Educational Services in Second Life

Among many changes influenced by the Internet, interactivity in spaces that promote relationships, entertainment, and businesses can be highlighted. Considering this, Second Life stands out because it is a tridimensional online environment which imitates human real social life. Despite social and commercial influences, Second Life suggests a new format for e-learning. Then, the question of how to explore the facets of an online learning environment may be answered by Flow Theory. Hence, the main objective of this paper is to analyze the most significant antecedent and subsequent relations of the Flow Experience in the Second Life’s educational environment, based on Novak, Hoffman, and Yung (2000). This research used tools from multivariate statistical analysis such as confirmatory factor analysis and structural equation modeling. The results confirmed the hypotheses, indicating that there is flow in Second Life’s e-learning environment, with interactive speed, exploratory behavior and telepresence as the most significant constructs detected.

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Educational Services in Second Life

International Journal of Web-Based Learning and Teaching Technologies ◽

10.4018/jwltt.2011040101 ◽

2011 ◽

Vol 6 (2) ◽

pp. 1-17 ◽

Cited By ~ 2

Author(s):

Joana Ancila Pessoa Forte ◽

Danielle Miranda de Oliveira Arruda Gomes ◽

Cláudio André Gondim Nogueira ◽

Carlos Felipe Cavalcante de Almeida

Keyword(s):

Learning Environment ◽

Second Life ◽

Social Life ◽

Structural Equation ◽

Equation Modeling ◽

Online Learning Environment ◽

Online Environment ◽

Multivariate Statistical ◽

Confirmatory Factor ◽

E Learning

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An Introduction to Path Analysis Modeling Using LISREL

Student Satisfaction and Learning Outcomes in E-Learning - Advances in Educational Marketing, Administration, and Leadership ◽

10.4018/978-1-60960-615-2.ch004 ◽

2011 ◽

pp. 57-81

Author(s):

Sean B. Eom

Keyword(s):

Path Analysis ◽

Statistical Models ◽

Structural Equation ◽

Structural Equation Models ◽

Statistical Tools ◽

Multivariate Statistical ◽

Wide Range ◽

Confirmatory Factor ◽

E Learning ◽

Research Stream

Over the past decades, there has been a wide range of empirical research in the e-learning literature. The use of multivariate statistical tools has been a staple of the research stream throughout the decade. Path analysis modeling is part of four related multivariate statistical models, including regression, path analysis, confirmatory factor analysis, and structural equation models. This chapter focuses on path analysis modeling for beginners using LISREL 8.70. Several topics covered in this chapter include foundational concepts, assumptions, and steps of path analysis modeling. The major steps in path analysis modeling explained in this chapter consist of specification, identification, estimation, testing, and modification of models.

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Miscellany of Students’ Satisfaction in an Asynchronous Learning Environment

Journal of Educational Technology Systems ◽

10.1177/0047239516667499 ◽

2017 ◽

Vol 45 (4) ◽

pp. 456-475 ◽

Cited By ~ 4

Author(s):

Otu Larbi-Siaw ◽

Yaw Owusu-Agyeman

Keyword(s):

Learning Environment ◽

Structural Equation ◽

Internet Usage ◽

Equation Modeling ◽

Asynchronous Learning ◽

Exploratory Research ◽

Teacher Interaction ◽

Asynchronous Learning Environment ◽

E Learning ◽

Students Satisfaction

This study investigates the determinants of students’ satisfaction in an asynchronous learning environment using seven key considerations: the e-learning environment, student–content interaction, student and student interaction, student–teacher interaction, group cohesion and timely participation, knowledge of Internet usage, and satisfaction. The empirical data were gathered through structured questionnaires from 500 students who took courses in an asynchronous learning environment and the analysis was done using structural equation modeling. Framed along the positivist paradigm, deductive epistemology, exploratory research design, the study showed that all the seven variables served as robust antecedents of students’ satisfaction in an asynchronous learning environment.

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Test the Effect of Perceived Satisfaction, Motivation and Anxiety on Second Life Environment in Distance Learning Model: Structural Equation Modeling

International Journal of Modern Education Studies ◽

10.51383/ijonmes.2018.19 ◽

2018 ◽

Vol 2 (1) ◽

pp. 34

Author(s):

Elnour Omar ◽

Azhari Drewsh ◽

Abdomalik Ahmed

Keyword(s):

Learning Environment ◽

Undergraduate Students ◽

Second Life ◽

Convergent Validity ◽

Structural Equation ◽

Equation Modeling ◽

Asynchronous Learning ◽

Perceived Satisfaction ◽

University Of Technology ◽

Asynchronous Learning Environment

The main purpose of the present study is to predict the relationship between motivation , anxiety, perceived satisfaction and Second Life within asynchronous learning environment specifically in EFL course. Data of the present study were collected from undergraduate students - Sudan University of Technology and Science (SUST) in Sudan. The Questionnaire was conducted manually. Structural Equation Modelling (SEM) Version 16 and SPSS Version 11.5 were used. A hypothesized model was tested for model fit in the present study. The convergent validity and discriminant validity were conducted. The exogenous variables showed that Motivation was positively and significantly related to anxiety statistically level was 0.29 and Motivation was positively and significantly related to second life statistically level was 0.32 , anxiety positively and significantly related to Second Life statistically level was 0,25 whereas, perceived satisfaction was excluded in this study because did not satisfy statistical requirement. Therefore, the study was conducted in asynchronous learning environment, particularly in EFL course which is offered to undergraduate students at Deanship of Distance Education - Sudan University of Technology and Science (SUST) - Sudan.

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A Psychometric Study of the Quality Perception Questionnaire

Swiss Journal of Psychology ◽

10.1024/1421-0185/a000076 ◽

2012 ◽

Vol 71 (2) ◽

pp. 101-106 ◽

Cited By ~ 4

Author(s):

Raffaele Cioffi† ◽

Anna Coluccia ◽

Fabio Ferretti ◽

Francesca Lorini ◽

Aristide Saggino ◽

...

Keyword(s):

Structural Equation ◽

Factor Model ◽

Equation Modeling ◽

Fit Indices ◽

Quality Perception ◽

Italian Survey ◽

Confirmatory Factor ◽

Instrument Measuring ◽

Hospital Structures

The present paper reexamines the psychometric properties of the Quality Perception Questionnaire (QPQ), an Italian survey instrument measuring patients’ perceptions of the quality of a recent hospital admission experience, in a sample of 4400 patients (Mage = 56.42 years; SD = 19.71 years, 48.8% females). The 14-item survey measures four factors: satisfaction with medical doctors, nursing staff, auxiliary staff, and hospital structures. First, we tested two models using a confirmatory factor analysis (structural equation modeling): a four orthogonal factor and a four oblique factor model. The SEM fit indices and the χ² difference suggested the acceptance of the second model. We then did a simulation using a bootstrap with 1000 replications. Results confirmed the four oblique factor solution. Third, we tested whether there were significant differences with respect to age or sex. The multivariate general linear model showed no significant differences in the factors with respect to sex or age.

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Working Memory, Mental Prospection, Time Orientation, and Cognitive Insight

Journal of Individual Differences ◽

10.1027/1614-0001/a000111 ◽

2013 ◽

Vol 34 (3) ◽

pp. 159-169 ◽

Cited By ~ 4

Author(s):

Sevtap Cinan ◽

Aslı Doğan

Keyword(s):

Working Memory ◽

Structural Equation ◽

Factor Model ◽

Time Orientation ◽

Equation Modeling ◽

Cognitive Insight ◽

Confirmatory Factor ◽

Negative Effect ◽

Positive Effect ◽

Three Factor

This research is new in its attempt to take future time orientation, morningness orientation, and prospective memory as measures of mental prospection, and to examine a three-factor model that assumes working memory, mental prospection, and cognitive insight are independent but related higher-order cognitive constructs by using confirmatory factor analysis (CFA). The three-factor model produced a good fit to the data. An alternative one-factor model was tested and rejected. The results suggest that working memory and cognitive insight are distinguishable, related constructs, and that both are distinct from, but negatively associated with, mental prospection. In addition, structural equation modeling (SEM) showed that working memory had a strong positive effect on cognitive insight and a moderate negative effect on mental prospection.

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Further Insights Into the German Version of the Multidimensional Assessment of Interoceptive Awareness (MAIA)

European Journal of Psychological Assessment ◽

10.1027/1015-5759/a000404 ◽

2019 ◽

Vol 35 (3) ◽

pp. 317-325 ◽

Cited By ~ 6

Author(s):

Dorota Reis

Keyword(s):

Structural Equation Modeling ◽

Structural Equation ◽

Equation Modeling ◽

Interoceptive Awareness ◽

Multidimensional Assessment ◽

New Instrument ◽

Confirmatory Factor ◽

Complex Models ◽

Approximate Zero ◽

Bayesian Structural Equation Modeling

Abstract. Interoception is defined as an iterative process that refers to receiving, accessing, appraising, and responding to body sensations. Recently, following an extensive process of development, Mehling and colleagues (2012) proposed a new instrument, the Multidimensional Assessment of Interoceptive Awareness (MAIA), which captures these different aspects of interoception with eight subscales. The aim of this study was to reexamine the dimensionality of the MAIA by applying maximum likelihood confirmatory factor analysis (ML-CFA), exploratory structural equation modeling (ESEM), and Bayesian structural equation modeling (BSEM). ML-CFA, ESEM, and BSEM were examined in a sample of 320 German adults. ML-CFA showed a poor fit to the data. ESEM yielded a better fit and contained numerous significant cross-loadings, of which one was substantial (≥ .30). The BSEM model with approximate zero informative priors yielded an excellent fit and confirmed the substantial cross-loading found in ESEM. The study demonstrates that ESEM and BSEM are flexible techniques that can be used to improve our understanding of multidimensional constructs. In addition, BSEM can be seen as less exploratory than ESEM and it might also be used to overcome potential limitations of ESEM with regard to more complex models relative to the sample size.

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How Communication in Organizations Is Related to Foci of Commitment and Turnover Intentions

Journal of Personnel Psychology ◽

10.1027/1866-5888/a000261 ◽

2020 ◽

pp. 1-12

Author(s):

Sebastian Holzwarth ◽

George Gunnesch-Luca ◽

Roman Soucek ◽

Klaus Moser

Keyword(s):

Organizational Communication ◽

Turnover Intention ◽

Structural Equation ◽

Affective Commitment ◽

Turnover Intentions ◽

Equation Modeling ◽

Cross Sectional ◽

Confirmatory Factor ◽

Communication In Organizations ◽

Cohesion Theory

Abstract. The current study analyzes how two components of perceived organizational communication (vertical and horizontal) are related to employee turnover intentions via three types of affective commitment foci (organization, supervisor, and team). Using second-order confirmatory factor analysis and structural equation modeling techniques with a large cross-sectional dataset ( n = 3,317), our results show that, in line with social cohesion theory, vertical communication (e.g., supportiveness from the organization) is strongly related to affective organizational commitment, whereas horizontal communication (e.g., supportiveness from colleagues) is primarily related to affective team commitment. Additionally, both communication dimensions are related to affective supervisory commitment. Finally, these three foci of affective commitment incrementally explain and differentially mediate the relationship between perceived organizational communication and turnover intention.

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Dynamics of Multimorbidity Resilience and Health Outcomes Over Time in Community-Residing Older Adults

Innovation in Aging ◽

10.1093/geroni/igaa057.3285 ◽

2020 ◽

Vol 4 (Supplement_1) ◽

pp. 890-891

Author(s):

Yingzhi Xu ◽

Zahra Rahmaty ◽

Eleanor McConnell ◽

Tingzhong (Michelle) Xue ◽

Bada Kang ◽

...

Keyword(s):

Older Adults ◽

Health Outcomes ◽

Physical Health ◽

Social Life ◽

Structural Equation ◽

Equation Modeling ◽

Psychological Resilience ◽

Pain Level ◽

Reciprocal Relationships ◽

Over Time

Abstract Multimorbidity resilience may mitigate the adverse effects of multiple chronic diseases on older adults’ health. Wister et al.’s (2018) multimorbidity resilience index was developed and tested in a cross-sectional sample of older adults in Canada. Building on these findings, we examined the reciprocal relationships of resilience on outcomes to test these potentially mitigating effects in a community-based, U.S. sample of older adults over time. The study sample includes 1,054 older adults from waves 2 and 3 of the National Social Life, Health, and Aging Project (NSHAP) study (Waite et al 2020). Wister et al.’s (2018) index was mapped to NSHAP measures, and reciprocal relationships of multimorbidity resilience and health outcomes over a 5-year period was tested using structural equation modeling (SEM). Results indicated significant effects of multimorbidity resilience on self-rated physical health and pain. Interestingly, a better functional resilience at baseline conferred better self-rated physical health at follow-up, while better psychological resilience predicted lower pain level. By contrast, the influence of health outcomes on any domain of multimorbidity resilience was not detectable at all, supporting the direction of these associations from resilience to outcomes. The study systematically investigated the dynamic hypotheses between multimorbidity resilience and health outcomes. That is, whether they are determinants or consequences, or both. Our findings suggest multimorbidity resilience predicts subsequent 5-year change in health outcomes, especially self-rated physical health and pain level, but not vice versa, strengthening the evidence of the importance of resilience in the health of older adults.

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Stress and Its Impact on Academic and Social Life of Undergraduate University Students in Ghana: A Structural Equation Modeling Approach

Open Education Studies ◽

10.1515/edu-2020-0100 ◽

2020 ◽

Vol 2 (1) ◽

pp. 37-44

Author(s):

Koawo Edjah ◽

Francis Ankomah ◽

Ebenezer Domey ◽

John Ekow Laryea

Keyword(s):

Structural Equation Modeling ◽

Undergraduate Students ◽

Social Life ◽

Structural Equation ◽

Positive Impact ◽

Survey Design ◽

Equation Modeling ◽

Cross Sectional Survey ◽

Satisfaction Scale ◽

Cross Sectional

AbstractStress is concomitant with students’ life and can have a significant impact on their lives, and even how they go about their academic work. Globally, in every five visits by patients to the doctor, three are stress-related problems. This study examined stress and its impact on the academic and social life among students of a university in Ghana. The descriptive cross-sectional survey design was employed. Using the stratified and simple random (random numbers) sampling methods, 500 regular undergraduate students were engaged in the study. A questionnaire made up of Perceived Stress Scale and Students’ Life Satisfaction Scale was used to gather data for the study. Frequencies, percentages, means and standard deviation, and Structural Equation Modeling (SEM), with AMOS were used for the analyses. It was found that majority of the students were moderately stressed. Paramount among the stressors were academic stressors, followed by institutional stressors, and external stressors. Stress had a significant positive impact on the academic and social life of students. It was concluded that undergraduate students, in one way or the other, go through some kind of stress during the course of their study. It was recommended that the university, through its Students’ Affairs, and Counselling Sections, continue to empower students on how to manage and deal with stress in order to enhance their academic life.

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