scholarly journals Confirmatory factor analysis with missing data in a small sample: cognitive reserve in people with Down Syndrome

2021 ◽  
Author(s):  
Cristina Cañete-Massé ◽  
Maria Carbó-Carreté ◽  
María Dolores Figueroa-Jiménez ◽  
Guillermo R. Oviedo ◽  
Myriam Guerra-Balic ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Down Syndrome ◽  
Missing Data ◽  
Confirmatory Factor Analysis ◽  
Sample Size ◽  
Cognitive Reserve ◽  
Small Sample Size ◽  
Small Sample ◽  
Confirmatory Factor ◽  
Bayesian Estimations

AbstractThe presence of missing data and small sample sizes are very common in social and health sciences. Concurrently to present a methodology to solve the small sample size and missing data, we aim to present a definition of Cognitive Reserve for people with Down Syndrome. This population has become an appealing focus to study this concept because of the high incidence of dementia. The accidental sample comprised 35 persons with DS (16–35 years). A total of 12 variables were acquired, four of them had missing data. Two types of multiple imputation were made. Confirmatory factor analysis with Bayesian estimations was performed on the final database with non-informative priors. However, to solve the sample size problem, two additional corrections were made: first, we followed the Jiang and Yuan (2017) schema, and second, we made a Jackknife correlation correction. The estimations of the confirmatory factor analysis, as well as the global fit, are adequate. As an applied perspective, the acceptable fit of our model suggests the possibility of operationalizing the latent factor Cognitive Reserve in a simple way to measure it in the Down Syndrome population.

scholarly journals A Confirmatory Factor Analysis of Student Engagement at the University of Hail, Saudi Arabia

2020 ◽  
Vol 9 (26) ◽  
pp. 181-190
Author(s):  
Ajay Singh
Keyword(s):  
Factor Analysis ◽  
Saudi Arabia ◽  
Student Engagement ◽  
Confirmatory Factor Analysis ◽  
Convergent Validity ◽  
Discriminant Validity ◽  
Small Sample Size ◽  
Small Sample ◽  
Confirmatory Factor ◽  
The University

The research aims to examine the validity of four factors (course structure, course learning outcomes, Constructiveness of learning environment, and instructors' skills) about student engagement at the University of Hail, Kingdom of Saudi Arabia. The research applied the Confirmatory factor analysis (CFA) technique to confirm and validate the four factors. The data of the small sample size of 380 students of business administration took place for analysis. These four factors consisting of 18 variables, have considered from the existing literature for examination. The research uses a survey questionnaire to collect the students' perception of these factors to validate the four-factor model. The study uses Convergent validity and Discriminant validity to construct validity. The research finding supports the construct of four-factors to confirm the model is adequately fit. The results of the research contribute to existing literature from the perspective of the students of the University of Hail, Saudi Arabia, and other similar institutions of the country and outside world. The research also has created the scope for further improvement by adding some more factors along with variables of equal importance.

Replication and extension of Little et al.'s (2003) forms and functions of aggression measure

2008 ◽  
Vol 32 (3) ◽  
pp. 238-242 ◽  
Author(s):  
Paula J. Fite ◽  
Kirstin Stauffacher ◽  
Jamie M. Ostrov ◽  
Craig R. Colder
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Aggressive Behavior ◽  
Small Sample ◽  
Original Model ◽  
Modified Model ◽  
American Sample ◽  
Confirmatory Factor ◽  
Exact Replication

The goal of the current study was to replicate the confirmatory factor analysis of Little et al.'s (2003) aggression measure in an American sample of 69 children (mean age = 12.93 years; SD = 1.27). Although an exact replication of the original model could not be estimated given the small sample, a modified model representing a conceptual replication provided a good fit to the data. Findings suggest that this child self-reported aggression measure can be used with American samples to distinguish four domains of aggressive behavior (relational, overt, instrumental, and reactive).

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 Performance of Polytomous IRT Models With Rating Scale Data: An Investigation Over Sample Size, Instrument Length, and Missing Data

2021 ◽  
Vol 6 ◽  
Author(s):  
Shenghai Dai ◽  
Thao Thu Vo ◽  
Olasunkanmi James Kehinde ◽  
Haixia He ◽  
Yu Xue ◽  
...  
Keyword(s):  
Missing Data ◽  
Sample Size ◽  
Rating Scales ◽  
Rating Scale ◽  
Small Sample Size ◽  
Small Sample ◽  
Item Parameter ◽  
Partial Credit Model ◽  
Irt Models ◽  
Polytomous Irt Models

The implementation of polytomous item response theory (IRT) models such as the graded response model (GRM) and the generalized partial credit model (GPCM) to inform instrument design and validation has been increasing across social and educational contexts where rating scales are usually used. The performance of such models has not been fully investigated and compared across conditions with common survey-specific characteristics such as short test length, small sample size, and data missingness. The purpose of the current simulation study is to inform the literature and guide the implementation of GRM and GPCM under these conditions. For item parameter estimations, results suggest a sample size of at least 300 and/or an instrument length of at least five items for both models. The performance of GPCM is stable across instrument lengths while that of GRM improves notably as the instrument length increases. For person parameters, GRM reveals more accurate estimates when the proportion of missing data is small, whereas GPCM is favored in the presence of a large amount of missingness. Further, it is not recommended to compare GRM and GPCM based on test information. Relative model fit indices (AIC, BIC, LL) might not be powerful when the sample size is less than 300 and the length is less than 5. Synthesis of the patterns of the results, as well as recommendations for the implementation of polytomous IRT models, are presented and discussed.

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