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.

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 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 Evaluation of the Social Benefits of Financial Innovation Towns Based on SEM Model——Taking Yuhuang Shannan Fund Town as an Example

2021 ◽  
Vol 251 ◽  
pp. 01075
Author(s):  
Guimei Wu ◽  
Yuting Ye ◽  
Ting Li ◽  
Xueqin Chen ◽  
Shasha Zhu
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Latent Variables ◽  
Evaluation System ◽  
Structural Equation ◽  
Financial Innovation ◽  
Social Benefits ◽  
The Social ◽  
Confirmatory Factor ◽  
The Impact

Taking Yuhuang Shannan Fund Town as a typical example, this paper established a financial innovation characteristic town social benefit evaluation system through on-site investigation and quantitative analysis. It can be summarized into five major aspects: social and livelihood development, socioeconomic development, ecological environment, infrastructure construction and related system construction. Then we constructed a structural equation model (SEM) for the evaluation of social benefits of towns, and made the assumption that the impact of the five latent variables on the total variable of social benefits is positive. Through the first-order confirmatory factor analysis and the second-order confirmatory factor analysis of the structural equation, it is concluded that the five latent variables have a positively significant impact on the social benefits and have strong internal consistency. According to the degree of influence, effective suggestions are given from private equity and industrial foundation, which provide reference and practical guidance of the construction of financial innovative towns in the future.

Testing Factorial Invariance across Groups: A Reconceptualization and Proposed New Method

1999 ◽  
Vol 25 (1) ◽  
pp. 1-27 ◽  
Author(s):  
Gordon W. Cheung ◽  
Roger B. Rensvold
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Latent Variables ◽  
Cross Cultural ◽  
Factorial Invariance ◽  
Published Data ◽  
Data Set ◽  
Test Items ◽  
Confirmatory Factor ◽  
Different Cultures

Many cross-cultural researchers are concerned with factorial invariance; that is, with whether or not members of different cultures associate survey items, or similar measures, with similar constructs. Researchers usually test items for factorial invariance using confirmatory factor analysis (CFA). CFA, however, poses certain problems that must be dealt with. Primary among them is standardization, the process that assigns units of measurement to the constructs (latent variables). Two standardization procedures and several minor variants have been reported in the literature, but using these procedures when testing for factorial invariance can lead to inaccurate results. In this paper we review basic theory, and propose an extension of Byrne, Shavelson, and Muthgn’s (1989) procedure for identifying non-invariant items. The extended procedure solves the standardization problem by performing a systematic comparison of all pairs of factor loadings across groups. A numerical example based upon a large published data set is presented to illustrate the utility of the new procedure, particularly with regard to partial factorial invariance.

scholarly journals Confirmatory Factor Analysis to Establish Determinants of Wireless Technology in the Indian Healthcare

2011 ◽  
pp. 811-829
Author(s):  
Raj Gururajan ◽  
Tiana Gurney ◽  
Abdul Hafeez-Baig
Keyword(s):  
Factor Analysis ◽  
Qualitative Study ◽  
Confirmatory Factor Analysis ◽  
Regression Model ◽  
Qualitative Data ◽  
Survey Instrument ◽  
Second Order ◽  
Interview Data ◽  
Wireless Technology ◽  
Confirmatory Factor

This study reports the determinants of wireless technology in the Indian healthcare validated by a second order regression model. In order to assert the determinants, a qualitative study was conducted with 30 physicians using interviews to arrive at a set of barriers and drivers. Further analysis of the qualitative data indicated that there is a third component emerging, namely, clinical influence. The interview data was used to develop a survey instrument and this was administered on the Indian clinicians with 200 completed surveys. This data was used to establish the sub-components of the three major determinants as identified in the qualitative study. This is reported in this article.

The confirmatory factor analysis of teacher’s research motivation scale

2018 ◽  
Vol 8 (3) ◽  
pp. 101-106
Author(s):  
Ujsara Prasertsin
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Intrinsic Motivation ◽  
Latent Variables ◽  
Latent Variable ◽  
Extrinsic Motivation ◽  
Class Action ◽  
P Value ◽  
Confirmatory Factor ◽  
Motivation Scale

The purpose of the research is to develop the measurement of motivation scale of in class action research conducted by school teachers. The sampling is 403 teachers, subordinated to Office of The Basic Education Commission. Data collection was conducted through questionnaires of 20 questions. The questions were designed into 5 levels following to the motivation scale in research measurement of Deemer, Mahoney, & Ball (2010). This 20 questions questionnaire is consisting of 3 latent variables that are 9 questions of intrinsic motivation, 6 questions of failure avoidance and 5 questions extrinsic motivation. The purpose of confirmatory factor analysis (CFA) is to test the construct validity of research latent variables that found the harmony correlation of empirical data contained in this research model, the value of Chi-Square ( )=89.224 at the degree of freedom=71, P value=0.071, GFI=0.978, AGFI=0.936, RMSEA=0.062, RMR=0.018, Model AIC=367.224, Saturated AIC=420.000, Model CAIC= 1062.076, Saturated CAIC = 1469.777. The weight factors of latent variable are 0.692, -0.066 and 0.894 retrospectively. The value of reliability according to cronbach’s alpha coefficient of correlation is 0.479, 0.004 and 0.800 retrospectively. Moreover correlation matrix of 20 observed variables shows the correlation among latent variables of intrinsic motivation and extrinsic motivation with the significant level of statistic correlation at 0.05, the correlation value ranged between 0.196-0.604 and 0.196-0.696 retrospectively. The highest value of correlation scored 0.696 is founded in observed variables of intrinsic motivation latent variable. Keywords: Confirmatory, factor analysis, teacher, research motivation

Exploratory and Confirmatory Factor Analysis

2018 ◽  
Author(s):  
Peter Miksza ◽  
Kenneth Elpus
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Music Education ◽  
Latent Variables ◽  
Data Driven ◽  
Factor Analyses ◽  
Statistical Tool ◽  
Analysis Techniques ◽  
Confirmatory Factor ◽  
Research Questions

This chapter consists of data-driven examples of how factor analysis as a statistical tool can be applied in music education research. The chapter presents examples of how factor analysis methods can be used to identify latent variables, which in turn can be used to represent a broad set of measured variables. Exploratory and confirmatory factor analysis techniques are compared and illustrated with data-driven examples. The examples highlight some of the major considerations and basic steps for performing factor analyses so that the reader can begin to imagine how to apply this technique to their own research 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.

scholarly journals Application of Confirmatory Factor Analysis for Correlate of Students Attitude, Self-Belief, Students Engagement in Mathematics Lessons and Mathematics Achievement

2018 ◽  
Vol 7 (2.29) ◽  
pp. 535 ◽  
Author(s):  
Desi Rahmatina
Keyword(s):  
Factor Analysis ◽  
Mathematics Achievement ◽  
Confirmatory Factor Analysis ◽  
Latent Variables ◽  
Negative Relationship ◽  
Mathematics Lesson ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
Data Set ◽  
Confirmatory Factor

The study aimed to propose the Confirmatory Factor Analysis via four latent variables : 1) Students Attitude toward mathematics, 2) Self-belief in mathematics, 3) Students engagement in mathematics lessons and 4) Mathematics Achievement  and 19 observed variables and then we conduct to the correlations between latent variables and observed variables. The subjects were 5795 eight grades students from the result of the Trends in International Mathematics and Science Study (TIMSS) 2011 assessment conducted in Indonesia. Data Analysis were undertaken using the Lisrel software to examine the effect of students attitude toward mathematics, students self belief and students engagement in mathematics lesson for mathematics achievement. The proposed Confirmatory Factor Analysis model of the latent variables and observed variables fit well with the empirical data set (RMSEA = 0,071). The results of multivariate analyses has shown a strong negative relationship between student attitude toward mathematics, self-belief in mathematics and their mathematics achievement (t value = -6.32 and t = -6.10, respectively) and a strong positive relationship between students engagement in mathematics lesson with mathematics achievement (t value = 8,28).   

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