scholarly journals The Impact of the Learning Effectiveness on Alumni Employability: A Case Study of a T/I Department in Taiwan

2016 ◽  
Vol 4 (4) ◽  
pp. 586
Author(s):  
Pin-Shan Hsiung
Keyword(s):  
Structural Equation ◽  
Goodness Of Fit ◽  
Core Competency ◽  
Equation Modeling ◽  
Parameter Estimates ◽  
Learning Effectiveness ◽  
Fit Indices ◽  
Academic Advancement ◽  
Current Employment Status ◽  
The Impact

<p><em>In recent years, the number of translation and interpretation courses offered in Taiwan</em><em> has increased rapidly</em><em>, but </em><em>few studies has looked at</em><em> the employability of their graduates. </em><em>T</em><em>his paper </em><em>is aimed to</em><em> investigate the direct effects of curriculum on the professional careers of alumni as reflected in their current employment status </em><em>and</em><em> level of academic advancement. </em><em>A </em><em>questionnaire</em><em> survey was carried out to</em><em> evaluate multiple aspects of teaching, including learning effectiveness</em><em>, </em><em>core competency</em><em>, c</em><em>urriculum design and repay the society. Through an analysis of 150 named and 300 anonymous questionnaires, this study analyz</em><em>ed </em><em>the learning effectiveness as the mediator for the careers of alumni, using the Amos statistical package for Structural Equation Modeling</em><em> </em><em>(SEM), along with other related techniques, such as Confirmatory Factor Analysis</em><em> </em><em>(CFA)</em><em>. The analyses have </em><em>produce</em><em>d</em><em> parameter estimates and goodness-of-fit indices, which could be useful for many purposes, such as examining longitudinal data and comparing groups. It is hoped that this brief study may provide a better understanding and a basis for future studies.</em><em></em></p>

scholarly journals Resilience Analysis of Crisis in Medical Environments: A Structural Equation Modeling based on Crisis Management Components

2022 ◽  
Author(s):  
SAMIRA GHIYASI ◽  
FATEMEH VERDI BAGHDADI ◽  
FARSHAD HASHEMZADEH ◽  
AHMAD SOLTANZADEH
Keyword(s):  
Structural Equation Modeling ◽  
Crisis Management ◽  
Work Experience ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Equation Modeling ◽  
Fit Indices ◽  
Cross Sectional ◽  
Resilience Index ◽  
The Impact

Enhancing the index of crisis resilience is one of the key goals in medical environments. Various parameters can affect crisis resilience. The current study was designed to analyze crisis resilience in medical environments based on the crisis management components. This cross-sectional and descriptive-analytical study was performed in 14 hospitals and medical centers, in 2020. A sample size of 343.5 was determined based on the Cochran's formula. We used a 44-item crisis management questionnaire of Azadian et al. to collect data. The components of this questionnaire included management commitment, error learning, culture learning, awareness, preparedness, flexibility, and transparency. The data was analyzed based on the structural equation modeling approach using IBM SPSS AMOS v. 23.0. The participants’ age and work experience mean were 37.78±8.14 and 8.22±4.47 years. The index of crisis resilience was equal to 2.96±0.87. The results showed that all components of crisis management had a significant relationship with this index (p <0.05). The highest and lowest impact on the resilience index were related to preparedness (E=0.88) and transparency (E=0.60). The goodness of fit indices of this model including RMSEA, CFI, NFI, and NNFI (TLI) was 2.86, 0.071, 0.965, 0.972, and 0.978. The index of crisis resilience in the medical environments was at a moderate level. Furthermore, the structural equation modeling findings indicated that the impact of each component of crisis management should be considered in prioritizing measures to increase the level of resilience.  

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.

Structural Equation Modeling: An Alternative to Traditional Validation Techniques

1986 ◽  
Vol 14 (4) ◽  
pp. 345-352
Author(s):  
Margaret E. Bell ◽  
Jean A. Massey
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Statistical Significance ◽  
Equation Modeling ◽  
Fit Indices ◽  
T Values ◽  
Conditions Of Learning ◽  
Nursing Course ◽  
Cardiovascular Nursing

Validation of the sequencing of objectives is an important step in structural design. Prior statistical techniques, such as the reproducibility coefficient, have yielded only summary information. In contrast, structural equation modeling provides both goodness-of-fit indices and effect coefficients for links or paths between time-ordered events, i.e., objectives. Discussed here is the application of structural equation modeling to a set of objectives in a senior-level cardiovascular nursing course. Consistent with the theory-based requirement of structural equation modeling, the objectives were developed using Robert Gagné's conditions of learning. Also discussed is the use of “t” values, which indicate statistical significance of the paths, for testing instructional links in the learning model.

The Impact of BIB Spiraling-Induced Missing Data Patterns on Goodness-of-Fit Tests in Factor Analysis

1995 ◽  
Vol 20 (1) ◽  
pp. 69-82 ◽  
Author(s):  
David Kaplan
Keyword(s):  
Factor Analysis ◽  
Missing Data ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Covariance Matrices ◽  
Equation Modeling ◽  
Continuous Variables ◽  
Goodness Of Fit Test ◽  
New Approach ◽  
The Impact

This article considers the impact of missing data arising from balanced incomplete block (BIB) spiraled designs on the chi-square goodness-of-fit test in factor analysis. Specifically, data arising from BIB designs possess a unique pattern of missing data that can be characterized as missing completely at random (MCAR). Standard approaches to factor analyzing such data rest on forming pairwise available case (PAC) covariance matrices. Developments in statistical theory for missing data show that PAC covariance matrices may not satisfy Wishart distribution assumptions underlying factor analysis, thus impacting tests of model fit. One approach, advocated by Muthén, Kaplan, and Hollis (1987) for handling missing data in structural equation modeling, is proposed as a possible solution to these problems. This study compares the new approach to the standard PAC approach in a Monte Carlo framework. Results show that tests of goodness-of-fit are very sensitive to PAC approaches even when data are MCAR, as is the case for BIB designs. The new approach is shown to outperform the PAC approach for continuous variables and is comparatively better for dichotomous variables.

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 Correcting the Bias of the Root Mean Squared Error of Approximation under Missing Data

2018 ◽  
Author(s):  
Cailey Elizabeth Fitzgerald ◽  
Ryne Estabrook ◽  
Daniel Patrick Martin ◽  
Andreas Markus Brandmaier ◽  
Timo von Oertzen
Keyword(s):  
Missing Data ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Mean Squared Error ◽  
Equation Modeling ◽  
Fit Indices ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Acceptable Model ◽  
Downward Bias

Missing data are ubiquitous in both small and large datasets. Missing data may come about as a result of coding or computer error, participant absences, or it may be intentional, as in planned missing designs. We discuss missing data as it relates to goodness-of-fit indices in Structural Equation Modeling (SEM), specifically the effects of missing data on the Root Mean Squared Error of Approximation (RMSEA). We use simulations to show that naive implementations of the RMSEA have a downward bias in the presence of missing data and, thus, overestimate model goodness-of-fit. Unfortunately, many state-of-the-art software packages report the biased form of RMSEA. As a consequence, the community may have been accepting a much larger fraction of models with non-acceptable model fit. We propose a bias-correction for the RMSEA based on information-theoretic considerations that take into account the expected misfit of a person with fully observed data. This results in an RMSEA which is asymptotically independent of the proportion of missing data for misspecified models. Importantly, results of the corrected RMSEA computation are identical to naive RMSEA if there are no missing data.

scholarly journals Driving value creation in the new economy following the COVID-19 crisis. Data-mining students’ satisfaction from online teaching in the virtual academic climate.

2021 ◽  
Vol 19 (5) ◽  
pp. pp452-468
Author(s):  
Eyal Eckhaus ◽  
Nitza Davidovitch
Keyword(s):  
Mixed Methods Research ◽  
Structural Equation ◽  
Online Teaching ◽  
Teaching And Learning ◽  
Goodness Of Fit ◽  
Equation Modeling ◽  
Academic Institutions ◽  
Ethical Implications ◽  
Advantages And Disadvantages ◽  
The Impact

  This study examines the advantages of online teaching from the perspective of students at eleven institutions of higher education, universities and colleges, in Israel. The study was conducted at the end of the second semester of their academic studies, after students had experienced “face to face” studies, and they were asked to reply freely to an open question on their evaluation of the benefits of transitioning to online teaching. Students were forced to cope with a new reality, where they were compelled to study in a “digital classroom”. The academic-social climate, the conduct of lecturer-student relations, and the relations among the students themselves, changed instantaneously, with no preparation by any of those involved. Hence, the research findings can illuminate the strengths of online teaching, with a view to the future. Was the impact of teaching and learning in the coronavirus circumstances a one-time event for the students or one from which it is possible to examine and embrace new ways of learning as they see them?   Based on 1,937 fully completed surveys from eleven academic institutions, we employed a mixed methods research design. Major themes were manually tagged, and an empirical model was developed. Structural Equation Modeling (SEM) was utilized to test the model’s goodness-of-fit. Findings present a host of parameters that have a significant positive influence on students’ positive perception of the transition to online teaching.   This study is the first to thoroughly examine advantages and disadvantages of the transition to online teaching among a large group of students, from several different academic institutions, and presents both qualitative and empirical results. Ethical implications of the findings are discussed.  

PENGARUH PENGALAMAN, KEPERCAYAAN, CITRA TERHADAP LOYALITAS MEREK DAN DAMPAKNYA TERHADAP SIKAP KONSUMEN PADA PERLUASAN MEREK OBAT FLU DAN BATUK

2018 ◽  
Vol 13 (1) ◽  
pp. 12-24
Author(s):  
Handono Handoyo
Keyword(s):  
Brand Loyalty ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Design Research ◽  
Brand Extension ◽  
Equation Modeling ◽  
Product Price ◽  
Over The Counter ◽  
The People ◽  
The Impact

The pharmaceutical sector in Indonesia has grown by 12.5% per year, as prescriptiondrugs, Over the Counter (OTC) medicines and dietary supplements (nutrition and vitamins) allexperience increased sales. Bodrex is one type of over-the-counter medication to overcome theheadache that has been well known and believed for more than 45 years by the people in Indonesiaand then innovate manufacturers to expand the brand by launching Bodrex Flu and Cough.IMS datamarket shared Flu and Cough drug of Bodrex sales decline every year ie in 2013 amounted to 1.10%,in 2014 amounted 0.89% and in 2014 amounted to 0.84%. The purpose of this research was todetermine the effect of marketing mix composed of product, price, promotion and place of the brandloyalty so that the impact on consumer brand extension attitude. Questionnaire with closed questionsare used as design research instrument, and as many as 200 respondents were sampled in theresearch. Methods of SEM (Structural Equation Modeling) is used as a technique of data analysis,SPSS 16 and Lisrel 8.7 for windows was used to analysis the data obtained after the research. Theresearch shows that the experience, trust and image simultaneously influence the brand loyalty sothat the impact on consumer brand extension flu and cough drug eligible Goodness of fit. Partiallybrand experience significantly influence brand loyalty with t values 2.12. Partially Brand Trustaffects loyalty because it has a t value of 0.97. Partially Brand Image significant effect on purchasingdecision because have value 2,93.Secara Partial Loyalitas significant influence to ConsumerAttitude on Brand Extension because have value 11,11.

scholarly journals Health of Youth in Transition in Hong Kong

2020 ◽  
Vol 17 (11) ◽  
pp. 3791
Author(s):  
Ka-Man Leung ◽  
Folake Orekoya ◽  
Adrian J. Bailey ◽  
Hor-Yan Lai ◽  
Ka-Yi Chan ◽  
...  
Keyword(s):  
Hong Kong ◽  
Structural Equation ◽  
Structural Model ◽  
Vigorous Physical Activity ◽  
Equation Modeling ◽  
Fit Indices ◽  
Or Education ◽  
Individual Level ◽  
Recreation Facilities ◽  
The Impact

This study aimed to examine the impact of individual (level of vigorous physical activity (VPA) and frequency of using sports and recreation facilities), interpersonal (perceived social cohesion (PSC)), and neighborhood environmental (availability of sports and recreation facilities) factors on youths’ health in transition in Hong Kong. A sample of 508 individuals aged 17–23 years from all Hong Kong council districts randomly completed validated questionnaires by telephone survey. Of 508,302 individuals with complete data pertaining to address geocoding were selected for further analyses. Overall, more than half of them (56.3%) used sports and recreation facilities once per month or less. Structural equation modeling was used to examine the relationship among the studies’ constructs. The results indicated that the proposed model sufficiently fitted the data (χ2 (24) = 32.23, p < 0.12; CFI = 0.977; SRMR = 0.051; RMSEA = 0.034 (90% CI = 0.000 to 0.061). However, two items of PSC were sequentially removed due to their low standardized factor loadings (<0.3). A structural model was reinserted into data analyses, and the modified model fitted the data well as indicated by fit indices (χ2 (11) = 15.29, p < 0.17; CFI = 0.987; SRMR = 0.054; RMSEA = 0.036 (90% CI = 0.000 to 0.075). Only VPA (β = 0.27, p = 0.0005) and PSC (β = 0.12, p = 0.048) were significantly related to perceived health at an individual level. To promote youth health, the Hong Kong government may work with the business sector, community groups, or education institutions to develop community programs to keep youths active (especially VPA) and to build more cohesive, trustful relationships among youths in the neighborhood.

Study on farmers land consolidation adaptation intention

2018 ◽  
Vol 10 (4) ◽  
pp. 666-682 ◽  
Author(s):  
Abrham Tezera Gessesse ◽  
Houjian Li ◽  
Ge He ◽  
Araya Alemie Berhe
Keyword(s):  
Social Network ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Rehabilitation Program ◽  
Land Consolidation ◽  
Equation Modeling ◽  
Content Type ◽  
Face To Face ◽  
Effects Of Media ◽  
The Impact

Purpose The purpose of this paper is to examine the effects of media and social network in the development of farmers land consolidation (LC) awareness, perception and adaptation intention decisions. Design/methodology/approach A face-to-face interview was carried out with randomly selected farmers from three districts (Neijiang, Ya’an and Dujiangyan) of Sichuan province, China. A structural equation modeling (SEM) technique was used to test the conceptual framework of this study. The SEM was set to confirmatory factor analysis of the measurement and structural models for maximum likelihood estimate by means of IBM-AMOS to capture the models goodness-of-fit. Findings The result indicates that social network contributes a significant role in the development of farmers LC awareness, perception and adaptation intention decisions than media. The total (direct and indirect) effect of social network and media toward the farmers LC adaptation intention is 56.7 and 14 percent, respectively. When farmers are aware and perceived of LC and rehabilitation program, their adaptation intention improves significantly. Therefore, social network is a useful tool in improving the awareness, perception and adaptation intention decisions of Sichuan farmers compared to media. Originality/value This study tests the applicability of SEM techniques to understand the farmers LC adaptation intentions; and synthesizes the impact of social network and media in the development of the farmers LC awareness, perception as well as adaptation intentions. This study serves as an outline for assessing the adaptation intention of farmers toward sustainable land management.

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