Exploratory Factor Analysis with a Common Factor with Two Indicators

1997 ◽  
Vol 24 (2) ◽  
pp. 129-145 ◽  
Author(s):  
Yutaka Kano
Keyword(s):  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Common Factor

Exploratory Factor Analysis

2018 ◽  
Author(s):  
Brian D. Haig
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Common Factor ◽  
Methodological Issues ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Scientific Inference ◽  
Confirmatory Factor ◽  
The Common

Chapter 6 argues that exploratory factor analysis is an abductive method of theory generation that exploits a principle of scientific inference known as the principle of the common cause. Factor analysis is an important family of multivariate statistical methods that is widely used in the behavioral and social sciences. The best known model of factor analysis is common factor analysis, which has two types: exploratory factor analysis and confirmatory factor analysis. A number of methodological issues that arise in critical discussions of exploratory factor analysis are considered. It is suggested that exploratory factor analysis can be profitably employed in tandem with confirmatory factor analysis.

CONCEPTUALISING COMMUNICATION MEDIA AND FARMER’S REACTION IN PROBLEM SOLVING

2018 ◽  
Vol 6 (1) ◽  
pp. 01-07
Author(s):  
Idris Bin Ismail ◽  
Rosidayu Sabran ◽  
Yahya Bin Mohamed Ariffin
Keyword(s):  
Factor Analysis ◽  
Problem Solving ◽  
Exploratory Factor Analysis ◽  
Common Factor ◽  
Quantitative Measure ◽  
Information Delivery ◽  
Communication Media ◽  
Local Context ◽  
Governance Reform ◽  
New Research

Purpose: Agricultural Extension Services (AES) under structural governance reform that aimed to curb AES situational problem like insufficient of information delivery, suggested communication media principally radio as a tool for farmers to keep up with newest idea and information about agriculture technology and practices. It shows that, radio role is still pertinent on delivery of information for production output. Beyond that, this study is to examine radio as new concept that allow feedback and influence farmer’s information behavior in dealing with AES problem. Methodology: This study propose the reaction of Malaysian paddy farmers could be shape to view insufficient of information delivery within AES as being intervention in increasing farmers’ utilization of communication media such radio. Drawing upon Kim and Grunig (2011) Situational Theory Problem Solving (STOPS) as the framework, aside from looking at the perception of publics towards certain problem. This study  construct a quantitative measure of radio through exploratory factor analysis using principal components with varimax rotation to assess the performance of each item in measuring their respective variables and to be examined within STOPS. An extensive reviews of literature then produced preliminary factor and items that were tested through a survey distributed among 110 respondents. Findings: Exploratory factor analysis found the items fell into common factor group and finalized with seven (7) items of measurement for future study. Practical Implications: These results offer initial guidance into the relevance of radio effected farmer’s reaction in problem solving within the proposed model of the STOPS especially at local context. Social Implications: Study forwarded the idea of communication method that assisted public to be connected towards identified problem. Radio can be used to create awareness, share information and give a voice to the community Research Limitations: New research to further investigate other needs of famers through the use of combination of media not specifically on radio only. Originality Value: Study examined the role of radio in farmer’s reaction in problem solving towards insufficient information delivery to fill the gap of knowledge within the framework of the STOPS.

scholarly journals Application of Financial Cloud in the Sustainable Development of Smart Cities

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Yangchun Cao ◽  
Guangyu Zhang ◽  
Chunyao Ou
Keyword(s):  
Sustainable Development ◽  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Structural Equation ◽  
Comprehensive Evaluation ◽  
Smart Cities ◽  
Evaluation Model ◽  
Common Factor ◽  
The Sustainable Development ◽  
Evaluation Score

This article first investigates the status of financial cloud development in smart cities and studies the sustainable development of smart cities. Secondly, it investigates the construction of the evaluation model, using the power of the financial cloud, through exploratory factor analysis, selecting the principal component analysis method to extract the factors, for screening and dimensionality reduction of the indicators, and making hypotheses. We used confirmatory factor analysis to establish the structural equation. The measurement model analyzes and validates the assumptions of the previous stage and finally determines the sustainable development evaluation index system. Then, starting from the scoring coefficient matrix of the exploratory factor analysis of the final model, the variance contribution rate of each common factor is weighted to construct a comprehensive evaluation model to calculate the comprehensive evaluation score. For the comprehensive evaluation score of the sustainable development level of the smart city, SPSS software performs cluster analysis, performs regional clustering, and determines the level of urban development. Finally, by comparing the sustainable development levels of smart cities, the related causes of the gaps are analyzed. Key factors affecting the sustainable development of smart cities are identified, and corresponding countermeasures are proposed.

scholarly journals One model to rule them all? Using machine learning algorithms to determine the number of factors in exploratory factor analysis.

2020 ◽  
Author(s):  
David Goretzko ◽  
Markus Bühner
Keyword(s):  
Machine Learning ◽  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Learning Algorithms ◽  
Common Factor ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
New Approach ◽  
Correct Number ◽  
Number Of Factors

Determining the number of factors is one of the most crucial decisions a researcher has to face when conducting an exploratory factor analysis. As no common factor retention criterion can be seen as generally superior, a new approach is proposed - combining extensive data simulation with state-of-the-art machine learning algorithms. First, data was simulated under a broad range of realistic conditions and three algorithms were trained using specially designed features based on the correlation matrices of the simulated data sets. Subsequently, the new approach was compared to four common factor retention criteria with regard to its accuracy in determining the correct number of factors in a large-scale simulation experiment. Sample size, variables per factor, correlations between factors, primary and cross-loadings as well as the correct number of factors were varied to gain comprehensive knowledge of the efficiency of our new method. A gradient boosting model outperformed all other criteria, so in a second step, we improved this model by tuning several hyperparameters of the algorithm and using common retention criteria as additional features. This model reached an out-of-sample accuracy of 99.3% (the pre-trained model can be obtained from https://osf.io/mvrau/). A great advantage of this approach is the possibility to continuously extend the data basis (e.g. using ordinal data) as well as the set of features to improve the predictive performance and to increase generalizability.

scholarly journals Exploratory factor analysis on situational judgment data after controling for scenario effect

2013 ◽  
Vol 26 (4) ◽  
pp. 599-624
Author(s):  
JunJae Namgung ◽  
Soonmook Lee ◽  
HyoSun Kim
Keyword(s):  
Factor Analysis ◽  
Structural Equation Modeling ◽  
Exploratory Factor Analysis ◽  
Structural Equation ◽  
Common Factor ◽  
Equation Modeling ◽  
Situational Judgment ◽  
Method Effect ◽  
Creative Personality ◽  
Judgment Data

We demonstrated how to perform exploratory factor analysis on situational judgment data of creative personality. Situational judgment test(SJT) has a long history of 80 years and has settled down in industrial/organizational settings for the last 20 years. However, we have not overcome the problem of estimating construct because item scores in SJT contain method effect generated by the scenarios as well as response scores to the substantive questions. We applied Asparouhov and Muthen’s(2009) logic of explorary structural equation modeling(ESEM) to open an approach to estimating common factor structures after controling for the method effect generated by scenarios. Using ESEM, it is possible to specify measurement error correlations in the frame of structural equation modeling and to use exploratory approach to factor analysis on the remaining part of data. As a result we could estimate a four-factor structure on data of 40 items with eight scenarios measuring creative personality of college students.

scholarly journals Development and Validation of the Readiness for End-of-Life Conversations (REOLC) Scale

2021 ◽  
Vol 12 ◽  
Author(s):  
Pia Berlin ◽  
Nico Leppin ◽  
Katharina Nagelschmidt ◽  
Carola Seifart ◽  
Winfried Rief ◽  
...  
Keyword(s):  
Factor Analysis ◽  
End Of Life ◽  
Exploratory Factor Analysis ◽  
Factor Structure ◽  
Community Sample ◽  
Common Factor ◽  
Death And Dying ◽  
Living Will ◽  
Equation Modeling ◽  
Fear Of Progression

Background: Engaging in end-of-life care considerations is beneficial when the time is right. The purpose of this study is to provide a valid instrument to assess peoples readiness for end-of-life conversations before they are initiated.Materials and Methods: A community sample was recruited in study one for exploratory factor analysis of a 13-item questionnaire. In study two, psychometric properties were analyzed with structural equation modeling in a population affected by cancer. Convergent and discriminant validity were assessed with questionnaires measuring distress, depression, anxiety, fear of progression, and distress of death and dying.Results: In study one (N = 349) exploratory factor analysis resulted in three subscales readiness (α = 0.84), communication (α = 0.76), and values (α = 0.56) with a possible common factor (α = 0.84) for a community sample. In study two (N = 84) the three-factor solution with 13 items was not supported for cancer patients. Factor structure was adapted to 12 items with one common factor readiness (α = 0.87). Model fit was good: χ2(50) = 59.18, p>0.05 (Satorra-Bentler-correction = 1.27), with χ2/df = 1.184, rRMSEA = 0.053 (90%-CI[0.000;0.100]), and rSRMR = 0.072. Convergent validity was supported by moderate correlations to trait gratitude, ratings of readiness to provide a living will or talk with family about the end of life. Divergent validity was supported by no or small correlation with distress, depression, general and death anxiety and fear of progression, respectively.Conclusions: Results support usage of the REOLC Scale in different settings with adapted factor structure. The questionnaire is interpreted as valid and reliable instrument to assess objective readiness for end-of-life conversations.

Measuring Impact of Stress in Sandwich Generation Caring for Demented Parents

GeroPsych ◽  
2014 ◽  
Vol 27 (4) ◽  
pp. 171-179 ◽  
Author(s):  
Laurence M. Solberg ◽  
Lauren B. Solberg ◽  
Emily N. Peterson
Keyword(s):  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Social Worker ◽  
Resource Availability ◽  
Well Being ◽  
Sandwich Generation ◽  
Item Questionnaire ◽  
Significant Difference ◽  
Adult Caregivers ◽  
The Impact

Stress in caregivers may affect the healthcare recipients receive. We examined the impact of stress experienced by 45 adult caregivers of their elderly demented parents. The participants completed a 32-item questionnaire about the impact of experienced stress. The questionnaire also asked about interventions that might help to reduce the impact of stress. After exploratory factor analysis, we reduced the 32-item questionnaire to 13 items. Results indicated that caregivers experienced stress, anxiety, and sadness. Also, emotional, but not financial or professional, well-being was significantly impacted. There was no significant difference between the impact of caregiver stress on members from the sandwich generation and those from the nonsandwich generation. Meeting with a social worker for resource availability was identified most frequently as a potentially helpful intervention for coping with the impact of stress.

The Hierarchical Model of Exercise Dependence

2015 ◽  
Vol 36 (4) ◽  
pp. 247-257 ◽  
Author(s):  
Gayatri Kotbagi ◽  
Laurence Kern ◽  
Lucia Romo ◽  
Ramesh Pathare
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Physical Exercise ◽  
Exploratory Factor Analysis ◽  
Convergent Validity ◽  
Large Population ◽  
Assessment Tools ◽  
Negative Consequences ◽  
Confirmatory Factor ◽  
Preliminary Version

Abstract. Physical exercise when done excessively may have negative consequences on physical and psychological wellbeing. There exist many scales to measure this phenomenon. The purpose of this article is to create a scale measuring the problematic practice of physical exercise (PPPE Scale) by combining two assessment tools already existing in the field of exercise dependency but anchored in different approaches (EDS-R and EDQ). This research consists of three studies carried out on three independent sample populations. The first study (N = 341) tested the construct validity (exploratory factor analysis); the second study (N = 195) tested the structural validity (confirmatory factor analysis) and the third study (N = 104) tested the convergent validity (correlations) of the preliminary version of the PPPE scale. Exploratory factor analysis identified six distinct dimensions associated with exercise dependency. Furthermore, confirmatory factor analysis validated a second order model consisting of 25 items with six dimensions and four sub-dimensions. The convergent validity of this scale with other constructs (GLTEQ, EAT26, and The Big Five Inventory [BFI]) is satisfactory. The preliminary version of the PPPE must be administered to a large population to refine its psychometric properties and develop scoring norms.

Subclinical Sadism and the Dark Triad

2019 ◽  
Vol 40 (3) ◽  
pp. 127-133 ◽  
Author(s):  
Laura K. Johnson ◽  
Rachel A. Plouffe ◽  
Donald H. Saklofske
Keyword(s):  
Factor Analysis ◽  
University Students ◽  
Personality Traits ◽  
Exploratory Factor Analysis ◽  
Convergent Validity ◽  
Dark Triad ◽  
Antisocial Personality ◽  
Factor Solution ◽  
Online Study ◽  
Antisocial Personality Traits

Abstract. The Dark Triad is a constellation of three antisocial personality traits: Machiavellianism, narcissism, and psychopathy. Recently, researchers have introduced a “Dark Tetrad” that includes subclinical sadism, although others suggest considerable overlap between psychopathy and sadism. To clarify the position of sadism within the Dark Triad, an online study was conducted with 615 university students. Exploratory factor analysis revealed that a six-factor solution fit the data best, representing Machiavellianism, psychopathy, physical sadism, verbal sadism, narcissism, and vicarious sadism. Furthermore, convergent validity was supported through sadism’s correlations with the HEXACO personality traits. The results support sadism’s inclusion within the Dark Tetrad as a unique construct but with some conceptual overlap with psychopathy.

Equivalent Factorial Structure of the Brief Symptom Inventory (BSI) in Clinical and Nonclinical Jordanian Populations

2010 ◽  
Vol 26 (2) ◽  
pp. 116-121 ◽  
Author(s):  
Fawzi S. Daoud ◽  
Amjed A. Abojedi
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Clinical Sample ◽  
Brief Symptom Inventory ◽  
Factorial Structure ◽  
Symptom Inventory ◽  
Confirmatory Factor ◽  
Item Checklist ◽  
General Psychological Distress

This study investigates the equivalent factorial structure of the Brief Symptom Inventory (BSI) in clinical and nonclinical Jordanian populations, using both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The 53-item checklist was administered to 647 nonclinical participants and 315 clinical participants. Eight factors emerged from the exploratory factor analysis (EFA) for the nonclinical sample, and six factors emerged for the clinical sample. When tested by parallel analysis (PA) and confirmatory factor analysis (CFA), the results reflected a unidimensional factorial structure in both samples. Furthermore, multigroup CFA showed invariance between clinical and nonclinical unidimensional models, which lends further support to the evidence of the unidimensionality of the BSI. The study suggests that the BSI is a potentially useful measure of general psychological distress in clinical and nonclinical population. Ideas for further research are recommended.

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