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 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.

A Comparison of Reliability Estimation Based on Confirmatory Factor Analysis and Exploratory Structural Equation Models

2021 ◽  
pp. 001316442110089
Author(s):  
Yuanshu Fu ◽  
Zhonglin Wen ◽  
Yang Wang
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Structural Equation ◽  
Reliability Estimation ◽  
Model Fit ◽  
Equation Modeling ◽  
Factor Loadings ◽  
Reliability Estimates ◽  
Confirmatory Factor ◽  
Composite Reliability

Composite reliability, or coefficient omega, can be estimated using structural equation modeling. Composite reliability is usually estimated under the basic independent clusters model of confirmatory factor analysis (ICM-CFA). However, due to the existence of cross-loadings, the model fit of the exploratory structural equation model (ESEM) is often found to be substantially better than that of ICM-CFA. The present study first illustrated the method used to estimate composite reliability under ESEM and then compared the difference between ESEM and ICM-CFA in terms of composite reliability estimation under various indicators per factor, target factor loadings, cross-loadings, and sample sizes. The results showed no apparent difference in using ESEM or ICM-CFA for estimating composite reliability, and the rotation type did not affect the composite reliability estimates generated by ESEM. An empirical example was given as further proof of the results of the simulation studies. Based on the present study, we suggest that if the model fit of ESEM (regardless of the utilized rotation criteria) is acceptable but that of ICM-CFA is not, the composite reliability estimates based on the above two models should be similar. If the target factor loadings are relatively small, researchers should increase the number of indicators per factor or increase the sample size.

scholarly journals Trisilas Local Wisdom Scale , Silih Asih, Silih Asah, Silih Asuh

2020 ◽  
Vol 5 (2) ◽  
pp. 121
Author(s):  
Nandlia Fauzia ◽  
Sri Maslihah ◽  
Diah Zaleha Wyandini
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Model Fit ◽  
Confirmatory Factor

Penelitian ini bertujuan untuk mengembangkan alat ukur nilai kearifan lokal trisilas yang berasal dari falsafah budaya suku Sunda. Responden pada penelitian ini adalah masyarakat suku Sunda sebanyak 310 orang. Jumlah butir skala nilai kearifan lokal trisilas sebelum diujikan adalah 45 butir, lalu setelah diujikan berjumlah 17 butir. Penelitian ini menggunakan analisis faktor yaitu dengan metode CFA (Confirmatory Factor Analysis) untuk dapat menganalisis validitas konstruk. CFA ini digunakan untuk menguji model faktor alat ukur nilai kearifan lokal Trisilas berdasarkan pada indeks kecocokan parameter model fit. CFA menunjukkan kecocokan model yang baik diantaranya nilai RMSEA sebesar 0.066, nilai GFI sebesar 0.904 serta nilai CFI sebesar 0.890 yang mana seluruh parameter yang digunakan peneliti untuk menganalisis faktor alat ukur nilai kearifan lokal Trisilas telah sesuai dengan kriteria minimum nilai indeks kecocokan suatu model. Kata kunci: Confirmatory Factor Analysis, masyarakat suku Sunda, nilai kearifan lokal trisilas

scholarly journals Psychometric Properties of the 25-Item Connor-Davison Resilience Scale: Preliminary Data for a Romanian Military Population

2021 ◽  
Vol 26 (1) ◽  
pp. 31-38
Author(s):  
Iulia-Clarisa Giurcă ◽  
Adriana Baban ◽  
Sebastian Pintea ◽  
Bianca Macavei
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Construct Validity ◽  
Psychometric Properties ◽  
Factor Structure ◽  
Preliminary Data ◽  
Model Fit ◽  
Military Population ◽  
Resilience Scale ◽  
Confirmatory Factor

AbstractThe following study is aimed at investigating the construct validity of the 25-item Connor-Davidson Resilience Scale (CD-RISC 25) on a Romanian military population. The exploratory factor analysis was conducted on 434 male military participants, aged between 24 and 50 years (M = 34.83, S.D. = 6.14) and the confirmatory factor analysis was conducted on a sample of 679 military participants, of 605 men and 74 women, aged between 18 and 59 years (M = 38.37, S.D. = 9.07). Factor analysis of the scale showed it to be a bidimensional, rather than a multidimensional instrument, as the original five-factor structure was not replicated in this military Romanian sample. Moreover, EFAs suggested that a 14-item bidimensional model should be retained and CFA confirmed that this model fit the data best.

scholarly journals The CSGU: A Measure of Controllability, Stability, Globality, and Universality Attributions

2008 ◽  
Vol 30 (5) ◽  
pp. 611-641 ◽  
Author(s):  
Pete Coffee ◽  
Tim Rees
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Construct Validity ◽  
Self Efficacy ◽  
Interactive Effects ◽  
Hierarchical Regression ◽  
Regression Analyses ◽  
Confirmatory Factor ◽  
And Performance

This article reports initial evidence of construct validity for a four-factor measure of attributions assessing the dimensions of controllability, stability, globality, and universality (the CSGU). In Study 1, using confirmatory factor analysis, factors were confirmed across least successful and most successful conditions. In Study 2, following less successful performances, correlations supported hypothesized relationships between subscales of the CSGU and subscales of the CDSII (McAuley, Duncan, & Russell, 1992). In Study 3, following less successful performances, moderated hierarchical regression analyses demonstrated that individuals have higher subsequent self-efficacy when they perceive causes of performance as controllable, and/or specific, and/or universal. An interaction for controllability and stability demonstrated that if causes are perceived as likely to recur, it is important to perceive that causes are controllable. Researchers are encouraged to use the CSGU to examine main and interactive effects of controllability and generalizability attributions upon outcomes such as self-efficacy, emotions, and performance.

scholarly journals Measuring adolescent attitudes towards dementia: The revalidation and refinement of the A-ADS

2020 ◽  
pp. 135910532095347
Author(s):  
Nicolas Farina ◽  
Alys W Griffiths ◽  
Laura J Hughes ◽  
Sahdia Parveen
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Concurrent Validity ◽  
Principal Component ◽  
Model Fit ◽  
Single Factor ◽  
Confirmatory Factor ◽  
Adolescent Attitudes ◽  
Good Internal Consistency

The A-ADS is one the first validated measures of attitudes of dementia in adolescents, though further validation is needed. 630 adolescents were recruited from secondary schools in England. A Principal Component Analysis was completed ( n = 230) followed by a Confirmatory Factor Analysis ( n = 400). Reducing the A-ADS into a single factor, 13-item measure (Brief A-ADS) improved the model fit of the measure (χ2 = 182.75, DF = 65, CMIN/DF = 2.81, p < 0.001, CFI = 0.90, RMSEA = 0.07). The scale demonstrated good internal consistency, good predictive and concurrent validity. Building on the validation of the A-ADS, the Brief A-ADS is suitable to capture attitudes towards dementia amongst adolescents.

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.

Sign in / Sign up

Export Citation Format

Share Document