scholarly journals VALUES OF YOUNG EMPLOYEES: Z-GENERATION PERCEPTION

2020 ◽  
Vol 21 (1) ◽  
pp. 10-17
Author(s):  
Jelena Titko ◽  
Anna Svirina ◽  
Viktorija Skvarciany ◽  
Inga Shina
Keyword(s):  
Professional Development ◽  
Factor Analysis ◽  
Personality Trait ◽  
Correlation Matrix ◽  
Principal Axis ◽  
Image Correlation ◽  
Processing Factor ◽  
Number Of Factors ◽  
Bartlett’S Test ◽  
Principal Axis Factoring

The current paper aims to analyse the importance of values of young employees now and in five years period. In order to achieve the aim, the questionnaire consisting of fifty statements was developed and disseminated between the Latvian students. The sample size was 392, which shows that the results represent the whole populations. For data processing, factor analysis was chosen as a tool. The data factorability was assessed via Bartlett’s Test of Sphericity, Kaiser-Myer-Olkin (KMO) statistic, initial estimates of communality and the anti-image correlation matrix. The factors were extracted via principal axis factoring (PAF). The number of factors was determined by the scree plot/Kaiser’s rule and was equal to five in both cases. The results showed the for the young employees the essential values today are those connected to the personality trait. However, in five years, the essential values would be those that are linked to professional development.

scholarly journals Design Thinking Mini Project Approach Using Factor Analysis

2021 ◽  
Vol 6 (17) ◽  
Author(s):  
Sharifah Zannierah Syed Marzuki ◽  
Che Asniza Osman ◽  
Siti Zahrah Buyong ◽  
Mohamad Zreik
Keyword(s):  
Factor Analysis ◽  
Design Thinking ◽  
Statistical Significance ◽  
Publishing House ◽  
Image Correlation ◽  
Student Interactions ◽  
Project Approach ◽  
Bartlett’S Test ◽  
International Publishing ◽  
The Relationship

This study used factor analysis to investigate relationships between educator to student interactions, the relationship between students, skills to critical thinking, and motivation. Factor analysis showed that there are three factors; passion, assistance, and guidance. Meanwhile, the adequacy is 0.945 surpassing the recommended value of 0.50 using Kaiser Meyer Olkin. The statistical significance from the test of Bartlett’s Test of Sphericity indicated that 0.00 and χ2 = 3552.973. Adequacy measurement of Anti-Image Correlation Matrix ranges from 0.49 to 0.69. It is significantly important that educators guide the students in becoming more creative and innovative by using and applying DTMP during the learning process. Keywords: design thinking;creative;innovative;factor analysis eISSN: 2398-4287© 2021. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians/Africans/Arabians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI:

A Proposed Solution to the Problem With Using Completely Random Data to Assess the Number of Factors With Parallel Analysis

2011 ◽  
Vol 72 (3) ◽  
pp. 357-374 ◽  
Author(s):  
Samuel B. Green ◽  
Roy Levy ◽  
Marilyn S. Thompson ◽  
Min Lu ◽  
Wen-Juo Lo
Keyword(s):  
Monte Carlo ◽  
Principal Axis ◽  
Monte Carlo Study ◽  
Heuristic Approach ◽  
Parallel Analysis ◽  
Analysis Method ◽  
Random Data ◽  
Five Dimensions ◽  
Number Of Factors ◽  
Principal Axis Factoring

A number of psychometricians have argued for the use of parallel analysis to determine the number of factors. However, parallel analysis must be viewed at best as a heuristic approach rather than a mathematically rigorous one. The authors suggest a revision to parallel analysis that could improve its accuracy. A Monte Carlo study is conducted to compare revised and traditional parallel analysis approaches. Five dimensions are manipulated in the study: number of observations, number of factors, number of measured variables, size of the factor loadings, and degree of correlation between factors. Based on the results, the revised parallel analysis method, using principal axis factoring and the 95th percentile eigenvalue rule, offers promise.

scholarly journals Identifying Items to Assess Methodological Quality in Physical Therapy Trials: A Factor Analysis

2014 ◽  
Vol 94 (9) ◽  
pp. 1272-1284 ◽  
Author(s):  
Susan Armijo-Olivo ◽  
Greta G. Cummings ◽  
Jorge Fuentes ◽  
Humam Saltaji ◽  
Christine Ha ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Physical Therapy ◽  
Methodological Quality ◽  
Principal Axis ◽  
Risk Of Bias ◽  
Controlled Trials ◽  
Randomized Controlled ◽  
Confirmatory Factor ◽  
Principal Axis Factoring

Background Numerous tools and individual items have been proposed to assess the methodological quality of randomized controlled trials (RCTs). The frequency of use of these items varies according to health area, which suggests a lack of agreement regarding their relevance to trial quality or risk of bias. Objective The objectives of this study were: (1) to identify the underlying component structure of items and (2) to determine relevant items to evaluate the quality and risk of bias of trials in physical therapy by using an exploratory factor analysis (EFA). Design A methodological research design was used, and an EFA was performed. Methods Randomized controlled trials used for this study were randomly selected from searches of the Cochrane Database of Systematic Reviews. Two reviewers used 45 items gathered from 7 different quality tools to assess the methodological quality of the RCTs. An exploratory factor analysis was conducted using the principal axis factoring (PAF) method followed by varimax rotation. Results Principal axis factoring identified 34 items loaded on 9 common factors: (1) selection bias; (2) performance and detection bias; (3) eligibility, intervention details, and description of outcome measures; (4) psychometric properties of the main outcome; (5) contamination and adherence to treatment; (6) attrition bias; (7) data analysis; (8) sample size; and (9) control and placebo adequacy. Limitation Because of the exploratory nature of the results, a confirmatory factor analysis is needed to validate this model. Conclusions To the authors' knowledge, this is the first factor analysis to explore the underlying component items used to evaluate the methodological quality or risk of bias of RCTs in physical therapy. The items and factors represent a starting point for evaluating the methodological quality and risk of bias in physical therapy trials. Empirical evidence of the association among these items with treatment effects and a confirmatory factor analysis of these results are needed to validate these items.

Kenny Music Performance Anxiety Inventory: Exploratory Factor Analysis of the Romanian version

2020 ◽  
pp. 030573561989641
Author(s):  
Andrada Lavinia Faur ◽  
Sebastian Vaida ◽  
Adrian Opre
Keyword(s):  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Parental Support ◽  
Music Performance ◽  
Performance Anxiety ◽  
Music Performance Anxiety ◽  
Number Of Factors ◽  
Rotation Method ◽  
Oblimin Rotation ◽  
Principal Axis Factoring

Kenny Music Performance Anxiety Inventory (K-MPAI) is one of the most widely used instruments in the research of music performance anxiety. The aim of this study was to investigate the factor structure of the Romanian version of K-MPAI. A sample of 420 (aged 18–66, M = 24.46, SD = 7.36; 48% women and 52% men) musicians completed the K-MPAI. Exploratory factor analysis with principal axis factoring and oblimin rotation method indicated eight factors which explained 49.16% of variance. Due to the overestimation of the number of factors by the Kaiser’s criterion of 1, parallel analysis with the syntax provided by O’Connor was implemented. Four factors were extracted which explained 41.37% of variance. They were named “music performance anxiety symptoms,” “depression and hopelessness,” “parental support,” and “memory self-efficacy.” Results partially support the theoretical model which sustained the development of K-MPAI, and further clinical implications for the Romanian musician population are discussed.

Factor Analysis, Random Data, and Patterned Results

1981 ◽  
Vol 46 (2) ◽  
pp. 272-283 ◽  
Author(s):  
Robert K. Vierra ◽  
David L. Carlson
Keyword(s):  
Factor Analysis ◽  
Correlation Matrix ◽  
Statistical Significance ◽  
Principal Component ◽  
Multivariate Statistical Techniques ◽  
Random Data ◽  
Multivariate Statistical ◽  
Data Set ◽  
Bartlett’S Test ◽  
The Matrix

Multivariate statistical techniques such as factor analysis are capable of producing patterned results with most, if not all, data matrices. This paper demonstrates that patterned results are obtainable when principal component analysis is applied to a random data set. It is suggested that Bartlett's test for the statistical significance of a correlation matrix be employed in deciding whether a factor analysis of the matrix is justified.

scholarly journals Exploring the Factor Structure of the Glasgow Children’s Benefit Inventory: New Recommendations for Reporting Results

2020 ◽  
pp. 000348942096563
Author(s):  
Haytham Kubba ◽  
William M. Whitmer
Keyword(s):  
Factor Analysis ◽  
Internal Consistency ◽  
Patient Reported Outcomes ◽  
Principal Axis ◽  
Ventilation Tube ◽  
Tube Insertion ◽  
Future Studies ◽  
Life Saving ◽  
Patient Reported ◽  
Principal Axis Factoring

Objective: Patient-reported outcomes can be useful for reporting benefit from non-life-saving interventions, but often they report a single overall score, which means that much information on the specific areas of benefit is lost. Our aim was to perform a new factor analysis on the Glasgow Children’s Benefit Inventory (GCBI) to create subscales reflecting domains of benefit. Further aims were to assess the internal consistency of the GCBI, and to develop guidelines for reporting both a total score and sub-scales in future studies. Methods: We collected 4 existing datasets of GCBI data from children who have undergone tonsillectomy, ventilation tube insertion, pinnaplasty, and submucous diathermy to the inferior turbinates. We performed exploratory factor analysis with principal axis factoring with varimax rotation, we sought redundancy in question items, and we measured internal consistency. Results: Using the combined dataset of 772 cases, we found 4 factors which accounted for 64% of the variance and which we have labeled “Psycho-social,” “Physical health,” “Behavior,” and “Vitality.” Subscale results varied in predictable ways depending on the nature of the intervention. Cronbach’s alpha was 0.928. Item-total correlations were high, and no item could be deleted to improve alpha. Floor effects were apparent for various questions but were not consistent between different interventions. Conclusions: The GCBI contains a range of questions which each add value in different clinical interventions. We can now make recommendations for reporting the results of the GCBI and its 4 new subscales.

scholarly journals Authenticating Factor Analysis of Attitude towards Mathematics Inventory (ATMI) in Thailand

2020 ◽  
Vol 3 (1) ◽  
pp. p26
Author(s):  
Hareesol Khun-inkeeree ◽  
M. S. Omar Fauzee
Keyword(s):  
Factor Analysis ◽  
Psychometric Properties ◽  
Correlation Matrix ◽  
Primary Schools ◽  
Attitudes Toward Mathematics ◽  
Thai Language ◽  
Self Confidence ◽  
Bartlett’S Test ◽  
Attitude Towards Mathematics ◽  
Students Attitudes

The study examine the psychometric properties of Attitudes towards Mathematics Inventory (ATMI) in the Thai context. To achieve the objective set by the authors, 259 students from 10 different primary schools in Nakhon Si Thammarat province, Thailand were selected. Furthermore, a forty items ATMI questionnaire having four scales that is, 15 items measuring self-confidence, 10 items measuring value, 10 items measuring enjoyment, and 5 items measuring motivation from the study of Khine and Afari (2014) was adapted. The questionnaire adapted was translated to Thai language by expert English Thai lecturer. Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett’s test of sphericity were carried out to ascertain the factorability of the correlation matrix. That ATMI can be a viable scale to measure students’ attitudes toward mathematics in Thai context.

Factor Analysis of the Image Correlation Matrix

1979 ◽  
Vol 39 (4) ◽  
pp. 711-714 ◽  
Author(s):  
Henry F. Kaiser ◽  
Barbara A. Cerny
Keyword(s):  
Factor Analysis ◽  
Correlation Matrix ◽  
Image Correlation

scholarly journals Assessing Teacher and Librarian Collaboration: A Preliminary Report

2021 ◽  
Author(s):  
Patricia Montiel-Overall
Keyword(s):  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Internal Consistency ◽  
Preliminary Report ◽  
Principal Axis ◽  
Oblique Rotation ◽  
Broad Distribution ◽  
Proposed Model ◽  
Teacher Surveys ◽  
Principal Axis Factoring

Exploratory factor analysis was used to examine the structure of a 32-item teacher and librarian collaboration survey (TLC-II). The survey consisted of two scales with 16 items in each scale, Frequency and Importance to Student Learning. Scores from teacher surveys (N=194) were examined using principal axis factoring and oblique rotation to identify underlying constructs. A four factor interpretable structure of teacher and librarian collaboration emerged providing support for a proposed model of teacher and librarian collaboration. Internal consistency was high for the overall scale and for each of the factors. The results of this study provide a basis for further refinement of the instrument in preparation for broad distribution among teachers and librarians.

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