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 Effect of Dichotomization on the Latent Structure of Data

2021 ◽  
Vol 7 ◽  
Author(s):  
Karl Schweizer ◽  
Andreas Gold ◽  
Dorothea Krampen
Keyword(s):  
Simulation Study ◽  
Latent Variables ◽  
Latent Variable ◽  
Factor Model ◽  
Latent Structure ◽  
Factor Loadings ◽  
Dichotomous Data ◽  
Level Data ◽  
Confirmatory Factor

We investigated whether dichotomous data showed the same latent structure as the interval-level data from which they originated. Given constancy of dimensionality and factor loadings reflecting the latent structure of data, the focus was on the variance of the latent variable of a confirmatory factor model. This variance was shown to summarize the information provided by the factor loadings. The results of a simulation study did not reveal exact correspondence of the variances of the latent variables derived from interval-level and dichotomous data but shrinkage. Since shrinkage occurred systematically, methods for recovering the original variance were fleshed out and evaluated.

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 PROFESIONALISME GURU SEKOLAH DASAR: KESIAPAN GURU SD PILOTTING MENGIMPLEMENTASIKAN KURIKULUM 2013

2018 ◽  
Author(s):  
zuryanty
Keyword(s):  
Factor Analysis ◽  
Elementary School ◽  
Confirmatory Factor Analysis ◽  
Elementary School Teachers ◽  
Latent Variable ◽  
Partial Correlation ◽  
Factor Model ◽  
School Teachers ◽  
Confirmatory Factor ◽  
Teacher Readiness

This research aims to confirming and measuring a factors model that supposing associated to elementary school teachers readiness in implementing the 2013th curriculum,as indicators of teacher profesionalism These factors are mind set transformation, curriculum concepts comprehension, ability to analizing subjects matter, and teaching design predicted their correlation to elementary school teachers readiness as a latent variable.This is a correlational research. The quantitative data analized by confirmatory factor analysis technique and partial correlation techniques. In the case of a finite population, 19 elementary school teachers are sampled as a total sampling. Confirmatory factor analysis results indicated that only 47 of 53 research questions are confirming for four factor model. Factor 1 explained 29.44%, factor 2 explained 20.32%, factor 3 explained 12.63%, and factor 4 explained 8.43% varians of teacher readiness. Totally 70,82% varians of teacher readiness as a latent variable can be explained by the model. Partial correlation analysis finding that a very significance correlation among four factor and elementary school teachers readiness: factor 1 (RX1Y,234 = 0,997: p<0,00 ), faktor 2 (RX2Y,134 = 0,995: p<0,00), faktor 3 (RX3Y,124 = 0.972: p<0,00), dan faktor 4 (RX4Y,123 = 0.983: p<0,00). This research confirming that four factor model as indicator variables are suitable and very significance correlated to elementary school teachers readiness in implementing the 2013th curriculum.

scholarly journals The Pandemic Coping Scale – Factorial Validity and Reliability of a Brief Measure of Coping During a Pandemic

2021 ◽  
Author(s):  
Annett Lotzin ◽  
Ronja Ketelsen ◽  
Sven Buth ◽  
Linda Krause ◽  
Ann-Kathrin Ozga ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Goodness Of Fit ◽  
Factor Model ◽  
General Factor ◽  
Factorial Validity ◽  
Validity And Reliability ◽  
Factor Loadings ◽  
Confirmatory Factor ◽  
Coping Scale

Abstract Background: This study aimed to assess the factorial validity and reliability of the Pandemic Coping Scale, a new brief measure of coping behavior in response to the stressors of a pandemic. Methods: The scale was administered to N = 2,316 German participants during the COVID-19 pandemic. An exploratory and a confirmatory factor analysis were applied among two random splits of the sample. The global goodness of fit (χ², RMSEA, SRMR, CFI, TLI), the local goodness of fit (factor loadings, communalities, factor reliability, discriminant validity), and test quality criteria (internal consistency, item discrimination, and difficulty) were evaluated for two models (Model 1: four-factor model; Model 2: four-factor model combined with a second-order general factor). Results: The exploratory factor analysis suggested a four-factor solution with factor loadings accounting for 44.6% of the total variance (Factor 1 ‘Healthy Lifestyle’, Factor 2 ‘Joyful Activities’, Factor 3 ‘Daily Structure’, Factor 4 ‘Prevention Adherence’). The confirmatory factor analysis showed a sufficient global fit for both specified models (Model 1: χ² (59, N =1172) = 366.97, p < .001, RMSEA = .067, SRMR = .043, CFI = .926, TLI = .902; Model 2: χ² (61, N = 1172) = 373.33, p < .001, RMSEA = .066, SRMR = .043, CFI = .925, TLI = .904). Model 1 and Model 2 did not significantly differ in their fit to the data (∆χ² (2, N = 1172) = 6.36, p = .042). Local goodness of fit indices were similar for both models and mostly showed moderate to large factor loadings, and good factor reliabilities except for ‘Prevention Adherence’. Conclusion: The Pandemic Coping Scale showed sufficient factorial validity for the four measured dimensions of coping and reliability for the scales except for ‘Prevention Adherence’ to assess coping during the current COVID-19 pandemic. The ‘Prevention Adherence’ subscale might be improved by adding items with higher item difficulties.

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.

An Assessment of the Construct Distinctiveness of Stress Arousal and Burnout

2006 ◽  
Vol 99 (2) ◽  
pp. 396-406 ◽  
Author(s):  
Kenneth J. Smith ◽  
Jeanette A. Davy ◽  
George S. Everly
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Latent Variables ◽  
Discriminant Validity ◽  
Factor Model ◽  
Strong Support ◽  
Maslach Burnout Inventory ◽  
Significant Loss ◽  
Staff Members ◽  
Confirmatory Factor

This study examined the construct and discriminant validity of stress arousal and burnout as measured on the Stress Arousal Scale and the multidimensional role-specific version of the Maslach Burnout Inventory, respectively. The analyses utilized data from 148 individuals randomly selected from a database of 563 respondents to a larger study. The sample responded to a survey sent to members of the American Institute of Certified Public Accountants (AICPA). Sample size used in this study fell within Loehlin's 1992 prescription that for confirmatory factor analysis with two to four factors, a minimum of 100 to 200 cases should be collected. Forty-six respondents indicated that they were partners, principals, or sole practitioners in accounting firms, and 103 indicated that they were staff members (juniors, seniors, or managers). Latent variables were first constructed for the stress arousal and burnout factors. Confirmatory factor analysis was then conducted on the scale data to assess whether the factors would load on their respective underlying theoretical constructs. Finally, a nested model constraining stress arousal and burnout to load on one underlying construct was tested against the hypothesized two-factor model. The results indicated good model fit for the two-factor model and a significant loss of fit for the one-factor model, thus providing strong support for the conceptualization of stress arousal and burnout as distinct constructs.

Reconsidering the Conditions for Conducting Confirmatory Factor Analysis

2020 ◽  
Vol 23 ◽  
Author(s):  
Daniel Ondé ◽  
Jesús M. Alvarado
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Sample Size ◽  
Goodness Of Fit ◽  
Factor Model ◽  
Stable Solution ◽  
Practical Significance ◽  
Monte Carlo Simulation Study ◽  
Factor Loadings ◽  
Confirmatory Factor

Abstract There is a series of conventions governing how Confirmatory Factor Analysis gets applied, from minimum sample size to the number of items representing each factor, to estimation of factor loadings so they may be interpreted. In their implementation, these rules sometimes lead to unjustified decisions, because they sideline important questions about a model’s practical significance and validity. Conducting a Monte Carlo simulation study, the present research shows the compensatory effects of sample size, number of items, and strength of factor loadings on the stability of parameter estimation when Confirmatory Factor Analysis is conducted. The results point to various scenarios in which bad decisions are easy to make and not detectable through goodness of fit evaluation. In light of the findings, these authors alert researchers to the possible consequences of arbitrary rule following while validating factor models. Before applying the rules, we recommend that the applied researcher conduct their own simulation studies, to determine what conditions would guarantee a stable solution for the particular factor model in question.

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