scholarly journals أثر طریقة التدویر المتعامد وحجم العینة فی التحلیل العاملی على عدد العوامل ونسبة التباین المفسر The Effect of Methods of Orthogonal Rotation and Sample Size in Factor Analysis on the Number of Factors and the Proportion of Explained Variance

2019 ◽  
Vol 38 (3) ◽  
pp. 147-169
Author(s):  
صبری حسن الطراونة
Keyword(s):  
Factor Analysis ◽  
Sample Size ◽  
Orthogonal Rotation ◽  
Number Of Factors ◽  
Explained Variance

scholarly journals Identification of Facet Models by Means of Factor Rotation: A Simulation Study and Data Analysis of a Test for the Berlin Model of Intelligence Structure

2020 ◽  
Vol 80 (5) ◽  
pp. 995-1019
Author(s):  
André Beauducel ◽  
Martin Kersting
Keyword(s):  
Factor Analysis ◽  
Data Analysis ◽  
Sample Size ◽  
Simulation Study ◽  
Simple Structure ◽  
Factor Models ◽  
Minimum Entropy ◽  
Factor Rotation ◽  
Number Of Factors ◽  
Target Rotation

We investigated by means of a simulation study how well methods for factor rotation can identify a two-facet simple structure. Samples were generated from orthogonal and oblique two-facet population factor models with 4 (2 factors per facet) to 12 factors (6 factors per facet). Samples drawn from orthogonal populations were submitted to factor analysis with subsequent Varimax, Equamax, Parsimax, Factor Parsimony, Tandem I, Tandem II, Infomax, and McCammon’s minimum entropy rotation. Samples drawn from oblique populations were submitted to factor analysis with subsequent Geomin rotation and a Promax-based Tandem II rotation. As a benchmark, we investigated a target rotation of the sample loadings toward the corresponding faceted population loadings. The three conditions were sample size ( n = 400, 1,000), number of factors ( q = 4-12), and main loading size ( l = .40, .50, .60). For less than six orthogonal factors Infomax and McCammon’s minimum entropy rotation and for six and more factors Tandem II rotation yielded the highest congruence of sample loading matrices with faceted population loading matrices. For six and more oblique factors Geomin rotation and a Promax-based Tandem II rotation yielded the highest congruence with faceted population loadings. Analysis of data of 393 participants that performed a test for the Berlin Model of Intelligence Structure revealed that the faceted structure of this model could be identified by means of a Promax-based Tandem II rotation of task aggregates corresponding to the cross-products of the facets. Implications for the identification of faceted models by means of factor rotation are discussed.

Model Selection and Average Proportion Explained Variance in Exploratory Factor Analysis

2020 ◽  
pp. 001316442096316
Author(s):  
Tenko Raykov ◽  
Lisa Calvocoressi
Keyword(s):  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Structural Equation ◽  
Equation Modeling ◽  
Model Choice ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
Number Of Factors ◽  
Average Proportion ◽  
Explained Variance

A procedure for evaluating the average R-squared index for a given set of observed variables in an exploratory factor analysis model is discussed. The method can be used as an effective aid in the process of model choice with respect to the number of factors underlying the interrelationships among studied measures. The approach is developed within the framework of exploratory structural equation modeling and is readily applicable with popular statistical software. The outlined procedure is illustrated using a numerical example.

Impact of Outliers Arising From Unintended and Unknowingly Included Subpopulations on the Decisions About the Number of Factors in Exploratory Factor Analysis

2012 ◽  
Vol 72 (3) ◽  
pp. 388-414 ◽  
Author(s):  
Yan Liu ◽  
Bruno D. Zumbo
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Sample Size ◽  
Exploratory Factor Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Chi Square ◽  
Number Of Factors ◽  
Decision Methods ◽  
Sequential Tests

There is a lack of research on the effects of outliers on the decisions about the number of factors to retain in an exploratory factor analysis, especially for outliers arising from unintended and unknowingly included subpopulations. The purpose of the present research was to investigate how outliers from an unintended and unknowingly included subpopulation affected the decisions about the number of factors to retain using four commonly used methods separately. The results showed that all the decision methods could provide biased results and the number of factors could be inflated, deflated, or remain the same depending on the decision methods used and outlier conditions. The findings also revealed that symmetric outliers did not affect the three principal component analysis–based methods but affected chi-square (ML) sequential tests. Finally, sample size did not play a role in the effect of outliers.

scholarly journals FACTORS ANALYSIS OF FOOTWEAR TRADE INDUSTRY OF INDONESIA MAIN EXPORT DESTINATION

2019 ◽  
Vol 1 (1) ◽  
pp. 70-81
Author(s):  
Darwin Simanjuntak
Keyword(s):  
United States ◽  
Factor Analysis ◽  
Hong Kong ◽  
Sample Size ◽  
Russian Federation ◽  
Final Solution ◽  
Factors Analysis ◽  
Number Of Factors ◽  
Export Destination ◽  
Variance Explained

This study aims to identify the factor that affecting the footwear trade industry in Indonesia based on Indonesia main export destination. Understanding these factors could help leaders in trade industry institutions to better plan their strategies and further research on footwear trading. A set of data was obtained from Badan Pusat Statistic (BPS) based on data from Indonesia footwear main export destination namely: United States, China, Belgium, Germany, Japan, United Kingdom, Netherlands, Korea, Italy, Australia, Mexico, France, Canada, Denmark, Singapore, Brazil, Hong Kong, Russian Federation, Chile, Argentina and Other Countries. Exploratory factor analysis was used to identify the underlying dimensions of countries as Indonesia main export destination. By applying the factor analysis, the study will decide the number of factors to be retained and the total variance explained by these factors; the study can identify the variables in each factor retained in the final solution, on the basis of its factor loadings; the study can give names to each factor retained on the basis of the nature of the variables included in it; the study can suggest the test battery for assessing the footwear trade main export destination in Indonesia; and the study can test the adequacy of sample size used in factor analysis. The result of the study shows that KMO value is 0.784 which is > 0.5; hence, the sample size is adequate for the analysis and the commonalities of all the variables are more than .4; hence, all the variables are useful in the model. Since the variables are identified in factor 0.7 or more, the result shows that all factors which are from the year 2012-2016 contributed to the exports of footwear in Indonesia.

scholarly journals The connection of committing and experiencing cyber-bullying peer violence with depression, anxiety and stress in high school students

2021 ◽  
Author(s):  
Marija Draganic
Keyword(s):  
High School ◽  
Factor Analysis ◽  
High School Students ◽  
Cyber Bullying ◽  
Online Identity ◽  
School Students ◽  
Peer Violence ◽  
Significant Difference ◽  
Identity Concealment ◽  
Explained Variance

<p></p><p>This research has aimed to determine whether cyber-bullying peer violence is associated with depression, anxiety and stress in high school students. The research results are based on the data obtained from a sample of 202 Montenegrin high school students. It has been established that committing and experiencing e-violence is associated with depression. Committing and experiencing verbal online violence, as well as involvement in online counterfeiting/criminal acts, is associated with depression, while involvement in online identity concealment and lies has no connection with depression. When it comes to anxiety, the results of this research show that involvement in verbal e-violence and in online counterfeiting/criminal acts leads to anxiety, while committing and experiencing online identity concealmentis not related to anxiety. When it comes to committing and experiencing verbal online violence and stress, although there is a statistically significant difference, it is not applicable for all values. Consequently, we cannot draw a strong enough conclusion about it. Victims of online counterfeiting and criminal acts, according to the results of our research, have a higher level of stress than those not involved in this type of cyber-bullying peer violence.</p><p><i>Cyber victim and bullying scale </i>has been used to collect data on committing and experiencing e-violence among peers (Cetin, Yaman and Peker, 2011).Permission to use this scale was requested and obtained. The scale examines experiencing and committing online violence. It consists of two parts, each part containing 22 particles. In the first part (<i>Experiencing cyber-bullying </i>subscale), participants have assessed whether the described behavior happened to them,on a scale from 1 (never) to 5 (always). In the second part (<i>Committing cyber-bullying </i>subscale), and with identical particles, respondents have assessed whether they themselves behaved in this way. </p><p></p><p> </p><p><i>In our research, we have performed a factor analysis of the </i><i>Cyber victim and bulling scale .</i></p> <p>When it comes to the <i>Committing cyber-bullying </i>subscale, the percentage of explained variance amounts to 55.141%, whereas factor analysis also points to 3 factors, namely: a) verbal online violence; b) online identity concealment and lies; and c) online counterfeiting and criminal acts.</p> <p>When it comes to the <i>Experiencing cyber-bullying</i> subscale, the percentage of explained variance amounts to 65,211%, whereas factor analysis also points to 3 factors, namely: a) verbal online violence; b) online identity concealment and lies; and c) online counterfeiting and criminal acts.<i> W</i>e have used a scale for assessing the depression, anxiety and stress levels (DASS21) to determine whether experiencing e-violence leads to depression, anxiety and stress,and to what extent. We have chosen this scale because the results of a research checking its psychometric qualities have indicated that the DASS-21 scale’s psychometric qualities recommend it for use in researching unpleasant emotional states in adolescents. DASS-21 is a 21-item self-report measure giving three subclasses of seven items each: depression , anxiety and stress. Participants have been asked to indicate which statement applied to them over the past week on the Likert 4-point scale. DASS-21 has high reliability, consistent factor structure and high convergent valiadity. We did not have to ask permission for this scale and it can be used freely.</p><p></p>

scholarly journals Psychometric properties of the Desensitization to Violence Scale for Adolescents

Nova Scientia ◽  
2019 ◽  
Vol 11 (22) ◽  
pp. 274-292
Author(s):  
Jaime Sebastián F. Galán Jiménez ◽  
Omar Sánchez-Armáss Cappello ◽  
Luis Felipe García y Barragán
Keyword(s):  
Factor Analysis ◽  
Psychometric Properties ◽  
Criminal Behavior ◽  
Convergent Validity ◽  
Physical Violence ◽  
Factor Model ◽  
Juvenile Detention ◽  
Psychological Violence ◽  
Sequential Method ◽  
Explained Variance

Introduction: Desensitization to violence is the result of exposure to violence. It reduces the negative emotions in cognitive and physiological responses to violence and can even generate a positive response to it.Method: A mixed sequential method yielding a transactional analytic design for Exploratory Factor Analysis and Confirmatory Factor Analysis with a sample of 1720 participants of 25 different high schools and a juvenile detention center. Followed by convergent validity (as a criterion validity) with a different sample.Results: The EFA’s cumulative explained variance was 52% with a GFI of .98 with a three-factor model: sensitivity to violence, amusement with physical violence, and enjoyment of psychological violence. The CFA showed scores above .50 in Average Explained Variance in every factor, and an ideal model fit in every measure (CFI, AGFI, RMSEA, SRMR, and ECVI). The remaining factors are only those related with enjoying or amusement with violent behavior, indicating that the desensitization to violence is related not only with the normalization and legitimation of violence, but the increasing of the performance and amusement of it.Discussion or Conclusion: The scale of desensitization to violence for adolescents has adequate psychometric properties and can be a valuable instrument to generate intervention or prevention programs, especially for its intimate relationship with high scores in people interned because of their criminal behavior.

scholarly journals FAKTOR - FAKTOR YANG MEMPENGARUHI MINAT KONSUMEN DALAM MENGGUNAKAN JASA TRANSPORTASI KRL / COMMUTER LINE DENGAN METODE ANALISIS FAKTOR

2019 ◽  
Vol 2 (1) ◽  
pp. 38
Author(s):  
Besse Arnawisuda Ningsi ◽  
Ani Putriyani
Keyword(s):  
Factor Analysis ◽  
Reduction Process ◽  
Principal Component ◽  
Dominant Factor ◽  
Transportation Services ◽  
Consumer Interest ◽  
Number Of Factors ◽  
Interference Test ◽  
Spss Software ◽  
Determining Factor

The purpose of this study was to find out how many factors were formed through the reduction process, to determine which factors most dominantly influence consumer interest in using KRL Commuter Line transportation services, to find out how much influence the most dominant factors influence consumer interest in using KRL Commuter transportation services Line. In this study, the Factor Analysis method was used with principal component analysis techniques using SPSS software. 7 stages of factor analysis, namely: (1) formulating a problem, (2) Variable Interference Test, (3) forming a correlation matrix, (4) determining factor analysis methods, (5) extracting factors, (6) rotating factors, and (7) interpret factors. Based on the results of the study, the number of factors formed from the reduction process is 7 factors. Consisting of one factor (F1) with a correlation value of 0.608, factor two (F2) with a correlation value of -0.277, factor three (F3) with a correlation value of -0.246, factor four (F4) with a correlation value of 0.419, factor five (F5) with the correlation value is 0.716, factor six (F6) with a correlation value of -0.392, factor seven (F7) with a correlation value of 0.570. The most dominant factor affecting consumer interest in using KRL Commuter Line transportation services is factor five (F5) with a correlation value of 0.716 or 71.6%.

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