Personal beliefs and values have a fundamental impact on people’s behavior, health, and well-being (Chen, Stanton, Gong, Fang, & Li, 2009; Chen et al., 2011; Goodwin & Hernandez Plaza, 2000; Kayser, Wind, & Ashok Shankar, 2008; Realo, Allik, & Vadi, 1997; Wagner & Moch, 1986). Individualism and collectivism are a pair of core values theorized to be opposite ends of a continuum that can be used to characterize individuals and cultures around the world (Brewer & Venaik, 2011; Oyserman, Coon, & Kemmelmeier, 2002; Triandis & Singelis, 1998; Zhang, Liang, & Sun, 2013). In an individualistic culture, the self is considered independent; therefore, each individual can pursue his or her own goals; personal goals take priority over the group’s goals; personal rights, needs, and attitudes, as well as contracts with others, determine behavior; communal relationships are not emphasized, and individuals often leave relationships with which they are not satisfied (Triandis & Gelfand, 2012).

In direct contrast to individualism, in a collectivistic culture, the self is considered interdependent with others; therefore, the group’s goals take priority over individuals’ goals; norms, obligations, and duties guide behavior; communal relationships are common; and people attempt to resolve interpersonal problems in a way that will maintain relationships and harmony (Kim, Sharkey, & Singelis, 1994; Oyserman et al., 2002; Triandis & Gelfand, 2012). Beyond the bipolar conceptualization described above, another two-dimensional definition has been used to categorize the individualism–collectivism continuum into the following four components: horizontal individualism, involving being unique and following one’s own wishes; vertical individualism, involving being unique but accepting competition to be valuable; horizontal collectivism, involving being an equal and valued part of a group; and vertical collectivism, involving being submissive to within-group authorities (Triandis, 2001).

Numerous instruments are available for assessing individualism–collectivism (Probst, Carnevale, & Triandis, 1999; Taras et al., 2014), 10 of the most commonly used of which are listed in the Appendix. An evaluation of these instruments revealed that few can be used directly for cross-cultural psychological and behavioral research, because of several limitations.

First, some of these scales consist of a large number of measurement items. One example is the Allocentric–Idiocentric Tendencies Scale (Triandis et al., 1985), which contains 132 items, and may take as long as 15 to 20 minutes for individuals with a limited education to complete. In addition, although the reliability of most subscales has been found to be adequate, no reliability measure (e.g., Cronbach’s alpha coefficient) has been provided for the scale as a whole. Second, a number of scales are available that contain fewer items, but their reliability has not been proven because of the lack of, or relatively low, Cronbach’s alpha values that have been reported. For example, the alpha value for Yamaguchi’s Collectivism Scale (Yamaguchi & Sugimori, 1992) was found to be only .63. Finally, the validity of several of these scales is also unproven. For example, the relationship between the Individualism–Collectivism Index (Vandello & Cohen, 1999) and social capital has been reported as positive in some studies (Allik & Realo, 2004), but negative in others (McBride, 1998; Realo & Beilmann, 2012).

In addition to the limitations described above, the majority of the published scales were developed and validated in individualistic cultures and in countries where the residents earn a high income (Chen, 2007; Steele & Lynch, 2013). It is, therefore, unclear whether or not these instruments would be reliable and valid for use in China, a country well-known for its long history of advocacy for collectivism among its people. Values and beliefs of people in China regarding the self, goals, norms, attitudes, relationships, and behavior may significantly differ from those in countries where individualism is advocated (Oyserman et al., 2002; Triandis & Gelfand, 2012). In addition, China has experienced rapid economic and technological growth since the reforms of the late 1970s began. Thus, there is a growing need for cross-cultural research on cultural beliefs, behavior, and health, and for the development of better tools for measuring individualism–collectivism in the context of China in the early 21st century (Chen, 2007).

To support this research, in which our aim was to develop the Concise Scale of Individualism–Collectivism (CSIC), we operationalized the definition of individualism as a set of norms, values, and behavioral beliefs reflecting the notion that all individuals in a society and the universe are born to be independent (Triandis, Chan, Bhawuk, Iwao, & Sinha, 1995). Therefore, people who believe in individualism will promote the values of autonomy, personal uniqueness, personal efficiency, self-decision making, and personal benefits (Triandis, 1994). Consequently, these people are more likely to rate their own value and success based on what they have achieved (Hofstede, 1980; Oyserman et al., 2002).

In previous studies, researchers have shown that believing in individualism is associated with lower social capital (McBride, 1998), less social support (Triandis, Bontempo, Villareal, Asai, & Lucca, 1988), and greater stress (Allik & Realo, 2004; Triandis et al., 1988). Following these study findings, we hypothesized that believing in individualism may also be associated with low levels of resilience, which is the ability to adapt and to overcome risks and adversity. The development of resilience often involves group collaboration and joint activities (Allik & Realo, 2004), whereas individualism promotes independence and discourages group cohesion (Hofstede, 1980).

In direct contrast to individualism, we operationalized the definition of collectivism as a set of norms, values, and behavioral beliefs reflecting the notion that all individuals in a society are born to be connected with each other (Triandis, 1994; Triandis et al., 1995). As a result, individuals who believe in collectivism are more likely to treat each other as part of a group, and to pursue in-group coherence, cooperation, and collaboration (Kim et al., 1996; Singelis & Brown, 1995). These people will tend to seek advice from others when confronted with difficulties and be willing to sacrifice their own benefits for group interests (Kim et al., 1994; Triandis, 1994). Collectivists tend to rate the recognition by peers and society highly when assessing their own values (Oyserman et al., 2002).

Previous researchers have documented the protective effect of collectivism on health and social well-being. People who believe in collectivism have high social capital (Realo & Beilmann, 2012), more social network connections than do those who are not collectivists, and are more likely than people who are not collectivists to receive social, emotional, and instrumental support from others when needed (Cordero, 2011; Goodwin & Hernandez Plaza, 2000). People who are procollectivism also show high levels of resilience (Bandura, 2000; Kayser et al., 2008), and are less likely than others are to experience stress (Triandis et al., 1988).

Although individualism and collectivism are conceptual opposites, there is consistent empirical support for a moderate but positive relationship between the two concepts (Le & Stockdale, 2005; Oyserman et al., 2002; Shulruf et al., 2011). One person can simultaneously score high or low in both collectivism and individualism (Shulruf et al., 2011), suggesting that there are four potential behavioral mechanisms. (a) People who believe in both individualism and collectivism may selectively utilize each in different settings to maximize their own benefits. (b) People who embrace neither individualism nor collectivism may be at increased risk of health problems. (c) Individuals who disbelieve in collectivism will be less likely than those who believe in collectivism to interact with others, reducing their likelihood for social capital accumulation and resilience development, and lacking social support to use as a buffer against stress. (d) Individuals who disbelieve in individualism will have low motivation for success and, thus, an increased likelihood of failure in career and life, which may lead to stress and associated health issues.

Capitalizing on the progress made in previous research regarding individualism and collectivism, our purpose in this study was to develop a new scale that had a small number of items (≤ 20 items) and demonstrated good reliability (Cronbach’s alpha ≥ .80) and validity. The ultimate goals in this study were to provide a new tool for use in research in China, and to advance cross-cultural understanding of individualism and collectivism as they relate to people’s behavior, health, and well-being.

Method

Participants

The main characteristics of the study sample are presented in Table 1. The final sample comprised 249 adults recruited in Wuhan, China.

We purposefully selected a diverse sample to enhance the generalizability of the developed scale. Wuhan is a metropolitan city and the capital city in Hubei Province. It consists of both urban and rural areas, and has a population of approximately 10 million people comprising urban residents, rural residents, and rural-to-urban migrants.

The rural-to-urban migrant residents were recruited at the Wuhan Center for Disease Prevention and Control from among those migrants who were visiting for a physical check-up for employment purposes, the nonmigrant rural resident group was drawn from residents in a rural village, and the nonmigrant urban participants were residents in consecutive households of one street in the downtown area of Wuhan. We selected one participant per household. For households with two or more eligible participants, one was selected using the random digits method. Among the participants we approached, 92% of the urban residents and 95% of the rural-to-urban migrants and rural residents agreed to participate.

Instrument

The Concise Scale of Individualism–Collectivism consists of 18 items set out as nine pairs (see Table 2 for item content). Participants rate their responses on a standard 5-point Likert scale, with response options ranging from 1 (totally disagree) to 5 (totally agree). To construct the scale, we used the concept mapping technique (Rosas & Camphausen, 2007), a top-down approach for item development that has been found to enhance reliability and validity. After the establishment of the conceptual framework, the lead author composed a set of draft items by adopting six items from published scales that were highly consistent with the conceptual framework we used to develop the CSIC (see Table 2). Following the style of these adopted items, the lead author then drafted a further 12 items and circulated them among the team members for feedback and revision to produce the pilot instrument. We carefully worded individual items to maximize the discriminative sensibility and minimize culture-specificity. The pilot version was tested with a sample of 12 adults, after which we conducted further revisions to produce the final version of the CSIC.

Procedure

Data were collected using the audio computer-assisted self-interviewing technique. The individual CSIC items were randomly ordered and then embedded in the Migration and Behavioral Health Survey (Chen et al., 2009; Triandis, 2001). The participants completed the survey in a private room or a room of the participant’s preference. Three trained data collectors from the Wuhan Center for Disease Prevention and Control (CDC) were in charge of the data collection. All participants signed an informed consent form before completing the survey. The study protocol was approved by the Institutional Review Boards at Wuhan CDC in China, and the Human Investigation Committees at Wayne State University and University of Florida, USA.

Measures

The six variables used for construct validity assessment were age (in years), gender (male/female), marital status (married or other), area of residence (rural, urban, or rural-to-urban migrant), level of education (≤ middle school, high school, college or higher), and monthly income (in Chinese RMB; <500, 500–999, 1000–1999, and ≥ 2000). Our choice of unequal intervals for income level group divisions was exponential-based, to ensure better frequency distributions. Previous researchers have found that individualism is associated with an urban area of residence, higher levels of education (Azuma, 1998), and higher income (Realo, Allik, & Greenfield, 2008), whereas collectivism is associated with a rural area of residence and low income (Oishi, 2010).

The four variables we used for predictive validity assessment were social capital, social support, resilience, and stress. Social capital (including bonding and bridging) was assessed using the Personal Social Capital Scale (Cronbach’s alpha = .94), which was developed and validated with Chinese participants (Chen et al., 2009). Social support was assessed using a brief index scale of informational, instrumental, emotional, and total support, that some of the authors of the current study had previously used in another study conducted in China (Cronbach’s alpha = .92; Chen et al., 2009). Resilience was assessed using the Essential Resilience Scale, a scale developed and validated (Cronbach’s alpha = .94) with data collected in China by a group that included some of the authors of the current study (Chen, Wang, & Yan, 2015). Stress was assessed using the Perceived Stress Scale (Cronbach’s alpha = .87; Cohen, 1988), which has been found to have good reliability among Chinese participants (Chen et al., 2009).

Statistical Analysis

We conducted a systematic psychometric analysis to assess the CSIC. Item sensibility was assessed with measures of central tendency and dispersion, such as M, SD, Mdn, and interquartile range (IQR). The reliability was assessed using Cronbach’s alpha, with the criteria of alpha ≥ .90 as excellent, between .70 and .90 as good, and between .60 and .70 as acceptable. Confirmatory factor analysis (CFA) was conducted to confirm the suitability of the conceptual framework we had proposed to develop the CSIC. A good data–model fit in the CFA is required to meet at least two of the following four criteria: goodness-of-fit index (GFI) > .90, comparative fit index (CFI) > .90, root mean square error of approximation (RMSEA) < .05, ratio of chi square to degrees of freedom (χ²/df) < 2.0.

We assessed construct validity by comparing individualism (IND) scores and collectivism (COL) scores across groups with Student’s t tests (for dichotomous variables) or F tests (for variables with three or more levels). We assessed predictive validity with analysis of variance (ANOVA). Four comparison groups were generated using the median IND and COL scores as the cutoff point to produce (a) low IND and low COL (LILC), (b) high IND and low COL (HILC), (c) low IND and high COL (LIHC), and (d) high IND and high COL (HIHC). Effect sizes with 95% confidence intervals (CI) were computed for both t test (Cohen’s d) and ANOVA (η²) results. A type I error of p < .05 was applied to all psychometric assessments. Statistical analyses were conducted using SAS version 9.3 (SAS Institute, Cary, NC).

Results

Item Sensibility and Scale Constructs

Results in Table 2 indicate high sensibility of the individual CSIC items.

Results from the CFA indicated that the proposed two-factor 18-item structure of the CSIC (see Figure 1) was supported by the data, and there was a significant correlation between the two subscales of IND and COL.

Reliability and Validity Assessments

Cronbach’s alpha coefficients were .91, .86, and .83 for the total CSIC scale, and for the IND and COL subscales, respectively, suggesting excellent reliability for the CSIC as a whole and very good reliability for both IND and COL subscales. Results from the construct validity analysis set out in Table 3 show that the IND scores were higher for older participants, and for participants with a higher income, and lower for participants with a higher level of education, and for those who resided in urban areas. COL scores were lower for participants with a higher level of education, and for those who resided in urban areas.

Predictive Validity Analysis

The ANOVA results in Table 4 indicate that the CSIC significantly predicted levels of social capital, social support, resilience, and stress among the participants.

Among the four groups, HILC participants scored the lowest for social capital, social support, and resilience, and the highest for stress. In contrast, the same outcome measures for the HIHC participants were either the highest (resilience, social support), higher than (for social capital), or equal to (stress) the other three groups. The same measures for the other two groups (LILC and LIHC) varied in between these extremes.

Discussion

In this study, after having operationalized individualism and collectivism, we developed and evaluated the Concise Scale of Individualism–Collectivism (CSIC). Despite a century’s worth of empirical research, there are few well- established instruments for measuring individualism–collectivism that have been shown to be reliable and valid through empirical testing. There is a particular lack of such instruments to measure this pair of cultural beliefs in China, where collectivism is strongly advocated. It is commonly accepted that beliefs, including individualism and collectivism, are shaped by culture (Chen et al., 2013; Oyserman et al., 2002). However, many participants in our study had high IND subscale scores, despite having grown up in the Chinese culture. In addition, our results showed there was a positive correlation between IND and COL in our study sample, suggesting the coexistence of the two seemingly opposite values (Shulruf et al., 2011).

With this newly developed instrument available, more cross-cultural psychosocial research can henceforth be conducted to investigate the complex relationship between cultural setting and personal beliefs with regard to individualism and collectivisms, and the relationship of these beliefs with people’s health.

A common practice when researchers are developing a new scale is to start by soliciting a large number of potential items from experts, followed by conducting an exploratory factor analysis to determine the dimensions and items to be included in the proposed scale. Capitalizing on existing research, we used the top-down approach of concept mapping (Rosas & Camphausen, 2007) instead, using the conceptual framework of our operationalization of the definition of individualism and collectivism to guide the adoption of validated items from published scales, as well as the development of new items dictated by the same conceptual framework for our CSIC. We then performed a CFA to confirm the proposed conceptual framework. As demonstrated in our study, and also in other studies previously conducted by some of the same authors (see e.g., Chen et al., 2013; Chen et al., 2009; Wang et al., 2014), this approach appears to be very effective in ensuring high reliability and validity for the developed scale.

Limitations and Directions for Future Work

We tested the CSIC with adults aged from 18 to 45 years who were residents of one city in China. Additional research is needed to evaluate this new instrument among people in other age ranges in Wuhan, China, as well as in other cities within, and outside of, China. It will be of particular significance to evaluate the CSIC in countries that advocate for individualism, to test its cross-cultural reliability and validity. In addition, despite the excellent reliability of the CSIC in the diverse group of rural-to-urban migrants and nonmigrant rural and urban residents in Wuhan, China that comprised our study sample, the test–retest reliability of the scale cannot be determined without longitudinal data. Given these limitations, our findings showed that the 18-item CSIC is a valid and reliable tool for measuring cultural beliefs with regard to individualism and collectivism among a group of Chinese adults.

Appendix

Summary of 10 Scales Commonly Used for Measuring Individualism and Collectivism

Scale	Country	Subscales	No. of items	Cronbach’s α
1. Allocentric–Idiocentric Tendencies Scale (Triandis et al., 1985)	USA	Total scale Subscales	132	n/a
		Perceived similarity	48	.89
		Paying attention	42	.94
		Taking a trip	4	.75
		Investing money	8	.73
		Lottery	4	.68
		Work request	8	.86
		Loans	5	.82
		Honor associated with Nobel Prize	6	.81
		Contribution to others winning Nobel Prize	7	.80
2. The Individualism–Collectivism Scale (INDCOL; Hui, 1988)	USA and China	Total scale Subscales	63	.67
		Spouse	8	.46
		Parents	16	.76
		Kin	8	.72
		Friends	10	.70
		Neighbors	10	.47
		Coworkers	11	.58
3. The Horizontal and Vertical Individualism–Collectivism Scale (Singelis et al., 1995)	USA	Total scale Subscales	32	n/a
		Horizontal individualism	8	.67
		Vertical individualism	8	.74
		Horizontal collectivism	8	.74
		Vertical collectivism	8	.68
4. Self-Construal Scale (SCS; Singelis, 1994)	USA	Total scale Subscales	24	n/a
		Interdependent	12	.73
		Independent	12	.69
5. The ESTCOL Scale (Realo et al., 1997)	Estonia	Total scale Subscales	24	n/a
		Family-related collectivism	8	.81
		Peer-related collectivism	8	.66
		Society-related collectivism	8	.82
6. Oyserman’s Individualism and Collectivism Scale (Oyserman, 1993)	Israel	Total scale Subscales	19	n/a
		Individualism	10	.49
		Collectivism	9	.74
7. Individualism–Collectivism Scale (Kim & Cho, 2011)	Korea	Total scale Subscales	13	n/a
		Source of identity	3	.67
		Goal priority	4	.80
		Mode of social relation	3	.70
		Norm acceptance	3	.68
8. Beliefs, Values, and Norms of Individualism– Collectivism Scale (Wagner & Moch, 1986)	USA	Total scale Subscales	11	.75
		Beliefs	3	n/a
		Values	3	n/a
		Norms	5	n/a
9. Yamaguchi’s Collectivism Scale (Yamaguchi & Sugimori, 1992)	Japan	Total scale	10	.63
10. U.S. Individualism–Collectivism Index (Vandello & Cohen, 1999)	USA	Total scale	8	.71

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This study was supported through a research grant from the National Institute of Health (Award #

R01 MH086322). We are grateful to those who participated in data collection and data processing.

Bin Yu, Department of Epidemiology, University of Florida, 2004 Mowry Road, Gainesville, FL 32610, USA. Email: [email protected]; or to Shiyue Li, School of Public Health, Wuhan University, 185 Donghu Road, Wuhan, Hubei, 430071, People’s Republic of China. Email: [email protected]