Wenqing Mi,Zepeng Gou,Zhihao Ma
Psychometric Properties of the Chinese Version of the 10-Item Social Provisions Scale in Chinese Populations
Social provisions scale,Psychometric properties,Chinese,Validation,Item response theory,Network analysis
This study performed a cross-cultural validation of the Chinese version of the 10-item Social Provisions Scale (C-SPS-10) in Chinese populations. Study 1 examined the factor structure, internal reliability, discrimination, criterion validity, and network structure of C-SPS-10 by utilizing a sample of disaster victims in the 2021 Henan floods. Study 2 substantiated the findings of Study 1 in a general population sample. Measurement invariances between populations and between sexes in terms of the C-SPS-10 were also tested using the network approach. Study 3 used three samples to examine the test-retest reliability of the C-SPS-10 over three different time periods. The general results showed that the C-SPS-10 has excellent factor structure, internal reliability, discrimination, and criterion validity. The C-SPS-10 was confirmed to have good psychometric properties. Although the full scale functions well, problems may exist at a domain level. Moreover, the full scale of the C-SPS-10 was varied as a useful tool to capture trait-like characteristics of individuals’ perceptions of social support for the general population. Supplementary Information The online version contains supplementary material available at 10.1007/s10862-023-10047-7.
Psychometric Properties of the Chinese Version of the 10-Item Social Provisions Scale in Chinese Populations

This study performed a cross-cultural validation of the Chinese version of the 10-item Social Provisions Scale (C-SPS-10) in Chinese populations. Study 1 examined the factor structure, internal reliability, discrimination, criterion validity, and network structure of C-SPS-10 by utilizing a sample of disaster victims in the 2021 Henan floods. Study 2 substantiated the findings of Study 1 in a general population sample. Measurement invariances between populations and between sexes in terms of the C-SPS-10 were also tested using the network approach. Study 3 used three samples to examine the test-retest reliability of the C-SPS-10 over three different time periods. The general results showed that the C-SPS-10 has excellent factor structure, internal reliability, discrimination, and criterion validity. The C-SPS-10 was confirmed to have good psychometric properties. Although the full scale functions well, problems may exist at a domain level. Moreover, the full scale of the C-SPS-10 was varied as a useful tool to capture trait-like characteristics of individuals’ perceptions of social support for the general population.

The online version contains supplementary material available at 10.1007/s10862-023-10047-7.

The Social Provisions Scale (SPS) is fast becoming a key measurement of perceived social support in studies around the globe (Brown et al., 2021; Caron, 2013; Steigen & Bergh, 2019; Zhuang et al., 2021). Not only has it been applied in studies focusing on diversified population groups (Bondevik & Skogstad, 1996; Cutrona, 1982; Cutrona & Russell, 1987; Milgrom et al., 2019), it has also attracted the attention of large-scale population health investigations—such as the annual Canadian Community Health Survey—for approximately a decade (Orpana et al., 2019). Moreover, in terms of the COVID-19 pandemic and the resultant economic, social, and health impacts, the SPS is showing great potential in measuring protective factors that shield individuals against adversities in this period (Zhuang et al., 2021). Several extensive studies are available, of which exceptional examples are the COVID-19 Survey in Two National Longitudinal Studies aimed at Millennium Cohort and Next Steps, performed in the United Kingdom, and the COVIDiSTRESS Global Survey conducted in 179 countries (Brown et al., 2021; Yamada et al., 2021b). The SPS was originally developed and validated by Cutrona and Russell (1987), who based their design on the theoretical framework initially proposed by Weiss (1974). Weiss’s (1974) theory posits that different social connections serve specific purposes, and that these are not interchangeable. Accordingly, the provisions of social relationships were categorized into six domains: attachment, social integration, reassurance of worth, sense of reliable alliance, guidance, and opportunity for nurturance (Weiss, 1974). Based on this theory, Cutrona and Russell (1987) attached two positively-worded and two negatively-worded items to each of the six provisions, thereby bringing forth the 24-item version of the SPS (SPS-24). The SPS-24 proved robust in a vast range of investigations (Cutrona & Russell, 1987; Li et al., 2017; Milgrom et al., 2019; Rapier et al., 2019); subsequently, investigators from diverse cultural backgrounds modified the SPS to suit specific research conditions (Brown et al., 2021; Caron, 2013; Steigen & Bergh, 2019; Zhuang et al., 2021). A 16-item SPS was implemented in one study to compare the emotional statuses of two groups of older adults in Norway, of which one group was receiving nursing home care and the other was receiving community care (Bondevik & Skogstad, 1996). The shortened versions of the scale that retain less than half of the original items—such as the two 10-item versions of the SPS-10 respectively from Caron (2013) and Iapichino et al. (2016), the 5-item SPS from Orpana et al. (2019), and the 3-item SPS (SPS-3) from Brown et al. (2021)—are mainly designed for national or international studies on massive populations, to reduce administration time and alleviate participant burden. Among the shortened versions of the SPS, Caron’s (2013) 10-item version of the SPS bolsters outstanding psychometric properties and has been employed in several studies in cross-cultural conditions (Steigen & Bergh, 2019; Yamada et al., 2021b; Zhuang et al., 2021), making it the focus of the current study. As stated earlier, two versions of the 10-item SPS exist. They both retain five domains from the original SPS-24 and exclude the domain “opportunity for nurturance.” This domain is excluded because it mainly addresses the support provided by an individual to others, rather than the assistance received by an individual (Caron, 2013; Iapichino et al., 2016). The two versions differ in that Caron’s (2013) version only retains positively-worded statements from the original scale, while the one created by Iapichino et al. (2016) keeps the negatively-worded statements. According to Caron’s (1996) validation of the SPS-24, the saturations of most negatively-worded items on their loading factors were lower than those of the positively-worded ones. Additionally, Caron (1996) finds in previous fieldwork that the negatively-worded items might create barriers for people with limited language or cognitive proficiency to understand. The 10-item SPS from Iapichino et al. (2016) has, to date, only been translated and adopted in four studies, of which two were in Italy (Bonavita, Sparaco, Russo, Borriello, & Lavorgna, 2021; Brenna, Mazza, Vergani, & Percudani, 2022) and two in Japan (Katsuki et al., 2020; A. Yamada et al., 2021a). In contrast, Caron’s (2013) 10-item SPS has been utilized in more widespread research scenarios. Researchers used Caron’s (2013) 10-item SPS to assess individuals’ social support across Canada (Orpana et al., 2019), Norway (Steigen & Bergh, 2019), China (Zhuang et al., 2021), France (Peppe et al., 2018), and Australia (Miller et al., 2019). Therefore, the term “SPS-10″ in the current study is exclusively used to denote the 10-item version of the SPS comprised purely of positively-worded items, as modified by Caron (2013). The SPS-10 has also been shown to outperform the SPS-5 and SPS-3 in terms of its goodness of fit and broad application range. The SPS-5, which presents the same five domains as the SPS-10, keeps half items of the SPS-10 by Orpana et al. (2019). In their research (Orpana et al., 2019), however, some of the model fit indices do not achieve the minimal cutoff (e.g. the root mean squared error approximation was 0.073 for the whole sample, 0.079 for male, and 0.067 for females) suggested by previous validation guidelines (Hu & Bentler, 1999). Additionally, each SPS-5 domain was evaluated by only one item, causing concern that the content validity may be diminished (Orpana et al., 2019). The SPS-3 retains only three modified items to be applied to the Millennium Cohort and the next steps across all three waves of the COVID-19 Survey in Two National Longitudinal Studies, performed in the United Kingdom (Brown et al., 2021). However, no explanations regarding the selection or the modification of the three items are offered. Furthermore, the scopes of application for both the SPS-3 and SPS-5 are rather limited, compared with the SPS-10. So far, the SPS-3 has only been implemented in the COVID-19 Survey in Two National Longitudinal Studies (Brown et al., 2021) and the SPS-5 has been applied in the Canadian Community Health Survey (Orpana et al., 2019) and another preliminary study conducted in Michigan, United States (Stryd, 2020). As the SPS-10 were more frequently adopted across multiple countries (Miller et al., 2019; Steigen & Bergh, 2019; Y. Yamada et al., 2021b; Zhuang et al., 2021), we consider the SPS-10 to be more promising as an instrument to be validated in cross-cultural contexts and further promoted as a global standardized measurement. Recently, the SPS-10 was utilized to measure perceived social support among Chinese populations. For example, the COVIDiSTRESS Global Survey translated the SPS-10 into 47 languages, including Chinese, to measure individuals’ social support during the COVID-19 pandemic (Y. Yamada et al., 2021b). Another study on vulnerable families in Hong Kong adopted and translated the SPS-10 to assess family assistance and community services, which were presumed to function as protective factors shielding susceptible families against hardships during the pandemic (Zhuang et al., 2021). Despite the two existent studies adopting the SPS-10 in China (Yamada et al., 2021b; Zhuang et al., 2021), it is worth noting that neither has attempted to validate this scale in China, which highlights the urgent need for validating the SPS-10 in the Chinese context. Furthermore, evaluating the cross-cultural adaptation of the SPS-10 in China is fundamental for establishing an international standardized measurement of individuals’ perceived social support. Hence, validating the SPS-10 in the Chinese context would provide a solid research toolkit for future researchers interested in health-related issues in China. Further, this validation could potentially pave the way for intercultural investigations and smooth the process of cross-cultural comparisons. In addition to the research gap relating to the cross-cultural validation of SPS-10, the current study also aims to evaluate the test–retest reliability of this measurement to determine its capability of capturing trait-like characteristics of individuals to perceive social support, which has rarely been examined in previous studies. Whether an individual’s perception of social support is a trait-like characteristic rooted in their early experience, or simply a reflection of their available support from current relationships, has aroused the interest of numerous researchers (Davis et al., 1998; Lakey & Cassady, 1990; Pierce et al., 1991; Sarason et al., 1986). Previous research demonstrates that one’s overall perception of social support might function as a personality trait, regardless of the amount of support in one’s current relationships (Davis et al., 1998; Sarason et al., 1986). In Davis et al.’s (1998) investigation into the relationship between perceptions of global support and domain-specific support, specifically, the SPS-24 was adopted to evaluate participants’ perceived levels of global support, or the relatively stable characteristic to sense support. Davis et al. (1998) discovered that perception of global support had its own changing patterns that could hardly be explained by the simple summation of relationship-specific support. Additionally, the perception of global support exerts unique influences on individuals’ well-being, which are independent of the influences exerted by relationship-specific support (Davis et al., 1998). Therefore, Davis et al. (1998) conclude that individuals’ perception of global support functions in a trait-like manner, representing people’s permanent perspective of social interactions. Davis et al. (1998)’s investigation also demonstrated that the SPS-24 had the potential to reveal individuals’ stable characteristics to sense social support. However, among previous studies into different versions of the SPS, few ever examined the test–retest reliability of this measurement. Therefore, whether this measurement bolsters the ability to capture individuals’ trait-like characteristics to perceive social support, remains unknown. Considering the above, this study aims to examine the stability of the Chinese 10-item SPS (C-SPS-10) using test–retest reliability to thereby determine its capability of evaluating participants’ stable perceptions of social support. In conclusion, this study aims to examine the reliability and validity of the C-SPS-10 among a population who experienced the 2021 Henan floods and a general population. Classical test theory (CTT), item response theory (IRT), and network analysis will be applied to the evaluation of the psychometric properties of the C-SPS-10.

This study aimed to determine whether the psychometric properties of the C-SPS-10 were consistent with those of the SPS-10 originally designed and validated by Caron (2013). Participants who experienced the 2021 Henan floods were recruited. A confirmatory factor analysis (CFA) was first conducted via structural equation modeling (SEM) to verify whether the five-factor construct simplified by Caron (2013) was empirically sound. IRT was employed to assess the item discrimination, difficulty, and test information of the C-SPS-10. Thereafter, the criterion validity of the C-SPS-10 was evaluated by correlating it with other well-developed measurements (the Patient Health Questionnaire-9 [PHQ-9], the Generalized Anxiety Disorder-7 [GAD-7], the PTSD Check List – Civilian Version [PCL-C], and the Chinese version of the Ten-Item Personality Inventory [TIPI-C]). Finally, we conducted a network analysis to examine internal linkages and thereby provide a deeper understanding of the theoretical construct of the C-SPS-10.

Consistent with the original version of the SPS-10, the C-SPS-10 comprises ten items with response options on a 4-point Likert scale, ranging from 1 = strongly disagree to 4 = strongly agree. Translation and back-translation of the English SPS-10 was performed independently by three bilingual researchers. Two independent English–Chinese researchers translated the SPS-10 into Chinese. Discrepancies were discussed and reviewed with another bilingual researcher, after which a final Chinese version was obtained. The full scale of C-SPS-10 can be found in Table S1 of the Appendix.

Using convenience sampling, we aimed to capture the immediate reaction of people who experienced the 2021 Henan floods. All participants were recruited via an online reward-based crowdsourcing platform, Credamo (https://www.credamo.com/), which is similar to Amazon’s “Mechanical Turk.” The survey was conducted from July 20 to August 6, 2021, a period in which the disaster was still influencing Henan citizens. Potential participants viewed a description of the survey before electing to participate. Interested participants followed a link from Credamo to the current questionnaire. Altogether, 937 individuals completed the questionnaire, of which 225 were excluded as they failed to pass the attention check or reported “not applicable” at the flood exposure question. One respondent was excluded because of being identified as a gender minority. The remaining 713 respondents (Mage = 26.88, SDage = ±7.05, 49.93% male) were included in the sample.

This study protocol was approved by authors’ affiliation. Electronic informed consent was obtained from all participants before the interviews and survey administration. Participants were assured of their anonymity and confidentiality, and their rights of withdrawal were respected.

First, the C-SPS-10 was evaluated according to the CTT. The internal reliability of all the items within the scale was assessed using Cronbach’s alpha coefficients (recommended value >0.7) (Furnham, 1987). Second, a CFA was conducted via SEM to confirm the five-factor model of the C-SPS-10. The goodness of fit was evaluated using the following indexes: the chi-squared value to degrees of freedom ratio (χ2/df) < 3 (Hoe, 2008), the comparative fit index (CFI) > 0.95, the Tucker–Lewis index (TLI) > 0.95, the root mean squared error of approximation (RMSEA) < 0.06, and the standardized root mean squared residual (SRMR) < 0.08 (Hu & Bentler, 1999). Next, we employed the IRT approach to measure the discrimination and difficulty of each item, as well as the test information of C-SPS-10. In this section, the graded response model (GRM) was fit to the C-SPS-10 (Samejima, 1969). The GRM is a generalization of the two-parameter logistic item response model for ordinal data (Samejima, 1969). Thereafter, the criterion validity of the C-SPS-10 was verified via its correlations with other well-developed measurements. Regarding a psychological network analysis, a Gaussian Graphical Model was used to estimate the studied variables. Nodes in the graph represent variables related to each C-SPS-10 item or domain, while edges presented the partial correlations between any two variables when controlling for other variables (Borsboom et al., 2021; Epskamp et al., 2018). Detailed information on the reference names of the ten items and five domains of the C-SPS-10 and their measurement statements are presented in Tables 1 and 2. In the current study, we employed the Bayesian Gaussian Graphical Model (BGGM) (Williams, 2021) to perform the network analysis in two steps: First, the BGGM was used to estimate the network models among the disaster-exposed participants. In this step, the scale of the prior distribution was set at 0.25, while the number of posterior samples was set as 5000. Further, the estimated edges with a 95% credible interval were selected to create the network visualization. Second, to quantify the significance of a certain node that is connected with other nodes within a given network (Epskamp & Fried, 2018), the strength centrality—which refers to the sum of absolute values of edges toward a certain node—was calculated. All the BGGM analyses were conducted using the R software package BGGM (Williams & Mulder, 2020). Recent years witnessed a surge in the number of studies adopting a network analysis approach to investigate psychometric issues. For instance, scholars in clinical psychology used this approach to examine the central symptoms that trigger depressive episodes (Fried et al., 2016), to detect PTSD symptoms among earthquake survivors (McNally et al., 2015), and to examine the associations between distinct domains of middle-aged adults’ childhood trauma and their perceived stress (Betz et al., 2021). Moreover, the associations between the external shocks and psychiatric reactions have also attracted researchers’ attention; these include the connections between parental abuse and transgender youths’ mental reactions (Wang et al., 2021) and the relationships between media-related exposure and psychological consequences arising among viewers who were not exposed to disastrous events (Liu & Ma, 2022). Network analyses have also been utilized to demonstrate the potential optimal interpretations for previous scale structures, such as the interpersonal reactivity index scale (Briganti et al., 2018) and the narcissistic personality inventory (Briganti & Linkowski, 2020). Therefore, we conceive that the network analysis approach may provide us with novel insights into the finer structure of the C-SPS-10. Labels of each item and domain within C-SPS-10 are listed in Tables 1 and 2.

To examine the criterion validity of C-SPS-10, a couple of well-established measurements were employed in this study; the PHQ-9, the GAD-7, the PCL-C, and the TIPI-C. The internal reliabilities of the measures are provided in Table 3. In this section, the mean score of the C-SPS-10 was calculated to represent participants’ overall level of perceived social support. Additionally, the mean score of the two items within each domain of C-SPS-10 was respectively calculated to represent diverse categories of support identified by participants.

The PHQ-9 is a self-administered questionnaire aimed at evaluating the severity of depression among individuals (Kroenke et al., 2001). It consists of nine items and measures how often the participants have experienced depressive episodes during the previous two weeks (Kroenke et al., 2001). The response options are 0 = “not at all,” 1 = “several days,” 2 = “more than half the days,” and 3 = “nearly every day” (Kroenke et al., 2001). The current study uses the Chinese version of the PHQ-9, which has demonstrated good psychometric properties in several studies (Liu et al., 2016; Wang et al., 2014; Xiong et al., 2015). Participants’ aggregate PHQ-9 scores were calculated to determine their mental status. The Chinese version of the PHQ-9 demonstrated excellent internal reliability (Cronbach’s alpha = 0.866). We predicted that the C-SPS-10 and each of its five domains would demonstrate a negative correlation with PHQ-9.

The GAD-7 is an instrument designed to identify anxiety disorders among individuals (Löwe et al., 2008). Participants are instructed to report their frequency of experiencing anxiety symptoms during the previous two weeks. The instrument comprises seven items. The response categories for each item are 0 = “not at all,” 1 = “several days,” 2 = “more than half the days,” and 3 = “nearly every day.” In the current study, the Chinese version of the GAD-7 was applied to evaluate participants’ anxiety levels (Gong et al., 2021; Tong et al., 2016). The aggregate score of GAD-7 was calculated to examine participants’ levels of anxiety. The Chinese version of the GAD-7 demonstrated good internal reliability in this study (Cronbach’s alpha = 0.890). We hypothesized that the C-SPS-10, along with each of its five domains, would be negatively correlated with GAD-7.

The PCL-C was employed to evaluate participants’ PTSD (Weathers et al., 1993). It comprised 17 items, with item scores ranging from 1 = “not at all” to 5 = “extremely”. The instrument has been shown to have good validity and reliability in a Chinese population (He et al., 2018; Ma et al., 2019; Xu & Song, 2011). The aggregate score of the 17 items expresses the severity of participants’ PTSD symptoms. In the current study, the Chinese version of the PCL-C has excellent internal reliability (Cronbach’s alpha = 0.949). We hypothesized that the whole C-SPS-10 and each of its five domains would negatively correlate with the PCL-C.

The Ten-Item Personality Inventory originally designed and validated by Gosling et al. (2003) assesses participants’ personality traits. It comprises five dimensions: neuroticism, openness, agreeableness, extraversion, and conscientiousness. Each dimension has two items, constituting a ten-item scale (Gosling et al., 2003). Participants are instructed to indicate the extent to which they agree with each statement on a 7-point scale from 1 = disagree strongly to 7 = agree strongly (Gosling et al., 2003). In the current study, the TIPI-C, revised by Li (2013) was applied. We predicted that the C-SPS-10, along each of its five domains, would correlate negatively with neuroticism, and positively with openness, extraversion, conscientiousness, and agreeableness.

The result of the internal reliability of C-SPS-10 can be found in Table 3, showing that C-SPS-10 has good internal reliability (Cronbach’s alpha = 0.809) (Furnham, 1987). The results of CFA are shown in Table 4 and Fig. 1a, which demonstrate that the scale has an excellent five-factor structure. Therefore, the five-factor structure of the C-SPS-10 is consistent with the original version of the SPS-10 designed and validated by Caron (2013).

The IRT model parameters from the C-SPS-10 via GRM are reported in Table 5. We observe that the estimation value of discrimination (α) of each item is above 0.65. According to previous guidelines (Baker & Kim, 2004), the results manifest that all ten items support excellent measuring efficiency for the C-SPS-10. The item characteristic curves (ICC) are displayed in Fig. 2a. The probability for a participant to select a particular response category at a given level of the latent construct are demonstrated in the plots. We found that the response categories were distinguishable and monotonically related to the latent trait theta for all items in the C-SPS-10. Figure 3a shows the test information curves (TIC), where the test information function of the full C-SPS-10 scale and the associated standard error are displayed. TIC demonstrates the information values obtained for each level of the latent trait. The values are equal to the inverse of the standard error; therefore, higher values indicate more reliable estimates. As shown in Fig. 3a, ranging from low to moderate C-SPS-10 scores, relatively high levels of information with low levels of standard error are demonstrated. Conversely, comparatively low levels of information and high levels of standard error are demonstrated with moderate to high C-SPS-10 scores. The results indicated that the C-SPS-10 adequately measures individuals’ perceived social support ranging from low to moderate levels, whereas it is less precise for the highest levels of the trait. Figure 3b presents the test characteristic curve (TCC) of the C-SPS-10, which indicates that 95% of the randomly-selected participants are expected to score between 26 and 38.3.

The results of the criterion validity are demonstrated in Table 6. The value of the correlation coefficients are interpreted according to Cohen (1988), who defined the effect size represented by r = 0.10 as small, the effect size represented by r = 0.30 as medium, and the effect size represented by r = 0.50 as large (p. 83). First, in line with our predictions, the full measure of the C-SPS-10 displayed a weak-to-moderate negative correlation with the PHQ-9. At a domain level, weak-to-moderate negative correlations were observed between the domains of the C-SPS-10 and PHQ-9. Second, inconsistent with our prediction, no significant correlation was detected between the full C-SPS-10 scale and the GAD-7. Neither was there any significant correlation between each of the five domains. What was also inconsistent with our hypothesis, was that no significant correlation was detected between the full measure of the C-SPS-10 and the PCL-C. At a domain level, the correlations between PCL-C and Domain 3, 4, and 5 of the C-SPS-10 were insignificant. The correlations between Domain 1 and Domain 2 of the PCL-C were significant but negligible. Finally, in agreement with what we predicted, the full C-SPS-10 scale demonstrated a moderate-to-strong and negative correlation with the neuroticism domain of the TIPI-C. Moreover, moderate-to-strong positive correlations between the full C-SPS-10 scale and the extraversion, conscientiousness, and openness domains were detected. There was also a weak-to-moderate correlation between agreeableness and the C-SPS-10. The domain-level results were mostly consistent with those of the full scale, where correlations were mostly a bit weaker than those obtained by the full measurement.

The network structures of the ten items and five domains are respectively displayed in Figs. 4a and 5a, with detailed information regarding the estimated relations among the ten items and five domains supplied in Tables S2 and S4 of the Appendix. As shown in the two figures, the weight (coefficient of partial association between two nodes) of each edge is represented in terms of thickness and color saturation. Positive partial correlations are indicated by blue edges, while negative partial correlations are indicated by red edges. All the edges in the network are colored in blue, signifying that all the internal linkages—either within items or domains of the C-SPS-10—are positive. The node strength centrality of items and domains representing the connectedness of a given subject with all other subjects in the network were displayed in Figs. 4b and 5b, with the detailed statistics supplied in Tables S3 and S5 in the Appendix. At an item level, we note that Item 7 is positioned in the center of the network, thus exhibiting the highest node strength. Conversely, Item 1 has the lowest node strength. At a domain level, Domain 1 (sense of reliable alliance) is found to possess the highest node strength among the five domains, whereas Domain 2 (reassurance of worth) has the lowest node strength in the network.

The results of Study 1 confirmed the internal reliability and factor structure of the C-SPS-10 in a sample of disaster victims from the 2021 Henan floods. The CFA showed that the five-factor structure of the SPS-10 proposed by Caron (2013) fit this sample well. Additionally, results from the IRT indicated that each item within the C-SPS-10 had excellent discrimination. TIC further manifested that more information was provided for people with low-to-moderate scores on C-SPS-10, whereas people with higher degrees of perceived social support were represented with less information and a higher standard error. This observation is highly consistent with Steigen and Bergh (2019)‘s findings of their SPS-10 validation among participants with mental or drug-related problems in Norway, where a lack of items representing individuals with higher levels of perceived social support was detected. Furthermore, the C-SPS-10 displayed appropriate and mostly predicted criterion validity, compared with other well-developed measures. The C-SPS-10 was negatively correlated with depression and neuroticism, indicating that people scoring higher on C-SPS-10 can be characterized as liable to suffer less from depressive disorders and be more emotionally stable. The positive correlations between the C-SPS-10 and extraversion, agreeableness, conscientiousness, and openness imply that people with higher levels of perceived social support tend to be more outgoing, frank, responsible, and cooperative. The results of the network analysis indicated that Domain 4 (sense of reliable alliance) was the most central domain in the network. In contrast, Domain 2 (reassurance of worth) displayed the lowest centrality in the network, indicating that it has the least connectivity with other domains. At an item level, Item 7, which belongs to Domain 4 (sense of reliable alliance), was the most central. On the contrary, Item 1, which belongs to Domain 1 (social integration), had the lowest strength in the whole network, and is therefore presented as the most marginal node. This observation is, in part, consistent with Steigen and Bergh (2019)‘s finding in their validation of SPS-10 in Norway, that the domain “social integration” is deviated from the overall scale. Problems within the C-SPS-10 domain construct was further detected through network analysis. Theoretically, items belonging to each domain within the SPS-24, the original version of the scale, were designed to reflect the same concept (Cutrona & Russell, 1987). Therefore, it is reasonable to expect that the partial correlations between items of the same domain outweigh the partial correlations between items belonging to separate domains. However, as displayed in Fig. 4a and Table S2 of the Appendix, complications arise between items 7, 8, and 9. Among these items, items 8 and 9 both belong to Domain 5 (guidance), whereas Item 7 belongs to Domain 4 (sense of reliable alliance). It was observed that the edge between items 7 and 9 was stronger than between items 8 and 9. A possible explanation for this complication might be that both items 7 and 9 are designed to reflect the support received when an individual is in predicament, which indicates that the message conveyed by these items may overlap to a certain extent in the interpretation of participants exposed to disaster. Therefore, it is likely that reorganizations of the items are required to modify C-SPS-10 when it is utilized among disaster-exposed populations.

Study 1 verified the psychometric properties of the C-SPS-10 among disaster-exposed participants. However, it is unclear whether these findings would apply to general population. We also tested the measurement invariance of the C-SPS-10 across sexes through network analysis. Therefore, in Study 2, we recruited a sample from the general population, regardless of whether they were exposed to certain traumatic events. We then compared the results of the disaster-exposed population in Study 1 and the general population in Study 2, as well as the results of males and females in the general population in this study.

A total of 2000 participants from the general population regardless of whether they were exposed to traumatic events were recruited using convenience sampling and the Credamo platform on February 18, 2022. Of these, 182 participants were excluded because they failed to pass the attention check. Two participants were excluded because they were identified as gender minorities. Another 46 participants were further excluded because they were younger than 18 years old. Finally, data from 1770 participants were retained (Mage = 30.46, SDage = ±7.95, 44.07% male).

This study protocol was approved by authors’ affiliation. Electronic informed consent was obtained from all participants before the interviews and survey administration. Participants were assured of their anonymity and confidentiality, and their rights of withdrawal were respected.

Consistent with Study 1, we utilized SEM to examine the model fit of the five-factor construct of the C-SPS-10. We also employed IRT to evaluate the discrimination of each of the ten items of the C-SPS-10. Criterion validity was further determined via correlation testing with personality traits and with loneliness, which were measured by TIPI-C and the UCLA Three-item Loneliness Scale, respectively. Thereafter, the network analysis approach was applied to the general population, with estimations among measurement items conducted via BGGM. In addition to the analyses in Study 1, we conducted a student’s t test in this study to determine whether there were any distinctions regarding the performance of the C-SPS-10 between diverse samples and different sexes. In Study 2, we investigated the mean scores of the full C-SPS-10 scale and each of its five domains and ten items, and made comparisons to see whether there were any significant discrepancies.

In this study, in addition to the TIPI-C used in Study 1, we added the UCLA Three-item Loneliness Scale to identify the criterion validity of C-SPS-10. The Three-item Loneliness Scale was developed by Hughes et al. (2004), based on the Revised UCLA Loneliness Scale originally designed by Russell et al. (1980). The Chinese version of the Three-item Loneliness Scale was adopted in one previous study (Chang et al., 2014). In the current study, we used a 5-point Likert scale—ranging from 1 = “strongly disagree” to 5 “strongly agree”—from the Three-item Loneliness Scale to measure participants’ loneliness. The descripitive statistics of the Three-item Loneliness Scale are shown in Table 3, which displays that the internal reliability of this measure proved robust (Cronbach’s alpha = 0.852). We hypothesized that the C-SPS-10, along with each of its five domains, would negatively correlate with the Three-item Loneliness Scale.

As shown in Table 3, the C-SPS-10 demonstrated great internal reliability (Cronbach’s alpha = 0.844) (Furnham, 1987). Results of the CFA are displayed in Table 4 and Fig. 1b, showing that the five-factor structure of the C-SPS-10 remains consistent with the original version of the SPS-10—designed and validated by Caron (2013)—among the general Chinese population.

The IRT model parameters from the C-SPS-10 via GRM are reported in Table 5, which largely replicated those of Study 1. All ten C-SPS-10 items bolster excellent measuring efficiency, with the estimation value of discrimination (α) of each item above 0.65. In addition, the ICC results of C-SPS-10 are displayed in Fig. 2b, showing that the response categories were distinguishable and monotonically related to the latent trait theta for all items in the C-SPS-10. Figure 3c demonstrates the TIC results of the C-SPS-10. Similar to the results of Study 1, it can be observed that relatively high levels of information with low levels of standard error are demonstrated from low to moderate C-SPS-10 scores. In contrast, comparatively low levels of information and high levels of standard error are demonstrated with moderate to high C-SPS-10 scores. These results indicate that the C-SPS-10 adequately measures individuals’ perceived social support for low-to-moderate levels; however, it is less accurate for the highest levels of the trait. Figure 3d presents the test characteristic curve, which indicates that 95% of randomly-selected participants are expected to score between 25.2 and 38.9, with the mean score being 32.5.

As displayed in Table 6, the results of the criterion validity were in line with our hypotheses. Consistent with Study 1, we illustrate the values of correlation coefficients based on Cohen (1988)‘s suggestions (p. 83). In line with our predictions, a strong negative correlation was detected between the full C-SPS-10 scale and the Three-item Loneliness Scale. At a domain level, strong and negative correlations were also observed between the Three-item Loneliness Scale and each of the five domains of the C-SPS-10. Consistent with the results in Study 1, it was found that the full C-SPS-10 scale was moderately-to-strongly and negatively correlated with neuroticism, whereas moderate-to-strong and positive correlations between the C-SPS-10 and extraversion, conscientiousness, and openness were identified. Additionally, C-SPS-10 was weakly-to-moderately and positively correlated with agreeableness. Moreover, at a domain level, the results were mostly consistent with those of Study 1.

The network structures of the five domains and ten items are respectively displayed in Figs. 4c and 5c, with detailed information supplied in Tables S6 and S8. The node strength at the item level and domain level is respectively shown in Figs. 4d and 5d, with detailed statistics provided in Tables S7 and S9 in the Appendix. At an item level, we observe that Item 4 is positioned in the center of the network, thus exhibiting the highest node strength. Conversely, Item 6 has the lowest node strength. At a domain level, Domain 5 (guidance) is found to possess the highest node strength, while Domain 2 (reassurance of worth) exhibits the lowest strength.

A comparison was drawn between the disaster-exposed population in Study 1 and the general population in Study 2 using a student’s t test. The results are displayed in Table 7. It can be observed that none of the absolute values of Cohen’s D obtained exceeded 0.2, manifesting no significant differences between the two samples (Cohen, 1988). Results of the network analysis, which are on the contrary, revealed distinctions between the two groups that is worthy of further investigation. It is evident that the central items, central domains, and marginal items of the two population groups differ from each other, while they share the same marginal domain, Domain 2 (reassurance of worth). For the disaster-exposed population, Item 7 is the central node in the network, while for the general population, Item 4 is the most central. As for marginal items, Item 1 is the least interconnected in the network for the disaster victims, whereas Item 6 is the most marginal for the general population. At a domain level, Domain 4 (sense of reliable alliance) shows the most interconnectivity for the disaster-exposed population, while Domain 5 (guidance) is the most central for the general population. Additionally, a network comparison test was conducted to compare networks from the disaster-exposed population and the general population. The differences regarding partial correlations between items and domains where results obtained in the general population are taken as a reference are respectively displayed in Fig. 8a, b, with detailed information supplied in Tables S18 and S19. It was found that no difference was detected at a domain level, while notable deviations were presented at an item level. The partial correlation between Items 1 and 6 for the disaster-exposed population is significantly weaker than for the general population.

Next, we performed t-tests involving the mean score of the full C-SPS-10 measure, each of its five domains, and its ten items to determine differences between male and female respondents. The results demonstrated that there were no significant differences between the two groups (see Table 8). For males in the general population, the network structures at the item and domain level are respectively demonstrated in Figs. 6a and 7a and the node strength is presented in Figs. 6b and 7b, with detailed information supplied in Tables S10, S11, S12 and S13 in the Appendix. As to females, the network structures at the item and domain level are represented in Figs. 6c and 7c and the node strength is displayed in Figs. 6d and 7d, with detailed information supplied in Tables S14, S15, S16 and S17 of the Appendix. For males, Item 9 is the central item while for females, Item 4 is the central item. For both males and females, Item 6 demonstrates the lowest node strength, which is also in agreement with the finding of the whole sample. At a domain level, Domain 5 (guidance) is the central domain for males, whereas Domain 4 (sense of reliable alliance) is the central domain for females. For males, Domain 4 (sense of reliable alliance) is the most marginal domain, while Domain 2 (reassurance of worth) is the most marginal. Figure 8c, d demonstrate the results of the network comparison test across sexes where results obtained among female participants are taken as a reference, with detailed statistics provided in Tables S20 and S21 in the Appendix. No difference was detected at a domain level, while noteworthy deviations were revealed at an item level. It was found that the edge between Item 4 and Item 5 for male participants is significantly weaker than that for female participants. Additionally, the edge between item 7 and item 9 for male participants is also significantly weaker than that of female participants.

Study 2 confirmed the internal reliability and factor structure of the C-SPS-10 with a general population sample, regardless of whether they had been exposed to any disastrous events. Using CFA, we observed that the factor model of C-SPS-10 fit this sample well, and that it is in compliance with the original version designed and validated by Caron (2013). The results of the IRT in the current study were shown to be highly consistent with the findings of Study 1, with each item in the C-SPS-10 displaying good discrimination, via ICC. Considering TIC, we observe that more information and less standard error were presented for people with low to moderate C-SPS-10 scores, while less information and more standard error were displayed for those who score higher. The findings also corresponded to those in Steigen and Bergh (2019)‘s research. In terms of the criterion validity, the outcomes are consistent with our hypotheses. The correlations between C-SPS-10 and TIPI-C are consistent with those obtained in Study 1. Additionally, a strong negative correlation was detected between C-SPS-10 and the UCLA three-item loneliness scale, indicating that people who report lower degrees of perceived social support tend to feel increased levels of loneliness. In the network analysis, participants’ perceived social support was conceptualized as a system where interconnections within the five domains and ten items of C-SPS-10 were demonstrated. We observed that Item 4, which belongs to Domain 3 (attachment), was the most central in the general population’s interpretation, conveying a message of social support. On the contrary, Item 6, which belongs to Domain 2 (reassurance of worth), had the least connectivity in the network. At the domain level, Domain 5 (guidance) was the most central in transferring the message of social support while Domain 2 (reassurance of worth) was the most marginal. Therefore, it can be inferred that Domain 2 (reassurance of worth) might be conceptually different from other domains in the C-SPS-10 for general people. This observation may illuminate possible directions for the simplification of the C-SPS-10 among general populations in the future. To address the comparison of the disaster-exposed population and the general population, no significant differences were found via student’s t test, indicating that the C-SPS-10 can be extended to measure perceived social support among the general public. This might be attributed to the reason that disaster victims are more vulnerable when they are faced with traumatic events. Therefore, the connectivity between the two items are weaker than that of the general population. Lastly, no sex-related bias was detected regarding the performance of C-SPS-10. It was further detected, via a network comparison test, that the messages conveyed by Item 7 and Item 9 are more intercorrelated in the interpretations of females than those of males. Moore (1990)‘s finding—that females are more inclined to discuss personal matters with and obtain guidance from those who are intimate with them, while males tend to turn to their colleagues for suggestions when faced with problems—may partly account for this variance detected between sexes. It can also be inferred from the results of the network comparison that in the interpretation of male participants, the message conveyed by Item 4 and Item 5, both of which belong to Domain 3 (attachment), is less connected, compared with female participants’ interpretation of the two items.

Study 1 and Study 2 demonstrated that the C-SPS-10 has good psychometric properties, both among a disaster-exposed population and a general population. Additionally, measurement invariance was detected between the different sexes. However, the test–retest reliability of the scale over time, which reflects its ability to capture trait-like characteristics that perceive social support, remains unknown. To address the issue, Study 3 used three samples across different time periods: Sample 3a—a subsample of Sample 1 used in Study 1 that includes participants who completed the 6-month follow-up survey, Sample 3b—a subsample of Sample 2 used in Study 2 that includes general participants who complete the 12-month follow-up survey, and Sample 3c—a new sample set that includes general participants who complete the 4-week follow-up survey. In Sample 3a, the criterion validity of the C-SPS-10 was further substantiated with reference to other well-development measurements (the Multidimensional Scale of Perceived Social Support [MSPSS] and the revised Life Orientation Test [LOT-R]).

The disaster-exposed population from Study 1 were followed up 3 and 6 months after the first study, again using the Credamo platform. At the 3-month follow-up, 410 participants of the original sample of 713 were traced. The 6-month follow-up was conducted based on the 3-month follow-up, and 279 participants from the sample of 410 at the 3-month follow-up were traced. In Sample 3a, the statistics from the 6-month follow-up were examined. All the 279 respondents in this wave provided full information on the questions (Mage = 26.88, SDage = ±7.05, 43.37% male).

186 participants of the general population from Study 2 were followed up 12 months after the baseline survey (Mage = 32.48, SDage = ±7.42, 39.25% male). In Sample 3b, the C-SPS-10 was measured twice to obtain a 12-month time interval test-retest reliability.

To conduct a relatively short time interval test-rest reliability, we also make a survey to obtain a new sample including general population. A total of 427 participants from the general population regardless of whether they were exposed to traumatic events were recruited using convenience sampling and the Credamo platform on January 19, 2023. Of these, 11 participants were excluded because they failed to pass the attention check. Two participants were excluded because they were younger than 18 years old. A further 18 participants were excluded from the survey because they completed the survey for an extremely short or long period of time. Among retained 398 participants, 205 of them finished the 4-week follow-up survey, one participant was excluded because he/she failed to pass the attention check. 4 participants were further excluded from the survey because they completed the survey for an extremely short or long period of time. Finally, data from 200 participants were used to obtain a 4-week time interval test-retest reliability (Mage = 31.01, SDage = ±8.10, 38.00% male).

This study protocol was approved by authors’ affiliation. Electronic informed consent was obtained from all participants before the interviews and survey administration. Participants were assured of their anonymity and confidentiality, and their rights of withdrawal were respected.

Following Study 1 and Study 2, the internal reliability of Sample 3a was evaluated and the model fit of C-SPS-10 examined via SEM. The criterion validity of C-SPS-10 in Sample 3a was evaluated by correlating C-SPS-10 with additional well-developed measures (the MSPSS and LOT-R). Specifically, the test–retest reliability was evaluated on the full scale, its five domains, and ten items using a Pearson’s correlation coefficient between the results obtained in Samples 3a, 3b, and 3c over different time intervals.

Aiming to substantiate the criterion validity of C-SPS-10 in this study, the PHQ-9, GAD-7, PCL-C, TIPI-C, and UCLA Three-item Loneliness Scale applied in the previous studies were employed. Further, the MSPSS and the LOT-R were additionally adopted in Sample 3a. The internal reliabilities of the measurements are displayed in Table 3.

The MSPSS is a 12-item instrument that measures individuals’ perceived social support (Zimet et al., 1988). Individuals’ sources of social support are categorized by the MSPSS into three dimensions: family, friends, and significant other, with four items attached to each dimension (Zimet et al., 1988). The scale has demonstrated great psychometric properties in numerous studies in China (Chan et al., 2017; Chou, 2000; Huang et al., 1996; Lau et al., 2015). In the Sample 3a, the Chinese version, translated by Huang et al. (1996), was adopted,1 with each statement on a 7-point scale from 1 = “strongly disagree” to 7 = “strongly agree”. The scale’s internal reliability was excellent (Cronbach’s alpha = 0. 905). The mean score of participants’ responses on MSPSS and each of its three dimensions were calculated. We hypothesized that the C-SPS-10 and each of its five domains would display a strong positive correlation with the MSPSS, along with its three dimensions.

The LOT-R was originally designed and validated by Scheier and Carver (1985) for use in identifying individuals’ dispositional optimism via their expectations for the future. The instrument comprises ten items of five points each, measured on a Likert scale ranging from 0 = strongly disagree to 4 = strongly agree to determine the extent to which participants agree with each of the statements (Scheier et al., 1994). The psychometric properties of the Chinese version of the LOT-R has been examined in previous studies (Lai et al., 1998; Lai & Yue, 2000). The three items that directly assess respondents’ degree of optimism in LOT-R are adopted in the current study, the mean score of which are calculated to present participants’ optimism levels. Additionally, the internal reliability of the scale was calculated (Cronbach’s alpha = 0.570). We hypothesized that the C-SPS-10, along with each of its five domains, would positively correlate with optimism.

As shown in Table 3, the C-SPS-10 demonstrated high internal reliability (Cronbach’s alpha = 0.767) (Furnham, 1987). Additionally, the test–retest reliability of the full C-SPS-10 scale, each of its five domains and ten items in Samples 3a, 3b, and 3c across three time periods are displayed in Table 9. The 6-month test–retest reliability of the full C-SPS-10 scale for disaster victims was 0.476. At a domain level, the correlation coefficients were lower than that of the full scale, among which the test–retest reliability of Domain 4 was relatively lower than other domains (r = 0.195). The items of C-SPS-10 demonstrated low test–retest reliabilities, with Item 7 in the two waves displaying no significant correlation with each other. While in the general population samples, both the 4-week test-retest reliability (r = 0.786) and the 12-month test-retest reliability (r = 0.682) of the full C-SPS-10 scale were high. Results of the CFA presented in Table 4 and Fig. 1c show that the C-SPS-10 has an excellent five-factor structure, indicating that the structure of the C-SPS-10 in Sample 3a is highly consistent with the original version of the SPS-10 proposed by Caron (2013).

Results of the criterion validity of C-SPS-10 in Sample 3a are displayed in Table 6. In compliance with Study 1 and Study 2, we interpret the values of correlation coefficients according to Cohen (1988)‘s suggestions (p. 83). First, the full measure of C-SPS-10 in Sample 3a displayed a weak-to-moderate and negative correlation with PHQ-9. At a domain level, correspondingly, weak-to-moderate correlations were observed between each domain of C-SPS-10 and PHQ-9. Second, the full C-SPS-10 scale in Sample 3a correlated weakly and negatively with GAD-7. At a domain level, Domains 3 and 5 were weakly and negatively correlated with GAD-7, whereas Domains 1, 2, and 4 demonstrated no significant correlations with GAD-7. Third, no significant correlation was detected between the full measure of C-SPS-10 and PCL-C in Sample 3a. Neither was there any significant correlation detected between each domain of C-SPS-10 and PCL-C. Fourth, as predicted, the full measure of C-SPS-10 in Sample 3a is strongly and positively correlated with the MSPSS and each of its three dimensions. When the C-SPS-10 is examined at a domain level, we observe that each of its five domains had moderate-to-strong correlations with the whole MSPSS. In addition, each of the five C-SPS-10 domains correlated moderately-to-strongly with each of the three dimensions of the MSPSS. We also observed that the full C-SPS-10 in Sample 3a has a moderate-to-strong negative correlation with the Three-item Loneliness Scale. At a domain level, correspondingly, moderate correlations between the five domains within C-SPS-10 and the Three-item Loneliness Scale were observed. Finally, in accordance with our predictions, the full C-SPS-10 in Sample 3a scale correlated moderately-to-strongly and positively with the LOT-R. At a domain level, weak-to-moderate correlations were detected between the five domains and LOT-R.

In Study 3, the factor structure of the C-SPS-10 was further validated with a sample of the 6-month follow-up of the disaster-exposed participants in Study 1. The results of the CFA demonstrated that the factor structure of C-SPS-10 fit this sample well, and is highly consistent with the factor structure of the scale originally designed and validated by Caron (2013). Both the 4-week test-retest reliability and the 12-month test-retest reliability of the full C-SPS-10 scale in two general population samples demonstrated that it is adequate for capturing trait-like characteristics of perceived social support. Numerous extant studies investigated the trait-like characteristics of individuals to perceive support, regardless of the actual support one receives at the moment (Davis et al., 1998; Pierce et al., 1991; Sarason et al., 1986). For instance, the 2-, 5-, and 36-month of social support questionnaire in two scoring formulas (number and satisfaction) were 0.78 and 0.86, 0.69 and 0.67, and 0.67 and 0.55 (Sarason et al., 1986), the 9-month test–retest reliability for a German single-item self-esteem scale is 0.72 (Robins et al., 2001), the 6-month test–retest reliability for the Resilience Scale ranges between 0.70–0.73 (Losoi et al., 2015), and the 6- to 9-month test–retest reliability for the International Personality Item Pool – Five Factor Model are 0.88, 0.79, 0.81, 0.82, and 0.82, respectively, for extraversion, agreeableness, conscientiousness, neuroticism, and intellect/imagination (Donnellan et al., 2006). Therefore, we conclude that the C-SPS-10 is appropriate to be adopted as a measure of trait-like characteristics for the general population at the full-scale level because it provides adequate stability compared to other well-established measures that capture traits or trait-like characteristics. However, the results of the 6-month test–rest reliability in Sample 3a of this study were undesirable, compared with the test–retest reliabilities obtained in previous studies concerning measurements of traits or trait-like characteristics. These results could be explained by the transactional model of stress (Lazarus & Folkman, 1987)—cognitive reappraisals toward the disaster may alter disaster victims’ perceptions of their relationships (Gou & Ma, 2023), which are highly related to social support status. The criterion validity of the C-SPS-10 was appropriate and mostly predicted. The strong correlations between the full C-SPS-10 scale and MSPSS, along with the moderate-to-strong correlations between each domain of C-SPS-10 and MSPSS, demonstrates that the C-SPS-10 is a valid measurement of perceived social support. The moderate-to-strong and positive correlation between C-SPS-10 and optimism indicates that people with higher levels of perceived social support tend to be more optimistic. Furthermore, C-SPS-10 was found to be negatively correlated with loneliness, depression, and anxiety, indicating that people scoring higher in the C-SPS-10 can be characterized as suffering less from solitary feelings, depressive disorders, and anxiety symptoms.

This study investigated the psychometric properties of the C-SPS-10 in a disaster-exposed population and general population samples via CTT, IRT, and network analysis. Results of the CTT demonstrated that the C-SPS-10 has good reliability and the five-factor structure of the scale is consistent with the original version proposed by Caron (2013). The scale also demonstrates adequate criterion validity via correlations with other well-established measurements. The IRT results show that the 10-item structure of the C-SPS-10 promotes good ability to discriminate among people with diverse levels of perceived social support. TIC in Studies 1 and 2 both reveal that people with low-to-moderate C-SPS-10 scores are presented with more information, while those who score higher are underrepresented with more standard error. This observation is highly consistent with the findings of Steigen and Bergh (2019), who detected that the SPS-10 failed to represent people with higher levels of perceived social support. Therefore, future endeavor are required to investigate this issue and optimize the C-SPS-10. Our CTT and IRT analyses also indicated that, as a whole, the C-SPS-10 functions well in measuring social support. However, the network analysis showed that some of the domains—social integration and reassurance of worth—are at risk of deviating from other domains. Therefore, we suggest that forthcoming studies adopt the full C-SPS-10 scale, instead of its specific domains, to detect individuals’ levels of perceived social support. Our findings further suggest that network analysis might have an edge in detecting underlying defects regarding the finer structure of instruments. The network analysis also revealed that the central and marginal items and domains differ between the disaster victims and the general population. Therefore, the approach toward further simplification of the C-SPS-10 may differ across diverse population samples. Scholars in health-related studies are often interested in simplifying the scale. For example, Steigen and Bergh (2019) removed two items from the SPS-10 based on a survey of individuals who were out of school or work due to mental health or substance use problems. However, our results showed that the C-SPS-10 is a valid scale to capture trait-like characteristics for the general population, while the test-retest reliability is relatively low for the population suffering from disasters. Individuals experiencing external stress may adopt the cognitive reappraisal strategy to cope with the changed environment, which further alters their perceptions of social support (Lazarus & Folkman, 1987). Thus, modification work based on suffering populations should be reconsidered. There are two limitations of this research. One is the possibility of selection bias resulting from the collection of all samples via an online platform. Consequently, certain groups who have difficulty in accessing the Internet were possibly not included in this research. Therefore, a further study with different data-recruiting method is suggested to ensure that C-SPS-10 works in the same way for all groups with diverse education levels. The other is that we didn’t take social desirability into our consideration, which may lead to potential bias of the results.

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