The Use of Exploratory Factor Analysis in Public Health: A Note on Parallel Analysis as a Factor Retention Criterion

2010 ◽  
Vol 24 (3) ◽  
pp. 178-181 ◽  
Author(s):  
Vivek H. Patil ◽  
Matthew Q. McPherson ◽  
Daniel Friesner
Keyword(s):  
Public Health ◽  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
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.

Factor Retention Decisions in Exploratory Factor Analysis: a Tutorial on Parallel Analysis

2004 ◽  
Vol 7 (2) ◽  
pp. 191-205 ◽  
Author(s):  
James C. Hayton ◽  
David G. Allen ◽  
Vida Scarpello
Keyword(s):  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Parallel Analysis ◽  
Retention Decisions

scholarly journals Using Fit Statistic Differences to Determine the Optimal Number of Factors to Retain in an Exploratory Factor Analysis

2019 ◽  
Vol 80 (2) ◽  
pp. 217-241 ◽  
Author(s):  
W. Holmes Finch
Keyword(s):  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Optimal Number ◽  
Parallel Analysis ◽  
Fit Indices ◽  
Number Of Factors ◽  
Indicator Variables ◽  
Model Fit Indices ◽  
Fit Index ◽  
Normally Distributed

Exploratory factor analysis (EFA) is widely used by researchers in the social sciences to characterize the latent structure underlying a set of observed indicator variables. One of the primary issues that must be resolved when conducting an EFA is determination of the number of factors to retain. There exist a large number of statistical tools designed to address this question, with none being universally optimal across applications. Recently, researchers have investigated the use of model fit indices that are commonly used in the conduct of confirmatory factor analysis to determine the number of factors to retain in EFA. These results have yielded mixed results, appearing to be effective when used in conjunction with normally distributed indicators, but not being as effective for categorical indicators. The purpose of this simulation study was to compare the performance of difference values for several fit indices as a method for identifying the optimal number of factors to retain in an EFA, with parallel analysis, which is one of the most reliable such extant methods. Results of the simulation demonstrated that the use of fit index difference values outperformed parallel analysis for categorical indicators, and for normally distributed indicators when factor loadings were small. Implications of these findings are discussed.

scholarly journals Conducting COVID-19-Related Research in Jordan: Are We Ready?

2020 ◽  
pp. 1-25
Author(s):  
Balqis Ikhmais ◽  
Alaa M. Hammad ◽  
Waleed Qirim ◽  
Osama H. Abusara ◽  
Jonathan Ling
Keyword(s):  
Public Health ◽  
Factor Analysis ◽  
Scientific Community ◽  
Factor Model ◽  
Five Factor Model ◽  
Public Health Emergency ◽  
Parallel Analysis ◽  
Suitable Model ◽  
Related Research ◽  
Bartlett’S Test

Abstract The Coronavirus Disease-2019 (COVID-19) pandemic is a public health emergency of international concern. This pandemic poses a challenge to research and scientific community. In this study, we developed and tested content reliability and content validity of a questionnaire designed for evaluating the readiness and willingness of researchers to participate in virology research in Jordan. The survey was hosted on an online platform, and the link was emailed. A total of 332 participants from universities across Jordan completed the survey. For factor analysis, Kaiser-Meyer-Olkin value (KMO) and Bartlett’s Test of Sphericity were conducted. Furthermore, exploratory factor analysis (EFA) with parallel analysis and scree plots were conducted to evaluate the most suitable model for the data. The result of the EFA suggested a five-factor model would fit the survey. Data showed that the lowest means were for researchers’ readiness to conduct virology research and readiness for virology research with means of 2.07 and 2.95, respectively. Moreover, years of experience and specialty had a significant effect on the readiness and willingness of virology research in Jordan. In conclusion, readiness for research and researchers should be addressed and authorities should pay attention to these shortcomings in virology research.

scholarly journals An exploratory factor analysis of core competencies of public health professionals at primary care service level in Northeastern Thailand

2020 ◽  
Vol 8 ◽  
pp. 205031212094053
Author(s):  
Songkramchai Leethongdissakul ◽  
Wilawun Chada ◽  
Supa Pengpid ◽  
Sangud Chualinfa
Keyword(s):  
Public Health ◽  
Primary Care ◽  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Health Professionals ◽  
Care Service ◽  
Core Competencies ◽  
Service Level ◽  
Primary Care Service ◽  
Public Health Professionals

Background: Public health professionals play a significant role in primary care services in Thailand. Although efforts are being taken to establish professional standards it has neither been outlined nor been officially announced. There is a lack of understanding of what is a suitable set of core competencies for a public health professional. Objectives: This study aimed to explore the core competencies of public health professionals at the primary care service level in Thailand. Methods: A quantitative survey using a questionnaire was conducted in 862 public health professionals in the northeast of Thailand. Exploratory factor analysis was applied to develop a tool to test the competencies of public health professionals. Results: The results revealed core competencies in the following five main proficiencies: (1) public health administration and laws; (2) disease prevention and control; (3) social and environmental determinant of health and health research; (4) health promotion and community; and (5) basic medical care, screening, and diagnosis. In addition, the five core competencies included 50 items suitable for this sample. These factors accounted for 71.90% of the variance. Conclusion: In conclusion, this study’s finding provides significant recommendations to policymakers to improve and initiate a new policy or a standard guideline for public health education and human resource for health production and management in Thailand.

scholarly journals DETERMINANTS OF BRAND PREFERENCE OF COMMERCIAL THREE WHEELER PASSENGER AUTO RICKSHAW DRIVERS IN ADAMA CITY, ETHIOPIA

2019 ◽  
Vol 7 (1) ◽  
pp. 484-492
Author(s):  
Mohd Arif Shaikh ◽  
Devi Prasad U ◽  
Pagadala Sugandha Devi
Keyword(s):  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Parallel Analysis ◽  
Primary Data ◽  
Brand Preference ◽  
Scree Plot ◽  
Component Solution ◽  
Number Of Factors ◽  
Eigen Values ◽  
Relevant Factors

Purpose of the study: The aim of this study is to find out the factors that influence drivers of three wheeler auto rickshaw in their brand preference towards different brands of commercial three wheeler passenger auto rickshaw in Adama City, Ethiopia Methodology:  Primary data was collected from 500 auto drivers using a pilot tested questionnaire consisting of 40 questions. Cronbach’s alpha measure was used to test constructs reliability and in order to identify brand preference, exploratory factor analysis and parallel analysis was conducted. Main Findings: PCA revealed that there are 11 factors whose Eigen values are above 1. A look at scree plot indicated that there is a need to reconsider the number of factors to be used for further analysis. This decision was supported by Parallel analysis and 8 relevant factors identified. This 8 component solution explained 64.25 % of the variance. Applications of this study: Identification of determinants of brand preference can be used by three wheeler passenger auto manufacturers and distributors in Ethiopia Novelty/Originality of this study: There is no study conducted on drivers brand preference of three wheeler passenger auto rickshaws in Ethiopia.

scholarly journals “The more I fear about COVID-19, the more I wear medical masks”: A survey on risk perception and medical masks’ uses

2020 ◽  
Author(s):  
Toan Luu Duc Huynh
Keyword(s):  
Public Health ◽  
Factor Analysis ◽  
Risk Perception ◽  
Exploratory Factor Analysis ◽  
Public Health Emergency ◽  
The Public ◽  
Weak Evidence

AbstractThe legal behaviors in using medical masks in public have been finally promulgated by the Vietnamese Government after 47 days since the WHO declared the Public Health Emergency of International Concern (PHEIC) due to the COVID-19 pandemic. From a sample of 345 Vietnamese respondents aged from 15 to 47 years, this brief note found that the risk perception of COVID-19 danger significantly increases the likelihood of wearing the medical masks. In addition, there is a weak evidence about the differences in age under the COVID-19 outbreaks. More noticeably, those who use masks before COVID-19 pandemic tend to maintain their behaviors. Our results offer the insightful into Vietnamese citizens’ responses in terms of using medical masks; even the uses of this method are still controversial. Our results are robust by performing Exploratory Factor Analysis for five features and further regressions.

scholarly journals Investigating Parallel Analysis in the Context of Missing Data: A Simulation Study Comparing Six Missing Data Methods

2019 ◽  
Vol 80 (4) ◽  
pp. 756-774
Author(s):  
David Goretzko ◽  
Christian Heumann ◽  
Markus Bühner
Keyword(s):  
Factor Analysis ◽  
Missing Data ◽  
Random Forest ◽  
Exploratory Factor Analysis ◽  
Simulation Study ◽  
Case Analysis ◽  
Parallel Analysis ◽  
Complete Case Analysis ◽  
Complete Case ◽  
Data Set

Exploratory factor analysis is a statistical method commonly used in psychological research to investigate latent variables and to develop questionnaires. Although such self-report questionnaires are prone to missing values, there is not much literature on this topic with regard to exploratory factor analysis—and especially the process of factor retention. Determining the correct number of factors is crucial for the analysis, yet little is known about how to deal with missingness in this process. Therefore, in a simulation study, six missing data methods (an expectation–maximization algorithm, predictive mean matching, Bayesian regression, random forest imputation, complete case analysis, and pairwise complete observations) were compared with respect to the accuracy of the parallel analysis chosen as retention criterion. Data were simulated for correlated and uncorrelated factor structures with two, four, or six factors; 12, 24, or 48 variables; 250, 500, or 1,000 observations and three different missing data mechanisms. Two different procedures combining multiply imputed data sets were tested. The results showed that no missing data method was always superior, yet random forest imputation performed best for the majority of conditions—in particular when parallel analysis was applied to the averaged correlation matrix rather than to each imputed data set separately. Complete case analysis and pairwise complete observations were often inferior to multiple imputation.

scholarly journals Time management as a main factor influencing outpa-tients satisfaction on quality of service in public health facilities

2017 ◽  
Vol 5 (1) ◽  
pp. 32
Author(s):  
Abner Kukeyinge Shopati ◽  
Kabwebwe Honore Mitonga ◽  
Anna Kauko Shilunga
Keyword(s):  
Public Health ◽  
Factor Analysis ◽  
Quality Of Service ◽  
Exploratory Factor Analysis ◽  
Age Groups ◽  
Public Hospitals ◽  
Important Predictor ◽  
Public Health Care ◽  
Outpatient Department

This study aims to explore factors influencing outpatient client satisfaction level with quality of service to recommend friends and family to the three intermediate public health hospitals in Namibia. Reviewing the literature, 14 variables were identified. The items were then reduced using exploratory factor analysis which is evaluated using Principal Axis Factoring with Direct Oblimin rotation. Using exploratory factor analysis, variables were categorized into 8 factors. This model, in the order of effect, identified Total Time Spent in the outpatient department before leaving (in minutes) is the most important predictor of ES (expected satisfaction) values of (0.499), Sum of Medication Communication (0.105), Contact Time with Doctors/Nurses (in minutes) (0.099), Sum of Nurse Communication (0.089), Sum of Doctor Communication (0.049), First Time Visit to this Outpatient Department (0.031), Age groups (0.030) and Facility Name (0.011) is the least important predictor for client to recommend their friends and family to the three intermediate public health care in Namibia. This implies that Total Time Spent in the outpatient department before leaving (in minutes) is the most important predictor of ES by outpatient clients on quality of service perceptions and has the largest impact of recommending friends and family to receive services at the three intermediate public hospitals.

Latent Parallel Analysis and Ordinal Exploratory Factor Analysis of the Vocational Fit Assessment

2015 ◽  
Vol 69 (Suppl. 1) ◽  
pp. 6911500106p1
Author(s):  
P. Cristian Gugiu ◽  
Jane Case-Smith ◽  
Dennis Cleary ◽  
Andrew Persch
Keyword(s):  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Parallel Analysis
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