scholarly journals Analysis of Psychometric Properties and Validation of the Personal Learning Environments Questionnaire (B-PLE) in Higher Education Students

2021 ◽  
Vol 13 (16) ◽  
pp. 8736
Author(s):  
José Luis Carrasco-Sáez ◽  
Marcelo Careaga Butter ◽  
María Graciela Badilla-Quintana ◽  
Juan Molina-Farfán
Keyword(s):  
Higher Education ◽  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Learning Environments ◽  
Latent Variables ◽  
Cultural Transition ◽  
Personal Learning ◽  
New Instrument ◽  
Confirmatory Factor ◽  
Personal Learning Environments

We are living through a cultural transition, characterized by technological disruption and the erosion of the modern ideology, which are redefining the behavior of citizens in their physical and digital spaces. Virtuality emerges as a new human dimension, making it necessary to rethink social and educational paradigms for a new two-dimensional citizen. In this context, the psychometric features and validation procedure of an instrument (B-PLE) for analyzing Personal Learning Environments (PLE) in students of higher education institutions in the Biobío Region of Chile are described. There were four phases to the validation method: (i) content validity, as determined by six experts in education and ICT; (ii) pilot test, with a non-probabilistic sample of 327 subjects; (iii) principal components analysis (PCA); and (iv) confirmatory factor analysis (CFA). The results of the dimensional analysis made it possible to define the structure of the new instrument, explaining 72% of the total variance. The reliability analysis yielded an alpha coefficient of 0.92. The confirmatory factor analysis showed fit indexes that support the proposed theoretical model. In conclusion, the instrument was composed of three latent variables: Open learning (OL), with six questions, Information management (IM), with two questions, and Knowledge creation and transfer (KCT), with three questions.

scholarly journals Sociological Importance and Validation of a Questionnaire for the Sustainability of Personal Learning Environments (PLE) in 8th Grade Students of the Biobío Region in Chile

2019 ◽  
Vol 11 (5) ◽  
pp. 1301 ◽  
Author(s):  
José Luis Carrasco-Sáez ◽  
Marcelo Careaga Butter ◽  
María Graciela Badilla-Quintana ◽  
Laura Jiménez Pérez ◽  
Juan Molina Farfán
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Latent Variables ◽  
Human Interaction ◽  
Two Dimensional ◽  
Human Beings ◽  
Personal Learning ◽  
New Instrument ◽  
Confirmatory Factor ◽  
8Th Grade

Contemporary society is going through a cultural transition that leads to new conceptions about the ways in which human beings organize socially and communicate. This process of deep social and cultural transformations is characterized by a technological disruption, in which virtuality forms a new dimension that behaves as an extension of human intelligence. This new form of human interaction impacts on the social imagination, demanding one to rethink social and educational paradigms for the two-dimensional citizen. In this context, this research article describes the sociological importance and the process of social adaptation of users to a personal learning environment (PLE). It includes the validation process of an instrument for the study of the PLE of 8th grade students belonging to 15 schools in the Biobío Region of Chile. A PLE is a frame of reference that can help to understand how two-dimensional citizens socially adapt and influence the sustainability of local and global systems. The validation method for this instrument considered four stages: i) Expert judgment: considering the opinions of six educators and experts in information and communication technologies (ICT); ii) a pilot test: that included a non-probabilistic sample of 472 subjects; iii) a principal components analysis (PCA); and iv) a confirmatory factor analysis (CFA). The Questionnaire on Work Habits and Learning for Professional Futures and the Context Questionnaire SIMCE TIC were used as a reference. When performing a psychometric analysis, a Cronbach alpha coefficient of 0.89 was obtained. This confirms that the adaptation of the instrument is good. The results of the dimensional analysis help us define a structure for the new instrument considering three components that explain 55% of the total variance. The results of the confirmatory factor analysis showed adjustment indexes that support the theoretical model proposed for the PLE study. In conclusion, the instrument was composed of three latent variables: Open self-regulated learning (OSRL) with eight questions, information management (IM) with four questions, and creation and transfer of knowledge (CTK) with four questions.

Probability-Based and Measurement-Related Hypotheses With Full Restriction for Investigations by Means of Confirmatory Factor Analysis

Methodology ◽  
2011 ◽  
Vol 7 (4) ◽  
pp. 157-164
Author(s):  
Karl Schweizer
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Cognitive Processing ◽  
Latent Variables ◽  
Repeated Measures ◽  
Latent Variable ◽  
Model Fit ◽  
Repeated Measures Data ◽  
Confirmatory Factor ◽  
And Performance

Probability-based and measurement-related hypotheses for confirmatory factor analysis of repeated-measures data are investigated. Such hypotheses comprise precise assumptions concerning the relationships among the true components associated with the levels of the design or the items of the measure. Measurement-related hypotheses concentrate on the assumed processes, as, for example, transformation and memory processes, and represent treatment-dependent differences in processing. In contrast, probability-based hypotheses provide the opportunity to consider probabilities as outcome predictions that summarize the effects of various influences. The prediction of performance guided by inexact cues serves as an example. In the empirical part of this paper probability-based and measurement-related hypotheses are applied to working-memory data. Latent variables according to both hypotheses contribute to a good model fit. The best model fit is achieved for the model including latent variables that represented serial cognitive processing and performance according to inexact cues in combination with a latent variable for subsidiary processes.

scholarly journals Approbation of the Decision Making Tendency Inventory in the Russian Sample

2018 ◽  
Vol 26 (3) ◽  
pp. 146-163
Author(s):  
A.Yu. Razvaliaeva
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Education Institution ◽  
Scale Structure ◽  
Personal Factors ◽  
Higher Education Institution ◽  
Confirmatory Factor ◽  
Ambiguity Intolerance

We present the results of approbating the Decision Making Tendency Inventory (Misuraca et al., 2015) in the Russian sample (N=423, Mage= 25,01, SD = 9,63). The development of H. Simon’s satisficing theory in the current studies is considered. Confirmatory factor analysis confirmed the theoretical three-scale structure of the inventory. We describe the relations between maximizing, minimizing and satisficing scales and personal factors of decision-making, age, and education (its level and difficulty). The study demonstrates that maximizing and satisficing are close tendencies, implemented in case of making important effortful and resource-consuming (e.g., time-consuming) decisions, whereas minimizing is connected to withdrawal from effort and knowledge, avoidant strategies and ambiguity intolerance. The yielded results suggest that satisficing needs to be trained in conditions of high demands for the cognitive sphere such as studying in a higher education institution.

ASSESSING STATISTICS ANXIETY IN AN ONLINE OR HYBRID SETTING: THE ADAPTION AND DEVELOPMENT OF A NEW INSTRUMENT SASOH

2021 ◽  
Vol 20 (1) ◽  
pp. 2
Author(s):  
Lu Liu
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Mathematics Anxiety ◽  
Discriminant Validity ◽  
Item Analysis ◽  
Statistics Anxiety ◽  
Four Dimensions ◽  
New Instrument ◽  
Confirmatory Factor ◽  
Attitudes Toward Statistics

With the purpose of developing an instrument for measuring statistics anxiety in the online or hybrid setting, this study tested the newly developed instrument in two stages. Results on item selection and exploratory factor analysis based on pilot testing (n = 115) are presented. Results on classical item analysis, the confirmatory factor analysis, the measurement invariance test results, and the predictive and discriminant validity of the final model based on formal testing (n = 709) are presented. The resulting Statistics Anxiety Scale in the Online or Hybrid setting instrument (SASOH) has 27 items and four dimensions. The four dimensions are Class and Interpretation Anxiety (CI), Fear of Asking for Help Anxiety (FA), Online System Anxiety (OS), and Pre-Conception Anxiety (PC). The results of the confirmatory factor analysis revealed that the four-factor SASOH model represents an adequate description of statistics anxiety in an online or hybrid setting. Moreover, multiple-groups confirmatory factor analysis affirmed that the resulting model achieved at least partial measurement and structural invariance across gender and program. In addition, attitudes toward statistics significantly predicts the four factors of statistics anxiety, and the discriminant validity from mathematics anxiety was confirmed. Recommendations for future studies are also provided.

scholarly journals Formation of Social Capital for Community College Students: A Second-Order Confirmatory Factor Analysis

2018 ◽  
Vol 47 (1) ◽  
pp. 3-30 ◽  
Author(s):  
Yu (April) Chen ◽  
Soko S. Starobin
Keyword(s):  
College Students ◽  
Community College ◽  
Social Capital ◽  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Community College Students ◽  
Latent Variables ◽  
Institutional Agents ◽  
Family Social Capital ◽  
Confirmatory Factor

Objective: This quantitative study constructed a statistical model to measure family social capital and college social capital among community college students. The authors also examined influences of these two types of social capital constructs on degree aspiration. Method: This study utilized the STEM (Science, Technology, Engineering and Mathematics) Student Success Literacy Survey (SSSL) to collect data in all 15 community college districts in Iowa. With more than 5,000 responses, the authors conducted descriptive analysis, exploratory and confirmatory factor analysis, and structural equation modeling (SEM) analysis. Results: College social capital was measured by three latent variables such as interaction with advisors, interaction with faculty members, and transfer capital. The three latent variables were further measured by 14 survey items. Family social capital was measured by six survey items that described parent–child interaction in high school. The SEM results indicated that college social capital had stronger direct influences on degree aspiration compared with family social capital. The impact of family social capital was delivered through the mediation of college social capital. Contributions: Findings contributed to the literature by emphasizing the important role of institutional agents in promoting degree aspiration. Intervention programs should be implemented to encourage interactions between institutional agents and underrepresented and disadvantaged students.

scholarly journals Student Burnout in Higher Education: A Demand-Resource Model Approach

2022 ◽  
Author(s):  
Balázs Jagodics ◽  
Éva Szabó
Keyword(s):  
Higher Education ◽  
Factor Analysis ◽  
Structural Equation Modeling ◽  
Confirmatory Factor Analysis ◽  
Correlation Analysis ◽  
Structural Equation ◽  
Equation Modeling ◽  
Resource Model ◽  
Confirmatory Factor ◽  
Student Burnout

AbstractStudent burnout is a serious problem in higher education. It is associated with harmful consequences, such as decreased engagement, performance, and motivation, which can lead to dropout. The job demand-resource model of burnout is a comprehensive framework to grasp the factors related to the emergence of burnout. Although numerous studies claim its suitability in explaining burnout in work environments, its applicability in the educational context is less explored. The study aimed to analyze the structure and reliability of the newly developed University Demand-Resource Questionnaire (UDRQ) and to explore the links between its subscales and symptoms of student burnout. Using the online survey method, 743 Hungarian undergraduate students participated in the data collection. The student version of the Maslach Burnout Inventory was used in addition to the UDRQ. In the data analysis procedure, confirmatory factor analysis, correlation analysis, and structural equation modeling were utilized. The confirmatory factor analysis identified a five-factor structure related to both demands and resources. Correlation analysis revealed burnout to be associated positively to the subscales of demands and negatively to resources. Structural equation modeling analysis indicated that all five demands and two resources subscales can be used to build a model that predicts a significant proportion of the variance of student burnout scores. The findings suggest the demand-resource theory is an appropriate framework to predict burnout in higher education. The newly developed UDRQ has stable structure and good reliability and can be a useful tool in subsequent research related to student burnout.

scholarly journals Expected Learning Outcomes: A Confirmatory Factor Analysis of Higher Education in Interior Architecture and Interior Design for the Thailand Development

2019 ◽  
Vol 10 (4) ◽  
pp. 11-19
Author(s):  
Pornthep Jewpairojkit ◽  
Thanin Rattanolarn ◽  
Songwut Ekwuttiwongsa
Keyword(s):  
Higher Education ◽  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Learning Outcomes ◽  
Interior Design ◽  
Professional Education ◽  
Interpersonal Relationship ◽  
Measurement Model ◽  
Interior Architecture ◽  
Confirmatory Factor

Abstract Nowadays, the standard of professional education, interior architecture and interior design at higher education in Thailand must meet the certification criteria from the Professional Council. However, the expected learning outcomes results in the past studies has not studied the components of expected learning outcomes that are explicit and consistent with the 20-Year National Strategy. The researchers therefore aim to study such components to lead to the development of a standard measurement model to further expected learning outcomes. The researchers synthesized the initial components through the document to create and develop a questionnaire to evaluate the level of performance by estimating 5 levels and collect data with the senior students in the curriculum that has been approved by the Professional Council. Divided into 362 samples in the analysis of survey elements and 364 samples in Confirmatory Factor Analysis by Cluster Random Sampling from state and private universities. The survey component analysis resulted in 6 components along with confirmatory factor analysis of empirical component. The results of the sequence analysis of weight components from descending order as follows: Cognitive for profession skill (CP)=.96 Interpersonal relationship and responsibility skill (IR)=.89 In numerical communication and information technology skill(NC)=.87 Profession future of Thailand skill (PF)=.85 Knowledge for Professional practice skill (KPP)=.73 Moral and ethical skill (ME) =.67.

scholarly journals DETERMINANTS OF ACADEMIC RESEARCH COMMERCIALIZATION IN IRAN GAS INDUSTRY

2013 ◽  
Vol 11 (1) ◽  
pp. 34-49
Author(s):  
Yaghoub Zahedi Anbardan
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Research Methodology ◽  
Latent Variables ◽  
Qualitative Data ◽  
Academic Research ◽  
Gas Industry ◽  
Research Commercialization ◽  
Confirmatory Factor ◽  
Quantitative Results

The aim of this research is to identify determinants of academic research commercialization in the Iranian gas industry. For this purpose, we have applied a mixed research methodology. After reviewing the literature we conducted interviews with academics that have experience in the gas industry commercialization in order to develop the research questionnaire. Qualitative data were analyzed by codifying the interviews. To analyze the quantitative results we applied the exploratory and confirmatory factor analysis (EFA, CFA). The results show that there are 6 latent variables and 28 observed variables including the gas industry academic research commercialization requirements and prerequisites in Iran.

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