Fit Of Structural Equation Models Research Articles

The relationship between childhood adversity and sleep quality among rural older adults in China: the mediating role of anxiety and negative coping

BackgroundStudies have revealed the effects of childhood adversity, anxiety, and negative coping on sleep quality in older adults, but few studies have focused on the association between childhood adversity and sleep quality in rural older adults and the potential mechanisms of this influence. In this study, we aim to evaluate sleep quality in rural older adults, analyze the impact of adverse early experiences on their sleep quality, and explore whether anxiety and negative coping mediate this relationship.MethodsData were derived from a large cross-sectional study conducted in Deyang City, China, which recruited 6,318 people aged 65 years and older. After excluding non-agricultural household registration and lack of key information, a total of 3,873 rural older adults were included in the analysis. Structural equation modelling (SEM) was used to analyze the relationship between childhood adversity and sleep quality, and the mediating role of anxiety and negative coping.ResultsApproximately 48.15% of rural older adults had poor sleep quality, and older adults who were women, less educated, widowed, or living alone or had chronic illnesses had poorer sleep quality. Through structural equation model fitting, the total effect value of childhood adversity on sleep quality was 0.208 (95% CI: 0.146, 0.270), with a direct effect value of 0.066 (95% CI: 0.006, 0.130), accounting for 31.73% of the total effect; the total indirect effect value was 0.142 (95% CI: 0.119, 0.170), accounting for 68.27% of the total effect. The mediating effects of childhood adversity on sleep quality through anxiety and negative coping were significant, with effect values of 0.096 (95% CI: 0.078, 0.119) and 0.024 (95% CI: 0.014, 0.037), respectively. The chain mediating effect of anxiety and negative coping between childhood adversity and sleep quality was also significant, with an effect value of 0.022 (95% CI: 0.017, 0.028).ConclusionsAnxiety and negative coping were important mediating factors for rural older adult’s childhood adversity and sleep quality. This suggests that managing anxiety and negative coping in older adults may mitigate the negative effects of childhood adversity on sleep quality.

Improvement of the GFI and AGFI indexes in structural equation models by the covariance between repetitions in the observed variables

Although there are methods that relate a set of repeated observed variables to the fit of structural equation models, there are some deficiencies in the quality-of-fit indexes proposed in the literature, as they do not include characteristics of data with repeated observed variables. Thus, this study aims to propose an improvement in the GFI and AGFI indexes by incorporating a correction that considers repetitions in the observed variables. Several designs are considered to validate this proposal as a function of the criteria that determine the quality of fit, as well as the presence of outliers. In this context, the improvement is given to the indexes built in the least square and likelihood approaches, and their advantages and disadvantages are compared with conventional indexes using the Monte Carlo simulation. The improvement of the GFI and AGFI indexes to incorporate the effect of repetitions of the observed variables was promising, as it presented robustness properties relative to the presence of outliers and showed diagnostic evidence of a model with good and poor fit. It is recommended to use up to four repetitions to avoid numerical problems when computing the index estimates.

Exploring Fashion Hair Dyeing Behavior through the Stimulus-Response Model: The Mediating Effects of Professional Technology and Transaction Interaction

This study was conducted to explore hair dyeing behaviors among Taiwanese adolescents; analyze future market trends for fashion hair dyes; investigate the effects of social media, fashion trends, and product awareness on behavioral intentions; identify the mediating effects of professional technology and transaction interactions on behavioral intentions; and validate the overall fit of a model outlining the interrelationships between the aforementioned factors.This study was conducted in general and vocational schools across northern, central, and southern Taiwan. Students were surveyed between February 7, 2023, and July 21, 2023.
 Drawing inspiration from the Stimulus-Organism-Response theory, we constructed a comprehensive model incorporating six dimensions—social media, fashion trends, product awareness, transaction interaction, professional technology, and behavioral intention. The survey gathered 370 valid responses, primarily from female students in technical senior high schools. Students’ responses on a 32-item questionnaire were rated on a 5-point Likert scale. Reliability analysis, partial least squares structural equation modeling, and validity analysis were performed to assess the study model.Our findings revealed a high reliability and a good fit in structural equation modeling. Validity analysis confirmed convergent and discriminant validity. Bootstrapping revealed substantial differences between ordinary and technical high schools in hair dyeing behaviors. Professional technology mediated the correlation between fashion trends and behavioral intentions and that between product awareness and behavioral intentions.This study advances our theoretical understanding and offers valuable practical insights for businesses in the hair care industry. The application of the Stimulus-Organism-Response theory revealed adolescents’ complex hair dyeing behaviors. The observed model fit indicates the relevance of this framework in explaining the multifaceted dynamics of consumer decision-making. Regarding practical benefits, this study guides businesses toward natural hair dyes aligned with consumers’ health and environmental preferences.

Öğrencilerin Yazma Kaygıları, Yazma Motivasyonları ve Akademik Motivasyonlarına İlişkin Bir Yapısal Eşitlik Modeli

Writing anxiety causes various problems in the writing process, makes it difficult for the person to produce text and can prevent the act of writing. Writing motivation supports writing skills by affecting the writing process positively. The purpose of this research is to determine relationships between students' writing anxiety, writing motivation and academic motivation. The research was carried out with the correlational desing in the quantitative method. The population of this research consist of secondary school students and the sample consist of 1037 secondary school students, 577 of whom are females (55.64%) and 460 of them are males (44.36%) in Elazig province centre in 2020-2021 academic year. As a result of the correlation test, hierarchical regression analysis, it was determined that students' writing anxiety meaningful was predicted the writing motivation (r=-.65/strong) and academic motivations (r=-.37/medium) by 53%. Structural equation model fit was verified. While writing process, avoidance and writing pleasure latent variables explain 11% of consubstantiated extrinsic motivation, this rate increased to 45% when writing motivation was added to the equality. Although writing process, avoidance and writing pleasure variables explained 39% of the unmotivation and 32% of the extrinsic motivation reflected internal, it was observed that the regression coefficient for the two equations increased to 70% with the addition of writing motivation as the predictive variable.

Direct and indirect effects of adiposity on markers of autonomic nervous system activity in older adults.

Several cardiovascular disease (CVD) risk factors (e.g., hypertension, poor glycemic control) can affect and be affected by autonomic nervous system (ANS) activity. Since excess adiposity can influence CVD development through its effect on hypertension and diabetes mellitus, it is important to determine how adiposity and altered ANS activity are related. The present study employed structural equation modeling to investigate the relation between adiposity and ANS activity both directly and indirectly through biological variables typically associated with glycemic impairment and cardiac stress in older adults. Utilizing the Atherosclerosis Risk in Communities (ARIC) dataset, 1,145 non-smoking adults (74±4.8 yrs, 62.8% female) free from known CVD, hypertension, and diabetes and not currently taking beta-blockers were evaluated for fasting blood glucose (FBG), insulin, and HbA1c concentrations, waist circumference (WC), blood pressure (BP), and markers of ANS activity. WC was recorded just above the iliac crest and was used to reflect central adiposity. Resting 2-minute electrocardiograph recordings, pulse wave velocity, and ankle-brachial index data were used to assess the root mean square of successive differences in RR intervals (RMSSD) and the pre-ejection period (PEP), markers of parasympathetic and sympathetic activity, respectively. FBG, insulin, and HbA1c inferred a latent variable termed glycemic impairment (GI), whereas heart rate and diastolic BP inferred a latent variable termed cardiac stress (CS). The structural equation model fit was acceptable [root mean square error of approximation = 0.050 (90% CI = .036, .066), comparative fit index = .970, Tucker Lewis Index = 0.929], with adiposity having both significant direct (β = 0.208, p = 0.018) and indirect (β = -.217, p = .041) effects on PEP through GI. Adiposity displayed no significant direct effect on RMSSD. CS displayed a significant pathway (β = -0.524, p = 0.035) on RMSSD, but the indirect effect of WC on RMSSD through CS did not reach statistical significance (β = -0.094, p = 0.137). These results suggest that adiposity's relation to ANS activity is multifaceted, as increased central adiposity had opposing direct and indirect effects on markers of sympathetic activity in this population of older adults.

Analysis of Weather Prediction, Resource Management, and Land Optimization on the Application of Big Data Analytics in Agricultural Land Utilization in Agrarian Areas of West Java

This research investigates the impact of Weather Prediction, Resource Management, and Land Optimization on the adoption of Big Data Analytics in agricultural land utilization within the agrarian region of West Java. Employing a quantitative approach, the study integrates measurement model analysis, structural equation modeling, demographic profiling, and model fit assessment to comprehensively explore the intricate dynamics of technological adoption in agriculture. Results indicate that Land Optimization, Resource Management, and Weather Prediction significantly influence the adoption of Big Data Analytics. Demographic factors such as gender, age, education, and farming experience demonstrate varying correlations with key variables. The model exhibits strong fit, and approximately 60.2% of the variance in Big Data Analytics adoption is explained by the combined influence of the identified factors. This study contributes nuanced insights to inform policy and practice for sustainable and technology-driven agriculture in West Java.

Mathematics study habits through an ethnomathematics approach and entrepreneurial behavior of mathematics education students

This research aims to determine whether there is a positive correlation between mathematics learning habits through an ethnomathematics approach and entrepreneurial behavior. This research is survey research conducted by mathematics education students in Bengkulu and Lubuklinggau. The sample for this research consisted of 70 students. There are two questionnaires as research instruments. Both are a questionnaire on mathematics learning habits using an ethnomathematics approach, and an entrepreneurial behavior questionnaire. The result of this research is that the complete structural equation model fit test is a good fit, which means that the empirical structural equation model is suitable with the theoretical (conceptual) structural equation model. The results of the hypothesis test show that the calculated t value is 11.364 > 1.96, which means Ho is rejected, meaning that this research hypothesis is accepted. The conclusion is that there is a positive correlation between mathematics learning habits through an ethnomathematics approach and entrepreneurial behavior.

Testing structural equation model fit in psychological studies: A replication study using equivalence testing

Testing structural equation model fit in psychological studies: A replication study using equivalence testing

Designing and Validation of the Leadership Competency Scale in Medicine

Background: Leadership competencies in medicine are the key factors in improving the performance of physicians and the health system. The aim of the present study was to design and validate the leadership competencies scale in medicine.
 Methods: This research was conducted with a qualitative-quantitative approach. The research sample consisted of 12 physician managers in the qualitative stage and 15 people in the content validity evaluation stage using purposive sampling method. In the preliminary implementation stage of the questionnaire, 35 people were selected by convenience sampling and the main implementation stage of the questionnaire 306 students, faculty members, and medical graduate managers were selected using relative stratified random method. Qualitative data were collected by semi-structured interview and quantitative data by questionnaire. To analyze the qualitative data, the Esterberg analysis method was used, and to evaluate the construct validity, confirmatory factor analysis with Smart-PLS3 software was used. To evaluate the validity of quantitative data, content validity index, content validity ratio, Cronbach's alpha coefficient, convergent validity, and structural equation model fit indices were used. The criteria of credibility, transferability, dependability, and conformability were used using triangulation to evaluate the validity of the qualitative research data.
 Results: Competencies and parameters of the questionnaire were extracted into 17 competences and 123 concepts through the coding of interview texts and converted into a questionnaire in 101 items. The content validity of 100 items was valid. The results of factor analysis showed that the factor load of 99 items was valid and 17 components explained the structure of leadership competencies. Based on the fit indices of the model, the measurement scale of leadership competencies in medicine was valid in 17 components and 99 items. These components include perception and impression management, professional ethics, conflict management, problem solving, discipline, informal organization, public communication skills, interpersonal relationship skills, listening, cultural management, professional culture, innovation and creativity, motivation, emotional intelligence, change management, feeling management, participative management.
 Conclusion: Attention to the curriculum of leadership competencies in medical education and the application of this scale can improve the quality of leadership in the health system.

Impact of Socialization Tactics on Socialization-Specific Adjustment Via PsyCap: A Lens of COR Theory

Based on conservation of resources (COR) theory, this study proposes an integrative model, whereby socialization caravan passageways—socialization tactics—shape a resource caravan of PsyCap (psychological capital) that newcomers rely upon to achieve socialization-specific adjustment outcomes. SmartPLS 3.0 is mainly used to analyze the data and this model exhibits acceptable levels of structural equation modeling (SEM) fit indices in a sample of 368 respondents from 16 luxury hotels in Hainan Province, China. Study results indicate that socialization tactics have a positive influence on PsyCap, which in turn affects all six adjustment outcomes including task mastery, fitting in, standing out, role negotiation, membership identification, and interpersonal relationships. Our findings are informative and valuable because socialization resources theory (SRT) represents a new approach to capturing the dynamics of newcomer socialization phenomenon with important implications for theory, research, and practice.

Evaluating the Effect of Planned Missing Designs in Structural Equation Model Fit Measures

In a planned missing design, the nonresponses occur according to the researcher’s will, with the goal of increasing data quality and avoiding overly extensive questionnaires. When adjusting a structural equation model to the data, there are different criteria to evaluate how the theoretical model fits the observed data, with the root mean square error of approximation (RMSEA), standardized root mean square residual (SRMR), comparative fit index (CFI) and Tucker–Lewis index (TLI) being the most common. Here, I explore the effect of the nonresponses due to a specific planned missing design—the three-form design—on the mentioned fit indices when adjusting a structural equation model. A simulation study was conducted with correctly specified model and one model with misspecified correlation between factors. The CFI, TLI and SRMR indices are affected by the nonresponses, particularly with small samples, low factor loadings and numerous observed variables. The existence of nonresponses when considering misspecified models causes unacceptable values for all the four fit indexes under analysis, namely when a strong correlation between factors is considered. The results shown here were performed with the simsem package in R and the full information maximum-likelihood method was used for handling missing data during model fitting.

Equivalence testing to judge model fit: A Monte Carlo simulation.

Decades of published methodological research have shown the chi-square test of model fit performs inconsistently and unreliably as a determinant of structural equation model (SEM) fit. Likewise, SEM indices of model fit, such as comparative fit index (CFI) and root-mean-square error of approximation (RMSEA) also perform inconsistently and unreliably. Despite rather unreliable ways to statistically assess model fit, researchers commonly rely on these methods for lack of a suitable inferential alternative. Marcoulides and Yuan (2017) have proposed the first inferential test of SEM fit in many years: an equivalence test adaptation of the RMSEA and CFI indices (i.e., RMSEAt and CFIt). However, the ability of this equivalence testing approach to accurately judge acceptable and unacceptable model fit has not been empirically tested. This fully crossed Monte Carlo simulation evaluated the accuracy of equivalence testing combining many of the same independent variable (IV) conditions used in previous fit index simulation studies, including sample size (N = 100-1,000), model specification (correctly specified or misspecified), model type (confirmatory factor analysis [CFA], path analysis, or SEM), number of variables analyzed (low or high), data distribution (normal or skewed), and missing data (none, 10%, or 25%). Results show equivalence testing performs rather inconsistently and unreliably across IV conditions, with acceptable or unacceptable RMSEAt and CFIt model fit index values often being contingent on complex interactions among conditions. Proportional z-tests and logistic regression analyses indicated that equivalence tests of model fit are problematic under multiple conditions, especially those where models are mildly misspecified. Recommendations for researchers are offered, but with the provision that they be used with caution until more research and development is available. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Correlates of musculoskeletal pain and kinesiophobia in older adults with heart failure: A structural equation model

The study aimed to study the influence of musculoskeletal pain on kinesiophobia in patients with heart failure. This cross-sectional study recruited 107 heart failure patients aged 73.18±12.68 years (57% men) from an outpatient setting. Participants self-reported pain using the Musculoskeletal System Assessment Inventory and the Cornell Musculoskeletal Discomfort Questionnaire. Kinesiophobia was assessed with the Tampa Scale for Kinesiophobia-11. About 62% reported musculoskeletal pain, with knees (16.8%) and lower back (12.%) being the most painful locations. About 31% reported moderate levels and 24% indicated high levels of kinesiophobia. There were positive and significant associations between the indicators of pain and kinesiophobia. Results showed an adequate structural equation model fit to the data with musculoskeletal pain factors explaining 22.09% of the variance in kinesiophobia. Assessment of kinesiophobia in patients with heart failure with musculoskeletal pain is essential to improve self-care and overall quality of life.

Anxiety in pregnancy and length of gestation: Findings from the healthy babies before birth study.

Anxiety is prevalent in pregnancy and predicts risk of adverse birth outcomes. Many instruments measure anxiety in pregnancy, some of which assess pregnancy anxiety defined as maternal concerns about a current pregnancy (e.g., baby, childbirth). The present study examined covariance among four anxiety or distress measures at two times in pregnancy and tested joint and individual effects on gestational length. We hypothesized that the common variance of the measures in each trimester would predict earlier delivery. Research staff interviewed 196 women in first and third trimester utilizing a clinical screener of anxiety severity/impairment, two instruments measuring pregnancy anxiety, and one on prenatal distress. Birth outcomes and medical risk factors were obtained from medical records after birth. Structural equation modeling fit latent factors for each trimester from the four measures. Subsequent models tested whether the latent factors predicted gestational length, and unique effects of each measure. The third-trimester pregnancy anxiety latent factor predicted shorter gestational length adjusting for mother's age, education, parity, and obstetric risk. Scores on a four-item pregnancy-specific anxiety measure (PSAS) in third trimester added uniquely to prediction of gestational length. In first trimester, scores on the clinical screener (OASIS) uniquely predicted shorter gestational length whereas the latent factor did not. These results support existing evidence indicating that pregnancy anxiety is a reliable risk factor for earlier birth. Findings point to possible screening for clinically significant anxiety symptoms in the first trimester, and pregnancy-specific anxiety thereafter to advance efforts to prevent earlier delivery. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Investigation on the Influencing Factors Related to Quality of Life of Adult Epilepsy Patients in Wenzhou, China, Based on Structural Equation Model.

Objective The aim of the study is to investigate the influencing factors of quality of life in adult patients with epilepsy in Wenzhou in China. Methods A total of 190 patients who visited our hospital from July 2019 to February 2021 were included in the study. Demographic data and disease status were collected. Moreover, QOLIE-31, PSQI, ESS, HAMD-17, and GAD-7 were used in the questionnaire survey. Structural equation model fitting was used to analyze the influencing factors of quality of life in adult patients with epilepsy. Results The scores of the dimension of onset worry in men were greater than those of women (P = 0.049), and the scores of the dimension of life satisfaction were lower than women (P = 0.047). The scores of cognitive function decreased with age (P = 0.047). The scores of quality of life of unemployed and drinking patients significantly decreased (P < 0.05). The score of quality of life positively correlated with good economic status and family relations (P < 0.05). The score of emotional health increased first and then decreased with the course of the disease. With the decrease in seizure frequency and the extension of months without a seizure, the score of quality of life gradually increased. Furthermore, the structural equation model showed that health status was directly correlated to the quality of life of patients with epilepsy. Conclusion Male, unemployment, drinking, older age, and disease are negatively related to the quality of life in patients with epilepsy. However, good economic status, good family relations, and good colleague relationships are positively related to the quality of life.

Presenting a Model of Co-Dependency in Women with Addicted Spouses based on their Self-Differentiation with the Mediating Role of Emotional Cognitive Regulation

Aim: The aim of this study was to present a model of co-dependency in women with addicted spouses based on their differentiation with the mediating role of emotional cognitive regulation. Methods: The research method is cross-sectional correlation. The statistical population of this study consisted of all women with drug-addicted wives who referred to counseling centers, health centers and addiction treatment camps and meetings in Tehran in 1400, of which 188 people by cluster sampling they were selected in stages. In this study, tools of co-dependency (Span &amp; Fisher, 1990), self-differentiation (Skorn and Friedlander, 1998) and cognitive-emotional regulation (Garnfsky and Craig, 2006) were used. Amos-V8.8 software were used to analyze the data. Structural equation modeling was also used to answer the research hypotheses. Findings showed that the model has a good fit. Results: The results also showed that self-differentiation had a significant direct negative effect on co-dependency (P &lt;0.01). Self-differentiation had a direct positive effect on adaptive cognitive emotion regulation strategies (P &lt;0.01). Adaptive strategies for cognitive emotion regulation have a direct and negative effect on co-dependency (P &lt;0.01). The results showed that non-adaptive cognitive emotion regulation strategies had a direct and positive effect on co-dependency (P &lt;0.01). Self-differentiation also had an indirect effect on codependency through cognitive emotion regulation (β = -0.181; P = 0.01). Conclusion: Structural equation modeling fit indices confirmed the overall research model and the direct and indirect effects between the research variables were confirmed. Therefore, according to the results, it can be said that co-dependency is an important variable in predicting self-differentiation and cognitive regulation of emotion.

The Effect of Latent and Error Non-Normality on Measures of Fit in Structural Equation Modeling.

Prior studies investigating the effects of non-normality in structural equation modeling typically induced non-normality in the indicator variables. This procedure neglects the factor analytic structure of the data, which is defined as the sum of latent variables and errors, so it is unclear whether previous results hold if the source of non-normality is considered. We conducted a Monte Carlo simulation manipulating the underlying multivariate distribution to assess the effect of the source of non-normality (latent, error, and marginal conditions with either multivariate normal or non-normal marginal distributions) on different measures of fit (empirical rejection rates for the likelihood-ratio model test statistic, the root mean square error of approximation, the standardized root mean square residual, and the comparative fit index). We considered different estimation methods (maximum likelihood, generalized least squares, and (un)modified asymptotically distribution-free), sample sizes, and the extent of non-normality in correctly specified and misspecified models to investigate their performance. The results show that all measures of fit were affected by the source of non-normality but with varying patterns for the analyzed estimation methods.

대학운동선수의 자기관리 척도개발

Purpose: The purpose of this study is to develop a measure for quantitative evaluation of self-management of university athletes and to provide information for establishing an efficient self-management strategy by verifying its validity through a series of processes. Methods: In Study 1, question analysis, exploratory factor analysis, confirmatory factor analysis, and feasibility verification were conducted on 568 university athletes in Seoul and Gyeonggi Province by using the eight factors (68 questions) explored in Lee and Choi (2018)’s study. In Study 2, with the profile verification method, 332 subjects were analyzed by using the self-management questionnaire of 27 questions, 7 factors developed in Study 1. Results: 68 questions of 8 factors were finally made into 27 questions of 7 factors through item analysis, exploratory factor analysis, and confirmatory factor analysis. The validity of the construct concept (convergence and discrimination) was verified through the final question, and validity was secured. In Study 2, according to demographic characteristics, the self-management scale with proven validity and reliability for university athletes was tested and differences were found by gender, categories of sports, career but no differences were found by grade. Finally, the structural equation model between athlete satisfaction and performance was verified to predict whether it was related to various measurement factors in sports situations, and the self-management scale was found to have a static influence on athlete satisfaction and performance. The structural equation model fit was Q(x²/df)= 2.921, GFI= .905, CFI= .903, IFI= .904, RMR= .050, RMSEA= .076. Conclusion: Therefore, it is believed that this study will provide a valid self-management scale for understanding athletes in depth and developing them into excellent athletes in the university sports environment, and will provide the necessary basic data for further self-care follow-up research.

Evaluation of model fit in structural equation models with ordinal missing data: a comparison of the D 2 and MI2S methods

ABSTRACT Social science research often utilizes measurement instruments that generate ordinal data (e.g., Likert scales). Many empirical studies also face the challenge of missing data, which can be addressed by performing multiple imputation followed by analyses of the imputed datasets. However, when missing data exist on ordinal variables, there has been limited research on how to evaluate model fit of structural equation models for ordinal variables. Recent studies suggest that two multiple-imputation-based approaches show great promise: The D 2 procedure, and the Multiple Imputation Two-step (MI2S) approach, though the two have not been systematically compared. This study extends previous research by comparing the D 2 with the MI2S fit statistics in a wider range of conditions than previous studies. Our findings revealed a number of factors that can influence the performance of these test statistics.

A Comparison of Reliability Estimation Based on Confirmatory Factor Analysis and Exploratory Structural Equation Models.

Composite reliability, or coefficient omega, can be estimated using structural equation modeling. Composite reliability is usually estimated under the basic independent clusters model of confirmatory factor analysis (ICM-CFA). However, due to the existence of cross-loadings, the model fit of the exploratory structural equation model (ESEM) is often found to be substantially better than that of ICM-CFA. The present study first illustrated the method used to estimate composite reliability under ESEM and then compared the difference between ESEM and ICM-CFA in terms of composite reliability estimation under various indicators per factor, target factor loadings, cross-loadings, and sample sizes. The results showed no apparent difference in using ESEM or ICM-CFA for estimating composite reliability, and the rotation type did not affect the composite reliability estimates generated by ESEM. An empirical example was given as further proof of the results of the simulation studies. Based on the present study, we suggest that if the model fit of ESEM (regardless of the utilized rotation criteria) is acceptable but that of ICM-CFA is not, the composite reliability estimates based on the above two models should be similar. If the target factor loadings are relatively small, researchers should increase the number of indicators per factor or increase the sample size.