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Article

Adaptation and Validation of a Short Acculturation Scale in a Multi-Ethnic Asian Population

1
Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore
2
Research Division, Institute of Mental Health, Singapore 539747, Singapore
3
Department of Nutrition, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA 02115, USA
*
Authors to whom correspondence should be addressed.
Submission received: 9 November 2020 / Revised: 29 January 2021 / Accepted: 19 February 2021 / Published: 26 February 2021

Abstract

:
The Short Acculturation Scale (SAS) has been widely used for assessing the level of the acculturation of migrants in Western countries. However, the validity of SAS for use in cosmopolitan settings without a single prevailing culture is unclear. We examined the validity and reliability of a version of the SAS adapted to a multi-ethnic Asian society. We used cross-sectional data from 12,610 Singaporean citizens and permanent residents, aged 21–75 years, of Chinese, Malay, and Indian ethnicity. Our version used 11 items, with 5 questions on language use, 3 on media use, and 3 on ethnic social relations, to measure acculturation. Our version of the SAS had good internal consistency. The three-factor CFA model had a good fit to our data. The results from the multiple group CFA supported metric invariance and partial scalar invariance across the three ethnic groups. The total score was positively correlated with generation in Singapore and the number of languages spoken. Among first generation immigrants, country of origin, but not the duration of residence was significantly associated with the acculturation score. Our three-factor version of the SAS is a reliable and valid tool for measuring acculturation in Singapore residents. These findings indicate that adapted SAS can be used to assess acculturation in multicultural settings.

1. Introduction

Acculturation is a complex and multidimensional process that involves psychological, social, behavioral, and cultural changes by migrants to adopt a new or different cultural context over time [1,2]. The process of acculturation takes place when migrants adjust to and adopt the host or dominant culture’s values, behaviors, and attitudes [3,4].
Berry (1997) introduced a bidimensional acculturation model involving four modes of acculturation, based on individuals’ attitudes toward their own ethnic group and the dominant group: assimilation, separation, integration, and marginalization [1]. In public health research, it has been suggested that acculturation could be a risk or protective factor with respect to health among immigrants. Specifically, acculturation may influence mental and physical health due to lifestyle changes and psychological distress in immigrant populations [5,6]. For example, a systematic review on mental health by Koneru at el. (2007) presented the consistent finding that greater acculturation was associated with increased substance use, but mixed findings were observed in terms of mental-health outcomes, which varied by country, population, and the measures used [7]. A meta-analysis of acculturation to Western society and blood pressure presents a negative acculturation effect; higher acculturation was associated with increased blood pressure [8]. A systematic review on acculturation and physical activity showed that higher acculturation was associated with higher leisure-time physical activity in most studies [9].
Although studies on acculturation experiences have been conducted across the world, most have focused on immigrants in Western countries that have a clearly dominant culture, such as the U.S. and European countries [10]. Only a few studies focused on community settings. Landrine and Klonoff (1994) developed and validated the African American Acculturation Scale (AAAS) to measure acculturation level, and this scale was normed on African Americans in a community setting [11,12]. Some studies have been conducted on acculturation experiences in Asia, including Hong Kong, Taiwan, South Korea, and Japan. However, these studies mostly focused on migrant workers and these countries have comparatively monocultural societies [13,14,15,16]. A few studies have assessed acculturation in multicultural settings. Falavarjani and Yeh (2018) measured acculturation among Iranian immigrants in Malaysia using the Vancouver Index Acculturation scale (VIA) [17]. Cheung-Bluden and Juang (2008) used the Acculturation Scale for Vietnamese Adolescents (ASVA), developed by Nguyen et al. (1999) to examine if acculturation towards Chinese (majority) culture or western (minority) culture among ethnic Chinese girls from a school located on Hong Kong Island, which has a longer colonial history than other districts in Hong Kong [18,19]. However, it is unclear whether these measures could be applied in multicultural settings with differing in terms of the dominant culture. To the best of our knowledge, no study has measured acculturation levels in a cosmopolitan setting with several prominent cultures, using a validated scale.
Singapore is a multicultural, multireligious and multiracial country with three major Asian ethnic groups: Chinese, Malay, and Indian [20]. A large proportion of Singapore’s overall population is foreign-born [21]. The four official languages include English, Mandarin, Malay, and Tamil, and common religions include Buddhism, Christianity, Islam, Taoism, and Hinduism [22]. A recent qualitative study by Mohanty et al. (2018) found large differences in acculturation experiences and strategies among immigrants in Singapore and demonstrated different adaption strategies based on their country of origin [23]. Ethnic differences in mental and physical health in the population of Singapore have been reported including differences in the risk of diabetes and cardiovascular diseases [24,25]. Therefore, an understanding of cultural orientation beyond mere ethnicity may contribute insights to determinants of variation in health in multicultural societies such as Singapore.
Given Singapore’s multiculturalism, where distinct cultures co-exist, measuring acculturation involves assessing the orientation towards different cultures rather than a single host culture. Until now, there is no suitable measurement for assessing level of acculturation in a multicultural society setting. Therefore, we adapted an existing acculturation scale, the Short Acculturation Scale (SAS) originally developed for Hispanics in the U.S. by Marin et al. (1987) [26]. The SAS is one of the most commonly used tools for measuring acculturation. It was also validated in Chinese Americans by Gupta and Yick and has also been used for Korean and Filipino immigrants in the US [27,28]. In addition, Chan et al. (2019) translated and validated the SAS-Urdu version for Pakistani women in Hong Kong [29]. The SAS instrument measures attitudinal and behavioral aspects of acculturation rather than surrogate measures, such as place of birth, length of residence, or language preference. We modified some of the questions to make them more appropriate for a multicultural Asian setting without a single dominant culture. In this context, ‘acculturation’ refers to a stronger orientation toward other cultures rather than adaptation to a dominant host culture.
The objectives of this study were to describe the adaption of the acculturation scale and establish the psychometric properties of our scale for measuring acculturation in the multicultural Singapore population. We examined acculturation among Singapore citizens and permanent residents rather than focusing on acculturation or coping experience among temporary residents. This scale can serve as a tool for understanding the role of cultural differences more fully, even within the same ethnic group, and benefit future studies on the effects of cultural orientation on health outcomes and healthcare utilization.

2. Materials and Methods

2.1. Adaptation of the SAS

We adapted the 12-item Short Acculturation Scales (SAS) for Hispanics, which was originally developed for Hispanics in the U.S. [26] and subsequently validated for Chinese [28], Filipino [30], and Korean [27] immigrants to the U.S., and Pakistani women in Hong Kong [29]. Our version uses 11 items to measure acculturation on three sub-scales, with five questions about language, three about language of media use, and three about ethnic social relations with other ethnic groups. We did not include one question from the original scale, “If you should choose your children’s friend, you would want them to be?”, as the participants might not have children. For media use questions, we revised 2 questions from original scale, because a substantial proportion of participants may not listen to the radio in a contemporary Singapore setting. Therefore, instead of asking “In what language(s) are the radio program you usually listen to?”, we asked “In what language(s) are the newspapers or magazines you usually read?”. Another media use question from original scale, “In general, in what language(s) are the movies, T.V., and radio programs you prefer to watch and listen to?” was modified to “In general, in what language(s) are movies you prefer to watch?”.
The response options of the original SAS version for Hispanics were in Likert format, ranging from 1 (Only Spanish) to 5 (Only English) with higher scores indicating higher levels of acculturation. We had to change these response options for the multilingual Singapore setting where more than two languages are relevant, the mother tongue, other Asian languages, and English. Therefore, our version included three additional detailed questions for each item on language and media use corresponding to the main question. For example, instead of asking the main question, “What language(s) do you think in?”, we asked three detailed sub-questions more appropriate to the Singapore setting: “How often do you think in your mother tongue?”; “How often do you think in Other Asian languages?”; and “How often do you think in English?” The response options for these three questions were “never”, “rarely”, “sometimes”, “often”, or “always”. Because the first question has a different direction to the second and third questions, we reverse-coded the first question and then added its answer to the scores of the second and third questions. Similar to the original scale, the questions on ethnic social relations had scores ranging from 1 = “all from your ethnicity” to 5 = “all from other ethnicities”. Pre-testing was carried out among 8 team members aged between 30–70 years old who are either of Chinese or Malay ethnicity. We reviewed each item to identify potential issues, such as understanding the intent of the questions, contextual relevance for different groups of people (working vs non-working people, parents vs non-parents), and words which may be difficult to achieve an equivalent translation. We also estimated the time taken to complete the questionnaire. Details of the questionnaire and scoring are presented in Supplemental Table S1. The total scores for acculturation and the three sub-scales were calculated by summing items. The possible range for the sum of the scores for the 11 items was from 27 to 135, and higher scores indicate a greater acculturation.

2.2. Study Population and Data Collection

We used cross-sectional data collected from the follow-up of the Singapore Multi-Ethnic Cohort Phase 2 study (MEC2). This is a population-based cohort study of Singapore citizens and permanent residents aged 21–75 years, mainly comprising three major ethnic groups in Singapore (Chinese, Malay, and Indian). Detailed information is available http://blog.nus.edu.sg/sphs/the-first-sphs-follow-up/, accessed on 5 November 2020 and a previous publication Teo et al. [31]. Data collection for the follow-up consisted of two parts: (1) a home interview and (2) a physical examination at a health screening center. For the current study, we excluded those who did not complete the 11 items of the acculturation questionnaire, including those whose mother tongue is English (n = 982) or were not of Chinese, Malay, or Indian ethnicity (n = 656). The final sample included 12,170 participants for analyses. Informed consent was obtained from all individuals before study enrollment and the study protocol was approved by the Institutional Review Board of the National University of Singapore (NUS-IRB-reference B-16-125). All data were collected through face-to-face interviews using standardized questionnaires filled out on computer tablets by trained interviewers. The interviews were typically conducted at the participants’ home or occasionally at another location of their choice.

2.3. Sociodemographic and Acculturation-Related Variables

We included sociodemographic variables; age and gender (male or female), ethnicity, average monthly household income, highest attained education level, and religion. Ethnicity was recorded by the interviewer from participant’s identity card (Chinese, Malay, or Indian). Participants were asked to provide information on the average monthly household income in Singapore dollars with response options: $2000, $2000–3999, $4000–5999, $6000–9999, and $10,000 SGD or more. For highest attained education level, response options were no formal qualifications/lower primary; primary education; secondary education; ‘A’ level/polytechnic/diploma; and university and above. For religion, response options included no religion, Buddhism, Taoism/Chinese traditional belief, Islam, Hinduism, Sikhism, Christianity Roman Catholic, Christianity other denomination, or other religion.
Characteristics related to acculturation were also collected. For generation in Singapore, we utilized one of the items from the Suinn Lew Asian Self-Identity Acculturation Scale (SL-ASIA) that has been widely used for Asian population in the U.S. [32]. The same item “What generation are you?” was used from the scale, but we changed the name of a country to Singapore in the response options. In addition, country of birth (Malaysia, China, India, other South East Asian countries, or other countries), years lived in Singapore (<10, 10–19, 20–29, 30–39, or ≥40 years), number of languages spoken (1, 2, 3, or ≥4), and mother tongue (Mandarin, other Chinese language, Malay, Tamil, other Indian languages, or others) were included. Only those who responded they were the “first generation” were asked questions about their country of birth and the number of years lived in Singapore.

2.4. Statistical Analyses

Prior to the data analysis, some variables were recoded. Age was originally a continuous variable, and we categorized into <40, 40–49, 50–59, or ≥60 years. We collapsed the highest two income categories to avoid small numbers and responses with “don’t know” or “refused to answer” were included as “unknown”. The lowest two education categories were also collapsed to avoid small numbers.
Descriptive statistics were calculated, including frequencies, percentages, means, and standard deviations. To compare differences across ethnic groups (Chinese, Malay, and Indians), the Pearson’s chi-square test or Kruskal–Wallis test was performed. Post hoc paired comparisons after Kruskal-Wallis Test were applied using a Dunn’s test. To test the reliability of our scale, Cronbach’s alpha and omega coefficient were calculated to examine the internal consistency, and >0.70 was considered acceptable reliability [33]. Spearman’s correlation coefficients (r) were calculated to examine the inter-relationship of the scales, and 0.30 to 0.70 indicates good internal consistency [34]. Because exploratory factor analyses of SAS had been conducted in previous studies [26,27,35], we performed confirmatory factor analysis (CFA) to establish the construct validity of the scale with three factors (language use, media use, and ethnic social relations) previously constructed and validated for SAS. Subsequently, because language use and media use were correlated (Spearman’s correlation ρ = 0.72), we also evaluated a two-factor model that combined the language-use and media-use scales to decide which model fits better. A uni-factor model and bi-factor model were also examined for comparative purposes. We examined the model fit for the uni-factor, the bi-factor, the two-factor and the three-factor model using the following indices: p-value of χ2 statistic, comparative fit index (CFI), root mean square error of approximation (RMSEA), standardized root mean square residual (SRMR), and Tucker–Lewis Index (TLI). The cut-off values for the indices were as follows: p-value of χ2, >0.05CFI, ≥0.9; RMSEA, <0.08; SRMR, <0.08; and TLI, ≥0.95 [36,37]. A series of nested multiple group CFA was conducted to assess three hierarchic levels of measurement invariance (configural, metric and scalar invariance) across three different ethnic groups. Finally, to assess construct validity of our scale, we investigated the associations between the acculturation score and other questions that are expected to reflect aspects of acculturation using Pearson’s chi-square test and Spearman’s rank correlation coefficient. We expect to find positive correlations between our scale and the surrogate measures (i.e., generation and length of residence), as well as significant differences in levels of acculturation based on their country of origin. CFA was performed with weighted least squares means and variance adjusted (WLSMV) estimation using Mplus version 8.2., because item 9 to 11 were categorical variables. All other analyses were conducted with Stata 14.0. Statistical significance was set at 0.05 for all statistical tests.

3. Results

Table 1 presents the socio-demographic characteristics of the 12,170 study participants. The median age was 51.0 (interquartile range [IQR] = 40.0, 61.0) years and 57% were women. Most participants were ethnic Chinese (73.2%), followed by Indians (16.6%) and Malay (10.2%). The most common religion was Buddhism/Taoism, but having no religion, Islam, Christianity, and Hinduism were also reported by a substantial proportion of participants. Most participants were first (32%) or second (32%) generation Singaporeans, but third (17%), fourth (7%), and older generation (12%) Singaporeans were also represented. First generation participants were mostly born in Malaysia (50%), India (22%), and China (20%). More than 70% of participants reported that they spoke 3 languages or more. The most common mother tongue language was Mandarin (43.6%), followed by other Chinese language (28.4%), Tamil (12.0%), and Malay (11.4%). There were significant differences across ethnic groups in age, income level, education level, and generation in Singapore (Table 1).
To confirm the factor structure of our scale, CFA was conducted with WLSMV estimation, testing the SAS factor structure constructed previously. The model consisting of three factors is presented in Figure 1. The standardized regression weights (factor loadings) for the 11 items ranged from 0.60 to 0.87. Given that the χ2 test is sensitive to large sample sizes [38], the p-value of χ2 statistic for our model was significant (p = 0.000). However, according to the model fit indices, the three-factor model had a good fit in our data (Table 2). The model fit indices were as follows: CFI = 0.97, RMSEA = 0.05, SRMR = 0.03, and TLI = 0.96. The bi-factor CFA model fit the data slightly better than the three-factor model. However, the three-factor model was chosen for use in this study that has advantages in terms of practical interpretability and its comparison with other international studies.
For the uni-factor CFA model, all the model fit indices did not meet the desired cutoff values, suggesting this model did not have a good fit of the current data (Table 2). The two-factor CFA model, collapsing the first two factors was a worse fit to the data. Factor loadings for the uni-factor, two-factor, and bi-factor models are presented in Supplemental Figure S1. Moreover, we conducted a series of nested multigroup CFA to test the measurement invariance of the three-factor SAS across three ethnic groups (Chinese, Malay, and Indian). The change in the model fit indices (△CFI = 0.001, △RMSEA = 0.003, △SRMR = 0.01) suggested that the fit of the metric invariance model was slightly better relative to the configural model [39]. The change in the model fit indices between the metric and the scalar invariance models (△CFI = 0.211, △RMSEA = 0.064, △SRMR = 0.023) suggested that the fit of the scalar invariance model was not adequate. Modification indices suggested that the misfit was due to two items belong to ethnic social relations. After the equality constraints of threshold for items 9 and 10 were lifted, we found that the change in the model fit indices of the scalar invariance model was improved (△CFI = 0.017, △RMSEA = 0.007, △SRMR = 0.005). Therefore, we concluded that the metric invariance and partial scalar invariance were supported.
Reliability measured by Cronbach’s alpha to examine internal consistency of the SAS instrument was 0.89 for language use, 0.88 for media use, and 0.81 for ethnic social relations, indicating high internal consistency of our version. Omega coefficient ranged 0.86 to 0.90. Spearman correlation coefficients, calculated to examine the inter-relationships of the three subscales (factors), ranged from 0.30 to 0.72, which is acceptable. All the correlations were significant, and the strongest correlation was found between language use and media use (r = 0.72) (Table 3).
We compared the SAS score with other related indicators in Table 4. The items were summed to produce subscale and overall scores. The overall SAS scores and the three subscale scores increased from the first to the fourth generation but did not further increase for older generations. As expected, the generation number in Singapore was significantly correlated with higher overall (r = 0.19), language use (r = 0.17), media use (r = 0.18), and ethnic social relation (r = 0.10) scores. For the first generation, those who were born in China had a significantly lower score for all SAS scales. Additionally, we compared the SAS score for ethnic Chinese from Malaysia and ethnic Chinese from mainland China to examine if there are differences even within the same ethnic group from different countries of origin. Ethnic Chinese from Malaysia had a significantly higher score for all SAS scales than ethnic Chinese from mainland China (Supplemental Table S2). The duration of living in Singapore was not associated with the overall SAS score, and no significant correlation was found (r = −0.002). Specifically, those who lived in Singapore for 10–19 years had a higher ethnic social relation scale than those living there shorter or longer. The number of languages spoken was significantly positively correlated with overall, language use (r = 0.22), and media use scores (r = 0.28), but not with ethnic social relation scores (r = −0.08). However, those who spoke one language had the lowest score for all acculturation scales.

4. Discussion

In this study, we adapted the SAS and validated our version to measure acculturation in a population-based study of 12,610 residents of Singapore, of Indian, Malay, and Chinese ethnicity. Since Singapore is a multicultural/multiethnic country, questions regarding acculturation were revised to measure individuals’ orientation towards their native culture as compared to other cultures, rather than towards a single host culture. The factor structure of the original version consisted of three components (language use, media use, and ethnic social relations) [27,28,30]. Our CFA results demonstrated that our 11-item adapted scale had a three-factor structure identical to that of the original SAS. Our findings also indicate that the adapted SAS had good internal consistency for each subscale and for the overall score. The internal consistency was similar to the original version of SAS and versions used for Chinese and Korean Americans. We also conducted a multiple group CFA to test the measurement invariance across ethnic group and found that the factor structure and factor loadings are comparable across three ethnic groups. SAS scores were also associated with related measures such as number of generations in Singapore, number of languages spoken and country of origin. These findings indicate that the adapted version of SAS can be a valid and appropriate measure for examining acculturation in a multicultural Asian country.
Various scales of acculturation have been validated and used to examine the attitudes of Asian immigrants. A systematic review by Zhang and Tsai (2014) provided an exhaustive summary of acculturation scales for various Asian American groups, including in terms of their validity, reliability, and assessment domains (e.g., language, identity, etc.) [40]. They found that the two most commonly used scales were Suinn Lew Asian Self-Identity Acculturation Scale (SL-ASIA) and Vancouver Index Acculturation scale (VIA) [32,41]. The SL-ASIA (ref) scale includes topics such as language use, food preferences, history, attitudes, cultural preferences, ethnic identity and friendship choices, and comprises of 21 items [32]. This scale was developed for Asian Americans and has been widely used to measure acculturation among Asian immigrants such as Chinese, Korean, Filipino and Japanese Americans [42,43,44,45]. The VIA, which was initially developed for ethnic Chinese Canadian [46], provides a bidimensional measure of acculturation, and has 20 items including three domains: adherence to traditions, social relationships, and values. Few studies have assessed acculturation in Asia countries. A recent study [47] of acculturation and its associations with health outcomes among Myanmar migrant workers in Thailand adapted the East Asian Acculturation Measure (EAAM) scale, which was originally developed for East Asian immigrants in the US [48]. However, that study did not present a validation of the scale. To the best of our knowledge, ours is the first study to adapt and validate the original validated version for measuring acculturation in a multicultural Asian country.
Proxy indicators of acculturation (e.g., languages spoken, proportion of life lived in the country, country of origin, and generation status) have been suggested to be simple, useful measures of acculturation levels when designated acculturation measures are unavailable or infeasible [49,50]. In our study, variables related to acculturation included generation number residing in Singapore, country of birth, years lived in Singapore for the first generation, and number of languages spoken. As expected, the SAS subscales language use and media use were each positively correlated with the number of languages spoken. The SAS score was not clearly correlated with the duration of residence for the first generation, while the subscale ethnic social relation was negatively correlated with the duration of residence. Thus, proxy measures may not be sufficiently thorough to capture the whole spectrum of the acculturation process and aid in understanding the complex nature of acculturation, as suggested by previous studies [3,51]. For example, the authors of a study of Asian-Indian immigrants in the US reported that proxy measures do not fully capture acculturation because these immigrants might have been proficient in English and may have lived in other Western countries prior to immigration to the US [51]. With these concerns, a research instrument that provides a reliable and comprehensive assessment of acculturation is needed to understand the multifaceted nature of acculturation.
In our sample, we observed large differences in acculturation by country of origin among the first-generation immigrants. Those who were born in China had the lowest scores for overall acculturation score and all subscales (language use, media use and ethnic social relations). The highest overall scores were observed among those who were born in Malaysia. This finding is consistent with a qualitative study conducted in Singapore by Mohanty et al. (2018), which revealed that country of origin was more relevant than ethnicity with respect to the choice of acculturation strategy [23]. Even among ethnic Chinese immigrants, acculturation strategies differed by country of origin. Chinese immigrants from China feel discriminated against more than Chinese immigrants from Malaysia and Indonesia, which leads to marginalization or the adoption of integration strategies, while Chinese immigrants from Malaysia and Indonesia tend to assimilate into Singapore society due to the positive attitudes of the host society.
There were some limitations to our study. First, the level of acculturation was assessed based on self-report measures, which can be prone to social desirability and recall bias. Also, due to the cross-sectional design of our study, longitudinal studies are needed to establish the reliability of our adapted version of the SAS, and to understand changes in acculturation experiences over time. Lastly, the characteristics of our sample are distinct (i.e., absence of host culture and focus on the general population, including permanent residents and citizens, instead of specific immigrants) from other populations studied using the SAS. Future research should validate our scale in other populations or specific subpopulations, such as temporary migrant workers and younger individuals.

5. Conclusions

In this study, we adapted and validated the acculturation scale among the general population of Singapore to capture multidimensional aspects of acculturation. Additionally, our scale was normed on a community sample rather than short-term residents (i.e., international students and foreign domestic workers). High internal consistency and construct validity were confirmed. Our use of CFA validated a three-factor model, including (1) language use, (2) media use of different languages, and (3) social relations with other ethnic groups; this model exhibited a good overall fit for our sample. Our scale should allow researchers to understand the underlying mechanisms of individuals’ behaviors and attitudes that may not be explained by ethnicity and can be useful for tailoring preventive health interventions, based on individuals’ cultural orientations. Further research, including longitudinal and qualitative studies, is warranted to evaluate the complex process of acculturation over time, and to determine how cultural orientation influences lifestyle factors and health outcomes.

Supplementary Materials

The following are available online at https://0-www-mdpi-com.brum.beds.ac.uk/2624-8611/3/1/4/s1, Figure S1: Confirmatory factor analyses of the short acculturation scale; Table S1: The adapted Singaporean version of Short Acculturation Scale (SAS) for Hispanic, Table S2: Score comparison between ethnic Chinese from Malaysia and ethnic Chinese from China.

Author Contributions

Conceptualization, S.H.P. and R.M.v.D.; methodology, S.H.P., E.A., L.N., M.S. and R.M.v.D.; software, S.H.P. and E.A.; validation, S.H.P. and E.A.; formal analysis, S.H.P., L.N. and R.M.v.D.; investigation, R.M.v.D. and L.W.L.T.; resources, R.M.v.D. and L.W.L.T.; data curation, S.H.P. and E.A.; writing—original draft preparation, S.H.P. and R.M.v.D.; writing—review and editing, S.H.P., E.A., L.N., M.S. and R.M.v.D.; visualization, S.H.P. and E.A.; supervision, R.M.v.D.; L.N. and M.S.; project administration, R.M.v.D.; funding acquisition, R.M.v.D. All authors have read and agreed to the published version of the manuscript.

Funding

Public Health Metrics and Analytics program funding from the Ministry of Health, Singapore, NUS and National University Health System, Singapore.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of the National University of Singapore (NUS-IRB-reference B-16-125).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Datasets that are restricted and not publicly available.

Conflicts of Interest

No potential competing interest was reported by the authors.

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Figure 1. Results of the confirmatory factor analysis of the short acculturation scale. A box represents an observed variable. A circle represents latent variables. A double-headed curved arrow represents covariance between two latent variables. A single headed arrow represents the standardized regression weights. A short arrow represents the measurement error.
Figure 1. Results of the confirmatory factor analysis of the short acculturation scale. A box represents an observed variable. A circle represents latent variables. A double-headed curved arrow represents covariance between two latent variables. A single headed arrow represents the standardized regression weights. A short arrow represents the measurement error.
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Table 1. Socio-demographic characteristics of the study population, N(%).
Table 1. Socio-demographic characteristics of the study population, N(%).
Total, N(%)ChineseMalaysIndiansp-Value a
Age, median (IQR)51.0 (40.0, 61.0)53.0 (42.0, 63.0)48.0 (36.0, 56.0)46.0 (38.0, 56.0)0.0001
Gender
Men5215 (42.9)3827 (43.0)511 (41.1)877 (43.5)0.37
Women6955 (57.2)5082 (57.0)733 (58.9)1140 (56.5)
Income level b
Less than $20002465 (20.3)1840 (20.7)317 (25.5)308 (15.3)<0.0001
$2000–$39992341 (19.2)1541 (17.3)345 (27.7)455 (22.6)
$4000–$59992158 (17.7)1473 (16.5)218 (17.5)467 (23.2)
$6000 or higher3531 (29.0)2728 (30.6)217 (17.4)586 (29.1)
Education level
Primary or lower3264 (26.8)2524 (28.3)371 (29.8)369 (18.3)<0.0001
Secondary2802 (23.0)2023 (22.7)381 (30.6)398 (19.7)
Post-secondary2998 (24.6)2063 (23.2)389 (31.3)546 (27.1)
University or above3099 (25.5)2292 (25.7)103 (8.3)704 (34.9)
Religion
No religion2096 (17.2)2065 (23.2)3 (0.2)28 (1.4)NA
Buddhism/Taoism4905 (40.3)4896 (55.0) 1 (0.1)8 (0.4)
Islam1679 (13.8)51 (0.6)1235 (99.3)393 (19.5)
Hinduism1337 (11.0)7 (0.1)1 (0.1)1329 (65.9)
Christianity2084 (17.1)1844 (20.7)4 (0.3)236 (11.7)
Other religion 69 (0.6)46 (0.5)0 (0.0)23 (1.1)
Generation
First generation3876 (31.9)2792 (31.3)112 (9.0)972 (48.2)<0.0001
Second generation3855 (31.7)2943 (33.0)339 (27.3)573 (28.4)
Third generation2092 (17.2)1673 (18.8)200 (16.1)219 (10.9)
Fourth generation854 (7.0)598 (6.7)151 (12.1)105 (5.2)
Older generation1493 (12.3)903 (10.1)442 (35.5)148 (7.3)
Country of birth for first generation
Malaysia1937 (50.0)1722 (61.7)88 (78.6)127 (13.1)NA
China 776 (20.0)774 (27.7)1 (0.9)1 (0.1)
India836 (21.6)4 (0.1)2 (1.8)830 (85.4)
Other South East Asian countries220 (5.7)197 (7.1)17 (15.2)6 (0.6)
Other countries107 (2.8)95 (3.4)4 (3.6)8 (0.8)
Years lived in Singapore for first generation
Less than 10 years205 (5.3)153 (5.5)2 (1.8)50 (5.1)NA
10–19 years1387 (35.8)854 (30.6)20 (17.9)513 (52.8)
20–29 years1049 (27.1)792 (28.4)19 (17.0)238 (24.5)
30–39 years431 (11.1)369 (13.2)4 (3.6)58 (6.0)
40 years or longer804 (20.7)624 (22.4)67 (59.8)113 (11.6)
Number of languages spoken c
1189 (1.6)116 (1.3)47 (3.8)26 (1.3)<0.0001
2 3064 (25.2)1276 (14.3)949 (76.3)839 (41.6)
33411 (28.0)2380 (26.7)192 (15.4)839 (41.6)
≥45506 (45.2)5137 (57.7)56 (4.5)313 (15.5)
Mother tongue
Mandarin5302 (43.6)5290 (59.4)3 (0.2)9 (0.5)NA
Other Chinese language3459 (28.4)3453 (38.8)2 (0.2)4 (0.2)
Malay1391 (11.4)42 (0.5)1225 (98.5)124 (6.2)
Tamil1456 (12.0)5 (0.1)6 (0.5)1445 (71.6)
Other Indian language357 (2.9)1 (0.01)0 (0.0)357 (17.7)
Others202 (1.7)116 (1.3)8 (0.6)78 (3.9)
NA, p-value was not calculated because of too many low counts in the cells. IQR, interquartile range. a Pearson’s chi-square test or Kruskal–Wallis test with pairwise comparison. b Includes the “Unknown” category for those who responded ‘don’t know’ or refused to answer (N = 1675). c Languages include Mandarin, Cantonese, Hokkien, Teochew, Other Chinese language, Malay, Tamil, Other Indian languages, English, or Others.
Table 2. Goodness-of-fit indices of uni-factor, bi-factor, two-factor, and three-factor models for the short acculturation scale (SAS) and measurement invariance testing of the three-factor model across ethnic group.
Table 2. Goodness-of-fit indices of uni-factor, bi-factor, two-factor, and three-factor models for the short acculturation scale (SAS) and measurement invariance testing of the three-factor model across ethnic group.
p-Value (χ2)CFI (≥0.90)RMSEA (<0.08)SRMR (<0.08)TLI (≥0.90)
Uni-factor SAS<0.00010.400.230.140.25
Bi-factor SAS<0.00010.990.030.010.98
Two-factor SAS<0.00010.940.070.040.93
Three-factor SAS<0.00010.970.050.030.96
Measurement invariance (3-factor SAS)
Configural invariance<0.00010.9580.0580.0280.944
Metric invariance<0.00010.9570.0550.0380.949
Scalar invariance<0.00010.7460.1190.0610.766
Partial scalar invariance<0.0010.9400.0620.0430.937
χ2, chi-square test; CFI, comparable fit index; RMSEA, root mean square error of approximation; SRMR, standardized root mean square residual; TLI, Tucker Lewis Index.
Table 3. Factor correlation and internal consistency of the 3-factor model of the acculturation scale.
Table 3. Factor correlation and internal consistency of the 3-factor model of the acculturation scale.
Factor 1: Language UseFactor 2: Media UseFactor 3: Ethnic Social Relations
Internal consistency
Cronbach’s alpha0.8930.8760.806
Omega’s coefficient0.8950.8760.860
Validity
Factor 1. Language use r = 0.72 *r = 0.33 *
Factor 2. Media use r = 0.30 *
Factor 3. Ethnic social relations
* p < 0.001. r, Spearman’s rho.
Table 4. The association between the short acculturation scale and related variables.
Table 4. The association between the short acculturation scale and related variables.
Overall Summary Score, Mean (SD)Language Use, Mean (SD)Media Use, Mean (SD)Ethnic Social
Relations, Mean (SD)
Total69.43 (16.65)38.19 (9.97)25.22 (6.80)6.02 (2.54)
Ethnicityp = 0.0001p = 0.0001p = 0.12p = 0.0001
Chinese68.95 (17.04)38.25 (10.19)25.29 (7.09)5.41 (2.17)
Malays67.43 (14.74)34.91 (8.47)25.06 (5.86)7.46 (2.68)
Indians72.77 (15.58)39.94 (9.32)24.99 (6.04)7.84 (2.72)
Generationp = 0.0001p = 0.0001p = 0.0001p = 0.0001
First generation64.32 (17.13)35.25 (10.09)23.27 (7.04)5.80 (2.43)
Second generation71.10 (16.38)39.38 (9.84)25.84 (6.82)5.89 (2.54)
Third generation72.93 (15.43)40.23 (9.39)26.60 (6.19)6.10 (2.54)
Fourth generation74.49 (14.41)40.77 (8.91)27.14 (5.79)6.58 (2.37)
Older generation70.55 (15.67)38.41 (9.53)25.63 (6.34)6.51 (2.80)
Country of birth for first generationp = 0.0001p = 0.0001p = 0.0001p = 0.0001
Malaysia66.69 (16.99)37.03 (10.01)24.16 (7.06)5.50 (2.34)
China 52.72 (14.14)28.57 (8.57)19.38 (6.07)4.77 (1.66)
India66.30 (15.43)35.97 (8.93)23.09 (6.32)7.23 (2.48)
Other South East Asian countries73.56 (15.45)38.87 (9.50)28.16 (6.79)6.53 (2.60)
Other countries71.16 (16.93)38.37 (10.68)26.59 (5.93)6.20 (2.57)
Years lived in Singapore for first generationp = 0.67p = 0.12p = 0.61p = 0.0001
Less than 10 years64.68 (17.00)34.91 (9.80)23.85 (6.96)5.92 (2.38)
10–19 years64.09 (16.40)34.81 (9.67)23.17 (6.62)6.12 (2.40)
20–29 years64.52 (17.20)35.36 (10.16)23.36 (7.06)5.80 (2.34)
30–39 years65.29 (17.71)36.35 (10.46)23.40 (7.25)5.54 (2.37)
40 years or longer63.85 (17.99)35.37 (10.54)23.11 (7.60)5.37 (2.57)
Number of languages spoken ap = 0.0001p = 0.0001p = 0.0001p = 0.0001
145.28 (17.67)23.14 (10.28)17.33 (6.69)4.81 (2.56)
2 63.92 (14.63)33.87 (8.49)23.65 (6.12)6.39 (2.56)
370.76 (15.74)39.21 (9.46)25.43 (6.39)6.12 (2.60)
≥472.49 (16.80)40.48 (9.81)26.22 (7.08)5.80 (2.46)
Mother tonguep = 0.0001p = 0.0001p = 0.0001p = 0.0001
Mandarin62.63 (14.83)35.27 (9.06)22.11 (5.90)5.25 (1.99)
Other Chinese language78.31 (15.86)42.73 (10.23)29.98 (5.99)5.59 (2.39)
Malay67.88 (14.70)35.17 (8.52)25.24 (5.86)7.47 (2.69)
Tamil70.62 (15.05)39.05 (9.04)23.81 (5.65)7.76 (2.76)
Other Indian language79.45 (15.25)43.16 (9.29)28.25 (6.04)8.04 (2.57)
Others80.00 (14.67)42.74 (9.24)29.75 (6.03)7.51 (2.54)
p-values were calculated using a Kruskal-Wallis test with pairwise comparison; SD, standard deviation a Languages include Mandarin, Cantonese, Hokkien, Teochew, Other Chinese language, Malay, Tamil, Other Indian languages, English, or Others.
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Park, S.H.; Abdin, E.; Nan, L.; Subramaniam, M.; Tan, L.W.L.; van Dam, R.M. Adaptation and Validation of a Short Acculturation Scale in a Multi-Ethnic Asian Population. Psych 2021, 3, 25-38. https://0-doi-org.brum.beds.ac.uk/10.3390/psych3010004

AMA Style

Park SH, Abdin E, Nan L, Subramaniam M, Tan LWL, van Dam RM. Adaptation and Validation of a Short Acculturation Scale in a Multi-Ethnic Asian Population. Psych. 2021; 3(1):25-38. https://0-doi-org.brum.beds.ac.uk/10.3390/psych3010004

Chicago/Turabian Style

Park, Su Hyun, Edimansyah Abdin, Luo Nan, Mythily Subramaniam, Linda Wei Lin Tan, and Rob M van Dam. 2021. "Adaptation and Validation of a Short Acculturation Scale in a Multi-Ethnic Asian Population" Psych 3, no. 1: 25-38. https://0-doi-org.brum.beds.ac.uk/10.3390/psych3010004

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