Testing the Performance of Level-Specific Fit Evaluation in MCFA Models With Different Factor Structures Across Levels

2022 ◽  
pp. 001316442110669
Author(s):  
Bitna Lee ◽  
Wonsook Sohn
Keyword(s):  
Model Misspecification ◽  
Intraclass Correlation ◽  
Monte Carlo Study ◽  
Fit Indices ◽  
Group Level ◽  
Multilevel Confirmatory Factor Analysis ◽  
Confirmatory Factor ◽  
Fit Index ◽  
Design Factors ◽  
Tucker Lewis Index

A Monte Carlo study was conducted to compare the performance of a level-specific (LS) fit evaluation with that of a simultaneous (SI) fit evaluation in multilevel confirmatory factor analysis (MCFA) models. We extended previous studies by examining their performance under MCFA models with different factor structures across levels. In addition, various design factors and interaction effects between intraclass correlation (ICC) and misspecification type (MT) on their performance were considered. The simulation results demonstrate that the LS outperformed the SI in detecting model misspecification at the between-group level even in the MCFA model with different factor structures across levels. Especially, the performance of LS fit indices depended on the ICC, group size (GS), or MT. More specifically, the results are as follows. First, the performance of root mean square error of approximation (RMSEA) was more promising in detecting misspecified between-level models as GS or ICC increased. Second, the effect of ICC on the performance of comparative fit index (CFI) or Tucker–Lewis index (TLI) depended on the MT. Third, the performance of standardized root mean squared residual (SRMR) improved as ICC increased and this pattern was more clear in structure misspecification than in measurement misspecification. Finally, the summary and implications of the results are discussed.

scholarly journals Internet and decision-making regarding health among pregnant woman: cross-cultural adaptation of a questionnaire for use in Brazil

2021 ◽  
Vol 37 (2) ◽  
Author(s):  
Ana Carolina Cleto Borges ◽  
Raquel Conceição Ferreira ◽  
Lorrany Gabriela Rodrigues ◽  
Matheus França Perazzo ◽  
Saul Martins Paiva ◽  
...  
Keyword(s):  
Decision Making ◽  
Pregnant Women ◽  
Intraclass Correlation ◽  
Cross Cultural ◽  
Confirmatory Factor ◽  
Back Translation ◽  
Use Of The Internet ◽  
Test Retest Reliability ◽  
Fit Index ◽  
Tucker Lewis Index

The aim of this study was to translate and cross-culturally adapt the Women’s Use of the Internet in Pregnancy Questionnaire (WUIPQ) to Brazilian Portuguese and analyze the psychometric properties of the Preparation for Decision Making Scale (PDMS). This study consisted of the following steps: translation, synthesis, back-translation, evaluation by the author of the original questionnaire, review by the panel of experts, and pretest of the WUIPQ. For such, Brazilian pregnant women and mothers who were members of Facebook groups participated in the study. We measured test-retest reliability as well as internal consistency and performed confirmatory factor analysis (CFA) of the B-PDMS. In the pretest, 88.14% of the participants considered the items of the B-WUIPQ to be clear and pertinent, and 84.09% rated the sequence and organization of the questionnaire as excellent/good. The intraclass correlation coefficient and Cronbach’s alpha coefficient for the B-PDMS were 0.850 (95%CI: 0.791-0.899) and 0.91, respectively. CFA revealed factor loadings higher than 0.70 for most items, with a comparative fit index of 0.989, Tucker-Lewis index of 0.984, and root mean square error of approximation of 0.08 (95%CI: 0.06-0.09). The B-WUIPQ presented cross-cultural adapted, and the B-PDMS demonstrated satisfactory psychometric proprieties to Brazilian pregnant women.

scholarly journals Understanding the Model Size Effect on SEM Fit Indices

2018 ◽  
Vol 79 (2) ◽  
pp. 310-334 ◽  
Author(s):  
Dexin Shi ◽  
Taehun Lee ◽  
Alberto Maydeu-Olivares
Keyword(s):  
Sample Size ◽  
Structural Equation ◽  
Model Misspecification ◽  
Factor Loading ◽  
Equation Modeling ◽  
Fit Indices ◽  
Finite Samples ◽  
Model Size ◽  
Fit Index ◽  
Tucker Lewis Index

This study investigated the effect the number of observed variables ( p) has on three structural equation modeling indices: the comparative fit index (CFI), the Tucker–Lewis index (TLI), and the root mean square error of approximation (RMSEA). The behaviors of the population fit indices and their sample estimates were compared under various conditions created by manipulating the number of observed variables, the types of model misspecification, the sample size, and the magnitude of factor loadings. The results showed that the effect of p on the population CFI and TLI depended on the type of specification error, whereas a higher p was associated with lower values of the population RMSEA regardless of the type of model misspecification. In finite samples, all three fit indices tended to yield estimates that suggested a worse fit than their population counterparts, which was more pronounced with a smaller sample size, higher p, and lower factor loading.

scholarly journals Multicultural Validation of the Zuckerman–Kuhlman–Aluja Personality Questionnaire Shortened Form (ZKA-PQ/SF) Across 18 Countries

Assessment ◽  
2019 ◽  
Vol 27 (4) ◽  
pp. 728-748
Author(s):  
Anton Aluja ◽  
Jérôme Rossier ◽  
Barry Oumar ◽  
Luis. F. García ◽  
Tarek Bellaj ◽  
...  
Keyword(s):  
Sensation Seeking ◽  
Personality Questionnaire ◽  
Fit Indices ◽  
Average Percentage ◽  
Five Factors ◽  
Congruence Coefficient ◽  
Confirmatory Factor ◽  
Fit Index ◽  
Tucker Lewis Index ◽  
Variance Explained

The aim of this study was to assess the psychometric properties of the Zuckerman–Kuhlman–Aluja Personality Questionnaire shortened form (ZKA-PQ/SF) in 18 cultures and 13 languages of different African, American, Asian, and European cultures and languages. The results showed that the five-factor structure with 20 facets replicated well across cultures with a total congruence coefficient of .97. A confirmatory factor analysis (CFA) resulted in adequate fit indices for the five factors based on the comparative fit index (CFI), Tucker–Lewis index (TLI; >.90), and RMSEA (.031-.081). A series of CFA to assess measurement invariance across cultures resulted in adequate CFIs and TLIs for configural and metric invariance. However, factors did not show scalar invariance. Alpha internal consistencies of five factors ranged between .77 (Sensation Seeking) and .86 (Neuroticism). The average alpha of the 20 facets was .64 with a range from .43 (SS4) to .75 (AG1). Nevertheless, alpha reliabilities were lower in some facets and cultures, especially for Senegal and Togo. The average percentage of the variance explained based on the adjusted R2 was 2.9%, 1.7%, and 5.1% for age, sex, and, cultures, respectively. Finally, multidimensional scaling suggested that geographically or culturally close cultures share mean profile similarities.

Development and Validation of a Perceived Barriers to Physical Activity Scale for Low-Income Adolescents

2021 ◽  
pp. 1-9
Author(s):  
Cheng Li ◽  
Christy Hullings ◽  
Wei Wang ◽  
Debra M. Palmer Keenan
Keyword(s):  
Physical Activity ◽  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Low Income ◽  
Concurrent Validity ◽  
Intraclass Correlation ◽  
Perceived Barriers ◽  
Confirmatory Factor ◽  
Fit Index

Background: Low-income adolescents’ physical activity (PA) levels fall below current recommendations. Perceived barriers to physical activity (PBPA) are likely significant predictors of PA levels; however, valid and reliable measures to assess PA barriers are lacking. This manuscript describes the development of the PBPA Survey for Low-Income Adolescents. Methods: A mixed-method approach was used. Items identified from the literature and revised for clarity and appropriateness (postcognitive interviews) were assessed for test–retest reliability with 74 adolescents using intraclass correlation coefficient. Items demonstrating low intraclass correlation coefficients or floor effects were removed. Both exploratory factor analysis and confirmatory factor analysis analyses (n = 1914 low-income teens) were used to finalize the scale; internal consistency was assessed by Cronbach’s alpha. Concurrent validity was established by correlating the PBPA with the PA questionnaire for adolescents using a Spearman correlation. Results: The exploratory factor analysis yielded a 38-item, 7-factor solution, which was cross-validated by confirmatory factor analysis (comparative-fit index, nonnormed fit index = .90). The scale’s Cronbach’s alpha was .94, with subscales ranging from .70 to .88. The PBPA Survey for Low-Income Adolescents’ concurrent validity was supported by a negative PA questionnaire for adolescents’ correlation values. Conclusion: The PBPA Survey for Low-Income Adolescents can be used to better understand the relationship between PBPA among low-income teens. Further research is warranted to validate the scale with other adolescent subgroups.

Model Persamaan Struktur untuk Mengkaji Penggunaan Kenderaan tidak Bermotor Berdasarkan Teori Tingkah Laku Terancang

2013 ◽  
Vol 62 (1) ◽  
Author(s):  
Muhamad Razuhanafi Mat Yazid
Keyword(s):  
Confirmatory Factor ◽  
Fit Index ◽  
Tucker Lewis Index

Konsep pengangkutan tidak bermotor adalah penting bagi menjamin kehidupan dalam persekitaran yang bersih, sihat dan berkualiti tinggi. Hari ini, sistem pengangkutan bandar-bandar di Malaysia mempunyai imej buruk seperti kesesakan, kemalangan, ketiadaan pengangkutan awam sebagai alternatif serta konflik pembebasan gas karbon ke ruang atmosfera menyumbang kepada pencemaran alam dan kepincangan dari aspek kualiti mobiliti kehidupan secara umumnya. Tujuan kajian ini adalah untuk mengukur kesahan dan kebolehpercayaan Instrumen model kenderaan tidak bermotor. Instrumen 4 konstruk yang mengandungi 17 item skala 5 mata telah digunakan dalam kajian ini. Instrumen ini telah ditadbirkan kepada 400 orang responden di bandar Kota Bharu yang dipilih secara rawak berkelompok. Perisian Amos versi 7 digunakan untuk menganalisis data. Nilai Comparative Fit Index (CFI), Tucker Lewis Index (TLI) dan RMSEA digunakan untuk mengekal dan menggugurkan item. Dapatan akhir kajian menggunakan model pengukuran confirmatory factor analisis telah menggugurkan 7 item dan mengekalkan 10 item yang sah dan boleh dipercayai untuk mengukur 4 konstruk. Instrumen ini boleh digunakan untuk membentuk model penggunaan kenderaan tidak bermotor berdasarkan teori tingkah laku terancang (TPB) iaitu berbasikal dan berjalan kaki dalam menjadikannya sebagai mod pengangkutan pilihan di Malaysia.

scholarly journals The Impact of Intraclass Correlation on the Effectiveness of Level-Specific Fit Indices in Multilevel Structural Equation Modeling

2016 ◽  
Vol 77 (1) ◽  
pp. 5-31 ◽  
Author(s):  
Hsien-Yuan Hsu ◽  
Jr-Hung Lin ◽  
Oi-Man Kwok ◽  
Sandra Acosta ◽  
Victor Willson
Keyword(s):  
Structural Equation ◽  
Structural Equation Models ◽  
Intraclass Correlation ◽  
Monte Carlo Study ◽  
Multilevel Structural Equation Modeling ◽  
Equation Modeling ◽  
Saturated Model ◽  
Fit Indices ◽  
The Impact ◽  
Multilevel Structural Equation

Several researchers have recommended that level-specific fit indices should be applied to detect the lack of model fit at any level in multilevel structural equation models. Although we concur with their view, we note that these studies did not sufficiently consider the impact of intraclass correlation (ICC) on the performance of level-specific fit indices. Our study proposed to fill this gap in the methodological literature. A Monte Carlo study was conducted to investigate the performance of (a) level-specific fit indices derived by a partially saturated model method (e.g., [Formula: see text] and [Formula: see text]) and (b) [Formula: see text] and [Formula: see text] in terms of their performance in multilevel structural equation models across varying ICCs. The design factors included intraclass correlation (ICC: ICC1 = 0.091 to ICC6 = 0.500), numbers of groups in between-level models (NG: 50, 100, 200, and 1,000), group size (GS: 30, 50, and 100), and type of misspecification (no misspecification, between-level misspecification, and within-level misspecification). Our simulation findings raise a concern regarding the performance of between-level-specific partial saturated fit indices in low ICC conditions: the performances of both [Formula: see text] and [Formula: see text] were more influenced by ICC compared with [Formula: see text] and SRMRB. However, when traditional cutoff values ( RMSEA≤ 0.06; CFI, TLI≥ 0.95; SRMR≤ 0.08) were applied, [Formula: see text] and [Formula: see text] were still able to detect misspecified between-level models even when ICC was as low as 0.091 (ICC1). On the other hand, both [Formula: see text] and [Formula: see text] were not recommended under low ICC conditions.

scholarly journals Transcultural Adaptation and Psychometric Proprieties of the Mental Toughness Inventory for Brazilian Athletes

2021 ◽  
Vol 12 ◽  
Author(s):  
Caio Rosas Moreira ◽  
Renan Codonhato ◽  
Lenamar Fiorese
Keyword(s):  
Well Being ◽  
Mental Toughness ◽  
Chi Square ◽  
Present Sample ◽  
Resilience Scale ◽  
Confirmatory Factor ◽  
Average Variance ◽  
And Performance ◽  
Fit Index ◽  
Tucker Lewis Index

This study has assessed the psychometric proprieties of the Mental Toughness Inventory (MTI) within the context of Brazilian sports. About 12 professionals participated in the process of adapting and translating the scale to Brazilian Portuguese. Subjects were 575 athletes (23.54 ± 5.79 years old; 58% males) who answered the MTI and the 10-item Connor–Davidson Resilience Scale (CD-RISC-10). Data were analyzed through confirmatory factor analysis (CFA), Cronbach's alpha (α), composite reliability (CR), average variance extracted (AVE), Spearman correlation, and model invariance tests. Results from CFA showed adequate fit for the original 8-item structure of the scale [Chi-square (χ2) = 27.041; p = 0.078; normalized chi-square (χ2/df) = 1.50; comparative fit index (CFI) = 0.988; Tucker–Lewis Index (TLI) = 0.981; root mean square error of approximation (RMSEA) = 0.03 [0.00–0.05]; standardized root mean residual (SRMR) = 0.030] assessing mental toughness (MT) as a single factor and the scale presented satisfactory internal consistency (CR = 0.81; α = 0.82). MT was correlated with resilience (r = 0.607), age (r = 0.276), and time of experience in the sport (r = 0.215). The MTI has also shown partial measurement invariance for sex and complete invariance across sport types. It was concluded that the MTI is a suitable tool for assessing MT in the present sample of Brazilian athletes; this instrument has potential practical application for researchers and sports psychologists who seek to develop the well-being and performance of athletes.

scholarly journals The psychometric properties of the Persian Menopause Rating Scale

2020 ◽  
Author(s):  
Leila Jahangiry ◽  
Robabeh Parviz ◽  
Mojgan Mirghafourvand ◽  
Maryam Khazaee-Pool ◽  
Koen Ponnet
Keyword(s):  
Factor Analysis ◽  
Psychometric Properties ◽  
Rating Scale ◽  
Intraclass Correlation ◽  
Principal Component ◽  
Fit Indices ◽  
Chi Square ◽  
Two Samples ◽  
Fit Index ◽  
Menopause Rating Scale

Abstract Background: To measure the severity of menopausal complaints and determine the pattern of menopausal symptoms, a valid and reliable instrument is needed in women’s healthcare. The Menopause Rating Scale (MRS) is one of the best-known tools in response to the lack of standardized scales. The purpose of this study was to examine the psychometric properties of the MRS in an Iranian example. Methods: Participants were randomly selected from women referred to healthcare centers in Miandoab, West Azerbaijan, Iran. A total of 330 questionnaires were completed (response rate of 96.9%). Two samples were considered for analysis in the validation process. An exploratory factor analysis (EFA) was conducted on the first sample (n1 =165), and a confirmatory factor analysis (CFA) was done using a second study sample (n2 = 165). The psychometric properties process was concluded with assessment of internal consistency and test-retest reliability. Results: The EFA with Principal Component Analysis extracted three factors explaining 75.47% cumulative variance. The CFA confirmed a three-factor structure of the 11-items MRS. All fit indices proved to be satisfactory. The relative chi-square (χ2/df) was 3.686 (p < .001). The Root Mean Square Error of Approximation (RMSEA) of the model was .04 (90% CI = .105 – .150). All comparative indices of the model, including the Comparative Fit Index, Normed Fit Index, and Relative Fit Index, were more than .80 (.90, .87, and .80, respectively). For the overall scale, Cronbach’s alpha was .931, whereas the alpha for the subscales ranged from 0.705-0.950. The intraclass correlation was .91 (95% CI = .89-.93), p < 0.001. Conclusion: The results of the study indicate that the Persian model of the MRS is a valid and reliable scale. As a screening tool, the Persian MRS could be used to identify the pattern of symptoms among menopausal, premenopausal, and postmenopausal women to care for and educate them on how to identify and treat the symptoms.

Psychometric Testing of the Modified Reminiscence Functions Scale

2014 ◽  
Vol 22 (3) ◽  
pp. 500-510 ◽  
Author(s):  
Juliette M. Shellman ◽  
Danjie Zhang
Keyword(s):  
Factor Model ◽  
Psychometric Testing ◽  
Community Dwelling ◽  
Black Adults ◽  
Chi Square ◽  
Convenience Sample ◽  
Number Of Factors ◽  
Confirmatory Factor ◽  
Fit Index ◽  
Tucker Lewis Index

Background and Purpose: The Modified Reminiscence Functions Scale (MRFS) measures the patterns and functions of reminiscence. The purpose of this study was to examine the factor structure of the MRFS in a sample of community-dwelling Black adults. Methods: A convenience sample (N= 335) of Black adults from the Northeast completed the 39-item MRFS. Seven- and 8-factor models were evaluated given the uncertainty regarding the number of factors in previous reminiscence research. Results: Confirmatory factor analysis established validity of the 7-factor model (relative chi-square [χ2/df] = 1.9, Tucker-Lewis index [TLI] = .919, comparative fit index [CFI] = .929, root mean square error of approximation [RMSEA] = .05). Reliability of the subscales ranged from .64 to .90. Conclusions: The MRFS is a reliable and valid measure of reminiscence patterns and functions in Black adults with similar characteristics.

scholarly journals Why We Need to Abandon Fixed Cutoffs for Goodness-of-Fit Indices: A Comprehensive Simulation and Possible Solutions

2021 ◽  
Author(s):  
Katharina Groskurth ◽  
Matthias Bluemke ◽  
Clemens M. Lechner
Keyword(s):  
Goodness Of Fit ◽  
Model Misspecification ◽  
Model Fit ◽  
Fit Indices ◽  
New Approach ◽  
Response Options ◽  
Factor Loadings ◽  
Confirmatory Factor ◽  
Main Effect ◽  
Extraneous Data

To evaluate model fit in confirmatory factor analysis, researchers compare goodness-of-fit indices (GOFs) against fixed cutoff values derived from simulation studies. However, these cutoffs may not be as broadly applicable as researchers typically assume, especially when used in settings not covered in the simulation scenarios from which these cutoffs were derived. Thus, we aim to evaluate (1) the sensitivity of GOFs to model misspecification and (2) their susceptibility to extraneous data and analysis characteristics (i.e., estimator, number of indicators, number of response options, distribution of response options, loading magnitude, sample size, and factor correlation). Our study includes the most comprehensive simulation on that matter to date. This enables us to uncover several previously unknown or at least underappreciated issues with GOFs. All widely used GOFs are far more susceptible to extraneous influences in even more complex ways than generally appreciated, and their sensitivity to misspecifications in factor loadings and factor correlations varies significantly across different scenarios. For instance, one of those strong influences on all GOFs constituted the magnitude of factor loadings (either as a main effect or two-way interaction with other characteristics). The strong susceptibility of GOFs to data and analysis characteristics showed that the practice of judging the fit of models against fixed cutoffs is more problematic than so-far assumed. Hitherto unnoticed effects on GOFs imply that no general cutoff rules can be applied to evaluate model fit. We discuss alternatives for assessing model fit and develop a new approach to tailor cutoffs for GOFs to research settings.

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