scholarly journals PMH83 ESTIMATING SAMPLE SIZE FOR PSYCHOMETRIC STUDIES USING CONFIRMATORY FACTOR ANALYSIS

2011 ◽  
Vol 14 (3) ◽  
pp. A201
Author(s):  
J.C. Cole ◽  
R. Cheng
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Sample Size ◽  
Confirmatory Factor ◽  
Psychometric Studies

Effect of Variable and Subject Sampling on Recovery of Weak Factors In CFA

Methodology ◽  
2007 ◽  
Vol 3 (2) ◽  
pp. 67-80 ◽  
Author(s):  
Carmen Ximénez
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Sample Size ◽  
Exploratory Factor Analysis ◽  
Estimation Method ◽  
Simulation Studies ◽  
Weak Factor ◽  
Variable Sampling ◽  
Improper Solutions ◽  
Confirmatory Factor

Abstract. Two general issues central to the design of a study are subject sampling and variable sampling. Previous research has examined their effects on factor pattern recovery in the context of exploratory factor analysis. The present paper focuses on recovery of weak factors and reports two simulation studies in the context of confirmatory factor analysis. Conditions investigated include the estimation method (ML vs. ULS), sample size (100, 300, and 500), number of variables per factor (3, 4, or 5), loading size in the weak factor (.25 or .35), and factor correlation (null vs. moderate). Results show that both subject and variable sample size affect the recovery of weak factors, particularly if factors are not correlated. A small but consistent pattern of differences between methods occurs, which favors the use of ULS. Additionally, the frequency of nonconvergent and improper solutions is also affected by the same variables.

Measuring the components of visitor experience on a mountain attraction: the case of the Nordkette, Tyrol, Austria

2017 ◽  
Vol 72 (4) ◽  
pp. 429-447 ◽  
Author(s):  
Ady Milman ◽  
Anita Zehrer ◽  
Asli D.A. Tasci
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Sample Size ◽  
Service Providers ◽  
Customer Experience ◽  
Dimensional Structure ◽  
Visitor Experience ◽  
Content Type ◽  
Confirmatory Factor ◽  
The Impact

Purpose Previous mountain tourism research addressed economic, environmental, social and political impacts. Because limited studies evaluated visitors’ perception of their experience, this study aims to examine the tangible and intangible visitor experience in a Tyrolean alpine tourist attraction. Design/methodology/approach The study adopted Klaus and Maklan’s (2012) customer experience model, suggesting that customers base their experience perception on the quality of product experience, outcome focus, moments of truth and peace-of-mind. Their model was used to validate the impact on overall customer experience quality at the mountain attraction through conducting a structured survey with 207 face-to-face interviews on-site. Findings The results of the confirmatory factor analysis did not confirm the four-dimensional structure, probably due to the differences between mountain tourism experience and the mortgage lending experience in the original study. Instead, principal component analysis suggested a different dimensional structure of components that were arbitrarily named as functional, social, comparative and normative aspects of the visitors’ experience. Research limitations/implications The results are based on a sample in a given period of time, using convenience sampling. While the sample size satisfied the data analysis requirements, confirmatory factor analysis would benefit from a larger sample size. Practical implications Consumer experience dimensions while visiting a mountain attraction may not be concrete or objective, and consequently may yield different types of attributes that influence behavior. Social implications The social exchange theory could explain relationships between visitors and service providers and their consequences. Attraction managers should increase benefits for visitors and service providers to enhance their relationships, and thus experience. Originality/value The study explored the applicability of an existing experiential consumption model in a mountain attraction context. The findings introduce a revised model that may be applicable in other tourist attractions.

scholarly journals Evaluating Model Fit in Bayesian Confirmatory Factor Analysis With Large Samples: Simulation Study Introducing the BRMSEA

2017 ◽  
Vol 78 (4) ◽  
pp. 537-568 ◽  
Author(s):  
Huub Hoofs ◽  
Rens van de Schoot ◽  
Nicole W. H. Jansen ◽  
IJmert Kant
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Sample Size ◽  
Simulation Study ◽  
Reliability And Validity ◽  
Model Fit ◽  
Model Complexity ◽  
Large Sample ◽  
Large Samples ◽  
Confirmatory Factor

Bayesian confirmatory factor analysis (CFA) offers an alternative to frequentist CFA based on, for example, maximum likelihood estimation for the assessment of reliability and validity of educational and psychological measures. For increasing sample sizes, however, the applicability of current fit statistics evaluating model fit within Bayesian CFA is limited. We propose, therefore, a Bayesian variant of the root mean square error of approximation (RMSEA), the BRMSEA. A simulation study was performed with variations in model misspecification, factor loading magnitude, number of indicators, number of factors, and sample size. This showed that the 90% posterior probability interval of the BRMSEA is valid for evaluating model fit in large samples ( N≥ 1,000), using cutoff values for the lower (<.05) and upper limit (<.08) as guideline. An empirical illustration further shows the advantage of the BRMSEA in large sample Bayesian CFA models. In conclusion, it can be stated that the BRMSEA is well suited to evaluate model fit in large sample Bayesian CFA models by taking sample size and model complexity into account.

scholarly journals Management Perceptions of Organizational Service Quality Practices

2017 ◽  
Vol 10 (3) ◽  
pp. 14
Author(s):  
Syahri Nehru Husain ◽  
Yasir Syam Husain
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Service Quality ◽  
Sample Size ◽  
The Other ◽  
Quality Practices ◽  
Confirmatory Factor ◽  
Importance Analysis ◽  
One Stop ◽  
Main Factor

The research purposed to investigate the management perception of organizational service quality practices. The study conducted in Institution of One-Stop Service of Southeast Sulawesi. Using SERVQUAL Instruments including tangibility; reliability; responsiveness; assurance; and empathy, customers were interviewed and filling a questionnaire. The sample size of 150 was selected purposively, but only 116 samples were analyzed. Data was analyzed with using confirmatory factor analysis and then the results were compared with using performance importance analysis (PIA). This research found that dimensions of responsiveness; reliability; and empathy was the main factor of organizational service quality. Otherwise, tangibility and assurance were not an important dimension for organizational service quality. This research limited on the perception of the customer of public services. This finding indicated that there were differences organizational service quality practices from the other sector and country. The study suggested that organizational service quality practices should have reliability; responsiveness; and empathy on the customer.

Goodness-of-fit indexes in confirmatory factor analysis: The effect of sample size.

1988 ◽  
Vol 103 (3) ◽  
pp. 391-410 ◽  
Author(s):  
Herbert W. Marsh ◽  
John R. Balla ◽  
Roderick P. McDonald
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Sample Size ◽  
Goodness Of Fit ◽  
Confirmatory Factor ◽  
Fit Indexes

Sample Size Requirements of the Robust Weighted Least Squares Estimator

Methodology ◽  
2014 ◽  
Vol 10 (2) ◽  
pp. 60-70 ◽  
Author(s):  
Morten Moshagen ◽  
Jochen Musch
Keyword(s):  
Factor Analysis ◽  
Maximum Likelihood ◽  
Confirmatory Factor Analysis ◽  
Sample Size ◽  
Least Squares ◽  
Ordinal Data ◽  
Weighted Least Squares ◽  
Weighted Least Squares Estimator ◽  
Confirmatory Factor ◽  
Different Levels

The present study investigated sample size requirements of maximum likelihood (ML) and robust weighted least squares (robust WLS) estimation for ordinal data with confirmatory factor analysis (CFA) models with 3–10 indicators per factor, primary loadings between .4 and .9, and four different levels of categorization (2, 3, 5, and 7). Additionally, the utility of the H-measure of construct reliability (an index combining the number of indicators and the magnitude of loadings) in predicting sample size requirements was examined. Results indicated that a higher number of indicators per factors and higher factor loadings increased the rates of proper convergence and solution propriety. However, the H-measure could only partly account for the results. Moreover, it was demonstrated that robust WLS was mostly superior to ML, suggesting that there is little reason to prefer ML over robust WLS when the data are ordinal. Sample size recommendations for the robust WLS estimator are provided.

Sign in / Sign up

Export Citation Format

Share Document