scholarly journals Genomic Bayesian confirmatory factor analysis and Bayesian network to characterize a wide spectrum of rice phenotypes

2018 ◽  
Author(s):  
Haipeng Yu ◽  
Malachy T. Campbell ◽  
Qi Zhang ◽  
Harkamal Walia ◽  
Gota Morota
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Bayesian Network ◽  
Flowering Time ◽  
Latent Variables ◽  
Complex Traits ◽  
Latent Variable ◽  
Grain Morphology ◽  
Hill Climbing ◽  
Confirmatory Factor

AbstractWith the advent of high-throughput phenotyping platforms, plant breeders have a means to assess many traits for large breeding populations. However, understanding the genetic interdependencies among high-dimensional traits in a statistically robust manner remains a major challenge. Since multiple phenotypes likely share mutual relationships, elucidating the interdependencies among economically important traits can better inform breeding decisions and accelerate the genetic improvement of plants. The objective of this study was to leverage confirmatory factor analysis and graphical modeling to elucidate the genetic interdependencies among a diverse agronomic traits in rice. We used a Bayesian network to depict conditional dependencies among phenotypes, which can not be obtained by standard multitrait analysis. We utilized Bayesian confirmatory factor analysis which hypothesized that 48 observed phenotypes resulted from six latent variables including grain morphology, morphology, flowering time, physiology, yield, and morphological salt response. This was followed by studying the genetics of each latent variable, which is also known as factor, using single nucleotide polymorphisms. Bayesian network structures involving the genomic component of six latent variables were established by fitting four algorithms (i.e., Hill Climbing, Tabu, Max-Min Hill Climbing, and General 2-Phase Restricted Maximization algorithms). Physiological components influenced the flowering time and grain morphology, and morphology and grain morphology influenced yield. In summary, we show the Bayesian network coupled with factor analysis can provide an effective approach to understand the interdependence patterns among phenotypes and to predict the potential influence of external interventions or selection related to target traits in the interrelated complex traits systems.

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.

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

scholarly journals Can Variances of Latent Variables be Scaled in Such a Way That They Correspond to Eigenvalues?

2017 ◽  
Vol 6 (6) ◽  
pp. 35 ◽  
Author(s):  
Karl Schweizer ◽  
Stefan Troche ◽  
Siegbert Reiß
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Simulation Study ◽  
Latent Variables ◽  
Latent Variable ◽  
Factor Model ◽  
Estimation Method ◽  
Factor Loadings ◽  
Confirmatory Factor ◽  
Theoretical Foundations

The paper reports an investigation of whether sums of squared factor loadings obtained in confirmatory factor analysis correspond to eigenvalues of exploratory factor analysis. The sum of squared factor loadings reflects the variance of the corresponding latent variable if the variance parameter of the confirmatory factor model is set equal to one. Hence, the computation of the sum implies a specific type of scaling of the variance. While the investigation of the theoretical foundations suggested the expected correspondence between sums of squared factor loadings and eigenvalues, the necessity of procedural specifications in the application, as for example the estimation method, revealed external influences on the outcome. A simulation study was conducted that demonstrated the possibility of exact correspondence if the same estimation method was applied. However, in the majority of realized specifications the estimates showed similar sizes but no correspondence. 

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 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.

scholarly journals HAKI MODUL PENGOLAHAN DATA DENGAN LISREL BETI NURBAITI

2019 ◽  
Author(s):  
beti nurbaiti
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Latent Variable ◽  
Goodness Of Fit ◽  
Confirmatory Factor ◽  
Fit Index

Tutorial singkat yang menjelaskan cara singkat dan praktif mempersiapkan kuesioner penelitian, mengimpor data dan mengolah dengan Lisrel, mulai dari uji validitas, reliabilitas, Confirmatory Factor Analysis hingga uji model struktural penelitian. Selain modul ini, dapat dipelajari melalui Channel Youtube untuk Tutorial Lisrel 1 hingga 7 dengan link sebagai berikut : a) Tutorial -1 Lisrel, Persiapan Kuesioner Untuk Diolah Dengan Lisrel, hingga Uji Statistik Deskriptif; Link Youtube :https://www.youtube.com/watch?v=QmdDMepmLCwb) Tutorial Lisrel -1 lanjutan, Import Data Hingga Uji Statistik Deskriptif (Output Lisrel); Link Youtube : https://www.youtube.com/watch?v=QmdDMepmLCwc) Tutorial -2 Lisrel , Convert Output Lisrel dalam MS Word; Link Youtube : https://www.youtube.com/watch?v=myWFkuWauH4d) Tutorial -3, Uji Model Pengukuran dan Kecocokan Model (Goodness Of Fit Index); Link Youtube : https://www.youtube.com/watch?v=aMEFdifPod4e) Tutorial -4, Uji Model Pengukuran dengan Kecocokan Model Perfect Fit; Link Youtube : https://www.youtube.com/watch?v=Sj2VR2VWVOAf) Tutorial -5, Penyederhanaan Model Penelitian (Latent Variable Score/LVS) Dengan Lisrel; Link Youtube : https://www.youtube.com/watch?v=41IlJsr-ESkg) Tutorial- 6 Lisrel, Uji Validitas dan Reliabilitas Dengan Lisrel; Link Youtube : https://www.youtube.com/watch?v=Ma1IlMWwZawh) Tutorial -7, Uji Confirmatory Factor Analysis (CFA) dan Uji Model Struktural Dengan Lisrel; Link Youtube : https://www.youtube.com/watch?v=CyQOfCooUwQ

scholarly journals Prediksi Minat Perilaku Menggunakan Jasa Pajak Elektronik: Analisis Faktor Konfirmatori

InFestasi ◽  
2018 ◽  
Vol 13 (2) ◽  
pp. 321
Author(s):  
Jullie J. Sondakh
Keyword(s):  
Factor Analysis ◽  
Structural Equation Modeling ◽  
Confirmatory Factor Analysis ◽  
Behavioral Intention ◽  
Latent Variable ◽  
Structural Equation ◽  
Structural Model ◽  
Measurement Model ◽  
Equation Modeling ◽  
Confirmatory Factor

<p class="Ventura-Abstract">The purpose of this research is to predict the tax payer behavioral intention of using the e-SPT through the application of Technology Acceptance Model (TAM).</p><p class="Ventura-Abstract">This research used survey method to collect primary data from the population of tax payer in the city of Manado and Bitung with 156 respondents while using judgement sampling method.The data analysis is using Structural Equation Modeling (SEM) that consists of two steps; the measurement model and structural model. The focus of this research is on the first step of SEM modeling, which is the measurement model by using the Confirmatory Factor Analysis (CFA). The purpose of this analysis is to test the validity and reliability from the indicator of the construct or latent variable researched, thus, we will obtain the fit construct or latent variable before proceeding to the next step of SEM which is the structural model.Based on the confirmatory factor analysis (CFA), we obtained the validity test result, convergent validity, and reliability test result, construct reliability and variance extracted, from the indicator of construct or latent variable which are perceived usefulness, perceived ease of use, attitude towards e-SPT, and behavioral intention to use e-SPT. The reliabilty and validity test result showed that there is no indicator from all the tested latent variable to be excluded for the next step of Structural Equation Modeling (SEM)  which is the structural model.</p><p class="Ventura-Abstract"> </p><p class="Ventura-Abstract">Tujuan penelitian ini adalah melakukan prediksi minat perilaku wajib pajak menggunakan  e-SPT melalui penerapanTechnology Acceptance Model (TAM). Penelitian ini menggunakan metode survei untuk mengumpulkan data primer dari populasi yaitu wajib pajak di Kota Manado dan Bitung dengan jumlah sampel sebanyak 156 responden serta penentuan sampel berdasarkan metode  judgment sampling. Teknik analisis data menggunakan pemodelan Structural Equation Modeling (SEM) yang terdiri  dari dua tahapan yaitu model pengukuran (measurement model) dan model struktural (structural model). Fokus penelitian ini adalah pada pemodelan SEM tahap pertama yaitu model pengukuran (measurement model)  melalui  analisis faktor konfirmatori (Confirmatory Factor Analysis - CFA). Analisis ini  bertujuan  untuk menguji validitas dan reliabilitas dari indikator-indikator pembentuk konstruk atau variabel laten yang diteliti sehingga diperoleh  konstruk atau variabel laten yang fit  sebelum lanjut ke tahap pemodelan SEM berikutnya  yaitu model struktural. Berdasarkan analisis faktor konfirmatori (Confirmatory Factor Analysis - CFA) diperoleh hasil uji validitas yaitu signifikansi  factor loading (convergent validity) dan reliabilitas (construct reliability dan variance extracted) dari indikator pembentuk konstruk atau variabel laten kegunaan persepsian (Perceived Usefulness), kemudahan penggunaan persepsian (Perceived ease of use), sikap terhadap penggunaan  e-SPT (Attitude towards e-SPT) dan  minat perilaku  menggunakan e-SPT (Behavioral intention to use e-SPT). Hasil uji validitas dan reliabilitas ini menunjukan bahwa tidak ada indikator dari variabel kegunaan persepsian (Perceived Usefulness), kemudahan penggunaan persepsian (Perceived ease of use), sikap terhadap penggunaan  e-SPT (Attitude towards e-SPT) dan  minat perilaku  menggunakan e-SPT (Behavioral intention to use e-SPT) yang di hilangkan pada analisis selanjutnya yaitu pemodelan Structural Equation Modeling (SEM) tahap kedua  sehingga  dapat dilakukan estimasi model persamaan struktural (structural model).</p>

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.

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