How many factors in factor analysis? New insights about parallel analysis with confidence intervals

2022 ◽  
Vol 139 ◽  
pp. 1026-1043
Author(s):  
Dawn Iacobucci ◽  
Ayalla Ruvio ◽  
Sergio Román ◽  
Sangkil Moon ◽  
Paul M. Herr
Keyword(s):  
Factor Analysis ◽  
Confidence Intervals ◽  
Parallel Analysis

Development, validity and reliability testing the Swedish Ethical Climate Questionnaire

2019 ◽  
Vol 26 (7-8) ◽  
pp. 2482-2493 ◽  
Author(s):  
Catarina Fischer Grönlund ◽  
Anna Söderberg ◽  
Vera Dahlqvist ◽  
Lars Andersson ◽  
Ulf Isaksson
Keyword(s):  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Ethical Climate ◽  
Factor Model ◽  
Parallel Analysis ◽  
Validity And Reliability ◽  
Cronbach’S Alpha ◽  
Cronbach's Alpha ◽  
Professional Perspective ◽  
Ethical Climate Questionnaire

Background: An ethical climate has been described as a working climate embracing shared perceptions about morally correct behaviour concerning ethical issues. Various ethical climate questionnaires have been developed and validated for different contexts, but no questionnaire has been found concerning the ethical climate from an inter-professional perspective in a healthcare context. The Swedish Ethical Climate Questionnaire, based on Habermas’ four requirements for a democratic dialogue, attempts to assess and measure the ethical climate at various inter-professional workplaces. This study aimed to present the construction of and to test the psychometric properties of the Swedish Ethical Climate Questionnaire. Method: An expert group of six researchers, skilled in ethics, evaluated the content validity. The questionnaire was tested among 355 healthcare workers at three hospitals in Sweden. A parallel analysis (PA), an exploratory factor analysis and confirmatory factor analysis were performed. Ethical considerations: The participants included in the psychometric analysis were informed about the study, asked to participate in person and informed that they could withdraw at any time without giving any reason. They were also assured of confidentiality in the reporting of the results. Findings: The parallel analysis (PA) recommended one factor as a solution. The initial exploratory factor analysis with a four-factor solution showed low concordance with a four-factor model. Cronbach’s alpha varied from 0.75 to 0.82; however, since two factors only consisted of one item, alpha could not be reported. Cronbach’s alpha for the entire scale showed good homogeneity (α = 0.86). A confirmatory factory analysis was carried out based on the four requirements and showed a goodness-of-fit after deleting two items. After deletion of these items, Cronbach’s alpha was 0.82. Discussion: Based on the exploratory factor analysis, we suggest that the scale should be treated as a one-factor model. The result indicates that the instrument is unidimensional and assesses ethical climate as a whole. Conclusion: After testing the Swedish Ethical Climate Questionnaire, we found support for the validity and reliability of the instrument. We found the 10-item version of Swedish Ethical Climate Questionnaire satisfactory. However, we found no support for measuring different dimensions and, therefore, this instrument should be seen as assessing ethical climate as of whole.

scholarly journals Implementing Horn's Parallel Analysis for Principal Component Analysis and Factor Analysis

2009 ◽  
Vol 9 (2) ◽  
pp. 291-298 ◽  
Author(s):  
Alexis Dinno
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Common Factor ◽  
Principal Component ◽  
Component Analysis ◽  
Parallel Analysis ◽  
Common Factor Analysis

I present paran, an implementation of Horn's parallel analysis criteria for factor or component retention in common factor analysis or principal component analysis in Stata. The command permits classical parallel analysis and more recent extensions to it for the pca and factor commands. paran provides a needed extension to Stata's built-in factor- and component-retention criteria.

scholarly journals Proportion of Indicator Common Variance Due to a Factor as an Effect Size Statistic in Revised Parallel Analysis

2018 ◽  
Vol 79 (1) ◽  
pp. 85-107 ◽  
Author(s):  
Yan Xia ◽  
Samuel B. Green ◽  
Yuning Xu ◽  
Marilyn S. Thompson
Keyword(s):  
Factor Analysis ◽  
Effect Size ◽  
Past Research ◽  
Parallel Analysis ◽  
Large Sample Size ◽  
Common Variance ◽  
Minimum Rank ◽  
Additional Information ◽  
Number Of Factors ◽  
Principal Axis Factor Analysis

Past research suggests revised parallel analysis (R-PA) tends to yield relatively accurate results in determining the number of factors in exploratory factor analysis. R-PA can be interpreted as a series of hypothesis tests. At each step in the series, a null hypothesis is tested that an additional factor accounts for zero common variance among measures in the population. Integration of an effect size statistic—the proportion of common variance (PCV)—into this testing process should allow for a more nuanced interpretation of R-PA results. In this article, we initially assessed the psychometric qualities of three PCV statistics that can be used in conjunction with principal axis factor analysis: the standard PCV statistic and two modifications of it. Based on analyses of generated data, the modification that considered only positive eigenvalues ([Formula: see text]) overall yielded the best results. Next, we examined PCV using minimum rank factor analysis, a method that avoids the extraction of negative eigenvalues. PCV with minimum rank factor analysis generally did not perform as well as [Formula: see text], even with a relatively large sample size of 5,000. Finally, we investigated the use of [Formula: see text] in combination with R-PA and concluded that practitioners can gain additional information from [Formula: see text] and make more nuanced decision about the number of factors when R-PA fails to retain the correct number of factors.

scholarly journals Revision of the Criticism Avoidance dimension of the Dimensional Clinical Personality Inventory

2017 ◽  
Vol 34 (2) ◽  
pp. 219-231 ◽  
Author(s):  
Lucas de Francisco CARVALHO ◽  
Catarina Possenti SETTE
Keyword(s):  
Factor Analysis ◽  
Standard Deviation ◽  
Personality Inventory ◽  
The Other ◽  
Parallel Analysis ◽  
Final Version ◽  
Neo Personality Inventory ◽  
Personality Functioning ◽  
Pathological Characteristics ◽  
Dsm 5

Abstract The aim of this study was to revise the Criticism Avoidance dimension of the Dimensional Clinical Personality Inventory and to investigate its psychometric properties. The participants included 213 subjects aged 18 to 69 years (Mean = 25.56; Standard Deviation = 8.70), mostly females (N = 159; 74.3%). All participants answered the Dimensional Clinical Personality Inventory and the Brazilian versions of the Revised NEO Personality Inventory and the Personality Inventory for DSM-5. A total of 470 new items were developed and selected using content analysis, and 39 items composed the final version. Based on the parallel analysis and factor analysis, three interpretable factors were found. The internal consistency coefficients showed adequate levels of reliability ranging between 0.80 and 0.91 for the factors. Additionally, expected correlations were found between the Dimensional Clinical Personality Inventory and the other tests. The present study demonstrated the adequacy of the dimension revised to assess pathological characteristics of the avoidant personality functioning.

scholarly journals Los tamaños de las muestras en encuestas de las ciencias sociales y su repercusión en la generación del conocimiento

2017 ◽  
Vol 11 (22) ◽  
Author(s):  
Juan Rositas Martínez
Keyword(s):  
Factor Analysis ◽  
Sample Size ◽  
Confidence Intervals ◽  
Structural Equation ◽  
Size Factor ◽  
Sample Sizes ◽  
Cronbach’S Alpha ◽  
Cronbach's Alpha ◽  
Alpha Effect ◽  
The Impact

Keywords: confidence intervals, Cronbach's alpha, effect size, factor analysis, hypothesis testing, sample size, structural equation modelingAbstract. The purpose of this paper is to contribute to fulfilling the objectives of social sciences research such as proper estimation, explanation, prediction and control of levels of social reality variables and their interrelationships, especially when dealing with quantitative variables. It was shown that the sample size or the number of observations to be collected and analyzed is transcendental for the adequacy of the method of statistical inference selected and for the impact degree achieved in its results, especially for complying with reports guidelines issued by the American Psychological Association. Methods and formulations were investigated to determine the sample sizes that contribute to have good levels of estimation when establishing confidence intervals, with reasonable wide and relevant and significative magnitudes of the effects. Practical rules suggested by several researchers when determining samples sizes were tested and as a result it was integrated a guide for determining sample sizes for dichotomous, continuous, discrete and Likert variables, correlation and regression methods, factor analysis, Cronbach's alpha, and structural equation models. It is recommended that the reader builds scenarios with this guide and be aware of the implications and relevance in scientific research and decision making of the sample sizes in trying to meet the aforementioned objectives.Palabras clave: análisis factorial, intervalo de confianza, alpha de Cronbach, modelación mediante ecuaciones estructurales, pruebas de hipótesis, tamaño de muestra, tamaño del efectoResumen. El propósito del presente documento es contribuir al cumplimiento de los objetivos de la investigación en las ciencias sociales de estimar, explicar, predecir y controlar niveles de variables de la realidad social y sus interrelaciones, en investigaciones de tipo cuantitativo. Se demostró que el tamaño de la muestra o la cantidad de observaciones que hay que recolectar y analizar es trascendente tanto en la pertinencia del método de inferencia estadístico que se utilice como en el grado de impacto que se logre en sus resultados, sobre todo de cara a cumplir con lineamientos emitidos por la Asociación Americana de Psicología que es la que da la pauta en la mayoría de las publicaciones del área social. Se investigaron métodos y formulaciones para determinar los tamaños de muestra que contribuyan a tener buenos niveles de estimación al momento de establecer los intervalos de confianza, con aperturas razonables y con magnitudes de los efectos que sean de impacto y se pusieron a prueba reglas prácticas sugeridas por varios autores lográndose integrar una guía tanto para variables dicotómicas, continuas, discretas, tipo Likert y para interrelaciones en ellas, ya se trate de análisis factorial, alpha de Cronbach, regresiones o ecuaciones estructurales. Se recomienda que el lector crear escenarios con esta guía y se sensibilice y se convenza de las implicaciones y de trascendencia tanto en la investigación científica como en la toma de decisiones de los tamaños de muestra al tratar de cumplir con los objetivos de la que hemos mencionado.

Psychometric Testing: Self-Efficacy for Calorie Control and Exercise

2021 ◽  
pp. 105477382110649
Author(s):  
Li-Chun Hsiao ◽  
Chi-Jane Wang
Keyword(s):  
Factor Analysis ◽  
Weight Management ◽  
Self Efficacy ◽  
Psychometric Testing ◽  
Model Fit ◽  
Parallel Analysis ◽  
Convenience Sample ◽  
Confirmatory Factor ◽  
Reliable Instrument ◽  
Three Factor

Enhancing self-efficacy for calorie control and exercise is a crucial strategy for successful weight management. This study developed and psychometrically tested a 13-item quick checklist for assessing self-efficacy for calorie control and exercise (QCSE-CCE). A convenience sample of 425 adults between 18 and 69 years old completed the QCSE-CCE online. The principle axis factor analysis and a parallel analysis demonstrated a three-factor structure that accounts for 72.8% of the total variance. The confirmatory factor analysis indicated a good model fit (χ2/ df = 2.168, GFI = .913, AGFI = .873, RMR = .049, RMSEA = .073, CFI = .959). The predictive validity was adequate (.38 <  r < .39, p < .000), with Cronbach’s alphas ranging from .87 to .91. The test-retest demonstrated good stability ( r = .69; p < .001). The QCSE-CCE is a valid and reliable instrument for assessing self-efficacy for calorie control and exercise for weight management purposes.

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