Reliability and validity of the Chinese version of the solution-focused inventory in college students.
Keywords: solution-focused, Chinese, resilience, psychological adjustment
Se estudio la psicometrfa del Inventario Chino Centrado en Soluciones (CSFI, por sus siglas en ingles) en estudiantes universitarios chinos. El analisis de confirmacion de factores confirmo la estructura de 3 factores. Todas las subescalas mostraron buena confiabilidad, ademas de validez convergente y progresiva. Los resultados de los analisis de regresion jerarquica indicaron que las 3 subescalas representaron la varianza adicional en el ajuste psicologico mas alia de la resiliencia. Estos hallazgos indicaron que el CSFI es valido y confiable. Se discuten las implicaciones, limitaciones y orientaciones para estudios futuros.
Palabras clave: centrado en soluciones, chino, resiliencia, ajuste psicologico
Solution-focused brief therapy (SFBT) is a strength-based approach aimed at assisting individuals or groups to make expected changes. It emphasizes the resources and resilience that individuals possess and how these can be activated and applied to the positive change process. As a flexible approach, SFBT has been enthusiastically accepted and applied to a wide range of contexts, such as family problems, mental health problems, problem behaviors, and child protection issues (e.g., Bond, Woods, Humphrey, Symes, & Green, 2013; Corcoran & Pillai, 2009; Lindfors, Knekt, Virtala, Laaksonen, & the Helsinki Psychotherapy Study Group, 2012). However, although almost all of the empirical literature reports the effectiveness of the solution-focused approach, little is known about the mechanisms underlying these effects. To fill this gap, a reliable and validated instrument is needed to measure the psychological mechanisms underpinning solution-focused change.
In this regard, Grant (2011) and Grant et al. (2012) developed the Solution-Focused Inventory (SFI). The SFI is a 12-item, multidimensional scale with three distinct dimensions underlying solution-focused change: problem disengagement, goal orientation, and resource activation. Problem disengagement captures a focus on disengaging from problems and problem-focused thinking (e.g., "I tend to get stuck in thinking about problems"; a reversed item). Goal orientation captures a focus toward desired goal states (e.g., "I imagine my goals and then work toward them"). Resource activation captures a focus on recognizing and utilizing strengths and resources (e.g., "There are always enough resources to solve a problem if you know where to look"). This tripartite taxonomy is believed to tap into the goal pursuit process that is central to solution-focused approaches. Consistent with these conceptualizations, initial psychometric results have shown adequate reliability and validity for subscales. For example, exploratory factor analysis showed that the three-factor model explained 47.2% of the total variance. Confirmatory factor analyses showed the same adequate fit for both three-factor and second-order, one-factor models (root mean square error of approximation [RMSEA] = .078, goodness-of-fit index = .93, Tucker-Lewis Index = .92). The SFI was also found to have acceptable internal consistency estimates of [alpha] = .83 (total scores) and [alpha] = .68 to .82 (subscales), and test-retest reliability of r = .84 (total scores only). Specifically, the SFI related significantly and in the expected direction with life satisfaction, psychological well-being, resilience, depression, anxiety, and stress. The SFI also appears to be sensitive to changes following a leadership development intervention (Grant et al., 2012). However, Grant et al. (2012) used the second-order, one-factor model in their correlational analyses; therefore, no information about the correlations of the three factors with outcomes was provided. Furthermore, the SFI was developed with Australian samples; its relevance and validity in other cultures, such as the Chinese culture, has not yet been examined.
Solution-focused brief therapy is attracting increasing interest in China in recent years. For example, a search of CNKI (China National Knowledge Infrastructure, a database of China) by title name in August 2013 revealed 86 articles on SFBT, a greater number than for two other approaches: positive psychotherapy (41) and family therapy (31). It is important to note that Chinese culture has rich thoughts about problem disengagement, goal orientation, and resource activation. For example, "Misfortune might be an actual blessing" teaches individuals not to focus on the negative (i.e., problem disengagement); "A journey of a thousand miles begins with single step" encourages individuals to work toward goals (i.e., goal orientation); and "Ideas are more than problems" tells individuals to actively find methods for problem solving (i.e., resource activation). However, similar to the empirical research in Western society, the majority of studies in China concentrate on the effect that the SFBT interventions have on outcomes such as psychological adjustment (e.g., Xi, 2011; F. Yang, Zhu, & Luo, 2005), with the study of psychological mechanisms underpinning the solution-focused change receiving far less research attention. Thus, validating the SFI cross-culturally with a Chinese sample will benefit future studies on the psychological process of SFBT in China and also the cultural differences of this process.
The present study presents the first psychometric findings with the Chinese version of the SFI: Chinese Solution-Focused Inventory (CSFI). The main purposes were to (a) examine the replicability of the three-factor structure previously obtained with the original English SFI; (b) examine the reliability (internal consistency and test-retest) and convergent validity of the CSFI's subscales with criterion measures of life satisfaction, resilience, and mental health symptoms; and (c) examine the incremental validity of the CSFI as a useful predictor, over and beyond resilience, of psychological adjustment.
Because the original three-factor model of SFI was confirmed in a sample of Australian university students (Grant et al., 2012), we used Chinese students as subjects in the present study. Consistent with the original findings, we hypothesized the CSFI to be positively related to life satisfaction and resilience and negatively related to mental health symptoms. Furthermore, because solution-focused change is believed to represent a positive process that involves the application of resilience, it was necessary to examine if the CSFI factors were redundant with resilience. Considering that the conceptualization of problem disengagement, goal orientation, and resource activation did not involve the central part of resilience--ego survivorship (Block & Kremen, 1996)--we hypothesized that the CSFI factors would predict life satisfaction and mental health symptoms over and beyond resilience.
Participants were 922 students (i.e., 399 men, 501 women, 22 no gender indicated) recruited from five universities (including one top national university and four average local universities) in the eastern coastal region of the People's Republic of China. Of the 884 participants who indicated class status, 201 were freshmen, 287 were sophomores, 237 were juniors, 134 were seniors, and 25 were graduate students. The mean age was 20.34 years (SD = 1.55; range = 15-27 years). To investigate the CSFI scores' stability, we retested 165 of the participants (83 men and 82 women) after 4 weeks.
Because there was no institutional review board at our university, we protected the human rights of the subjects by telling them that they had the right to accept or refuse our request for research. First, three research assistants went to classrooms and reading rooms after class and asked for students' help to complete the measures. As a result, 787 students volunteered to perform the test, and 740 completed it. Second, to examine the test-retest reliability, we collected data from a public selective course of psychology at the national university. Among 206 students who selected the course, 182 completed the first administration, 187 completed the second 4 weeks later, and 165 completed both. We then combined the sample of 740 students recruited by research assistants with the sample of 182 students recruited in the first course, thus forming the main study sample of 922 students. Because the Box's M test comparing the variance-covariance matrices of female and male students for the data of the main sample was not significant, F(6, 5119293) = 1.76, p = ns, all analyses were based on responses provided by the participants, collapsed across sex, who completed all study measures. Because Box's M test is highly sensitive to even minor differences between variance-covariance matrices, we tested the significances on the p < .001 level as recommended by Tabachnick and Fidell (2007). We tested all other significances on the conventional p < .05 level.
SFI. The SFI (Grant et al., 2012) consists of 12 items measuring problem disengagement (four items), goal orientation (four items), and resource activation (four items). Items are rated on a Likert scale that ranges from 1 (strongly disagree) to 6 (strongly agree). Grant et al. (2012) reported that Cronbach's alpha coefficients for the subscales were .78, .82, and .68, respectively. The Chinese translation was achieved following established guidelines for cross-cultural translation of instruments (Brislin, 1970): First, two graduate students translated the original measure from English into Chinese; then, two other graduate students, independently from the first two, translated it back to English; finally, discrepancies were discussed (among the four students and the first author), and the final translation was agreed upon.
Ego Resilience Scale. The Ego Resilence Scale (ERS; Block & Kremen, 1996) consists of 14 items using a 4-point, Likert-type scale ranging from 1 (does not apply at all) to 4 (applies very strongly). The ERS is a one-dimensional scale measuring the ability to respond adaptively and resourcefully to new situations (e.g., "I quickly get over and recover from being startled"). Li (2005) translated it into Chinese and reported that the scale's Cronbach's alpha and split-half reliability values were .73 and .71, respectively. In this study, the Cronbach's alpha coefficient was .79.
Satisfaction With Life Scale. The Satisfaction With Life Scale (SWLS; Diener, Emmons, Larsen, & Griffin, 1985) consists of five items measuring satisfaction with life (e.g., "In most ways, my life is close to my ideal"). Items are rated on a 7-point, Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree). Previous research supports the reliability and validity of the scale. Chen and Yang (2003) translated it into Chinese. H. Yang et al.(2009) reported that the scale's Cronbach's alpha and split-half reliability values were .81 and .73, respectively. In this study, the Cronbach's alpha coefficient was .85.
Mental Symptom, Scale for College Students. The Mental Symptom Scale for College Students (MSSCS; H. Yang & Liu, 2006) consists of 30 items rated on a 5-point, Likert-type scale ranging from 1 (very much like me) to 5 (not at all like me). For convenience in making subsequent interpretations, we used reversed scoring such that higher scores would be indicative of greater symptom severity. The MSSCS comprises five subscales measuring self-inferiority (nine items; e.g., "I always worry about bad evaluations from others"), social sensitivity (nine items; e.g., "I feel unable to communicate with others"), Internet addiction (five items; e.g., "I always want to surf the Internet"), depression (four items; e.g., "I am feeling blue"), and hostility (three items; e.g., "I demand perfection from others"). H. Yang and Liu (2006) reported that the Cronbach's alpha coefficients for the subscales were .67 to .90, and .91 for the whole scale. The split-half reliabilities for the subscales were .65 to .88, and .92 for the whole scale. In this study, the Cronbach's alphas for self-inferiority, social sensitivity, Internet addiction, depression, and hostility were .84, .88, .82, .73, and .66, respectively, and the Cronbach's alpha for the whole scale was .94.
CONFIRMATORY FACTOR ANALYSIS OF THE CSFI
Following the factorial structure for the SFI proposed by Grant et al. (2012), we computed a confirmatory factor analysis (CFA) using LISREL 8.80 (Joreskog & Sorbom, 2007) to test the original three-factor model in which the items of the three subscales were specified to load only on their target factor and all factors were allowed to correlate. Because most of the items displayed significant deviation from normality (i.e., skewness and kurtosis), we used the robust maximum likelihood estimation, which provides robust parameter and model fit estimates for data that deviate from normality (Brown, 2006).
Considering the well-known problems with the chi square statistic as a measure of model fit--most notably its extreme sample size sensitivity (Hu & Bender, 1995; Kaplan, 1990)--we restricted the use of this statistic to testing the differences between the two nested models. Instead, we examined the following indices of goodness-of-fit: (a) the comparative fit index (CFI), for which values between .90 and .94 indicate adequate fit (McDonald & Ho, 2002) and values of .95 and higher indicate good fit (Kline, 2005); (b) the nonnormed fit index, for which values higher than .90 indicate adequate model fit and values of .95 and higher indicate good fit (Hu & Bender, 1999); (c) RMSEA, for which values of .06 to .08 indicate adequate fit and values of .05 and less indicate a good fit (Hu & Bender, 1999); and (d) the standardized root mean square residual, for which values of .08 or less indicate a good fit (Kline, 2005). Consistent with the comparisons of three models of SFI in previous studies (Grant et al., 2012), we examined the original three-factor model and two alternative models: a one-factor model with 16 items assumed to represent one SFI dimension and a second-order, one-factor factor model with three first-order factors assumed to represent one SFI dimension.
As shown in Table 1, the one-factor model showed inadequate fit, whereas the three-factor model showed good fit. The two-level hierarchical model with one second-order factor showed the same fit indices as for the three-factor model. Thus, both the three-factor model and the second-order, one-factor model were acceptable as constituting the CSFI.
RELIABILITY AND VALIDITY OF THE CSFI
Correlations for the CSFI subscales are presented in Table 2. As the table shows, the pattern of scale and factor intercorrelation suggests that the three subscales are positively correlated. Consistent with the CFA results, the weak to medium associations among the three subscales indicate their independence from each other. As the table also shows, the internal consistency of CSFI and the three subscales was satisfactory. We examined the test-retest stability of the CFA by examining scores of 165 students across a 4-week time interval. Results indicated test-retest associations of .61 to .72 (all ps < .001). Taken together, these results lend support to the internal consistency and test-retest reliability of the CSFI subscales.
To evaluate the convergent validity of the CSFI, we examined the associations between the CSFI subscales with measures of resilience, life satisfaction, and mental health symptoms (i.e., self-inferiority, social sensitivity, Internet addiction, depression, hostility). Results of the correlation analyses are presented in Table 3. As the table shows, the CSFI and its subscales were positively and significantly associated with resilience and life satisfaction, and negatively and significantly associated with the five indices of mental health symptoms measured by the MSSCS.
To investigate the incremental validity of the CSFI subscales in accounting for psychological adjustment, we performed a series of hierarchical regression analyses with life satisfaction and each of the five mental health symptoms as our outcome. For each regression model, however, we entered resilience in Step 1 to test the predictive utility of the CSFI subscales more rigorously. If the response to positive affect was not redundant with resilience, then we would have found evidence for the utility of the CSFI. Accordingly, we entered the scores on all three of the CSFI subscales as a set in Step 2. To compare the effect sizes of the predictors that accounted for the variance in functioning, we used Cohen's (1988) suggestions of small ([f.sup.2] [greater than or equal to] .02), medium ([f.sup.2] [greater than or equal to] .15), and large effects ([f.sup.2] [greater than or equal to] .35) as a general guide.
The results of these regressions showed two key patterns of prediction (see Table 4). First, resilience was consistently found to account for small ([f.sup.2] = .03 to .12), but significant (2.6% to 10.6%), variances in the outcome measures examined. Second, the CSFI predictor set was consistently found to account for small to medium ([f.sup.2] = .05 to .15), but significant (6.0% to 12.0%), variances in the outcome measures examined, above and beyond what was accounted for by resilience. Within the CSFI predictor set, the most robust predictor on life satisfaction was goal orientation, followed by problem disengagement and resource activation, whereas the most consistent significant predictor on mental health symptoms was problem disengagement, followed by goal orientation and resource activation.
This study empirically tested the factor structure and psychometric properties of a Chinese version of the SFI to establish its convergent and incremental validity. Results of the CFA indicate that there was a good fit between the original three-factor model; the second-order, one-factor model; and the data. Also, the internal consistencies and test-retest reliabilities of the subscales and aggregate scale were shown to be satisfactory. In accord with the findings of Grant et al. (2012), the present study shows that three subscales correlated with each other positively. Overall, SFI appears to be relevant and valid in a Chinese college student population. The replication of the original factor structure and intercorrelation pattern in the present sample of Chinese college students provides promising evidence for the cross-cultural validity of CSFI.
The correlational analyses indicating that the CSFI, including its three factors, correlated with criterion measures in the hypothesized direction further supported the validity of the CSFI. Moreover, expanding on Grant's (2012) findings based on correlational analyses, this study's hierarchical regression analyses found that the CSFI scores were able to add significant incremental validity in predicting variance in life satisfaction and in all five indices of mental health symptoms, above and beyond resilience. Goal orientation emerged as the most robust positive predictor within the CSFI set with regard to predicting life satisfaction. This finding is consistent with the goal approach to well-being (Oishi, 2000), which assumes that markers of well-being vary across individuals, depending on their goals and values. Interestingly, problem disengagement emerged as the most robust negative predictor in predicting mental health symptoms. This finding suggests that improving problem disengagement is an important methodology to cope with mental health problems, such as self-inferiority, social sensitivity, Internet addiction, depression, and hostility. The present findings may have some practical implications for both practitioners and researchers working with Chinese college students in counseling services. First, as Chinese college students with higher CSFI scores are likely to be more satisfied with life and less psychologically distressed, counselors could develop solution-focused programs that help foster and maintain high levels of solution-focused thinking. Specifically, counselors could develop goal-striving coaching programs to improve life satisfaction and address problem disengagement to decrease mental health symptoms. In other words, because problem disengagement and goal orientation show more powerful prediction on psychological adjustment, counselors might pay more attention to improve clients' problem disengagement and goal orientation in a solution-focused approach. Second, because the SFI is sensitive to changes following development interventions (Grant et al., 2012), researchers may use the CSFI as an outcome measure so that the psychological processes central to the solution-focused changes across different settings can be compared with each other. Considering that die solution-focused approach might be more effective than the problem-focused approach (Grant, 2012), researchers could use the CSFI to explore the mechanics of differential impacts of problem-focused versus solution-focused approaches. In addition, using the three-factor model as a guiding theoretical framework, solution-focused practitioners and researchers working with Chinese individuals might not only purposefully adapt and change the existing solution-focused techniques but also develop new techniques. Finally, with this theoretical framework, teachers might develop new methodologies for teaching SFBT and thus help students develop sophisticated understandings of the essential aspects of professional practice.
Despite these implications, the results of the present study should be considered with some limitations. First, the use of convenience sampling and collecting data in one setting limited the generalizability of these findings. Consequently, different Chinese samples (e.g., school students, older adults, clinical samples) need to be recruited to assess the psychometrics of the CSFI in future studies. Second, the present study was cross-sectional, in which the correlations between the CSFI factors and psychological adjustment could also be interpreted by suggesting that psychological adjustment contributes to how participants make changes on CSFI factors. Therefore, we used the term prediction only in the statistical sense and could not draw a causal-effect relationship between the CSFI factors and psychological adjustment from the present findings. Future studies will need to be designed prospectively to investigate if the CSFI factors also predict changes in people's psychological adjustment across time. Additionally, the lack of previous studies on the psychological mechanisms of SFBT in Chinese culture makes it questionable whether the CSFI really taps the central dimensions of the positive change process of a solution-focused approach for Chinese individuals.
The present findings demonstrate the promise of using the CSFI as a useful and efficient instrument to capture problem disengagement, goal orientation, and resource activation. What is needed now is further research that extends the present findings and helps examine the role of CSFI dimensions in the psychological underpinnings of a solution-focused approach. We hope that this study represents a useful step in developing knowledge about what constitutes effective solution-focused practice and how to apply a solution-focused approach in work with Chinese communities.
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Hongfei Yang and. Tang Hai, Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou City, Zhejiang Province, People's Republic of China. Tang Hai is now at The Affiliated High School to Hangzhou University, Hangzhou City, Zhejiang Providence, People's Republic of China. Correspondence concerning this article should be addressed to Hongfei Yang, Department of Psychology and Behavioral Sciences, Zhejiang University, Xixi Campus, Hangzhou City, Zhejiang Province, 310028, People's Republic of China (e-mail: email@example.com).
TABLE 1 Goodness-of-Fit Indices for Three Models of the Solution-Focused Inventory Model df [chi square] S-B [chi square] CFI One-factor 54 858.96 392.75 .88 Three-factor 51 445.62 188.86 .95 Second-order 51 445.62 188.86 .95 Model NNFI RMSEA 90% CI SRMR One-factor .85 .08 [0.08, 0.09] .10 Three-factor .94 .05 [0.05, 0.06] .07 Second-order .94 .05 [0.05, 0.06] .07 Note. N = 922. All fit indices are based on the robust maximum likelihood procedure. S-B [chi square] = Satorra-Bentler scaled chi-square; CFI = comparative fit index, NNFI = nonnormed fit index; RMSEA = root mean square error of approximation; CI = confidence interval; SRMR = standardized root mean square residual. TABLE 2 Correlations, Means, Standard Deviations, and Reliabilities for the Chinese Solution-Focused Inventory Subscales Subscale 1 2 3 M SD 1. PD -- 14.33 3.33 2. GO .18 ** -- 15.94 3.28 3. RA .08 * .39 ** -- 16.83 3.67 Total .61 ** .76 ** .71 ** 47.10 6.82 Subscale Skewness Kurtosis [alpha] Test-Retest r 1. PD -0.04 -0.17 .61 .62 2. GO -0.30 0.36 .76 .72 3. RA -0.27 0.20 .57 .61 Total 0.03 0.33 .70 .66 Note. N= 922. PD = Problem Disengagement; GO = Goal Orientation; RA = Resource Activation. * p< .05. ** p< .001. TABLE 3 Correlations Between Chinese Solution-Focused Inventory Subscales and Study Measures Study Measure PD GO RA Total Resilience .14 .42 .31 .42 Life satisfaction .23 .39 .30 .44 Self-inferiority -.31 -.28 -.19 -.38 Social sensitivity -.33 -.30 -.26 -.43 Internet addiction -.20 -.23 -.15 -.28 Depression -.30 -.31 -.20 -.39 Hostility -.21 -.19 -.22 -.30 Note. N = 922. All ps < .001. PD = Problem Disengagement; GO = Goal Orientation; RA = Resource Activation. TABLE 4 Results of Hierarchical Regression Analyses Showing Amount of Variance in Psychological Adjustment Accounted for by Resilience and the Chinese Solution-Focused Inventory (CSFI) in Chinese Students Outcome and Predictor [beta] [R.sup.2] [DELTA] [R.sup.2] Life satisfaction Step 1: Resilience .33 ** .11 Step 2: CSFI .22 .12 Problem Disengagement .15 ** Goal Orientation .23 ** Resource Activation .15 ** Self-inferiority Step 1: Resilience -.33 ** .10 Step 2: CSFI .19 .09 Problem Disengagement -.26 ** Goal Orientation -.12 ** Resource Activation -.06 ** Social sensitivity Step 1: Resilience -.31 ** .10 Step 2: CSFI .22 .12 Problem Disengagement -.27 ** Goal Orientation -.13 ** Resource Activation -.13 ** Internet addiction Step 1: Resilience -.16 ** .03 Step 2: CSFI .09 .06 Problem Disengagement -.16 ** Goal Orientation -.16 ** Resource Activation -.05 Depression Step 1: Resilience -.33 ** .11 Step 2: CSFI .20 .10 Problem Disengagement -.24 ** Goal Orientation -.15 ** Resource Activation -.05 Hostility Step 1: Resilience -.19 ** .04 Step 2: CSFI .10 .06 Problem Disengagement -.18 ** Goal Orientation -.06 * Resource Activation -.15 ** Outcome and Predictor F P [f.sup.2] Life satisfaction Step 1: Resilience 108.48 <.001 .12 Step 2: CSFI 65.50 <.001 .15 Problem Disengagement Goal Orientation Resource Activation Self-inferiority Step 1: Resilience 103.66 <.001 .12 Step 2: CSFI 54.88 <.001 .10 Problem Disengagement Goal Orientation Resource Activation Social sensitivity Step 1: Resilience 100.46 <.001 .11 Step 2: CSFI 64.18 <.001 .15 Problem Disengagement Goal Orientation Resource Activation Internet addiction Step 1: Resilience 24.45 <.001 .03 Step 2: CSFI 21.48 <.001 .05 Problem Disengagement Goal Orientation Resource Activation Depression Step 1: Resilience 115.26 <.001 .12 Step 2: CSFI 58.73 <.001 .13 Problem Disengagement Goal Orientation Resource Activation Hostility Step 1: Resilience 33.47 <.001 .04 Step 2: CSFI 24.85 <.001 .11 Problem Disengagement Goal Orientation Resource Activation Note. N = 922. * p< .05. ** p< .001.
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|Author:||Yang, Hongfei; Hai, Tang|
|Publication:||Journal of Multicultural Counseling and Development|
|Date:||Oct 1, 2015|
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