Examining the Factor Structure and Psychometric Properties of the Chinese Version of the Life Orientation Test in Older Chinese Immigrants

Abstract

The current study examined the factor structure and psychometric properties of the Chinese version of the Revised Life Orientation Test (CLOT-R) in a sample of 342 community-dwelling older Chinese immigrants in Canada (mean age = 71.99, SD = 5.62; 58.5% female). Confirmatory factor analysis demonstrated that the CLOT-R yields a two-factor model with one item cross-loading on two latent constructs of optimism and pessimism. Analysis further revealed poor internal consistency and convergent validity. Evidence for discriminant and convergent validity was found between optimism and perceived stress, as well as optimism and quality of life. Compared with the factor structure reported in previous Chinese-speaking samples, the modified two-factor structure found in the current group of older Chinese immigrants could be attributed to the heterogeneity of the sample and possible configural variance across culture and age. Overall, the current findings suggest that the CLOT-R may not be a reliable and valid measure to assess dispositional optimism and pessimism among older Chinese immigrants. Theoretical implications and suggestions for further scale development and research is discussed.

Keywords

Chinese factor analysis immigrant measures and statistics optimism psychometrics translated questionnaire validity

Introduction

Over three decades of research has reported a robust association between dispositional optimism and outcomes of health and well-being, including coping behaviors, physical health, and psychological well-being (e.g., Andersson, 1996; Rasmussen, Scheier, & Greenhouse, 2009; Scheier & Carver, 2018; Solberg Nes & Segerstrom, 2006). Optimism is a dimension of personality defined by having a positive outcome expectancy for the future (Carver, Scheier, & Segerstrom, 2010). Optimists believe that their goals are attainable and are likely to persist in goal-directed behaviors in the face of adversity (Carver et al., 2010; Scheier & Carver, 1992). Past findings demonstrate that higher levels of optimism foster health and well-being through proactive actions that minimize health risks by engaging in health promoting lifestyle behaviors (e.g., treatment adherence, exercise, etc. Kim, Chopik, & Smith, 2014), which leads to better psychological and physical health (e.g., Boehm et al., 2018; Conversano et al., 2010; Kleiman et al., 2017; Rasmussen et al., 2009; Scheier, Carver, & Bridges, 2001).

Often viewed as the polar opposite of optimism, dispositional pessimism reflects the degree to which an individual holds negative expectations for the future (Scheier & Carver, 1992). Pessimists are skeptical about goal attainment and surrender their goals in the face of adversity. Dispositional pessimism is associated with poor psychological well-being (e.g., Augusto-Landa, Pulido-Martos, & Lopez-Zafra, 2011; Pinquart, Fröhlich, & Sibereisen, 2007) and health-related outcomes (e.g., Kivimäki et al., 2005; Petersen et al., 2008; Serlachius et al., 2015; Sherman & Cotter, 2013). However, there has been an on-going debate surrounding the dimensionality of dispositional optimism and pessimism (Kubzansky, Kubzansky, & Maselko, 2004; Marshall, Wortman, Kusulas, Hergib, & Vickers, 1992; Rauch, Schweizer, & Moosbrugger, 2008; Segerstrom, Evens, & Eisenlohr-Moul, 2011).

The Life Orientation Test (LOT) was developed in the 1980s to assess dispositional optimism and pessimism (Scheier & Carver, 1985). Due to initial psychometric issues, the LOT was revised in the early 1990s (LOT-R; Scheier, Carver, & Bridges, 1994) to better represent the underlying theoretical model, by explicitly assessing whether individuals expect future life outcomes to be positive or negative (Scheier & Carver, 1992). Stemming from the original conceptualization of the bipolar construct, the LOT-R was constructed as a single-factor unidimensional scale that contains three positively worded items and three negatively worded items, as well as four filler items (Scheier et al., 1994). However, several studies have reported a two-factor model of the LOT-R, suggesting that optimism and pessimism should be considered as two independent constructs (Creed, Patton, & Bartrum, 2002; Glaesmer et al., 2012; Herzberg, Glaesmer, & Hoyer, 2006). Furthermore, weak correlations between optimism and pessimism have been reported, which provides further support for the two-factor model (Glaesmer et al., 2012). While some researchers suggest that the inconsistent factor structure is a result of methodological artifact (i.e., wording of the items; e.g., Chiesi, Galli, Primi, Innocenti Borgi, & Bonacchi, 2013; Rauch, Schweizer, & Moosbrugger, 2007; Vautier & Raufaste, 2006), others argue that the issue is fundamental to the conceptualization of optimism, suggesting that individuals can be partially optimistic and pessimistic at the same time (Chang, Maydeu-Olivares, & D’Zurilla, 1997).

Despite the LOT-R's inconsistent factor structure, the scale has been translated into many languages and has been validated in various cultural groups and populations (e.g., Lyrakos, Damigos, Mavreas, Georgia, & Dimoliatis, 2010; Sumi, 2004; Vautier & Raufaste, 2006; Veljko Jovanoić & Garvrilov-Jerković, 2012). To examine the physical and psychological correlates of optimism in China, Lai, Cheung, Lee, and Yu (1998) adapted the LOT-R by removing the four filler items, to develop the Chinese version of the revised Life Orientation Test (CLOT-R). However, initial studies using the CLOT-R demonstrated that one of the items (“If something can go wrong for me, it will”) exhibited unstable and low item-total correlations, which resulted in poor internal consistency of the scale (Lai, Hamid, & Cheng, 2000; Lai & Yue, 2000). As such, the CLOT-R was revised, and the item was replaced by a new item (“Looking into the future, I do not see any positive scenarios”), which improved internal consistency of the scale (Chan, Lai, & Wong, 2006; Lai, 2003). The revised CLOT-R demonstrates acceptable internal consistency and further demonstrates good convergent and discriminant validity with Chinese versions of the Hopelessness Scale and the Health Locus of Control Scale (Lai et al., 1998; Lai & Yue, 2000).

Although the revised CLOT-R has been used in healthy and clinical samples of Chinese adults (Chan et al., 2006; Lai et al., 2005), the revised CLOT-R has displayed inconsistent factor structures across two different Chinese populations. Specifically, Lai and Yue (2000) found a two-factor CLOT-R model in a Mainland Chinese sample, whereas a unidimensional model was found in a Hong Kong Chinese sample. Furthermore, a weaker correlation between optimism and pessimism was found in the Mainland Chinese sample (r = –0 .12) compared to the Hong Kong Chinese sample (r = –0.38) (Lai & Yue, 2000). Although the authors were able to find convergent and discriminant validity of the revised CLOT-R in both Chinese samples (Lai & Yue, 2000), the dimensionality of the revised CLOT-R remains unclear. Similar to other translated versions of the LOT-R (Cano-García et al., 2015; Chiesi et al., 2013), it is plausible that measurement artifacts may contribute to the inconsistent factor structures found in these two homogenous Chinese samples. Furthermore, little is known about how the CLOT-R performs among Chinese older adults and Chinese immigrant populations.

To the authors’ knowledge, there are no studies to date that have examined the CLOT-R in an older Chinese immigrant population, a heterogeneous subsample of the population that continues to grow in the context of an aging population and immigration influx. Both Hong Kongese and Mainland Chinese who immigrate to Western nations are exposed to processes of acculturation that may further influence life orientation. Thus, the current study aimed to examine the factor structure and other psychometric properties of the CLOT-R in a group of older Chinese immigrants living in Canada. Given this unique population, the factor structure of the CLOT-R was examined via two optimism–pessimism models based on the literature: (1) the one-factor model as proposed by Scheier et al. (1994) which is based on a Western cultural context; and (2) the two-factor model as proposed by Lai (2003) which is based on an Eastern cultural context. The reliability, convergent, and discriminant validity of the CLOT-R was also examined in order to evaluate the psychometric properties of the CLOT-R in Chinese immigrants.

Methods

Participants and procedure

Data were obtained from a larger study that examined the impact of culture on well-being among older Chinese immigrants living in Canada. Inclusion criteria for the larger study entailed being a Chinese immigrant aged 60 years or older, with a minimum of one living child at the time of study recruitment. A total of 346 community-dwelling Chinese adults, between 60 and 95 years of age, were recruited from the Greater Toronto Area through advertisement postings at local community centers and on Chinese online chat groups. Informed consent was obtained from all eligible participants prior to completing a battery of validated questionnaires, including the CLOT-R. All recruitment and testing procedures were conducted in either Cantonese or Mandarin, depending on the participant’s preferred language. The study was approved by the Research Ethics Board at Ryerson University (REB 2016–322).

Measures

All English documents and questionnaires were translated according to the World Health Organization’s (2007) guideline. Two bilingual translators independently conducted forward and backward translations. Meetings were held between the translators and the first author (V. H.) to discuss any discrepancies between back-translation and the original documents as well as to ensure linguistic equivalence and cultural appropriateness of the Chinese documents.

The Chinese version of the Revised Life Orientation Test (CLOT-R)

The translated version of the CLOT-R by Lai (2003) was used in the current study, which consists of three positively worded items (e.g., “I am always optimistic about my future.”) and three negatively worded items (e.g., “I rarely count on good things happening to me.”). Participants rated the extent to which they agree with each item on a five-point Likert-type scale, ranging from 0 (strongly disagree) to 4 (strongly agree). The score of optimism was computed by summing responses of the six items. Higher scores indicate greater degree of optimism.

The Perceived Stress Scale – 10 item (PSS-10; Cohen, Kamarck, & Mermelstein, 1983)

The PSS-10 is a 10-item questionnaire that measures the degree to which a person perceives various aspects of life as uncontrollable, unpredictable, and overloading within the last month. Each item is responded on a 5-point Likert-type scale, ranging from 0 (Never) to 4 (Always). The total score ranges from 0 to 40, with higher scores indicating greater perceived levels of stress. The Chinese version of the PSS-10 has demonstrated acceptable reliability in previous study samples (e.g., Dong & Zhang, 2016) and yielded acceptable internal consistency within the current sample (α = .78). The PSS-10 was used to assess discriminant validity of the CLOT-R.

The Quality of Life Scale (QOLS; Flanagan, 1978 )

The QOLS is a 16-item measure that assesses whether needs are met with satisfaction. The QOLS assesses six domains of quality of life: physical and material well-being, relationships with other people, social and civic activities, personal development, and independence. Each time is rated on a 7-point Likert-type scale, ranging from 1 (Terrible) to 7 (Delighted). The total score ranges from 16 to 112, with higher scores indicating individuals are more satisfied with their needs being met, and better quality of life. The QOLS has demonstrated excellent internal consistency in previous studies (Burckhardt & Anderson, 2003) and yielded excellent internal consistency within the current sample (α = .93). The QOLS was used to assess concurrent validity of the CLOT-R.

Planned analysis

Item-level data were analyzed to ensure that the current dataset met all statistical assumptions. Assumption of normality was assessed through the inspection of histograms. Multivariate normality was assessed using Mardia’s statistics via the “NVM” package (Korkmaz, Goksuluk, & Zararsiz, 2014) in R (R Core Team, 2018). A scatterplot of all items was conducted to assess for assumption of linearity. A bivariate correlation was conducted to assess for multicollinearity between items.

Confirmatory factor analysis (CFA) was conducted to compare the three hypothesized factor structures of the CLOT-R (see Figure 1). Each model was evaluated using the following five fit-criteria to assess for overall goodness-of-fit of the model: (1) the chi-square goodness-of-fit index, with a nonsignificant chi-square (χ²) statistic indicating a satisfactory model fit; (2) the root mean square error of approximation (RMSEA) value, with values between 0.05 and 0.08 and a significant equivalence test for the RMSEA indicating a satisfactory model fit; (3) the standardized root mean square residual (SRMR), with values less than 0.05 suggesting a satisfactory model fit; (4) the comparative fit index (CFI), with values closer to one indicating better model fit; and (5) the Tucker-Lewis index (TLI), with values closer to one indicating better model fix (Hu & Bentler, 1999; Kline, 2015). The best-fit model was determined by comparing the different fit indices across the two models.

Figure 1.

Theoretical models of the CLOT-R factor solutions: (a) the original one-factor of the LOT-R and (b) the two-factor solution of the CLOT-R proposed by Lai (2003).

Composite reliability (CR; Raykov, 1997) was used to determine internal consistency of the CLOT-R in the current sample via an online calculator (Colwell, 2016). Compared to Cronbach’s alpha, CR is a better method for internal consistency estimation as it does not assume unidimensionality and accounts for the varying factor loadings of each item. We also conducted Cronbach’s alpha to supplement the information provided by CR. To assess convergent validity, all CLOT-R item loadings must be statistically significant and item loadings must be greater than 0.707 (Carmines & Zeller, 1979). Discriminant validity of the CLOT-R was examined using Kendall’s tau correlation between the CLOT-R and PSS-10. Concurrent validity of the CLOT-R was examined using Kendall’s tau correlation between the CLOT-R and QOLS. All analyses were conducted using R via the “lavaan” (Rosseel, 2018) and “psych” (Revelle, 2017) statistical package.

Results

Of the 346 participants, 8% included some missing data. Listwise deletion was used for cases with more than 10% incomplete data, which resulted in the removal of data points from four participants. This resulted in a final sample size of 342 (mean age = 71.99, SD = 5.62; 58.50% female). See sample characteristics in Table 1.

Table 1.

Sample characteristics.

	N = 342
Age (SD)	71.99 (5.62)
Sex (% women)	58.5%
Marital status (%)^a
Married	262 (76.9%)
Separated/divorce	21 (6.4%)
Widow	58 (17.3%)
Education (%)^b
High school or less	54 (15.8%)
Above high school	285 (83.3%)
Birthplace (%)^a
Mainland China	264 (77.2%)
Hong Kong	64 (18.7%)
Other	14 (4.1%)

^aWith one case missing.

^bWith six cases missing.

Visual inspection of the histograms and scatterplot matrix revealed that all item-level data violated assumptions of normality and linearity. The histograms also suggested that item responses should not be treated as continuous variables but should be treated as ordinal data. Furthermore, a significant Mardia’s statistic indicated that multivariate assumptions were violated. As such, CFA was conducted with the robust diagonally weighted least square, with mean variance adjusted estimator (i.e., WLSMV estimator). Compared to maximum likelihood (ML) estimation, the WLSMV estimation is specifically designed for ordinal data or when data are continuous but are not normally distributed (Browne, 1984). The WLSMV estimator does not make any distributional assumptions (Herzberg, 2006; Li, 2016). Given that the current dataset contained ordinal, nonnormal data and violated multivariate assumptions, the WLSMV estimation was used instead of the ML estimation. Finally, a polychoric correlation, which assumes an underlying bivariate normal distribution (Choi, Peters, & Mueller, 2010) was used to assess for multicollinearity between items. The polychoric correlation matrix showed that there was no strong multicollinearity between items (see Table 2 for correlation matrix).

Table 2.

Correlation matrix of CFA analysis of all CLOT-R items.

CLOT-R items	1	2	3	4	5	6
1	1.00
2	0.05	1.00
3	0.39	0.20	1.00
4	0.05	0.38	0.00	1.00
5	–0.05	0.26	–0.03	0.40	1.00
6	0.35	0.20	0.44	0.03	–0.02	1.00
Mean (SD)	2.81 (0.86)	2.37 (1.04)	3.02 (0.74)	1.73 (1.01)	1.92 (1.04)	3.01 (0.87)

Note. CLOT-R: Chinese version of the Revised Life Orientation Test.

Model comparison: One-factor and two-factor models

As shown in Table 3, the one-factor model for the CLOT-R yielded poor model fit within the current sample of Chinese immigrants, wherein a significant model chi-square test and all other model fit indices did not meet the recommended values for acceptable model fit. Although the two-factor CLOT-R model yielded an improved model fit compared to the one-factor model, a significant chi-square model fit test and some fit indices were above the recommended values for acceptable model fit (see Table 3). As such, the two-factor model demonstrated poor fit between the hypothesized model and the observed data.

Table 3.

Model Fit Indices for all models.

	χ² (df)	CFI	TLI	RMSEA [90% CI]	SRMR
One-factor model	227.31 (9)*	.56	.26	.27 [0.24, 0.30]	.14
Two-factor model	39.00 (8)*	.94	.88	.11 [0.08, 0.14]	.06
Modified two-factor model	10.42 (7)^a	.99	.99	.04 [0.00, 0.09]	.03

Note. CFI: Comparative fit index; TLI: Tucker-Lewis Index; RMSEA: root mean square error of approximation; SRMR: standardized root mean square residual; CLOT-R: Chinese version of the Revised Life Orientation Test.

^ap = .17.

*p < .001.

Given that poor model fit was found in both models, model misspecification of important parameters could have been missed. Moreover, given that the CLOT-R has not been validated in an older Chinese immigrant population, exploratory model modifications were conducted to identify a parsimonious model for the current sample. Modification indices suggested that cross-loading item 2 of the CLOT-R onto both latent variables accounted for 23.96 units of residual variance. Similarly, adding a parameter by covarying items 4 and 5 (i.e., “I hardly ever expect things to go my way” and “I rarely count on good things happening to me”) accounted for 23.96 units of residual variance, which improved the two-factor model fit. Theoretically, item 2 could measure both optimism and pessimism. As such, a modified two-factor model was conducted by allowing cross-loading on item 2; however, the error covariance between items 4 and 5 was not included as this was not grounded in theory.

The modified two-factor model yielded an improved model fit, wherein a nonsignificant model fit chi-square test and all other fit indices were all within the recommended values, which indicated an acceptable and/or good model fit (see Table 3). As such, the modified two-factor model solution with item 2 cross-loaded onto both latent variables of optimism and pessimism provided the best model fit for the current observed data. See Figure 2 for the path diagram of the modified two-factor model of the CLOT-R with standardized parameter estimates. The relatively small residual covariance matrix between items in the modified two-factor model suggests that the relationship between items are adequately captured in the modified two-factor model (see Table 4).

Figure 2.

Path model of the CLOT-R two-factor solution with standardized parameter estimates. *p < .001

Table 4.

Residual covariance matrix between items.

CLOT-R items	1	3	6	2	4	5
1	0.00
3	0.01	0.00
6	0.01	–0.01	0.00
2	–0.10	0.03	0.03	0.00
4	0.05	0.002	0.03	–0.002	0.00
5	–0.06	–0.03	–0.02	0.004	0.00	0.00

Note. CLOT-R: Chinese version of the Revised Life Orientation Test.

Reliability and validity

The two latent constructs of optimism and pessimism of the CLOT-R current sample yielded CR values of 0.62 and 0.64, respectively, suggesting poor internal consistency between items that assess each latent variable. Poor internal consistency for each latent construct remained after removing item 2, with CR values of 0.67 for optimism and 0.72 for pessimism. Cronbach’s alpha also revealed similar findings – poor internal consistency was found for the two latent variables: 0.47 (95% CI [0.37, 0.47]) and 0.56 (95%CI [0.48, 0.56]). Similarly, low Cronbach’s alpha values remained for each latent construct, even after removing item 2: 0.54 (95% CI [0.46, 0.54]) and 0.60 (95% CI [0.51, 0.60]). Overall, the CLOT-R yielded poor internal consistency within the current sample of Chinese immigrants.

Convergent validity was not obtained for the CLOT-R based on recommendations by Carmines and Zeller (1979) as not all of the standardized item loadings were above 0.707. Specifically, only items 3 and 4 had standardized loadings above the recommended values (0.73 and 0.79, respectively). Furthermore, items were only allowed to load onto one latent construct; however, item 2 cross-loaded onto both latent constructs. Although removal of item 2 may improve validity, the remaining number of CLOT-R items may not be adequately assessing the latent constructs of optimism and pessimism in the present sample of Chinese immigrants. Discriminant validity was demonstrated by negative, weak correlations between the optimism subscale score and PSS-10 (τ = –.27, p < .001) and the pessimism subscale score and the PSS-10 (τ = –.17, p < .001). Concurrent validity was demonstrated by positive, weak correlations between the optimism subscale score and QOLS (τ = .18, p < .001) and the pessimism subscale score and QOLS (τ = .13, p < .001).

Discussion

Although the revised CLOT-R is a measure of optimism–pessimism that has been previously used in the Chinese population, the examination of its factor structure has yielded inconsistent results across different Chinese subsamples (Lai & Yue, 2000). The different factor structures found between Chinese-speaking populations suggest that Mainland and Hong Kong Chinese groups may have different conceptualizations of optimism and pessimism. The current study aimed to examine the factor structure and psychometric properties of the revised CLOT-R in a group of community-dwelling older Chinese immigrants, a diverse and heterogeneous group of Chinese-speaking older adults in Canada. By comparing three different factor models, results suggest that the modified two-factor model, with item 2 cross-loaded onto optimism and pessimism, provides the best model fit for the revised CLOT-R in older Chinese-speaking immigrants. The current findings further suggest discriminant validity between the latent constructs of optimism and pessimism, and perceived stress levels; however, poor internal consistency and convergent validity was found.

The current revised CLOT-R factor structure in Chinese immigrants is in partial agreement with previous studies conducted in Hong Kong and Mainland China. Lai and Yue (2000) reported a one-factor model in a sample of Hong Kong Chinese young adults and a two-factor model in a sample of Mainland Chinese young adults. The variant factor structure of the revised CLOT-R between Hong Kong and Mainland China may be attributed to different cultural influences between the two Chinese-speaking regions, which may further impact the conceptualization of optimism–pessimism. According to Koo (1987), Chinese individuals construe optimism as positively accepting one’s current life situations, rather than expecting good things to happen in one’s life. This is in contrast to Western conceptualization of the construct. As Hong Kong was once a British colony, exposure to Western cultural norms may have transformed the conceptualization of optimism–pessimism among Chinese individuals from Hong Kong, relative to Mainland Chinese.

Indeed, the LOT-R was originally designed as a unidimensional measurement from a Western cultural perspective and studies conducted in a Western context have provided support for the unidimensionality of the LOT-R (e.g., Cano-García et al., 2015; Segerstrom et al., 2011). However, recent studies suggest the bidimensional factor structure yields better model fit than the unidimensional model (e.g., Hinz et al., 2017). Of note, a majority of these studies suggesting a bidimensional model have examined translated versions of the LOT-R (e.g., German). Cano-García et al. (2015) found that a unidimensional model with methodology effects (e.g., response bias) incorporated into the model provided adequate model fit, which suggests that the bidimensional model of LOT-R may be contributing to methodology or measurement artifacts (Alessandri et al., 2010; Chiesi et al., 2013). Furthermore, a strong correlation between optimism and pessimism of a bidimensional model emerged from the Spanish version of the LOT-R, suggesting that both factors form a higher-order factor (i.e., life-orientation), indicative of unidimensionality (Cano-García et al., 2015). Accordingly, additional research is needed to further compare and examine the factor structure of the revised CLOT-R among different Chinese populations.

The current sample comprised of Chinese immigrants from Hong Kong, Mainland China, and other Chinese-speaking countries (e.g., Taiwan), encompassing a more heterogeneous cultural background than previous research examining the psychometric properties of the revised CLOT-R. Although the modified two-factor model provided the best fit between the model and the observed data, a large amount of residual variance was not accounted for by the six CLOT-R items, calling into question the utility of the CLOT-R in measuring the constructs of optimism and pessimism among older Chinese immigrants. It is plausible that the large amount of residual variance observed was a result of measurement artifact. The authors conducted a bi-factor model to examine whether a method factor that accounts for the measurement artifact should be considered in the current sample. However, the bi-factor model failed to converge. This is likely due to complexity of the bi-factor model relative to the small number of items and the model estimator used in the current study, precluding model estimation (findings not presented here).

Given that the majority of the current sample was comprised of older Mainland Chinese immigrants, it is plausible that this subsample may be driving the bidimensional structure found in the current study. A subsequent analysis was conducted to examine the CLOT-R factor structure in the subsample of Mainland Chinese. However, inadequate model fit was found for both of the unidimensional and bidimensional structures within the Mainland Chinese subsample (data not presented here). This may be due to decreased statistical power stemming from the relatively small sample size when restricting analysis to the Mainland Chinese subsample. It is also plausible that other cultural factors, such as acculturation, may contribute to the inconsistent factor structure found in the current study compared to previous studies conducted in Chinese-speaking countries. Future studies should examine whether there is configural variance of the CLOT-R based on acculturation levels.

Although the CLOT-R demonstrated acceptable discriminant and concurrent validity, poor internal consistency and convergent validity were found in the current sample of older Chinese immigrants, which suggests that the scale items may not adequately capture the latent constructs within this population. This may be due to the small number of items in the revised CLOT-R, in which three items were used to measure each latent construct. Moreover, it is also postulated that the restricted range in scale items and item responses may have contributed to the poor internal consistency of the revised CLOT-R in this sample of older Chinese immigrants. However, poor internal consistency found in the current study is somewhat consistent with previous findings.

Past studies in other populations have reported that the CLOT-R and LOT-R yield poor to adequate internal consistency (e.g., Lai et al., 2005; Wong & Lim, 2009). Although a meta-analysis revealed that the LOT-R yields an acceptable internal consistency, large variability was found for coefficient alphas of the total score (range = 0.35–0.87), the optimism subscale score (range = 0.55–0.83), and the pessimism subscale score (range = 0.58–0.84) (Vassar & Bradley, 2010). Furthermore, it was reported that the English language version of the LOT-R had higher internal consistency estimates of the pessimism subscale than non-English versions of the scale (Vassar & Bradley, 2010).

With respect to poor convergent validity demonstrated in the current sample, it is important to note that validation studies often use alternative proxy measures of the same construct to assess convergent validity; however, the current study employed a SEM method of convergent validity test as proxy measures were not available from the larger study. The current findings of convergent validity simply indicate that the items do not appropriately capture the latent construct. This study limitation must be address by future research, by using at least two proxy measures (Carlson & Herdman, 2012; e.g., scales assessing hope or hopelessness) to adequately assess convergent validity of the CLOT-R within older Chinese immigrant populations. Moreover, the current study did not include any criterion variables to assess the criterion validity of the CLOT-R.

In addition to including a heterogeneous sample of Chinese immigrants, the current sample was comprised of adults aged 60 years and older. Although the original LOT-R was validated in a sample with a relatively large age range, from 18 years to 103 years of age (Herzberg et al., 2006; Scheier, 1994), the revised CLOT-R has mostly been assessed in university and college student samples, with a mean age ranging from 17.9 to 21.5 (Lai & Yue, 2000). It is possible that there is configural variance not only across cultural groups, but across age groups as well. Herzberg et al. (2006) found that with an increase in age, the latent constructs of optimism and pessimism become less correlated. Furthermore, research shows that older Chinese individuals are less optimistic compared with their older American and younger Chinese counterparts (You, Fung, & Issacowitz, 2009). It is plausible that the age range of the current sample contributed to the inconsistent CLOT-R factor structure relative to what was previously reported in university and college students from Mainland China and Hong Kong.

Although item 2 of the CLOT-R was revised by Lai (2003) to improve the internal consistency of the scale in a sample of Hong Kong Chinese adults, and subsequently validated in healthy and clinical Chinese populations (Chan et al., 2006; Lai, 2009; Lai et al., 2005), the present findings suggest that item 2 is a problematic item, as it cross-loaded onto both latent constructs. This may not be problematic with a unidimensional construct; however, cross-loading of a single item may obstruct identification of a bidimensional construct. In the current study, latent variables of optimism and pessimism were not statistically correlated, providing evidence for treating optimism and pessimism as separate constructs. As indicated above, differences in the conceptualization of optimism and pessimism may be attributed to differences between Western and Chinese culture. Indeed, it is suggested that Chinese optimism can be defined as the ability of individuals to accept conditions in life, rather than expecting good outcomes in the future (Koo, 1987; Lai & Yue, 2000). Other findings have also demonstrated that Chinese individuals place less emphasis on optimism (Chang, Asakawa, & Sanna, 2001; You et al., 2009).

In a meta-analytical study across 22 nations, it was reported that lower individualistic culture was associated with lower levels of optimism (Fischer & Chalmers, 2008). This further lends support to the notion that there are conceptual differences in optimism and pessimism across cultures and further suggests that the operationalization and development of LOT-R items may not be congruent with Chinese cultural beliefs, which is largely a collectivistic culture (i.e., low individualism). This becomes more complex in the context of colonization and immigration, in which exposure to Western culture may account for inconsistent factor structures.

Altogether, the revised CLOT-R may not be a reliable and valid measure to assess optimism and pessimism among older Chinese immigrants. However, additional investigation of the CLOT-R is still needed. Based on the current findings, future research should consider further revision of the CLOT-R to ensure strong psychometric properties across different Chinese-speaking populations, by including additional items and evaluating the cultural appropriateness of each scale item. To ensure that the scale is representative of Chinese-speaking individuals, research should also include immigrant populations who maintain their cultural practice in the host country. This is especially important with continued migration and an increasing interest in understanding health outcomes among immigrant populations.

Footnotes

Acknowledgments

The authors would like to thank the participants and community partners for their contribution and support. The authors would also like to thank Dr. Alyssa Council for her guidance and support in data analysis.

Article Notes

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