scholarly journals RGLS and RLS in Covariance Structure Analysis

2021 ◽  
Author(s):  
Bang Quan Zheng
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Least Squares ◽  
Goodness Of Fit ◽  
Weight Matrix ◽  
Equation Modeling ◽  
Covariance Structure Analysis ◽  
Factor Analysis Model ◽  
Monte Carlo Simulation Study ◽  
Confirmatory Factor

This paper assesses the performance of regularized generalized least squares (RGLS) and reweighted least squares (RLS) methodologies in a confirmatory factor analysis model. Normal theory maximum likelihood (ML) and GLS statistics are based on large sample statistical theory. However, violation of asymptotic sample size is ubiquitous in real applications of structural equation modeling (SEM), and ML and GLS goodness-of-fit tests in SEM often make incorrect decisions on the true model. The novel methods RGLS and RLS aim to correct the over-rejection by ML and under-rejection by GLS. Proposed by Arruda and Bentler (2017), RGLS replaces a GLS weight matrix with a regularized one. Rediscovered by Hayakawa (2019), RLS replaces this weight matrix with one that derives from an ML function. Both of these methods outperform ML and GLS when samples are small, yet no studies have compared their relative performance. A confirmatory factor analysis Monte Carlo simulation study with normal data was carried out to examine the statistical performance of these two methods at different sample sizes. Based on empirical rejection frequencies and empirical distributions of test statistics, we find that RLS and RGLS have equivalent performance when N≥70; whereas when N<70, RLS outperforms RGLS. Both methods clearly outperform ML and GLS with N≤400.

scholarly journals Mengakomodasi Efek Metode dalam Pengujian Validitas Konstruk Melalui Analisis Faktor Konfirmatori

2017 ◽  
Vol 1 (1) ◽  
pp. 37
Author(s):  
Wahyu Widhiarso
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Equation Modeling ◽  
Fit Indices ◽  
Method Effect ◽  
Confirmatory Factor ◽  
Method Effects ◽  
Item Writing

Literatures in the field of psychometrics recommend researchers to employvarious of methods on measuring individual attributes. Ideally,each methods are complementary and measuresthe construct designed to be measured. However, some problems arise when among the methods is unique and unrelated to the construct being measured. The uniqueness of method can lead what is called the method effect. In testing construct validity using confirmatory factor analysis, the emergence of this effect tend to reducing the goodness of fit indices of the model. There are many ways to solve these problem, one of themis controling the method effects and accommodate it to the model. This paper introduces how to accommodate method effecton the confirmatory factor analysis using structural equation modeling. In the application section, author identify the emergence of method effects due to the differences item writing direction (favorable-unfavorable). The analysis showed that method effectemerge from different writing direction.

scholarly journals Exploratory and Confirmatory Factor Analysis of the Jefferson Scale of Empathy among Chinese Medical Students

2019 ◽  
Author(s):  
Xuyu Chen ◽  
Li Ran ◽  
Yaohua Gu ◽  
Yuting Zhang ◽  
Wenwen Wu ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Medical Students ◽  
Confirmatory Factor Analysis ◽  
Goodness Of Fit ◽  
Medical Training ◽  
Equation Modeling ◽  
Emotional Detachment ◽  
Confirmatory Factor ◽  
Chinese Medical Students ◽  
Jefferson Scale Of Empathy

Abstract Background: Empathy is critical for medical training, clinical practice, even the future professionalism of medical students. The Jefferson scale of empathy- Students version (JSE-S) was developed for estimating empathy among medical students and it’s a psychometrically sound instrument. The purpose of the study is to develop a validated translation of JSE-S that used by Chinese medical students and to confirm the psychometric properties, underlying component and latent variable structure of the Chinese JSE-S. Methods: The JSE-S was translated into Chinese based on standardized guidelines. The sample was divided to two parts. The first half of the data was used for exploratory factor analysis (EFA) using principal component analysis(PCA) with oblique rotation(promax), and the latter was used for confirmatory factor analysis(CFA) using structural equation modeling(SEM). Results: 749 questionnaires (94.81%) were eligible. Cronbach’s alpha coefficient of the JSE-S for the entire sample was 0.89. EFA indicated that three-component solution was acceptable, and the three components are "Perspective Taking", "Compassionate Care" and "Emotional Detachment" respectively. Item 18 was proposed to move to the third factor. CFA indicated that the three-factors model showed an acceptable goodness of fit. Conclusions: Reliability and validity of the Chinese JSE-S were satisfactory among Chinese medical students. The results supporting the underlying factor structure of the Chinese JSE-S.

scholarly journals Psychometric Properties of School Refusal Assessment Scale-Revised: Parent Version in Iranian Population

2020 ◽  
Vol 8 (2) ◽  
pp. 123-132
Author(s):  
Khoshdavi Ebrahimzadeh ◽  
◽  
Farhad Ghadiri Sourman Abadi ◽  
Soraya Anvari Anbi ◽  
Karim Abdolmohamadi ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Construct Validity ◽  
Goodness Of Fit ◽  
School Refusal ◽  
Assessment Scale ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
Confirmatory Factor ◽  
Parent Version

Objective: This study aimed at evaluating the factor validity of the Kearny school refusal assessment scale-revised: parent version among parents of school students in Urmia City, Iran. Methods: The study population comprised students in the first, second, and third grades of elementary schools in Urmia (N=18750). Of them, 351 students from 5 schools were selected using a multistage cluster sampling method. Then, They responded to the Kearny school refusal assessment scale-revised: parent version. To assess the construct validity of this scale, confirmatory factor analysis and internal consistency were used.  Results: The goodness of fit index of the confirmatory factor analysis model indicated a relatively good fit of the data with factor structure of the school refusal assessment scale-revised and confirming the existence of four characters of school stimulus, evaluative situations, seeking caregivers’ attention, and tangible reinforcements, as school refusal characters. Also, the Cronbach alpha coefficient values indicate the stability of the measurement of the whole scale as well as its subscales. Conclusion: Based on these results, the school refusal scale has good statistic characters and the 4-factor mentioned model has good construct validity and help clinicians to determine the symptoms and causes of school refusal behavior.

scholarly journals Efek Mediasi Variabel Motivasi pada Pengaruh Komitmen Organisasi dan Disiplin Kerja Terhadap Kinerja Pegawai

2021 ◽  
Vol 6 (10) ◽  
pp. 5232
Author(s):  
Nasruddin Nasruddin ◽  
Surajiyo Surajiyo ◽  
Suhaipa Suhaipa ◽  
Herman Paleni
Keyword(s):  
Factor Analysis ◽  
Structural Equation Modeling ◽  
Confirmatory Factor Analysis ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Equation Modeling ◽  
Confirmatory Factor

Komitmen seorang pegawai merupakan hasil (resultante) dari dukungan dan penghargaan yang diberikan organisasi pada pegawai. Penelitian ini bertujuan untuk memperoleh hasil tentang pengaruh Komitmen Organisasi Terhadap Motivasi Pegawai, Disiplin Kerja Terhadap Motivasi Pegawai, Komitmen Organisasi Terhadap Kinerja Pegawai, Disiplin Kerja Terhadap Kinerja Pegawai, Motivasi Pegawai Terhadap Kinerja Pegawai, Motivasi dapat memediasi pengaruh komitmen organisasi terhadap kinerja pegawai, dan Motivasi dapat memediasi pengaruh disiplin kerja terhadap kinerja pegawai pada Lembaga Pemasyarakatan Narkotika Kelas II Lubuklinggau (Muara Beliti). Tahapan penelitian dengan melakukan observasi, pengelompokkan data hasil observasi, mengidentifikasi masalah, merumuskan masalah, penelusuran referensi/literatur, membuat kerangka berpikir dan hipotesis, menentukan teknik pengumpulan data, penentuan populasi dan sampel, membuat instrumen penelitian atau kuesioner, melakukan penyebaran kuesioner, menganalisis data dari hasil jawaban 96 pegawai ASN di Lembaga Pemasyarakatan Narkotika Klas IIA Lubuklinggau (Muara Beliti) menggunakan teknik analisis Structural Equation Modeling (SEM) dengan alat bantu komputer menggunakan program Lisrel 8.70. Adapun langkah-langkah Structural Equation Modeling (SEM) yang dilakukan Confirmatory Factor Analysis (CFA), Identifikasi model, modifikasi model dan menguji kecocokan model (Goodness-of-Fit). Hasil penelitian menunjukkan nilai koefesien pengaruh langsung komitmen organisasi terhadap motivasi pegawai sebesar 0,43, dan nilai t hitung sebesar 3,35. Karena nilai t hitung sebesar 3.35 > 1,96, maka komitmen organisasi berpengaruh signifikan terhadap motivasi pegawai. Dengan demikian dapat diinterpretasikan semakin tinggi komitmen organisasi, maka semakin tinggi motivasi pegawai

Reconsidering the Conditions for Conducting Confirmatory Factor Analysis

2020 ◽  
Vol 23 ◽  
Author(s):  
Daniel Ondé ◽  
Jesús M. Alvarado
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Sample Size ◽  
Goodness Of Fit ◽  
Factor Model ◽  
Stable Solution ◽  
Practical Significance ◽  
Monte Carlo Simulation Study ◽  
Factor Loadings ◽  
Confirmatory Factor

Abstract There is a series of conventions governing how Confirmatory Factor Analysis gets applied, from minimum sample size to the number of items representing each factor, to estimation of factor loadings so they may be interpreted. In their implementation, these rules sometimes lead to unjustified decisions, because they sideline important questions about a model’s practical significance and validity. Conducting a Monte Carlo simulation study, the present research shows the compensatory effects of sample size, number of items, and strength of factor loadings on the stability of parameter estimation when Confirmatory Factor Analysis is conducted. The results point to various scenarios in which bad decisions are easy to make and not detectable through goodness of fit evaluation. In light of the findings, these authors alert researchers to the possible consequences of arbitrary rule following while validating factor models. Before applying the rules, we recommend that the applied researcher conduct their own simulation studies, to determine what conditions would guarantee a stable solution for the particular factor model in question.

Confirmatory Factor Analysis of the Long and Short Forms of the Worry Domains Questionnaire

2009 ◽  
Vol 25 (4) ◽  
pp. 239-243
Author(s):  
Roberto Nuevo ◽  
Andrés Losada ◽  
María Márquez-González ◽  
Cecilia Peñacoba
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Factor Structure ◽  
Goodness Of Fit ◽  
Factorial Validity ◽  
Fit Indices ◽  
Short Forms ◽  
Confirmatory Factor ◽  
Positive Correlations ◽  
Future Work

The Worry Domains Questionnaire was proposed as a measure of both pathological and nonpathological worry, and assesses the frequency of worrying about five different domains: relationships, lack of confidence, aimless future, work, and financial. The present study analyzed the factor structure of the long and short forms of the WDQ (WDQ and WDQ-SF, respectively) through confirmatory factor analysis in a sample of 262 students (M age = 21.8; SD = 2.6; 86.3% females). While the goodness-of-fit indices did not provide support for the WDQ, good fit indices were found for the WDQ-SF. Furthermore, no source of misspecification was identified, thus, supporting the factorial validity of the WDQ-SF scale. Significant positive correlations between the WDQ-SF and its subscales with worry (PSWQ), anxiety (STAI-T), and depression (BDI) were found. The internal consistency was good for the total scale and for the subscales. This work provides support for the use of the WDQ-SF, and potential uses for research and clinical purposes are discussed.

512 Confirmatory Factor Analysis of the Sleep Inertia Questionnaire in a Clinical Sample with Sleep Disorders

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A201-A202
Author(s):  
Kristina Puzino ◽  
Susan Calhoun ◽  
Allison Harvey ◽  
Julio Fernandez-Mendoza
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Construct Validity ◽  
Sleep Disorders ◽  
Goodness Of Fit ◽  
Criterion Validity ◽  
Clinical Samples ◽  
Sleep Inertia ◽  
Confirmatory Factor ◽  
Sensitivity Specificity

Abstract Introduction The Sleep Inertia Questionnaire (SIQ) was developed and validated in patients with mood disorders to evaluate difficulties with becoming fully awake after nighttime sleep or daytime naps in a multidimensional manner. However, few data are available regarding its psychometric properties in clinical samples with sleep disorders. Methods 211 patients (43.0±16.4 years old, 68% female, 17% minority) evaluated at the Behavioral Sleep Medicine (BSM) program of Penn State Health Sleep Research & Treatment Center completed the SIQ. All patients were diagnosed using ICSD-3 criteria, with 111 receiving a diagnosis of chronic insomnia disorder (CID), 48 of a central disorder of hypersomnolence (CDH), and 52 of other sleep disorders (OSD). Structural equation modelling was used to conduct confirmatory factor analysis (CFA) of the SIQ. Results CFA supported four SIQ dimensions of “physiological”, “cognitive”, “emotional” and “response to” (RSI) sleep inertia with adequate goodness-of-fit (TLI=0.90, CFI=0.91, GFI=0.85, RMSEA=0.08). Internal consistency was high (α=0.94), including that of its dimensions (physiological α=0.89, cognitive α=0.94, emotional α=0.67, RSI α=0.78). Dimension inter-correlations were moderate to high (r=0.42–0.93, p<0.01), indicating good construct validity. Convergent validity showed moderate correlations with Epworth sleepiness scale (ESS) scores (r=0.38) and large correlations with Flinders fatigue scale (FFS) scores (r=0.65). Criterion validity showed significantly (p<0.01) higher scores in subjects with CDH (69.0±16.6) as compared to those with CID (54.4±18.3) or OSD (58.5±20.0). A SIQ cut-off score ≥57.5 provided a sensitivity/specificity of 0.77/0.65, while a cut-off score ≥61.5 provided a sensitivity/specificity of 0.71/0.70 to identify CDH vs. ESS<10 (AUC=0.76). Conclusion The SIQ shows satisfactory indices of reliability and construct validity in a clinically-diverse sleep disorders sample. Its criterion validity is supported by its divergent association with hypersomnia vs. insomnia disorders, as well as its adequate sensitivity/specificity to identify patients with CDH. The SIQ can help clinicians easily assess the complex dimensionality of sleep inertia and target behavioral sleep treatments. Future studies should confirm the best SIQ cut-off score by including good sleeping controls, while clinical studies should determine its minimal clinically important difference after pharmacological or behavioral treatments. Support (if any):

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

2021 ◽  
pp. 001316442110089
Author(s):  
Yuanshu Fu ◽  
Zhonglin Wen ◽  
Yang Wang
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Structural Equation ◽  
Reliability Estimation ◽  
Model Fit ◽  
Equation Modeling ◽  
Factor Loadings ◽  
Reliability Estimates ◽  
Confirmatory Factor ◽  
Composite Reliability

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.

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