearson Upper Saddle River, NJ.
Kline, R. B. (2015). Principles and practice of structural equation modeling: Guilford publications.
Mahmud, W. M. R. W., Awang, A., & Mohamed, M. N. (2004). Psychometric evaluation of the Medical Outcome Study (MOS) Social Support Survey among malay postpartum women in kedah, north west of Peninsular Malaysia. The Malaysian Journal of Medical Sciences : MJMS, 11(2), 26-33.
Margolis, R., Bellin, M. H., Sacco, P., Harrington, D., & Butz, A. (2018). Evaluation of MOS social support in low-income caregivers of African American children with poorly controlled asthma. Journal of Asthma, 1-8. doi: 10.1080/02770903.2018.1510504
Nevitt, J., & Hancock, G. R. (2001). Performance of bootstrapping approaches to model test statistics and parameter standard error estimation in structural equation modeling.
Structural equation modeling, 8(3), 353-377.
Ng, C. G., Amer Siddiq, A. N., Aida, S. A., Zainal, N. Z., & Koh, O. H. (2010). Validation of the Malay version of the Multidimensional Scale of Perceived Social Support (MSPSS- M) among a group of medical students in Faculty of Medicine, University Malaya.
Asian Journal of Psychiatry, 3(1), 3-6. doi: 10.1016/j.ajp.2009.12.001
Osborne, J. W., Costello, A. B., & Kellow, J. T. (2008). Best practices in exploratory factor analysis. Best Practices in Quantitative Methods, 86-99.
Procidano, M. E., & Heller, K. (1983). Measures of perceived social support from friends and from family: Three validation studies. American Journal of Community Psychology, 11(1), 1-24. doi: 10.1007/bf00898416
Robitaille, A., Orpana, H., & McIntosh, C. N. (2011). Psychometric properties, factorial structure, and measurement invariance of the English and French versions of the Medical Outcomes Study social support scale. Health Reports, 22(2), 33.
Saddki, N., Sulaiman, Z., Abdullah, S., Zakaria, N., Mohamad, N., Ab Razak, A., & Zainan
Abidin, Z. (2017). Psychometric properties of the Malay version of the Medical Outcomes Study Social Support Survey (MOS-SSS) in a sample of patients with HIV.
Journal of HIV/AIDS & Social Services, 16(1), 60-74. doi:
10.1080/15381501.2015.1107801
Samad, S. A., & Mansor, N. (2017). Population ageing and social protection in Malaysia.
Malaysian Journal of Economic Studies, 50(2)
Schreiber, J. B., Nora, A., Stage, F. K., Barlow, E. A., & King, J. (2006). Reporting structural equation modeling and confirmatory factor analysis results: A review. The Journal of Educational Research, 99(6), 323-338. doi: 10.3200/joer.99.6.323-338
Sheikh, J., & Yesavage, J. (1986). Geriatric Depression Scale (GDS): Recent findings and development of a shorter version. ClinicalGerontology: A Guide to Assessment and Intervention. New York: Howarth Press.
Sherbourne, C. D., & Stewart, A. L. (1991). The MOS social support survey. Social Science
& Medicine, 32(6), 705-714. doi: 10.1016/0277-9536(91)90150-B
Shyu, Y.-I. L., Tang, W.-R., Liang, J., & Weng, L.-J. (2006). Psychometric testing of the social support survey on a Taiwanese sample. Nursing Research, 55(6), 411-417.
Soares, A., Biasoli, I., Scheliga, A., Baptista, R. L., Brabo, E. P., Morais, J. C., . . . Spector, N. (2012). Validation of the Brazilian Portuguese version of the Medical Outcomes Study-Social Support Survey in Hodgkin's lymphoma survivors. Supportive Care in Cancer, 20(8), 1895-1900. doi: 10.1007/s00520-011-1292-8
Teh, E. E., & Hasanah, C. I. (2004). Validation of Malay version of Geriatric Depression Scale among elderly inpatients. Age, 17(65). 64.
Wang, W., Zheng, X., He, H.-G., & Thompson, D. R. (2013). Psychometric testing of the Chinese Mandarin version of the Medical Outcomes Study Social Support Survey in patients with coronary heart disease in mainland China. Quality of Life Research, 22(8), 1965-1971. doi: 10.1007/s11136-012-0345-x
Westaway, M. S., Seager, J. R., Rheeder, P., & Van Zyl, D. G. (2005). The effects of social support on health, well-being and management of diabetes mellitus: A black South African perspective. Ethnicity & Health, 10(1), 73-89. doi:
10.1080/1355785052000323047
Yeh, S.-C. J., & Liu, Y.-Y. (2003). Influence of social support on cognitive function in the elderly. BMC Health Services Research, 3, 9. doi: 10.1186/1472-6963-3-9
Yu, D. S. F., Lee, D. T. F., & Woo, J. (2004). Psychometric testing of the Chinese version of the medical outcomes study social support survey (MOS-SSS-C). Research in Nursing
& Health, 27(2), 135-143. doi: 10.1002/nur.20008
Yusoff, M. S. B. (2009). The sensitivity, specificity and reliability of the Malay version 12- items general health (GHQ-12) in questionnaire detecting distressed medical students.
ASEAN Journal of Psychiatry, 11(1).
Zimet, G. D., Dahlem, N. W., Zimet, S. G., & Farley, G. K. (1988). The Multidimensional Scale of Perceived Social Support. Journal of Personality Assessment, 52(1), 30-41. doi:
10.1207/s15327752jpa5201_2
Zimet, G. D., Powell, S. S., Farley, G. K., Werkman, S., & Berkoff, K. A. (1990).
Psychometric Characteristics of the Multidimensional Scale of Perceived Social Support. Journal of Personality Assessment, 55(3-4), 610-617. doi:
10.1080/00223891.1990.9674095