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Karimian Zahra Farrokhi Majid Reza Moghadami Mohsen Zarifsanaiey Nahid Mehrabi Manoosh Khojasteh Laleh Salehi Nasim 《Education and Information Technologies》2022,27(3):3299-3320
Education and Information Technologies - The COVID-19 crisis has had a profound effect on higher education, especially medical education due to its sensitive nature, dealing with people’s... 相似文献
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The analysis of longitudinal data has received widespread interest in the behavioral, educational, medical, and social sciences for many years. Many modeling techniques have been suggested for conducting such analyses, especially when the data exhibit complex nonlinear trajectory patterns. A major problem with many of these modeling techniques, however, is that they often either impose overly restrictive assumptions or can be computationally demanding. The purpose of this paper is to introduce a less known but highly effective modeling procedure that can be used to model complex nonlinear longitudinal data patterns. The procedure is illustrated using empirical data along with an easy to use computerized implementation. 相似文献
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A Monte Carlo simulation study was conducted to evaluate the sensitivities of the likelihood ratio test and five commonly used delta goodness-of-fit (ΔGOF) indices (i.e., ΔGamma, ΔMcDonald’s, ΔCFI, ΔRMSEA, and ΔSRMR) to detect a lack of metric invariance in a bifactor model. Experimental conditions included factor loading differences, location and number of noninvariant items, and sample size. The results indicated all ΔGOF indices held Type I error to a minimum and overall had adequate power for the study. For detecting the violation of metric invariance, only ΔGamma and ΔCFI, in addition to Δχ2, are recommended to use in the bifactor model with values of ?.016 to ?.023 and ?.003 to ?.004, respectively. Moreover, in the variance component analysis, the magnitude of the factor loading differences contributed the most variation to all ΔGOF indices, whereas sample size affected Δχ2 the most. 相似文献
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Latent growth modeling (LGM) is a popular and flexible technique that may be used when data are collected across several different measurement occasions. Modeling the appropriate growth trajectory has important implications with respect to the accurate interpretation of parameter estimates of interest in a latent growth model that may impact educational policy decisions. A Monte Carlo simulation study was conducted to examine the accuracy of six information-based criteria (i.e., AIC, CAIC, AICC, BIC, nBIC, and HQIC) when selecting among various growth trajectories modeled using LGM under different sample size, number of time points, and growth trajectory scenarios. The accuracy of the information criteria generally improved as sample size increased. The cubic and linear growth models were distinguished most accurately by the information criteria. All of the nonlinear models were more easily distinguished as the number of time points increased. The comparative performance of the six information criteria was dependent upon the manipulated conditions. Implications of the findings are discussed. 相似文献
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