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Relevant bibliographies by topics / Multiple Indicators-Multiple Causes (MIMIC) model

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Academic literature on the topic 'Multiple Indicators-Multiple Causes (MIMIC) model'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Multiple Indicators-Multiple Causes (MIMIC) model"

1

Lee, Soo, Okan Bulut, and Youngsuk Suh. "Multidimensional Extension of Multiple Indicators Multiple Causes Models to Detect DIF." Educational and Psychological Measurement 77, no. 4 (2016): 545–69. http://dx.doi.org/10.1177/0013164416651116.

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A number of studies have found multiple indicators multiple causes (MIMIC) models to be an effective tool in detecting uniform differential item functioning (DIF) for individual items and item bundles. A recently developed MIMIC-interaction model is capable of detecting both uniform and nonuniform DIF in the unidimensional item response theory (IRT) framework. The goal of the current study is to extend the MIMIC-interaction model for detecting DIF in the context of multidimensional IRT modelling and examine the performance of the multidimensional MIMIC-interaction model under various simulation conditions with respect to Type I error and power rates. Simulation conditions include DIF pattern and magnitude, test length, correlation between latent traits, sample size, and latent mean differences between focal and reference groups. The results of this study indicate that power rates of the multidimensional MIMIC-interaction model under uniform DIF conditions were higher than those of nonuniform DIF conditions. When anchor item length and sample size increased, power for detecting DIF increased. Also, the equal latent mean condition tended to produce higher power rates than the different mean condition. Although the multidimensional MIMIC-interaction model was found to be a reasonably useful tool for identifying uniform DIF, the performance of the model in detecting nonuniform DIF appeared to be questionable.

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2

Chen, Yong, and Kai Jiang. "A multiple indicators multiple causes (mimic) model of the behavioral consequences of hotel guests." Tourism Management Perspectives 30 (April 2019): 197–207. http://dx.doi.org/10.1016/j.tmp.2019.02.014.

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3

Remeikiene, Rita, Martin Cepel, and Ligita Gaspareniene. "Evaluation of E-business micro and macro determinants by Multiple Indicators Multiple Causes model." Engineering Economics 30, no. 5 (2019): 591–600. http://dx.doi.org/10.5755/j01.ee.30.5.19639.

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The methods employed for the research include comparative and systematic literature analysis, statistical data analysis, expert evaluation and the Multiple Indicators Multiple Causes (MIMIC) model. There are several research gaps in the field of e-business: 1) there is no simplified factor model which would denote the development of e-business, including its causes and consequences, and which, due to its universality, could be adapted to any country; 2) the studies of e-business development lack a comprehensive assessment which would consider this phenomenon in both quantitative and qualitative terms. On the basis of the theoretical analysis, a model for assessment of e-business development has been formed. The results of the empirical evaluation have revealed that in the group of micro environmental determinants development of e-business in Lithuania is, to the largest extent, determined by business managers’ motivation to reach the defined aims and their positive viewpoint that e-business may become a perfect auxiliary tool to ensure business competitiveness. In the group of macro environmental determinants, the determinants of socio-cultural environment can be indicated as the major ones that affect the development of e-business. Application of the MIMIC model has enabled to identify the following macro economical determinants (for the euro area): real effective exchange rate for the euro area; level of households with the Internet access; quantity of individuals using the Internet for ordering goods or services; government deficit/surplus; youth employment, and individuals using the Internet for interaction with public authorities.

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4

Proitsi, P., G. Hamilton, M. Tsolaki, et al. "A Multiple Indicators Multiple Causes (MIMIC) model of Behavioural and Psychological Symptoms in Dementia (BPSD)." Neurobiology of Aging 32, no. 3 (2011): 434–42. http://dx.doi.org/10.1016/j.neurobiolaging.2009.03.005.

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5

Qi, Chang, Brian C. Kelly, Yanhui Liao, et al. "A Multiple Indicators Multiple Causes (MIMIC) model of internal barriers to drug treatment in China." Drug and Alcohol Dependence 148 (March 2015): 143–49. http://dx.doi.org/10.1016/j.drugalcdep.2014.12.033.

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6

Son, Sookyoung, and Sehee Hong. "Multiple Group Analysis in Multilevel Data Across Within-Level Groups: A Comparison of Multilevel Factor Mixture Modeling and Multilevel Multiple-Indicators Multiple-Causes Modeling." Educational and Psychological Measurement 81, no. 5 (2021): 904–35. http://dx.doi.org/10.1177/0013164420987899.

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The purpose of this two-part study is to evaluate methods for multiple group analysis when the comparison group is at the within level with multilevel data, using a multilevel factor mixture model (ML FMM) and a multilevel multiple-indicators multiple-causes (ML MIMIC) model. The performance of these methods was evaluated integrally by a series of procedures testing weak and strong invariance models and the latent group mean differences testing after holding for factorial invariance. Two Monte Carlo simulation studies were conducted under the following conditions: number of clusters, cluster size, and the design type in groups. A multilevel one-factor confirmatory factor analysis (CFA) model as a research model in Study 1 was investigated to compare the results under different conditions with those of previous studies. A multilevel two-factor CFA model as a research model in Study 2 was evaluated by fitting alternative models that can be applied when the model is complicated. The results indicated that the two methods were reasonable in multilevel multiple groups analysis across within-level groups. However, pros and cons were found between the two methods. In the multilevel one-factor CFA model, ML MIMIC model was slightly better when the sample size is small. In the multilevel complex model, two alternative models of ML FMM were recommended because the weak invariance testing of ML MIMIC was considerably time-consuming. Finally, it was shown that information criteria, which are criteria for determining whether factorial invariance is established, need to be applied differently according to the sample size conditions. Guidelines for this situation are provided.

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7

Kunchai, Jetnipit, Dissakoon Chonsalasin, and Buratin Khampirat. "Psychometric Properties and a Multiple Indicators Multiple Cause Model of the Career Aspiration Scale with College Students of Rural Thailand." Sustainability 13, no. 18 (2021): 10377. http://dx.doi.org/10.3390/su131810377.

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To help address educational inequalities in student backgrounds and career plans, the measurement of career aspirations can provide crucial information about inequality related to career opportunities. Therefore, this study analyzed the factor structure and psychometric properties of the Career Aspiration Scale-Revised (CAS-R) and the effects of sociodemographic variables on the CAS-R. The study participants were 590 undergraduates at three community colleges in rural Thailand. Confirmatory factor analysis (CFA) was conducted to investigate the validity of the CAS-R three-factor model. The Multiple Indicators Multiple Causes (MIMIC) model with and without differential item functioning (DIF) were applied to investigate the effects of sociodemographic factors as covariates on the CAS-R and specific item response. The CFA results supported the 18-item CAS-R’s three-factor structure because the items well represented latent factors and because the subscales met research standards for reliability and validity. The MIMIC model showed that only the year of study (first-year students) had a positive direct effect on leadership, achievement, and educational aspirations. The MIMIC model with DIF indicated that gender, year of study, major, and paternal education caused inequality in 10 items. Overall, the results show that the 18-item CAS-R has strong psychometric properties and can accurately assess the career aspirations of Thai students. The MIMIC model’s application allowed researchers to show that sociodemographic background affected the leadership, achievement, and education subscales. Obtaining information on the CAS-R scale’s measurements would be useful for researchers, practitioners, and career counselors interested in helping students develop career aspirations and choices.

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8

Iliceto, Paolo, Maurizio Pompili, Sally Spencer-Thomas, et al. "Occupational stress and psychopathology in health professionals: An explorative study with the Multiple Indicators Multiple Causes (MIMIC) model approach." Stress 16, no. 2 (2012): 143–52. http://dx.doi.org/10.3109/10253890.2012.689896.

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9

Normand, Sébastien, Amori Yee Mikami, Victoria Savalei, and Joanna Guiet. "A Multiple Indicators Multiple Causes (MIMIC) model of friendship quality and comorbidities in children with attention-deficit/hyperactivity disorder." Psychological Assessment 32, no. 7 (2020): 698–704. http://dx.doi.org/10.1037/pas0000824.

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10

Lim, Young-Jin. "Relations between virtues and positive mental health in a Korean population: A Multiple Indicators Multiple Causes (MIMIC) model approach." International Journal of Psychology 50, no. 4 (2014): 272–78. http://dx.doi.org/10.1002/ijop.12096.

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