The Chinese Adaptation of the Teachers’ Sense of Efficacy Scale in Early Childhood Pre-service Teachers: Validity, Measurement Invariance, and Reliability

The Chinese Adaptation of the Teachers’ Sense of Efficacy Scale in Early Childhood Pre-service Teachers: Validity, Measurement Invariance, and Reliability | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Chinese Adaptation of the Teachers’ Sense of Efficacy Scale in Early Childhood Pre-service Teachers: Validity, Measurement Invariance, and Reliability Mingxing Shao, Mohd Mokhtar Muhamad, Fazilah Razali, Nasnoor Juzaily Mohd Nasiruddin, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4868390/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Teachers’ sense of efficacy (TSE) is a crucial construct for evaluating the quality of pre-service teachers. While the Teachers’ Sense of Efficacy Scale (TSES) is the most widely used and promising instrument for measuring TSE, there is no existing literature assessing the appropriateness of the TSES for early childhood pre-service teachers in China. This study aimed to translate the English version of the TSES into Chinese and test its factor structure, validity, measurement invariance across gender, age, and college year, as well as reliability. Methods This study used a cross-sectional design. The sample included 402 participants in China. The TSES was translated into Chinese using the standard back-to-back translation method. The psychometric properties of the TSES, including construct validity, concurrent validity, convergent validity, criterion-related validity, measurement invariance, internal consistency reliability, and composite reliability, were examined. Results CFA results indicated that the TSES is best represented by a modified three-factor model, demonstrating strong preliminary, overall, and internal structure fit. The concurrent validity, convergent validity, criterion-related validity, internal consistency reliability, and composite reliability of the Chinese version TSES were robust. The measurement invariance across gender, age, and college year was also confirmed. Conclusions This study addresses a gap in the literature by providing robust empirical evidence on the factor structure, validity, measurement invariance, and reliability of the Chinese version of the TSES for early childhood pre-service teachers, thereby enhancing understanding of TSE in Chinese-speaking context. Teachers’ sense of efficacy Teachers’ sense of efficacy scale Early childhood pre-service teachers Psychometric properties Figures Figure 1 Introduction According to Bandura [1], teachers’ sense of efficacy (TSE) is commonly considered a form of self-efficacy. For pre-service teachers (PSTs), TSE refers to their confidence in their capacity to organize and carry out teaching-related behaviors effectively [2, 3]. In recent years, there has been a growing body of research focused on the TSE of PSTs [4–12]. Evidence suggests that PSTs need to cultivate a strong sense of efficacy, as it is critical to their effective teaching practice and pedagogical knowledge [12–15]. Previous research has established that PSTs with greater self-efficacy, the more effective pedagogical approaches they implement [10, 16, 17]. Additionally, evidence in the literature shows that teachers with greater self-efficacy also have a higher passion for teaching [18, 19]. High-quality teachers are the cornerstone of education. The quality of early childhood PSTs (EC-PSTs) has gained renewed focus with the introduction of key education policies in China, such as the Professional Competency Standards of Normal Students of Preschool Education (2021) and the Opinions on the implementation of the Plan of Action for the expansion and quality of basic Education in the New era (2023). TSE has been recognized as a crucial construct for evaluating PSTs’ quality, serving as an assessment of their capacity to implement effective instructional practices [20, 21]. For EC-PSTs, TSE has also been identified as a valuable predictor of their future instructional practices [22]. Thus, it is vital to study EC-PSTs’ TSE. Over the past few decades, various instruments have been developed to assess TSE, such as the Teacher Efficacy Scale [23], Bandura’s Teacher Self-Efficacy Scale [1], Science Teaching Efficacy Belief Instrument B [24], and Teachers’ Sense of Efficacy Scale (TSES) [2]. The TSES, which includes a 12-item short form and a 24-item long form, is the most widely used and promising instrument for measuring TSE [20, 21, 25–33] as it aligns with Bandura’s [1] theory and the recommendations of critics [5, 30, 34]. According to Duffin et al. [31], the TSES has become the predominant instrument for evaluating PSTs’ TSE. A review of previous studies indicates that while both forms of TSES exhibit excellent internal consistency reliability among in-service teachers (ISTs) and PSTs [2, 28, 33, 35], the short form shows better psychometric properties and cross-cultural adaptation [33, 36, 37]. However, there is significant debate and conflicting statistical outcomes regarding the factor structure of short form TSES (TSES-SF) when utilized with PSTs [2, 4, 26, 34, 38], indicating that the factor structure of the scale remains unresolved. Prior studies have extensively examined the TSES-SF’s factor structure. Besides the initial three-factor model proposed by Tschannen-Moran and Hoy, other researchers have suggested different two-factor and three-factor models. For example, Tsui and Kennedy [39] recommended a two-factor model based on Hong Kong ISTs. Ruan et al. [36] proposed a modified three-factor model based on ISTs from China, Korea, and Japan by removing item 8. This model has also recently been found applicable in Spanish and Vietnamese contexts [4, 40]. Building on this model, Lu et al. [41] introduced another edition of the three-factor model with 11 items based on Chinese special education ISTs, by revising item 12 as a “Efficacy for instructional strategies (IS)” factor rather than a “Efficacy for student engagement (SE)” factor. Furthermore, Tschannen-Moran et al. [2] noted that the single-factor model was more ideal for American PSTs than the three-factor model. This finding was validated by subsequent research employed American PSTs as the sample [31, 34]. However, Burgueño et al. [4] suggested that, for Spanish PSTs, the modified three-factor model recommended by Ruan et al. [36] was more ideal. Recently, Chan et al. [38] proposed a complex bifactor model, suggesting it was preferred for Australian EC-PSTs. Another significant gap in the literature is the lack of studies assessing the appropriateness of the TSES for EC-PSTs in China. Given that TSE is context-dependent and may vary according to cultural values and demographic variables such as gender and teaching field [42], this study aims to translate the English version of the TSES into Chinese, and then test its factor structure, validity, measurement invariance across gender, age, and college year, as well as reliability among Chinese EC-PSTs. This is essential for supporting and improving the quality of EC-PSTs in Chinese-speaking contexts. Materials and Methods Research design and participants This study employed a cross-sectional survey methodological design to assess the TSE of EC-PSTs at a normal university in Hainan province, China. Data were collected from 402 early childhood education (ECE) undergraduate PSTs (ages ranged from 18 to 24, M age = 20.42, SD = 1.35) using a simple random sampling method. Participants were 92.3% female (n = 371) and 7.7% male (n = 31). According to the latest data from the Ministry of Education of China, there were 324,4204 full-time early childhood ISTs (EC-ISTs) in China as of 2022, with 316, 6616 (97.61%) being female [43]. Additionally, as of 2024, the sampled normal university had 1,946 EC-PSTs enrolled, of which 1,823 (93.68%) were female. Therefore, the gender distribution of our sample was representative of the EC-PSTs in Hainan and EC-ISTs in China. Regarding college year, 23.40% (n = 94) of the respondents were freshmen, 29.60% (n = 119) were sophomores, 42.50% (n = 171) were juniors, and 4.50% (n = 18) were seniors. Sample sizes considered appropriate for factor analysis are typically more than 10 times the total number of items [44], or at least 300 cases [45]. Given that the TSES-SF has 12 items, the sample size of this study (n = 402) was satisfactory for meeting the minimum requirement. The sociodemographic descriptions of the sample are shown in Table 1 . Table 1 Sociodemographic descriptions of the sample Variables N (%) SD Gender 0.27 Male 31 (7.7) Female 371 (92.3) Age 1.35 18 22 (5.5) 19 75 (18.7) 20 132 (32.8) 21 100 (24.9) 22 40 (10) 23 21 (5.2) 24 12 (3) College year 0.87 Freshman 94 (23.4) Sophomore 119 (29.6) Junior 171 (42.5) Senior 18 (4.5) Measures Teachers’ Sense of Efficacy Scale Short Form, TSES-SF The measurement of EC-PSTs’ TSE was conducted using the TSES-SF with 12 items [2]. The TSES-SF was translated from English into Chinese using back-to-back translation to ensure translation validity. As shown in Table 2 , all 12 items were categorized into three sub-scales: “Efficacy for instructional strategies (IS)”, “Efficacy for classroom management (CM)”, and “Efficacy for student engagement (SE)”. The TSES-SF was measured using a Likert 9-point scale, where 1 = “Nothing”, 3 = “Very little”, 5 = “Some degree”, 7 = “Quite a bit”, and 9 = “A great deal”. The overall TSES score is derived from an average of the three factors. The α for TSES-SF in the present study was 0.93. Table 2 Dimensions of the TSES-SF Factor Item Item No. IS 1, 2, 3, 4 4 CM 5, 6, 7, 8 4 SE 9, 10, 11, 12 4 Student Teacher Professional Identity Scale, STPIS EC-PSTs’ professional identity (PI) was measured using the STPIS [46] with 12 items (e.g., “I think PSTs are respected”) on a Likert 5-point scale where 1 = “strongly disagree” and 5 = “strongly agree”. The 12 items were divided into four sub-scales: “Professional willingness (PW)”, “Professional values (PVa)”, “Professional efficacy (PE)”, and “Professional volition (PVo)”. Higher scores suggest a stronger sense of PI. The α for STPIS was 0.77 in the present study. Procedures All data were collected using the wjx.cn online questionnaire at the beginning of the second half of the 2022–2023 school year. Participants were briefed on the aim of this research and the procedure. They then voluntarily completed questionnaires anonymously. Data analysis All of the data in this research were analyzed using SPSS version 22 and Amos version 24. Preliminary analyses, including SD and mean, were conducted. Skewness and kurtosis of the TSES-SF were utilized to examine its normality. Given the previous research on the factor structure of TSES-SF, we employed CFA to investigate the factorial structures of the Chinese version of TSES-SF (C-TSES-SF) among EC-PSTs. The specific factor structures of the six models investigated are as follows: “Model 1” [2]: one factor (Items 1–12); “Model 2” [39]: Teaching and Support [TS] (Items 1–4, 9, 10, 12), CM (Items 5–8, 11); “Model 3” [2]: IS (Items 1–4), CM (Items 5–8), SE (Items 9–12); “Model 4” [36]: IS (Items 1–4), CM (Items 5–7), SE (Items 9–12); “Model 5” [41]: IS (Items 1, 2, 3, 4, 12), CM (Items 5, 6, 7), SE (Items 9, 10, 11); “Model 6” [38]: IS (Items 1–4), CM (Items 5–8), SE (Items 9–11), General (Items 1–12). We run CFA respectively with the same sample (n = 402) to test the fitness of the factor structures of the six models. Prior to CFA, KMO tests (> 0.80) and Bartlett’s test were conducted to determine the appropriateness of the sample for factor analysis. CFA was then performed using maximum likelihood estimation to validate the factorial structure of the six models. The assessment of the model’s adequacy was conducted utilizing three dimensions of preliminary fit, overall fit, and internal structure fit advocated by Bagzzi and Yi [47]. According to Bagozzi and Yi [47], criteria for preliminary fit include all error variances of indicators are positive and p-values are significant; standard errors are not “very large”; factor loadings are between 0.5 to 0.95. The overall model fit indices include χ 2 /df 0.95, TLI (NNFI) > 0.94 [49], RMSEA 0.90 [51], NFI > 0.90 [52], IFI > 0.90 [53], the lower the value for AIC and ECVI, the better model fit [31]. The internal structure model fit indices include high individual items (> 0.50), AVE > 0.50 [47, 54], and CR > 0.70 [54]. Next, the measurement invariance of the C-TSES-SF model across gender, age, and college year, including configural, metric, scalar, and residual invariance[55], was further evaluated by multigroup CFAs. For measurement invariance across age and college year, the sample was separated into two groups respectively: age group 1 (18–20 years, n = 229), age group 2 (21–24 years, n = 173); college year group 1 (freshmen and sophomores, n = 213), and college year group 2 (juniors and seniors, n = 189). The criteria of measurement invariance of C-TSES-SF are ΔCFI ≤ 0.01 and ΔRMSEA ≤ 0.015 [56, 57]. Following the selection of the suitable model, we assessed the concurrent validity among C-TSES-SF’s three sub-scales, the convergent validity of the C-TSES-SF, as well as the criterion-related validity with STPIS using two-tailed Pearson correlation analyses. Correlation strengths (r) were classified as follows: extremely high, r > 0.70; large, 0.30 < r < 0.70; medium, 0.10 < r < 0.30; low, r < 0.10 [58]. The concurrent validity criterion was r < 0.8 and p < 0.05 [50]. Criteria for convergent validity include significant p-values, and larger (smaller) r-values indicate larger (smaller) convergent validity [54]. The criterion-related validity would be supported if the correlation between the STPIS (including its four sub-scales) and the C-TSES-SF (including its three sub-scales) is positive and substantial, as Zhang [59] and Hong et al. [60] verified that self-efficacy was significantly positively correlated with PI. Lastly, the reliability of C-TSES-SF and its sub-scales was calculated using Cronbach alpha’s coefficient and composite reliability values (ρ) [61] for all participants (n = 402). Internal consistency was classified as follows: α > 0.7, acceptable; 0.9 > α > 0.8, good; α > 0.9, perfect [62]. Regarding ρ, an acceptable range is 0.60 ≤ ρ ≥ 0.70, a satisfactory range is 0.70 ≤ ρ ≥ 0.90, and a perfect range is ρ ≥ 0.90 [63]. Results Preliminary analyses The normality of the sample was examined before conducting CFA. Table 3 presents descriptive statistics for all 12 items of the scale. Both skewness and kurtosis were in the range of -1 to + 1, indicating that the data distributions of the TSES-SF were normal [64, 65]. Table 3 Descriptive statistics for the TSES-SF Item Sample (n = 402) M SD Skewness Std. Error Kurtosis Std. Error 1 5.72 1.22 -0.03 0.12 0.44 0.24 2 6.06 1.19 0.00 0.12 0.20 0.24 3 6.00 1.26 -0.33 0.12 0.48 0.24 4 5.72 1.26 0.02 0.12 0.09 0.24 5 5.67 1.23 -0.11 0.12 -0.33 0.24 6 5.88 1.24 -0.06 0.12 -0.31 0.24 7 5.82 1.23 0.13 0.12 0.04 0.24 8 5.65 1.24 -0.07 0.12 -0.12 0.24 9 6.14 1.20 0.06 0.12 -0.26 0.24 10 6.08 1.16 -0.11 0.12 0.03 0.24 11 5.86 1.16 -0.07 0.12 -0.06 0.24 12 5.66 1.28 0.01 0.12 -0.31 0.24 Construct validity The preliminary model fit The KMO statistic and Bartlett’s sphericity test outcomes illustrated that the factor analysis was appropriate, as KMO = 0.94 and Bartlett test, χ 2 (66) = 3131.39 (p < 0.001). According to the CFA outcomes (n = 402), the preliminary fit of the first five models was good, except for Model 6. All error variances from e1 to e11 or e12 among the first five models were positive and significant (p 0.05). The t-values of e1 to e11 or e12 in the six models ranged from 8.65 to 13.50 (Model 1: 12.50 to 13.50, Model 2: 11.78 to 13.28, Model 3: 10.60 to 12.56, Model 4: 8.65 to 12.42, Model 5: 10.60 to 12.56, Model 6: -0.48 to 13.25). Standard errors of all parameters among the first five models ranged from 0.04 to 0.10 (Model 1: 0.04 to 0.10, Model 2: 0.04 to 0.10, Model 3: 0.04 to 0.08, Model 4: 0.04 to 0.08, Model 5: 0.04 to 0.08), and were not “very large”. However, in Model 6, the standard error of W7 (16.07) was more than 16 times the largest standard error in the first five models (0.10 in Model 1 and Model 2) and was “very large”. The standardized factor loadings between the latent variables and their measurement pointers among the first five models varied between 0.64 and 0.87 (Model 1: 0.64 to 0.80, Model 2: 0.66 to 0.82, Model 3: 0.71 to 0.84, Model 4: 0.71 to 0.87, Model 5: 0.69 to 0.87), meeting the criteria of greater than 0.50 and less than 0.95 [47]. However, in Model 6, the standardized factor loadings varied between 0.08 and 1.24, which did not meet the above criteria. The overall model fit The outcomes of the overall model fit, as indicated in Table 4 , showed that Model 4 displayed the best fit compared to the other five models. The IFIs were sufficient for all six models (ranging from 0.91 to 0.96 > 0.90). In Model 1 and Model 2, none of the indices except IFI were sufficient. In Model 3, the RMSEA was not satisfied (RMSEA = 0.085 > 0.08). In Model 5, the RMSEA was insufficient (RMSEA = 0.088 > 0.08). Both Model 4 and Model 6 showed relatively good fit according to the overall model fit indices. However, the differences in AIC and ECVI values led us to prefer Model 4. Figure 1 displays the factor structure of Model 4. Table 4 Overall model fit for existing models No. of factors Item No. χ 2 df χ 2 /df GFI RMSEA NFI IFI TLI (NNFI) CFI AIC ECVI Model 1 1 12 337.53 54 6.25 0.87 0.114 0.89 0.91 0.89 0.91 385.53 0.96 Model 2 2 12 314.00 53 5.92 0.88 0.111 0.90 0.92 0.90 0.92 364.00 0.91 Model 3 3 12 197.24 51 3.87 0.92 0.085 0.94 0.95 0.94 0.95 251.24 0.63 Model 4 3 11 142.67 41 3.48 0.94 0.079 0.95 0.96 0.95 0.96 192.67 0.48 Model 5 3 11 168.74 41 4.12 0.92 0.088 0.94 0.95 0.94 0.95 218.74 0.55 Model 6 bifactor model 12 134.44 44 3.06 0.95 0.072 0.96 0.94 0.96 0.97 202.44 0.51 Note. Model 6 with the residual variance of item 1 set to 0. The internal structure model fit Regarding the internal structure model fit, it was discovered that the individual item reliability of all observations in Model 3, Model 4, and Model 6 was adequate (Model 3: 0.50 to 0.71 > 0.50, Model 4: 0.51 to 0.76 > 0.50, Model 6: 0.53 to 2.07 > 0.50). In contrast, some observations in Model 1, Model 2, and Model 5 were not satisfied (Model 1: 0.41 to 0.62, Model 2: 0.43 to 0.67, Model 5: 0.48 to 0.76). As illustrated in Table 5 , the CR of the three latent variables in Model 3, Model 4, and Model 5 all met the criterion of greater than 0.70 [54]. The AVE of the three latent variables in Model 3, Model 4, and Model 5 also satisfied the criterion of greater than 0.50 [47]. However, in Model 1 (CR = 0.94, AVE = 0.56), Model 2, and Model 6, these indices were not met. The analysis revealed that the internal structure fit of Model 3 and Model 4 was good. Table 5 AVE and CR for existing models CR AVE IS CM SE TS IS CM SE TS Model 2 - 0.89 - 0.89 - 0.62 - 0.54 Model 3 0.86 0.88 0.85 - 0.61 0.64 0.58 - Model 4 0.86 0.86 0.85 - 0.61 0.67 0.58 - Model 5 0.87 0.86 0.83 - 0.58 0.67 0.61 - Model 6 0.36 0.31 0.57 - 0.19 0.13 0.51 - To recap, considering that only Model 4 fully met all indices of the preliminary fit, overall model fit, and internal structure model fit, Model 4 was considered the best representation of the C-TSES-SF among EC-PSTs and was employed in the subsequent analysis. Measurement invariance According to the results of the multigroup CFA analysis on Model 4 (C-TSES-SF), summarized in Table 6 , the ΔCFI and ΔRMSEA values of configural, metric, scalar, and residual invariance across gender, age, and college year were all less than or equal to the thresholds of 0.01 and 0.015, respectively (gender: ΔCFI ranged from 0.001 to 0.004 < 0.01, ΔRMSEA ranged from 0.001 to 0.004 < 0.015; age: ΔCFI ranged from 0.001 to 0.01 ≤ 0.01, ΔRMSEA ranged from 0.001 to 0.003 < 0.015; college year: ΔCFI = 0.002 < 0.01, ΔRMSEA ranged from 0.004 to 0.005 < 0.015). This suggested that configural, metric, scalar, and residual invariance were established across gender, age, and college year. Table 6 Measurement invariance across gender, age, and college year Model χ 2 df CFI RMSEA Δχ 2 Δdf ΔCFI ΔRMSEA Gender Configural invariance 188.08 82 0.961 0.057 - - - - Metric invariance 201.67 90 0.959 0.056 13.59 8 0.002 0.001 Scalar invariance 210.93 101 0.960 0.052 9.26 11 0.001 0.004 Residual invariance 240.30 118 0.956 0.051 29.37 * 17 0.004 0.001 Age Configural invariance 235.38 82 0.945 0.068 - - - - Metric invariance 241.94 90 0.946 0.065 6.56 8 0.001 0.003 Scalar invariance 256.99 101 0.944 0.062 15.05 11 0.002 0.003 Residual invariance 301.46 118 0.934 0.062 44.47 *** 17 0.010 0.001 College Year Configural invariance 244.81 82 0.941 0.070 - - - - Metric invariance 247.32 90 0.943 0.066 2.51 8 0.002 0.004 Scalar invariance 251.79 101 0.945 0.061 6.98 11 0.002 0.005 Residual invariance 264.32 118 0.947 0.056 12.53 17 0.002 0.005 Note: Model comparison. * p < 0.05 , *** p < 0.001. Concurrent validity and convergent validity Table 7 shows that all three C-TSES-SF sub-scales had statistically significant high-level correlations, with r ranging from 0.69 to 0.73 (below 0.80). This finding supports the concurrent validity among the three C-TSES-SF sub-scales. Furthermore, the results in Table 7 demonstrated that the convergent validity of the C-TSES-SF was satisfactory, with significant extremely high-level correlations (p < 0.001) between the C-TSES-SF and its three sub-scales (IS: r = 0.90, CM: r = 0.89, SE: r = 0.92). Table 7 Correlations between the C-TSES-SF and sub-scales C-TSES-SF IS CM SE C-TSES-SF 1 IS 0.90 *** 1 CM 0.89 *** 0.69 *** 1 SE 0.92 *** 0.73 *** 0.76 *** 1 Note. *** p ≤ 0.001. Criterion-related validity Regarding the criterion-related validity of the C-TSES-SF, the results (Table 8 ) indicated that all factors from the C-TSES-SF were significantly positively correlated with the STPIS scale and its sub-scales. Specifically, STPIS, PE, and PW exhibited a significant large correlation strength with C-TSES-SF, IS, CM, and SE respectively. PVa showed a significant large correlation strength with C-TSES-SF and CM, and a medium correlation strength with IS and SE. PVo displayed a significant large correlation strength with C-TSES-SF, CM, and SE, and a significant medium correlation strength with IS. These findings support the criterion-related validity of the C-TSES-SF. Table 8 Correlations between the C-TSES-SF and STPIS C-TSES-SF IS CM SE STPIS 0.56 *** 0.45 *** 0.62 *** 0.48 *** PVa 0.30 *** 0.23 *** 0.34 *** 0.25 *** PE 0.55 *** 0.45 *** 0.63 *** 0.44 *** PW 0.39 *** 0.34 *** 0.39 *** 0.33 *** PVo 0.33 *** 0.23 *** 0.36 *** 0.32 *** Note. *** p ≤ 0.001. Reliability The C-TSES-SF’s Cronbach’s alpha coefficient was 0.93, and its sub-scales internal consistency scores were IS (α = 0.86), CM (α = 0.86), and SE (α = 0.85), respectively. In addition, the ρ-values were 0.92 for C-TSES-SF, 0.81 for IS, 0.81 for CM, and 0.80 for SE individually. These results indicated that the C-TSES-SF’s internal consistency reliability and composite reliability were perfect, and the internal consistency reliability and composite reliability of its three sub-scales were good. Discussion This study addressed a gap in the literature by adapting a C-TSES-SF and examining its factor structure, validity, measurement invariance, and reliability in the context of Chinese EC-PSTs. Our findings suggest that the TSES-SF is best represented by a modified three-factor model for EC-PSTs. The psychometric properties of the C-TSES-SF were robust, validating its use for assessing the TSE of Chinese EC-PSTs across different genders, ages, and college years. These findings enhance the cross-cultural applicability of the TSES scale and understanding of TSE among Chinese EC-PSTs. Given that cultural factors have a deep impact on TSE [39], we employed the standard double-back translation method to translate the TSES-SF scale from English to Chinese to minimize translation-related validity and reliability issues. Considering the specific characteristics of ECE in China, where children aged 3 to 6 years attend kindergartens, we replaced “students/student” with “幼儿” (children/child) in items 2, 3, 5, 7, 8, 9, 10, and 11, and “school” with “幼儿园” (kindergarten) in item 12. Additionally, recognizing that ECE does not have academic performance requirements and uses play-based pedagogies [66] and activities, we replaced “schoolwork” with “教育教学活动” (educational and instructional activities) in items 9 and 11. Overall, the present study may yield some practical significance for ECE, as these modifications ensured the translated TSES-SF accurately conveyed its intended meaning in Chinese and was appropriate for use in ECE settings. While previous studies have considered various rival models for TSES-SF, including single-factor, two-factor, three-factor, and bifactor models [2, 36, 38, 39, 41], we evaluated the construct validity of TSES-SF using more stringent criteria (preliminary fit, overall fit, and internal structure fit). This approach may be more advantageous as it provides detailed information regarding the model’s structure. Our results indicate that TSE of Chinese EC-PSTs is best represented by a modified three-factor model excluding item 8. This suggests that the TSE factor structure among EC-PSTs is also multi-dimensional as that of general ISTs. Our findings align with previous studies on ISTs in four Asian countries [36, 40] and PSTs in Spain [4]. The poor fit of item 8 may be because it is not common to group children for varied instruction in China [36] due to the sociocultural characteristics of the Chinese education system, such as collectivist culture and whole-group teaching [67]. This finding is noteworthy as it highlights that cultural values, educational contexts, and professional backgrounds may influence the appropriateness of the TSES [39, 42]. Our findings offer new insights into the factor structure of TSES-SF, suggesting that the structure of TSES-SF is stable within Asian cultures due to certain cultural commonalities. We then assessed the measurement invariance of the C-TSES-SF across gender, age, and college year using multigroup CFA. The results indicated robust measurement invariance across gender, consistent with a prior study on Spanish PSTs [4]. Although our male sample was small, CFI and RMSEA are less sensitive to sample size [56], making sample size less critical for measuring invariance [68]. Compared to previous studies, our findings further revealed measurement invariance across age and college year, indicating that there are no differences in the C-TSES-SF’s three-factor model (Model 4) across various ages and college years among Chinese EC-PSTs. Given the measurement invariance is a prerequisite of the comparison of group means [68], our findings are meaningful as they provide evidence for comparisons of TSE across gender, age, and college year for EC-PSTs in China. Moreover, our outcomes also provide the feasibility of evaluating the effectiveness of relevant targeted intervention measures. The convergent validity and criterion-related validity of C-TSES-SF, as well as concurrent validity among its three sub-scales, were evaluated using Pearson correlation analyses. Our results demonstrated sufficient convergent and concurrent validity. However, the correlation coefficients of C-TSES-SF with its three factors, and between its three sub-scales, were higher than those in previous studies on ISTs [2, 40], indicating stronger psychometric properties for the C-TSES-SF. Additionally, our findings present evidence for the criterion-related validity of C-TSES-SF by validating the correlation between the STPIS and the C-TSES-SF. Compared to previous studies [4, 26, 33, 40, 41, 69], our results enhance the understanding of TSES’s criterion validity in EC-PSTs. This is achieved by introducing a new questionnaire to verify the TSES’s criterion-related validity, building on earlier research that demonstrated a correlation between self-efficacy and PI [59, 60]. The α and ρ values provide evidence for the good reliability of the scale. The ρ values were consistent with those obtained in a prior study among PSTs in Spain [4]. However, the α values in this study were higher than those in previous studies among ISTs [2, 33, 40], indicating superior internal consistency in this study and contributing new evidence to knowledge about the internal consistency of TSES-SF among ISTs and PSTs. Our findings indicate that TSES-SF is applicable in different cultural and teacher contexts. Finally, our EC-PST participants demonstrated lower TSE (M PSTs = 5.87 < M ISTs = 6.83) compared to EC-ISTs in Hong Kong [70], potentially due to the more practical experience of ISTs positively affecting their TSE. This explanation is speculative, and it is important to note that a comparison of specific factors between ISTs and PSTs cannot be realized due to the use of a two-factor structure in the TSES-SF adopted for ISTs. Therefore, future research should further collect qualitative data from EC-ISTs to better understand the reasons behind this difference. Limitations This study has several limitations. Firstly, the evaluation of TSE among EC-PSTs in Hainan province was conducted using a cross-sectional survey research methodology. Random sampling using the probability technique should be used in the future to ensure the sample covers a wider range and different educational levels to improve the generalizability of the study findings. Secondly, we did not assess the psychometric properties of the long-form TSES, which could be considered in future research to facilitate further cross-cultural comparative studies among EC-PSTs using different forms of TSES. Conclusion and Contribution This study fills a gap in understanding the TSE of EC-PSTs by providing robust empirical evidence on the factor structure, validity, measurement invariance, and reliability of the C-TSES-SF. Importantly, our findings enhance the cross-cultural applicability of the TSES, demonstrating its appropriateness in Chinese-speaking contexts. Additionally, our findings have significant implications for ECE by offering a reliable tool for teacher education programs to dynamically assess TSE in EC-PSTs. This enables targeted interventions to enhance the teaching effectiveness and overall quality of EC-PSTs. Abbreviations TSE Teachers’ sense of efficacy TSES Teachers’ Sense of Efficacy Scale TSES-SF Teachers’ Sense of Efficacy Scale Short Form C-TSES-SF Chinese Version of Teachers’ Sense of Efficacy Scale Short Form IS Efficacy for instructional strategies CM Efficacy for classroom management SE Efficacy for student engagement TS Teaching and Support PI Professional identity STPIS Student Teacher Professional Identity Scale PW Professional willingness PVa Professional values PE Professional efficacy PVo Professional volition PSTs Pre-service teachers EC-PSTs Early childhood pre-service teachers ISTs In-service teachers EC-ISTs Early childhood in-service teachers ECE Early childhood education SD Standard deviation M Mean CFA Confirmatory factor analysis KMO Kaiser-Meyer-Olkin χ 2 /df Chi-square by degrees of freedom ratio CFI Comparative fit index TLI (NNFI) Tucker-Lewis coefficient (Bentler-Bonett non-normed fit index) RMSEA Root mean square error of approximation GFI Goodness of fit index NFI Normed fit index IFI Incremental fit index AIC Akaike information criterion ECVI Expected cross validation index AVE Average variance extracted CR Composite reliability ΔCFI CFA change ΔRMSEA RMSEA change Δdf df change Declarations Ethics approval and consent to participate Before data collection, this study obtained approval from the ethical committee of Universiti Putra Malaysia (Project ID: JKEUPM-2023-323) as well as permission from the sampled normal university (Qiongtai Normal University). All participants voluntarily signed informed consent forms. This study was conducted in accordance with the principles expressed in the Declaration of Helsinki (2008). Additionally, provisions from the Nuremberg Code of 1946, pertinent to the gathering of information in social science fields, were also utilized in the present set of guidelines. Consent for publication Not applicable. Availability of data and materials Upon reasonable request, the data used are available from the corresponding author. Competing interests The authors declare that they have no competing interests. Funding This study was supported by Hainan Province’s “South China Sea Rising Star” Education Platform Project (JYNHXX2023-24T), Educational and Teaching Reform of Hainan’s Higher Education Institutions Research Project (Hnjgzc2022-67), Hainan Province Philosophy and Social Sciences Planning Project [HNSK(ZC)22-157], and China National Social Science Fund Education Youth Project (CGA200246). Authors' contributions SMX designed the study. MMM and NJMN gave methodological guidance. NJMN, SXC, and YGQ involved in the double back translation of the scale. SXC collected the data and performed the preliminary analysis of the data. SMX completed follow-up data analysis and wrote the manuscript. MMM and FR supervised the manuscript. All authors approved the final manuscript. Acknowledgments Not applicable. References Bandura A. Self-efficacy: the Exercise of Control. NY: Freeman; 1997. Tschannen-Moran M, Hoy AW. Teacher Efficacy: Capturing an Elusive Construct. Teaching and Teacher Education. 2001;17 (7): 783-22. Plourde LA. The influence of student teaching on pre-service elementary teachers’ science self-efficacy and outcome expectancy beliefs. Journal of Instructional Psychology. 2002;29(4):245-8. Burgueño R, Sicilia A, Medina-Casaubón J, Alcaraz-Ibañez M, Lirola MJ. Psychometry of the teacher’s sense of efficacy scale in Spanish teachers’ education. J. Exp. 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The Teacher Efficacy Scale: A Reliability and Validity Study. The Asia-Pacific Education Researcher. 2012;21(2):414-7. Leung YW, Mak TCT, Chan DKC, Capio CM. Early Childhood Educators’ Physical Literacy Predict Their Self-Efficacy and Perceived Competence to Promote Physical Activity. Early Education and Development. 2023; doi:10.1080/10409289.2023.2243187. Additional Declarations No competing interests reported. Supplementary Files Appendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4868390","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":338270445,"identity":"c66934a3-0668-4500-942a-d7527e79e07d","order_by":0,"name":"Mingxing Shao","email":"","orcid":"","institution":"Universiti Putra Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Mingxing","middleName":"","lastName":"Shao","suffix":""},{"id":338270446,"identity":"9daa1d3e-ab49-464d-8aac-380aa562d5ab","order_by":1,"name":"Mohd Mokhtar Muhamad","email":"","orcid":"","institution":"Universiti Putra Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Mohd","middleName":"Mokhtar","lastName":"Muhamad","suffix":""},{"id":338270447,"identity":"b5153e12-cc62-414a-a836-547ebe290304","order_by":2,"name":"Fazilah Razali","email":"","orcid":"","institution":"Universiti Putra Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Fazilah","middleName":"","lastName":"Razali","suffix":""},{"id":338270448,"identity":"0ac86d84-401b-42f1-a23b-6074e95fc26c","order_by":3,"name":"Nasnoor Juzaily Mohd Nasiruddin","email":"","orcid":"","institution":"University of Malaya","correspondingAuthor":false,"prefix":"","firstName":"Nasnoor","middleName":"Juzaily Mohd","lastName":"Nasiruddin","suffix":""},{"id":338270449,"identity":"0ccc25be-69f8-4db0-8e6d-743ccf7a7182","order_by":4,"name":"Xinchong Sha","email":"data:image/png;base64,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","orcid":"","institution":"Universiti Putra Malaysia","correspondingAuthor":true,"prefix":"","firstName":"Xinchong","middleName":"","lastName":"Sha","suffix":""},{"id":338270450,"identity":"0a9c216c-50a9-415e-b4c4-bfa5ee89877b","order_by":5,"name":"Guoqiang Yin","email":"","orcid":"","institution":"Central China Normal University","correspondingAuthor":false,"prefix":"","firstName":"Guoqiang","middleName":"","lastName":"Yin","suffix":""}],"badges":[],"createdAt":"2024-08-06 12:15:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4868390/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4868390/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64568871,"identity":"b79c8d9d-d4f6-4a81-82df-04ed8c26be75","added_by":"auto","created_at":"2024-09-16 00:44:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":85963,"visible":true,"origin":"","legend":"\u003cp\u003eThe best-fit model of TSES-SF (Model 4)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4868390/v1/7659f3ceffb410078fccd200.png"},{"id":72867731,"identity":"fcce4f07-f1da-4eda-b27f-e9b1204344e8","added_by":"auto","created_at":"2025-01-03 06:31:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":942690,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4868390/v1/f40c1c6b-966c-4747-96ce-38060d0bdebf.pdf"},{"id":64568872,"identity":"16c87547-1055-4d30-bf5a-8819258fa0ca","added_by":"auto","created_at":"2024-09-16 00:44:03","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18872,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-4868390/v1/17c9c064b3e036a420fe0bd5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Chinese Adaptation of the Teachers’ Sense of Efficacy Scale in Early Childhood Pre-service Teachers: Validity, Measurement Invariance, and Reliability","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAccording to Bandura [1], teachers\u0026rsquo; sense of efficacy (TSE) is commonly considered a form of self-efficacy. For pre-service teachers (PSTs), TSE refers to their confidence in their capacity to organize and carry out teaching-related behaviors effectively [2, 3]. In recent years, there has been a growing body of research focused on the TSE of PSTs [4\u0026ndash;12]. Evidence suggests that PSTs need to cultivate a strong sense of efficacy, as it is critical to their effective teaching practice and pedagogical knowledge [12\u0026ndash;15]. Previous research has established that PSTs with greater self-efficacy, the more effective pedagogical approaches they implement [10, 16, 17]. Additionally, evidence in the literature shows that teachers with greater self-efficacy also have a higher passion for teaching [18, 19].\u003c/p\u003e \u003cp\u003eHigh-quality teachers are the cornerstone of education. The quality of early childhood PSTs (EC-PSTs) has gained renewed focus with the introduction of key education policies in China, such as \u003cem\u003ethe Professional Competency Standards of Normal Students of Preschool Education (2021) and the Opinions on the implementation of the Plan of Action for the expansion and quality of basic Education in the New era (2023).\u003c/em\u003e TSE has been recognized as a crucial construct for evaluating PSTs\u0026rsquo; quality, serving as an assessment of their capacity to implement effective instructional practices [20, 21]. For EC-PSTs, TSE has also been identified as a valuable predictor of their future instructional practices [22]. Thus, it is vital to study EC-PSTs\u0026rsquo; TSE.\u003c/p\u003e \u003cp\u003eOver the past few decades, various instruments have been developed to assess TSE, such as the Teacher Efficacy Scale [23], Bandura\u0026rsquo;s Teacher Self-Efficacy Scale [1], Science Teaching Efficacy Belief Instrument B [24], and Teachers\u0026rsquo; Sense of Efficacy Scale (TSES) [2]. The TSES, which includes a 12-item short form and a 24-item long form, is the most widely used and promising instrument for measuring TSE [20, 21, 25\u0026ndash;33] as it aligns with Bandura\u0026rsquo;s [1] theory and the recommendations of critics [5, 30, 34]. According to Duffin et al. [31], the TSES has become the predominant instrument for evaluating PSTs\u0026rsquo; TSE.\u003c/p\u003e \u003cp\u003eA review of previous studies indicates that while both forms of TSES exhibit excellent internal consistency reliability among in-service teachers (ISTs) and PSTs [2, 28, 33, 35], the short form shows better psychometric properties and cross-cultural adaptation [33, 36, 37]. However, there is significant debate and conflicting statistical outcomes regarding the factor structure of short form TSES (TSES-SF) when utilized with PSTs [2, 4, 26, 34, 38], indicating that the factor structure of the scale remains unresolved.\u003c/p\u003e \u003cp\u003ePrior studies have extensively examined the TSES-SF\u0026rsquo;s factor structure. Besides the initial three-factor model proposed by Tschannen-Moran and Hoy, other researchers have suggested different two-factor and three-factor models. For example, Tsui and Kennedy [39] recommended a two-factor model based on Hong Kong ISTs. Ruan et al. [36] proposed a modified three-factor model based on ISTs from China, Korea, and Japan by removing item 8. This model has also recently been found applicable in Spanish and Vietnamese contexts [4, 40]. Building on this model, Lu et al. [41] introduced another edition of the three-factor model with 11 items based on Chinese special education ISTs, by revising item 12 as a \u0026ldquo;Efficacy for instructional strategies (IS)\u0026rdquo; factor rather than a \u0026ldquo;Efficacy for student engagement (SE)\u0026rdquo; factor. Furthermore, Tschannen-Moran et al. [2] noted that the single-factor model was more ideal for American PSTs than the three-factor model. This finding was validated by subsequent research employed American PSTs as the sample [31, 34]. However, Burgue\u0026ntilde;o et al. [4] suggested that, for Spanish PSTs, the modified three-factor model recommended by Ruan et al. [36] was more ideal. Recently, Chan et al. [38] proposed a complex bifactor model, suggesting it was preferred for Australian EC-PSTs.\u003c/p\u003e \u003cp\u003eAnother significant gap in the literature is the lack of studies assessing the appropriateness of the TSES for EC-PSTs in China. Given that TSE is context-dependent and may vary according to cultural values and demographic variables such as gender and teaching field [42], this study aims to translate the English version of the TSES into Chinese, and then test its factor structure, validity, measurement invariance across gender, age, and college year, as well as reliability among Chinese EC-PSTs. This is essential for supporting and improving the quality of EC-PSTs in Chinese-speaking contexts.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eResearch design and participants\u003c/h2\u003e \u003cp\u003eThis study employed a cross-sectional survey methodological design to assess the TSE of EC-PSTs at a normal university in Hainan province, China. Data were collected from 402 early childhood education (ECE) undergraduate PSTs (ages ranged from 18 to 24, M\u003csub\u003eage\u003c/sub\u003e = 20.42, SD\u0026thinsp;=\u0026thinsp;1.35) using a simple random sampling method. Participants were 92.3% female (n\u0026thinsp;=\u0026thinsp;371) and 7.7% male (n\u0026thinsp;=\u0026thinsp;31). According to the latest data from the Ministry of Education of China, there were 324,4204 full-time early childhood ISTs (EC-ISTs) in China as of 2022, with 316, 6616 (97.61%) being female [43]. Additionally, as of 2024, the sampled normal university had 1,946 EC-PSTs enrolled, of which 1,823 (93.68%) were female. Therefore, the gender distribution of our sample was representative of the EC-PSTs in Hainan and EC-ISTs in China. Regarding college year, 23.40% (n\u0026thinsp;=\u0026thinsp;94) of the respondents were freshmen, 29.60% (n\u0026thinsp;=\u0026thinsp;119) were sophomores, 42.50% (n\u0026thinsp;=\u0026thinsp;171) were juniors, and 4.50% (n\u0026thinsp;=\u0026thinsp;18) were seniors. Sample sizes considered appropriate for factor analysis are typically more than 10 times the total number of items [44], or at least 300 cases [45]. Given that the TSES-SF has 12 items, the sample size of this study (n\u0026thinsp;=\u0026thinsp;402) was satisfactory for meeting the minimum requirement. The sociodemographic descriptions of the sample are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSociodemographic descriptions of the sample\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e371 (92.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75 (18.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e132 (32.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100 (24.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreshman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94 (23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSophomore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119 (29.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e171 (42.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSenior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eTeachers\u0026rsquo; Sense of Efficacy Scale Short Form, TSES-SF\u003c/h2\u003e \u003cp\u003eThe measurement of EC-PSTs\u0026rsquo; TSE was conducted using the TSES-SF with 12 items [2]. The TSES-SF was translated from English into Chinese using back-to-back translation to ensure translation validity. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, all 12 items were categorized into three sub-scales: \u0026ldquo;Efficacy for instructional strategies (IS)\u0026rdquo;, \u0026ldquo;Efficacy for classroom management (CM)\u0026rdquo;, and \u0026ldquo;Efficacy for student engagement (SE)\u0026rdquo;. The TSES-SF was measured using a Likert 9-point scale, where 1 = \u0026ldquo;Nothing\u0026rdquo;, 3 = \u0026ldquo;Very little\u0026rdquo;, 5 = \u0026ldquo;Some degree\u0026rdquo;, 7 = \u0026ldquo;Quite a bit\u0026rdquo;, and 9 = \u0026ldquo;A great deal\u0026rdquo;. The overall TSES score is derived from an average of the three factors. The α for TSES-SF in the present study was 0.93.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDimensions of the TSES-SF\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItem No.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1, 2, 3, 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5, 6, 7, 8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9, 10, 11, 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eStudent Teacher Professional Identity Scale, STPIS\u003c/h2\u003e \u003cp\u003eEC-PSTs\u0026rsquo; professional identity (PI) was measured using the STPIS [46] with 12 items (e.g., \u0026ldquo;I think PSTs are respected\u0026rdquo;) on a Likert 5-point scale where 1 = \u0026ldquo;strongly disagree\u0026rdquo; and 5 = \u0026ldquo;strongly agree\u0026rdquo;. The 12 items were divided into four sub-scales: \u0026ldquo;Professional willingness (PW)\u0026rdquo;, \u0026ldquo;Professional values (PVa)\u0026rdquo;, \u0026ldquo;Professional efficacy (PE)\u0026rdquo;, and \u0026ldquo;Professional volition (PVo)\u0026rdquo;. Higher scores suggest a stronger sense of PI. The α for STPIS was 0.77 in the present study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003eProcedures\u003c/h2\u003e \u003cp\u003eAll data were collected using the wjx.cn online questionnaire at the beginning of the second half of the 2022\u0026ndash;2023 school year. Participants were briefed on the aim of this research and the procedure. They then voluntarily completed questionnaires anonymously.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eAll of the data in this research were analyzed using SPSS version 22 and Amos version 24. Preliminary analyses, including SD and mean, were conducted. Skewness and kurtosis of the TSES-SF were utilized to examine its normality. Given the previous research on the factor structure of TSES-SF, we employed CFA to investigate the factorial structures of the Chinese version of TSES-SF (C-TSES-SF) among EC-PSTs. The specific factor structures of the six models investigated are as follows: \u0026ldquo;Model 1\u0026rdquo; [2]: one factor (Items 1\u0026ndash;12); \u0026ldquo;Model 2\u0026rdquo; [39]: Teaching and Support [TS] (Items 1\u0026ndash;4, 9, 10, 12), CM (Items 5\u0026ndash;8, 11); \u0026ldquo;Model 3\u0026rdquo; [2]: IS (Items 1\u0026ndash;4), CM (Items 5\u0026ndash;8), SE (Items 9\u0026ndash;12); \u0026ldquo;Model 4\u0026rdquo; [36]: IS (Items 1\u0026ndash;4), CM (Items 5\u0026ndash;7), SE (Items 9\u0026ndash;12); \u0026ldquo;Model 5\u0026rdquo; [41]: IS (Items 1, 2, 3, 4, 12), CM (Items 5, 6, 7), SE (Items 9, 10, 11); \u0026ldquo;Model 6\u0026rdquo; [38]: IS (Items 1\u0026ndash;4), CM (Items 5\u0026ndash;8), SE (Items 9\u0026ndash;11), General (Items 1\u0026ndash;12).\u003c/p\u003e \u003cp\u003eWe run CFA respectively with the same sample (n\u0026thinsp;=\u0026thinsp;402) to test the fitness of the factor structures of the six models. Prior to CFA, KMO tests (\u0026gt;\u0026thinsp;0.80) and Bartlett\u0026rsquo;s test were conducted to determine the appropriateness of the sample for factor analysis. CFA was then performed using maximum likelihood estimation to validate the factorial structure of the six models. The assessment of the model\u0026rsquo;s adequacy was conducted utilizing three dimensions of preliminary fit, overall fit, and internal structure fit advocated by Bagzzi and Yi [47]. According to Bagozzi and Yi [47], criteria for preliminary fit include all error variances of indicators are positive and p-values are significant; standard errors are not \u0026ldquo;very large\u0026rdquo;; factor loadings are between 0.5 to 0.95. The overall model fit indices include χ\u003csup\u003e2\u003c/sup\u003e/df\u0026thinsp;\u0026lt;\u0026thinsp;5 [48], CFI\u0026thinsp;\u0026gt;\u0026thinsp;0.95, TLI (NNFI)\u0026thinsp;\u0026gt;\u0026thinsp;0.94 [49], RMSEA\u0026thinsp;\u0026lt;\u0026thinsp;0.08 [50], GFI\u0026thinsp;\u0026gt;\u0026thinsp;0.90 [51], NFI\u0026thinsp;\u0026gt;\u0026thinsp;0.90 [52], IFI\u0026thinsp;\u0026gt;\u0026thinsp;0.90 [53], the lower the value for AIC and ECVI, the better model fit [31]. The internal structure model fit indices include high individual items (\u0026gt;\u0026thinsp;0.50), AVE\u0026thinsp;\u0026gt;\u0026thinsp;0.50 [47, 54], and CR\u0026thinsp;\u0026gt;\u0026thinsp;0.70 [54].\u003c/p\u003e \u003cp\u003eNext, the measurement invariance of the C-TSES-SF model across gender, age, and college year, including configural, metric, scalar, and residual invariance[55], was further evaluated by multigroup CFAs. For measurement invariance across age and college year, the sample was separated into two groups respectively: age group 1 (18\u0026ndash;20 years, n\u0026thinsp;=\u0026thinsp;229), age group 2 (21\u0026ndash;24 years, n\u0026thinsp;=\u0026thinsp;173); college year group 1 (freshmen and sophomores, n\u0026thinsp;=\u0026thinsp;213), and college year group 2 (juniors and seniors, n\u0026thinsp;=\u0026thinsp;189). The criteria of measurement invariance of C-TSES-SF are ΔCFI\u0026thinsp;\u0026le;\u0026thinsp;0.01 and ΔRMSEA\u0026thinsp;\u0026le;\u0026thinsp;0.015 [56, 57].\u003c/p\u003e \u003cp\u003eFollowing the selection of the suitable model, we assessed the concurrent validity among C-TSES-SF\u0026rsquo;s three sub-scales, the convergent validity of the C-TSES-SF, as well as the criterion-related validity with STPIS using two-tailed Pearson correlation analyses. Correlation strengths (r) were classified as follows: extremely high, r\u0026thinsp;\u0026gt;\u0026thinsp;0.70; large, 0.30\u0026thinsp;\u0026lt;\u0026thinsp;r\u0026thinsp;\u0026lt;\u0026thinsp;0.70; medium, 0.10\u0026thinsp;\u0026lt;\u0026thinsp;r\u0026thinsp;\u0026lt;\u0026thinsp;0.30; low, r\u0026thinsp;\u0026lt;\u0026thinsp;0.10 [58]. The concurrent validity criterion was r\u0026thinsp;\u0026lt;\u0026thinsp;0.8 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 [50]. Criteria for convergent validity include significant p-values, and larger (smaller) r-values indicate larger (smaller) convergent validity [54]. The criterion-related validity would be supported if the correlation between the STPIS (including its four sub-scales) and the C-TSES-SF (including its three sub-scales) is positive and substantial, as Zhang [59] and Hong et al. [60] verified that self-efficacy was significantly positively correlated with PI.\u003c/p\u003e \u003cp\u003eLastly, the reliability of C-TSES-SF and its sub-scales was calculated using Cronbach alpha\u0026rsquo;s coefficient and composite reliability values (ρ) [61] for all participants (n\u0026thinsp;=\u0026thinsp;402). Internal consistency was classified as follows: α\u0026thinsp;\u0026gt;\u0026thinsp;0.7, acceptable; 0.9\u0026thinsp;\u0026gt;\u0026thinsp;α\u0026thinsp;\u0026gt;\u0026thinsp;0.8, good; α\u0026thinsp;\u0026gt;\u0026thinsp;0.9, perfect [62]. Regarding ρ, an acceptable range is 0.60\u0026thinsp;\u0026le;\u0026thinsp;ρ\u0026thinsp;\u0026ge;\u0026thinsp;0.70, a satisfactory range is 0.70\u0026thinsp;\u0026le;\u0026thinsp;ρ\u0026thinsp;\u0026ge;\u0026thinsp;0.90, and a perfect range is ρ\u0026thinsp;\u0026ge;\u0026thinsp;0.90 [63].\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePreliminary analyses\u003c/h2\u003e \u003cp\u003eThe normality of the sample was examined before conducting CFA. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents descriptive statistics for all 12 items of the scale. Both skewness and kurtosis were in the range of -1 to +\u0026thinsp;1, indicating that the data distributions of the TSES-SF were normal [64, 65].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics for the TSES-SF\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eSample (n\u0026thinsp;=\u0026thinsp;402)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eConstruct validity\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eThe preliminary model fit\u003c/h2\u003e \u003cp\u003eThe KMO statistic and Bartlett\u0026rsquo;s sphericity test outcomes illustrated that the factor analysis was appropriate, as KMO\u0026thinsp;=\u0026thinsp;0.94 and Bartlett test, χ\u003csup\u003e2\u003c/sup\u003e (66)\u0026thinsp;=\u0026thinsp;3131.39 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). According to the CFA outcomes (n\u0026thinsp;=\u0026thinsp;402), the preliminary fit of the first five models was good, except for Model 6. All error variances from e1 to e11 or e12 among the first five models were positive and significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, in Model 6, the error variance e10 was negative (-1.45), and the p-values of e6 and e10 were not significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The t-values of e1 to e11 or e12 in the six models ranged from 8.65 to 13.50 (Model 1: 12.50 to 13.50, Model 2: 11.78 to 13.28, Model 3: 10.60 to 12.56, Model 4: 8.65 to 12.42, Model 5: 10.60 to 12.56, Model 6: -0.48 to 13.25). Standard errors of all parameters among the first five models ranged from 0.04 to 0.10 (Model 1: 0.04 to 0.10, Model 2: 0.04 to 0.10, Model 3: 0.04 to 0.08, Model 4: 0.04 to 0.08, Model 5: 0.04 to 0.08), and were not \u0026ldquo;very large\u0026rdquo;. However, in Model 6, the standard error of W7 (16.07) was more than 16 times the largest standard error in the first five models (0.10 in Model 1 and Model 2) and was \u0026ldquo;very large\u0026rdquo;. The standardized factor loadings between the latent variables and their measurement pointers among the first five models varied between 0.64 and 0.87 (Model 1: 0.64 to 0.80, Model 2: 0.66 to 0.82, Model 3: 0.71 to 0.84, Model 4: 0.71 to 0.87, Model 5: 0.69 to 0.87), meeting the criteria of greater than 0.50 and less than 0.95 [47]. However, in Model 6, the standardized factor loadings varied between 0.08 and 1.24, which did not meet the above criteria.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eThe overall model fit\u003c/h2\u003e \u003cp\u003eThe outcomes of the overall model fit, as indicated in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, showed that Model 4 displayed the best fit compared to the other five models. The IFIs were sufficient for all six models (ranging from 0.91 to 0.96\u0026thinsp;\u0026gt;\u0026thinsp;0.90). In Model 1 and Model 2, none of the indices except IFI were sufficient. In Model 3, the RMSEA was not satisfied (RMSEA\u0026thinsp;=\u0026thinsp;0.085\u0026thinsp;\u0026gt;\u0026thinsp;0.08). In Model 5, the RMSEA was insufficient (RMSEA\u0026thinsp;=\u0026thinsp;0.088\u0026thinsp;\u0026gt;\u0026thinsp;0.08). Both Model 4 and Model 6 showed relatively good fit according to the overall model fit indices. However, the differences in AIC and ECVI values led us to prefer Model 4. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e displays the factor structure of Model 4.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverall model fit for existing models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. of\u003c/p\u003e \u003cp\u003efactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e/df\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eIFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTLI (NNFI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eECVI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e337.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e385.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e314.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e364.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e197.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e251.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e142.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e192.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e168.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e218.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ebifactor model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e134.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e202.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003e\u003cem\u003eNote. Model 6 with the residual variance of item 1 set to 0.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eThe internal structure model fit\u003c/h2\u003e \u003cp\u003eRegarding the internal structure model fit, it was discovered that the individual item reliability of all observations in Model 3, Model 4, and Model 6 was adequate (Model 3: 0.50 to 0.71\u0026thinsp;\u0026gt;\u0026thinsp;0.50, Model 4: 0.51 to 0.76\u0026thinsp;\u0026gt;\u0026thinsp;0.50, Model 6: 0.53 to 2.07\u0026thinsp;\u0026gt;\u0026thinsp;0.50). In contrast, some observations in Model 1, Model 2, and Model 5 were not satisfied (Model 1: 0.41 to 0.62, Model 2: 0.43 to 0.67, Model 5: 0.48 to 0.76). As illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the CR of the three latent variables in Model 3, Model 4, and Model 5 all met the criterion of greater than 0.70 [54]. The AVE of the three latent variables in Model 3, Model 4, and Model 5 also satisfied the criterion of greater than 0.50 [47]. However, in Model 1 (CR\u0026thinsp;=\u0026thinsp;0.94, AVE\u0026thinsp;=\u0026thinsp;0.56), Model 2, and Model 6, these indices were not met. The analysis revealed that the internal structure fit of Model 3 and Model 4 was good.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAVE and CR for existing models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo recap, considering that only Model 4 fully met all indices of the preliminary fit, overall model fit, and internal structure model fit, Model 4 was considered the best representation of the C-TSES-SF among EC-PSTs and was employed in the subsequent analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement invariance\u003c/h2\u003e \u003cp\u003eAccording to the results of the multigroup CFA analysis on Model 4 (C-TSES-SF), summarized in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the ΔCFI and ΔRMSEA values of configural, metric, scalar, and residual invariance across gender, age, and college year were all less than or equal to the thresholds of 0.01 and 0.015, respectively (gender: ΔCFI ranged from 0.001 to 0.004\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ΔRMSEA ranged from 0.001 to 0.004\u0026thinsp;\u0026lt;\u0026thinsp;0.015; age: ΔCFI ranged from 0.001 to 0.01\u0026thinsp;\u0026le;\u0026thinsp;0.01, ΔRMSEA ranged from 0.001 to 0.003\u0026thinsp;\u0026lt;\u0026thinsp;0.015; college year: ΔCFI\u0026thinsp;=\u0026thinsp;0.002\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ΔRMSEA ranged from 0.004 to 0.005\u0026thinsp;\u0026lt;\u0026thinsp;0.015). This suggested that configural, metric, scalar, and residual invariance were established across gender, age, and college year.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMeasurement invariance across gender, age, and college year\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eΔχ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eΔdf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eΔCFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eΔRMSEA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConfigural invariance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e188.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetric invariance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e201.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScalar invariance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e210.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual invariance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e240.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.37\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConfigural invariance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e235.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetric invariance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e241.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScalar invariance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e256.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual invariance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e301.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44.47\u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege Year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConfigural invariance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e244.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetric invariance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e247.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScalar invariance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e251.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual invariance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e264.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cem\u003eNote: Model comparison.\u003c/em\u003e \u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e, \u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eConcurrent validity and convergent validity\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows that all three C-TSES-SF sub-scales had statistically significant high-level correlations, with r ranging from 0.69 to 0.73 (below 0.80). This finding supports the concurrent validity among the three C-TSES-SF sub-scales. Furthermore, the results in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e demonstrated that the convergent validity of the C-TSES-SF was satisfactory, with significant extremely high-level correlations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) between the C-TSES-SF and its three sub-scales (IS: r\u0026thinsp;=\u0026thinsp;0.90, CM: r\u0026thinsp;=\u0026thinsp;0.89, SE: r\u0026thinsp;=\u0026thinsp;0.92).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelations between the C-TSES-SF and sub-scales\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC-TSES-SF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-TSES-SF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.90\u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.89\u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69\u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.92\u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.73\u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.76\u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote.\u003c/em\u003e \u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003cem\u003ep\u0026thinsp;\u0026le;\u0026thinsp;0.001.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCriterion-related validity\u003c/h2\u003e \u003cp\u003eRegarding the criterion-related validity of the C-TSES-SF, the results (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) indicated that all factors from the C-TSES-SF were significantly positively correlated with the STPIS scale and its sub-scales. Specifically, STPIS, PE, and PW exhibited a significant large correlation strength with C-TSES-SF, IS, CM, and SE respectively. PVa showed a significant large correlation strength with C-TSES-SF and CM, and a medium correlation strength with IS and SE. PVo displayed a significant large correlation strength with C-TSES-SF, CM, and SE, and a significant medium correlation strength with IS. These findings support the criterion-related validity of the C-TSES-SF.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelations between the C-TSES-SF and STPIS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC-TSES-SF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTPIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.56\u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.45\u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.62\u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.48\u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePVa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.30\u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.55\u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.45\u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.63\u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.44\u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.39\u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.34\u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.39\u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.33\u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePVo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.33\u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.36\u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote.\u003c/em\u003e \u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003cem\u003ep\u0026thinsp;\u0026le;\u0026thinsp;0.001.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eReliability\u003c/h2\u003e \u003cp\u003eThe C-TSES-SF\u0026rsquo;s Cronbach\u0026rsquo;s alpha coefficient was 0.93, and its sub-scales internal consistency scores were IS (α\u0026thinsp;=\u0026thinsp;0.86), CM (α\u0026thinsp;=\u0026thinsp;0.86), and SE (α\u0026thinsp;=\u0026thinsp;0.85), respectively. In addition, the ρ-values were 0.92 for C-TSES-SF, 0.81 for IS, 0.81 for CM, and 0.80 for SE individually. These results indicated that the C-TSES-SF\u0026rsquo;s internal consistency reliability and composite reliability were perfect, and the internal consistency reliability and composite reliability of its three sub-scales were good.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study addressed a gap in the literature by adapting a C-TSES-SF and examining its factor structure, validity, measurement invariance, and reliability in the context of Chinese EC-PSTs. Our findings suggest that the TSES-SF is best represented by a modified three-factor model for EC-PSTs. The psychometric properties of the C-TSES-SF were robust, validating its use for assessing the TSE of Chinese EC-PSTs across different genders, ages, and college years. These findings enhance the cross-cultural applicability of the TSES scale and understanding of TSE among Chinese EC-PSTs.\u003c/p\u003e \u003cp\u003eGiven that cultural factors have a deep impact on TSE [39], we employed the standard double-back translation method to translate the TSES-SF scale from English to Chinese to minimize translation-related validity and reliability issues. Considering the specific characteristics of ECE in China, where children aged 3 to 6 years attend kindergartens, we replaced \u0026ldquo;students/student\u0026rdquo; with \u0026ldquo;幼儿\u0026rdquo; (children/child) in items 2, 3, 5, 7, 8, 9, 10, and 11, and \u0026ldquo;school\u0026rdquo; with \u0026ldquo;幼儿园\u0026rdquo; (kindergarten) in item 12. Additionally, recognizing that ECE does not have academic performance requirements and uses play-based pedagogies [66] and activities, we replaced \u0026ldquo;schoolwork\u0026rdquo; with \u0026ldquo;教育教学活动\u0026rdquo; (educational and instructional activities) in items 9 and 11. Overall, the present study may yield some practical significance for ECE, as these modifications ensured the translated TSES-SF accurately conveyed its intended meaning in Chinese and was appropriate for use in ECE settings.\u003c/p\u003e \u003cp\u003eWhile previous studies have considered various rival models for TSES-SF, including single-factor, two-factor, three-factor, and bifactor models [2, 36, 38, 39, 41], we evaluated the construct validity of TSES-SF using more stringent criteria (preliminary fit, overall fit, and internal structure fit). This approach may be more advantageous as it provides detailed information regarding the model\u0026rsquo;s structure. Our results indicate that TSE of Chinese EC-PSTs is best represented by a modified three-factor model excluding item 8. This suggests that the TSE factor structure among EC-PSTs is also multi-dimensional as that of general ISTs. Our findings align with previous studies on ISTs in four Asian countries [36, 40] and PSTs in Spain [4]. The poor fit of item 8 may be because it is not common to group children for varied instruction in China [36] due to the sociocultural characteristics of the Chinese education system, such as collectivist culture and whole-group teaching [67]. This finding is noteworthy as it highlights that cultural values, educational contexts, and professional backgrounds may influence the appropriateness of the TSES [39, 42]. Our findings offer new insights into the factor structure of TSES-SF, suggesting that the structure of TSES-SF is stable within Asian cultures due to certain cultural commonalities.\u003c/p\u003e \u003cp\u003eWe then assessed the measurement invariance of the C-TSES-SF across gender, age, and college year using multigroup CFA. The results indicated robust measurement invariance across gender, consistent with a prior study on Spanish PSTs [4]. Although our male sample was small, CFI and RMSEA are less sensitive to sample size [56], making sample size less critical for measuring invariance [68]. Compared to previous studies, our findings further revealed measurement invariance across age and college year, indicating that there are no differences in the C-TSES-SF\u0026rsquo;s three-factor model (Model 4) across various ages and college years among Chinese EC-PSTs. Given the measurement invariance is a prerequisite of the comparison of group means [68], our findings are meaningful as they provide evidence for comparisons of TSE across gender, age, and college year for EC-PSTs in China. Moreover, our outcomes also provide the feasibility of evaluating the effectiveness of relevant targeted intervention measures.\u003c/p\u003e \u003cp\u003eThe convergent validity and criterion-related validity of C-TSES-SF, as well as concurrent validity among its three sub-scales, were evaluated using Pearson correlation analyses. Our results demonstrated sufficient convergent and concurrent validity. However, the correlation coefficients of C-TSES-SF with its three factors, and between its three sub-scales, were higher than those in previous studies on ISTs [2, 40], indicating stronger psychometric properties for the C-TSES-SF. Additionally, our findings present evidence for the criterion-related validity of C-TSES-SF by validating the correlation between the STPIS and the C-TSES-SF. Compared to previous studies [4, 26, 33, 40, 41, 69], our results enhance the understanding of TSES\u0026rsquo;s criterion validity in EC-PSTs. This is achieved by introducing a new questionnaire to verify the TSES\u0026rsquo;s criterion-related validity, building on earlier research that demonstrated a correlation between self-efficacy and PI [59, 60].\u003c/p\u003e \u003cp\u003eThe α and ρ values provide evidence for the good reliability of the scale. The ρ values were consistent with those obtained in a prior study among PSTs in Spain [4]. However, the α values in this study were higher than those in previous studies among ISTs [2, 33, 40], indicating superior internal consistency in this study and contributing new evidence to knowledge about the internal consistency of TSES-SF among ISTs and PSTs. Our findings indicate that TSES-SF is applicable in different cultural and teacher contexts.\u003c/p\u003e \u003cp\u003eFinally, our EC-PST participants demonstrated lower TSE (M\u003csub\u003ePSTs\u003c/sub\u003e = 5.87\u0026thinsp;\u0026lt;\u0026thinsp;M\u003csub\u003eISTs\u003c/sub\u003e = 6.83) compared to EC-ISTs in Hong Kong [70], potentially due to the more practical experience of ISTs positively affecting their TSE. This explanation is speculative, and it is important to note that a comparison of specific factors between ISTs and PSTs cannot be realized due to the use of a two-factor structure in the TSES-SF adopted for ISTs. Therefore, future research should further collect qualitative data from EC-ISTs to better understand the reasons behind this difference.\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations. Firstly, the evaluation of TSE among EC-PSTs in Hainan province was conducted using a cross-sectional survey research methodology. Random sampling using the probability technique should be used in the future to ensure the sample covers a wider range and different educational levels to improve the generalizability of the study findings. Secondly, we did not assess the psychometric properties of the long-form TSES, which could be considered in future research to facilitate further cross-cultural comparative studies among EC-PSTs using different forms of TSES.\u003c/p\u003e \u003c/div\u003e "},{"header":"Conclusion and Contribution","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003cp\u003eThis study fills a gap in understanding the TSE of EC-PSTs by providing robust empirical evidence on the factor structure, validity, measurement invariance, and reliability of the C-TSES-SF. Importantly, our findings enhance the cross-cultural applicability of the TSES, demonstrating its appropriateness in Chinese-speaking contexts. Additionally, our findings have significant implications for ECE by offering a reliable tool for teacher education programs to dynamically assess TSE in EC-PSTs. This enables targeted interventions to enhance the teaching effectiveness and overall quality of EC-PSTs.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eTSE Teachers\u0026rsquo; sense of efficacy\u003c/p\u003e \u003cp\u003eTSES Teachers\u0026rsquo; Sense of Efficacy Scale\u003c/p\u003e \u003cp\u003eTSES-SF Teachers\u0026rsquo; Sense of Efficacy Scale Short Form\u003c/p\u003e \u003cp\u003eC-TSES-SF Chinese Version of Teachers\u0026rsquo; Sense of Efficacy Scale Short Form\u003c/p\u003e \u003cp\u003eIS Efficacy for instructional strategies\u003c/p\u003e \u003cp\u003eCM Efficacy for classroom management\u003c/p\u003e \u003cp\u003eSE Efficacy for student engagement\u003c/p\u003e \u003cp\u003eTS Teaching and Support\u003c/p\u003e \u003cp\u003ePI Professional identity\u003c/p\u003e \u003cp\u003eSTPIS Student Teacher Professional Identity Scale\u003c/p\u003e \u003cp\u003ePW Professional willingness\u003c/p\u003e \u003cp\u003ePVa Professional values\u003c/p\u003e \u003cp\u003ePE Professional efficacy\u003c/p\u003e \u003cp\u003ePVo Professional volition\u003c/p\u003e \u003cp\u003ePSTs Pre-service teachers\u003c/p\u003e \u003cp\u003eEC-PSTs Early childhood pre-service teachers\u003c/p\u003e \u003cp\u003eISTs In-service teachers\u003c/p\u003e \u003cp\u003eEC-ISTs Early childhood in-service teachers\u003c/p\u003e \u003cp\u003eECE Early childhood education\u003c/p\u003e \u003cp\u003eSD Standard deviation\u003c/p\u003e \u003cp\u003eM Mean\u003c/p\u003e \u003cp\u003eCFA Confirmatory factor analysis\u003c/p\u003e \u003cp\u003eKMO Kaiser-Meyer-Olkin\u003c/p\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e/df Chi-square by degrees of freedom ratio\u003c/p\u003e \u003cp\u003eCFI Comparative fit index\u003c/p\u003e \u003cp\u003eTLI (NNFI) Tucker-Lewis coefficient (Bentler-Bonett non-normed fit index)\u003c/p\u003e \u003cp\u003eRMSEA Root mean square error of approximation\u003c/p\u003e \u003cp\u003eGFI Goodness of fit index\u003c/p\u003e \u003cp\u003eNFI Normed fit index\u003c/p\u003e \u003cp\u003eIFI Incremental fit index\u003c/p\u003e \u003cp\u003eAIC Akaike information criterion\u003c/p\u003e \u003cp\u003eECVI Expected cross validation index\u003c/p\u003e \u003cp\u003eAVE Average variance extracted\u003c/p\u003e \u003cp\u003eCR Composite reliability\u003c/p\u003e \u003cp\u003eΔCFI CFA change\u003c/p\u003e \u003cp\u003eΔRMSEA RMSEA change\u003c/p\u003e \u003cp\u003eΔdf df change\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBefore data collection, this study obtained approval from the ethical committee of Universiti Putra Malaysia (Project ID: JKEUPM-2023-323) as well as permission from the\u0026nbsp;sampled\u0026nbsp;normal university (Qiongtai Normal University). All\u0026nbsp;participants voluntarily signed informed consent forms.\u0026nbsp;This study was conducted in accordance with the principles expressed in the Declaration of Helsinki (2008). Additionally, provisions from the Nuremberg Code of 1946, pertinent to the gathering of information in social science fields, were also utilized in the present set of guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUpon reasonable request, the data used are available from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Hainan Province\u0026rsquo;s \u0026ldquo;South China Sea Rising Star\u0026rdquo; Education Platform Project (JYNHXX2023-24T), Educational and Teaching Reform of Hainan\u0026rsquo;s Higher Education Institutions Research Project (Hnjgzc2022-67),\u0026nbsp;Hainan Province Philosophy and Social Sciences Planning Project\u0026nbsp;[HNSK(ZC)22-157], and\u0026nbsp;China National Social Science Fund Education Youth Project (CGA200246).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSMX designed the study. MMM and NJMN gave methodological guidance. NJMN,\u0026nbsp;SXC, and YGQ involved in the double back translation of the scale. SXC collected the data and performed the preliminary analysis of the data. SMX completed follow-up data analysis and wrote the manuscript. MMM and FR supervised the manuscript. All authors approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBandura A. Self-efficacy: the Exercise of Control. NY: Freeman; 1997.\u003c/li\u003e\n\u003cli\u003eTschannen-Moran M, Hoy AW. Teacher Efficacy: Capturing an Elusive Construct. Teaching and Teacher Education. 2001;17 (7): 783-22.\u003c/li\u003e\n\u003cli\u003ePlourde LA. The influence of student teaching on pre-service elementary teachers\u0026rsquo; science self-efficacy and outcome expectancy beliefs. 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Early Education and Development. 2023; doi:10.1080/10409289.2023.2243187.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Teachers’ sense of efficacy, Teachers’ sense of efficacy scale, Early childhood pre-service teachers, Psychometric properties","lastPublishedDoi":"10.21203/rs.3.rs-4868390/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4868390/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTeachers\u0026rsquo; sense of efficacy (TSE) is a crucial construct for evaluating the quality of pre-service teachers. While the Teachers\u0026rsquo; Sense of Efficacy Scale (TSES) is the most widely used and promising instrument for measuring TSE, there is no existing literature assessing the appropriateness of the TSES for early childhood pre-service teachers in China. This study aimed to translate the English version of the TSES into Chinese and test its factor structure, validity, measurement invariance across gender, age, and college year, as well as reliability.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study used a cross-sectional design. The sample included 402 participants in China. The TSES was translated into Chinese using the standard back-to-back translation method. The psychometric properties of the TSES, including construct validity, concurrent validity, convergent validity, criterion-related validity, measurement invariance, internal consistency reliability, and composite reliability, were examined.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCFA results indicated that the TSES is best represented by a modified three-factor model, demonstrating strong preliminary, overall, and internal structure fit. The concurrent validity, convergent validity, criterion-related validity, internal consistency reliability, and composite reliability of the Chinese version TSES were robust. The measurement invariance across gender, age, and college year was also confirmed.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study addresses a gap in the literature by providing robust empirical evidence on the factor structure, validity, measurement invariance, and reliability of the Chinese version of the TSES for early childhood pre-service teachers, thereby enhancing understanding of TSE in Chinese-speaking context.\u003c/p\u003e","manuscriptTitle":"The Chinese Adaptation of the Teachers’ Sense of Efficacy Scale in Early Childhood Pre-service Teachers: Validity, Measurement Invariance, and Reliability","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-16 00:43:59","doi":"10.21203/rs.3.rs-4868390/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6f0a6d0e-5a31-4ddf-8cba-1998f2aa7513","owner":[],"postedDate":"September 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-03T06:23:41+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-16 00:43:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4868390","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4868390","identity":"rs-4868390","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}