Determining the number of factors to retain in an exploratory factor analysis using comparison data of known factorial structure.

Ruscio, John; Roche, Brendan

doi:10.1037/a0025697

Cited by 426 publications

(402 citation statements)

References 24 publications

Supporting

Mentioning

376

Contrasting

Unclassified

Order By: Relevance

“…It is primarily used in statistics to describe variance among observed correlated variables in terms of potentially a smaller number of unobserved variables, usually referred to as factors [27]. In this work, EFA was employed to search for confounding ecological factors that are latent [8,27] from the set of observed meteorological variables.…”

Section: Factor Analysismentioning

confidence: 99%

Towards a Predictive Analytics-Based Intelligent Malaria Outbreak Warning System

et al. 2017

View full text Add to dashboard Cite

Malaria, as one of the most serious infectious diseases causing public health problems in the world, affects about two-thirds of the world population, with estimated resultant deaths close to a million annually. The effects of this disease are much more profound in third world countries, which have very limited medical resources. When an intense outbreak occurs, most of these countries cannot cope with the high number of patients due to the lack of medicine, equipment and hospital facilities. The prevention or reduction of the risk factor of this disease is very challenging, especially in third world countries, due to poverty and economic insatiability. Technology can offer alternative solutions by providing early detection mechanisms that help to control the spread of the disease and allow the management of treatment facilities in advance to ensure a more timely health service, which can save thousands of lives. In this study, we have deployed an intelligent malaria outbreak early warning system, which is a mobile application that predicts malaria outbreak based on climatic factors using machine learning algorithms. The system will help hospitals, healthcare providers, and health organizations take precautions in time and utilize their resources in case of emergency. To our best knowledge, the system developed in this paper is the first publicly available application. Since confounding effects of climatic factors have a greater influence on the incidence of malaria, we have also conducted extensive research on exploring a new ecosystem model for the assessment of hidden ecological factors and identified three confounding factors that significantly influence the malaria incidence. Additionally, we deploy a smart healthcare application; this paper also makes a significant contribution by identifying hidden ecological factors of malaria.

show abstract

Section: Factor Analysismentioning

confidence: 99%

Towards a Predictive Analytics-Based Intelligent Malaria Outbreak Warning System

et al. 2017

View full text Add to dashboard Cite

show abstract

“…As some of the sub-scale items proposed were new, or first time adaptations of recent constructs, including: affective, cognitive and material personal values; student social capital; and PSE skills, it was deemed prudent to conduct an exploratory factor analysis (EFA) (Ruscio and Roche 2012). In the absence of previous empirical studies, or appropriate sub-scale validation, an EFA allows researchers to examine the underlying factor structure and communality of items before finalizing hypotheses and proceeding to CFA and structural analysis (Anderson and Gerbing 1998 The final EFA converged on 6 rotations and the 5 new latent factors accounted for 51% of total explained variance.…”

Section: Exploratory Factor Analysis (Efa)mentioning

confidence: 99%

Personal values, social capital, and higher education student career decidedness: a new ‘protean’-informed model

Fearon

Nachmias

McLaughlin

et al. 2016

Studies in Higher Education

View full text Add to dashboard Cite

This study investigates the role of personal values as motivational antecedents for understanding HE student career decidedness among university business school (UBS) students. We propose a new 'protean' informed HE student career decidedness model for theorizing how both personal values and social capital mediators (student social capital; personal, social and enterprise skills; access to resources) help in the student-centric and self-directed processes of career decision-making. A mixed methods study combines a (stage 1) survey of 308 UBS students from five (UK) university business schools, with results from (stage 2) four student focus groups, and (stage 3) two staff-student interactive seminars. From an employability perspective, arguably, the ultimate responsibility for becoming a 'protean graduate' rests with each UBS student, whilst the obligation of HE staff is to effectively facilitate and nurture all possible personal growth and skills development opportunities.

show abstract

“…For the extraction of confounding hidden variables, we performed factor analysis using exploratory factor analysis (EFA) [17]. From the results, three hidden factors were identified namely: Factor I (related to minimum temperature and relative humidity), Factor II (related to maximum temperature and solar radiation) and Factor III (related to precipitation and wind speed), respectively.…”

Section: Resultsmentioning

confidence: 99%

“…It is primarily used in statistics to describe variance among observed correlated variables in terms of potentially smaller number of unobserved variables, usually referred to as factors [17]. In this work, EFA was employed to search for confounding ecological factors that are latent [8], [17] from the set of observed meteorological variables.…”

Section: Factor Analysismentioning

confidence: 99%

An Assessment on the Hidden Ecological Factors of the Incidence of Malaria

Modu

Asyhari

Konur

et al. 2017

Proceedings of the IS4SI 2017 Summit DIGITALISATION FOR a SUSTAINABLE SOCIETY, Gothenburg, Sweden, 12–16 June 2017.

View full text Add to dashboard Cite

Confounding effects of climatic factors poses a greater influence in the incidence of malaria and its considerable changes over space and time. In this study, we explore a new framework for assessment and identification of hidden ecological factors to the incidence of malaria with cognizance to its ecosystem. Partial least squares path modeling (PLS-PM) and exploratory factor analysis (EFA) techniques are employed for the identification of hidden ecological factors from climate data and the reported cases of malaria in Ejisu-Juaben, Ghana. Three hidden factors are identified: Factor I is related to minimum temperature and relative humidity, Factor II is related to maximum temperature and solar radiation and Factor III is related to precipitation and wind speed, respectively. Factor I is identified as the most influential hidden ecological factor of malaria incidence in the study area, as evaluated by communality and Dillon-Goldstein's indices. This result is also validated using cross-correlation, where Factor I indicate positive association with malaria incidence at Lag1 (a month in advance). We conclude that minimum temperature and relative humidity are accountable for high malaria incidence and should be given consideration in strategizing policy for prevention and control in the study area.

show abstract

Determining the number of factors to retain in an exploratory factor analysis using comparison data of known factorial structure.

Cited by 426 publications

References 24 publications

Towards a Predictive Analytics-Based Intelligent Malaria Outbreak Warning System

Towards a Predictive Analytics-Based Intelligent Malaria Outbreak Warning System

Personal values, social capital, and higher education student career decidedness: a new ‘protean’-informed model

An Assessment on the Hidden Ecological Factors of the Incidence of Malaria

Contact Info

Product

Resources

About