scholarly journals The Translation and Validation of the Dutch Monash Dog–Owner Relationship Scale (MDORS)

Animals ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 249
Author(s):  
Emmy A.E. van Houtert ◽  
Nienke Endenburg ◽  
Joris J. Wijnker ◽  
T. Bas Rodenburg ◽  
Hein A. van Lith ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Factor Model ◽  
Principal Component ◽  
Component Analysis ◽  
Cronbach’S Alpha ◽  
Cronbach's Alpha ◽  
Dutch Translation ◽  
English Speaking ◽  
Translation And Validation ◽  
Three Factor

The Monash Dog–Owner Relationship Scale (MDORS) is a questionnaire that is used to evaluate the perceived relationship between humans and their dog. This questionnaire was originally only formulated and validated in English, which limits its use among non-English speaking individuals. Although a translation could be made, the translation of questionnaires without additional validation often impairs the reliability of that questionnaire. Therefore, the aim of this study was to validate a translation of the MDORS that is suitable for use among native Dutch speakers. To achieve this, a Dutch translation of the MDORS was made and checked for spelling/grammar mistakes, readability, feasibility, and clarity. A test–retest comparison was subsequently performed on the translation together with a calculation of Cronbach’s alpha score and principal component analysis (PCA). Through the PCA, we found that the three-factor model of the original MDORS was also largely present in the Dutch translation. However, deviations were also found, as several questions did not achieve high PCA scores in their original factor. Therefore, we propose that these questions are excluded from the Dutch MDORS.

scholarly journals Pengembangan Skala Pemaafan Diri (Self-forgiveness)

2020 ◽  
Vol 2 (1) ◽  
pp. 80-90
Author(s):  
Dyah Ayu Larasati ◽  
Tria Widyastuti
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Cronbach’S Alpha ◽  
Cronbach's Alpha ◽  
Self Forgiveness

Individu memiliki cara yang berbeda dalam merespon kesalahan yang diperbuat. Ada yang cepat memaakan kesalahan diri sendiri, namun ada yang butuh waktu lama atau bahkan membenci diri sendiri karena kesalahan yang diperbuat. Konsep penting terkait hal tersebut yaitu pemaafan diri yang ternyata berimbas positif pada kesejahteraan psikologis dan kesehatan mental. Pengukuran pemaafan diri kemudian menjadi penting untuk mendukung semakin banyaknya penelitian pemaafan diri. Namun begitu, meski di luar sudah dikembangkanbeberapa skala pemaafan diri, di Indonesia belum banyak ditemukan penelitian yang khusus mengembangkan atau mengadaptasi skala ini. Dalam tulisan ini, peneliti bertujuan mengembangkan skala pemaafan diri yang diharapkan menjadi alat ukur yang valid dan reliabel. Peneliti melakukan tujuh tahapan pengembangan skala yaitu: (1) penentuan konstrak ukur, (2) penyusunan indikator-aitem, (3) pengujian validitas isi, (4) uji coba, (5) uji lapangan, (6) seleksi aitem, dan (7) penyusunan skala final. Hasil akhir penelitian ini dihasilkan 13 aitem skala pemaafan diriyang memiliki daya beda berkisar .337 hingga .676 dan reliabilitas Cronbach’s alpha ditemukan .798 (dimensi mengakui tanggung jawab) dan .779 (dimensi memperbarui diri). Skala pemaafan diri juga memiliki struktur internal yang cukup baik. Hasil analisis faktor dengan principal component analysis pada 13 aitem didapat nilai KMO=.833, Barlett’s Test of sphrecity X2(78)=447.594, p<.01. Dua dimensi pemaafan diri mampu menjelaskan sebesar 49.891% varian.

scholarly journals Pengembangan Skala Deception Behavior in Social Media

2020 ◽  
Author(s):  
Clara Moningka ◽  
Maria Selviana
Keyword(s):  
Social Media ◽  
Principal Component Analysis ◽  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Cronbach’S Alpha ◽  
Cronbach's Alpha ◽  
Confirmatory Factor

Kemajuan teknologi Internet di dunia memunculkan fenomena baru dalam penggunaan media sosial. Media sosial tidak hanya digunakan untuk berhubungan dengan orang lain, namun juga untuk membentuk kesan yang baik, bahkan untuk melakukan manipulasi. Pengguna media sosial seringkali memanipulasi penampakan mereka di media sosial, seperti mengedit foto atau memalsukan identitas mereka. Fenomena ini termasuk dalam Deception Behavior (perilaku menipu). Meski fenomena ini sering dijumpai, belum banyak studi yang berupaya untuk mengukur kecenderungan seseorang untuk terlibat dalam perilaku menipu di media sosial. Oleh karena itu, penelitian ini bertujuan untuk membuat skala Deception Behavior in Social Media berdasarkan teori Utz (2005). Partisipan penelitian adalah pengguna aktif media sosial sebanyak 457 orang di wilayah Jabodetabek dengan rentang usia 15-40 tahun. Penelitian ini meliputi pembuatan butir dan pengujian dengan menggunakan Principal Component Analysis (PCA), dilanjutkan dengan Confirmatory Factor Analysis (CFA). Sementara itu, reliabilitas diuji dengan menggunakan koefisien Cronbach’s Alpha. Hasil penelitian menunjukkan bahwa skala ini reliabel untuk mengukur deception behavior di media sosial.

scholarly journals Study of risk factors for healthcare-associated infections in acute cardiac patients using categorical principal component analysis (CATPCA)

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Emilio Renes Carreño ◽  
Almudena Escribá Bárcena ◽  
Mercedes Catalán González ◽  
Francisco Álvarez Lerma ◽  
Mercedes Palomar Martínez ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Heart Disease ◽  
Principal Component ◽  
Component Analysis ◽  
Apache Ii Score ◽  
Cronbach’S Alpha ◽  
Cronbach's Alpha ◽  
Icu Stay ◽  
Categorical Principal Component Analysis ◽  
Using Data

AbstractUsing categorical principal component analysis, we aimed to determine the relationship between health care-associated infections (HAIs) and diagnostic categories (DCs) in patients with acute heart disease using data collected in the Spanish prospective ENVIN-HELICS intensive care registry over a 10-year period (2005–2015). A total of 69,876 admissions were included, of which 5597 developed HAIs. Two 2-component CATPCA models were developed. In the first model, all cases were included; the first component was determined by the duration of the invasive devices, the ICU stay, the APACHE II score and the HAIs; the second component was determined by the type of admission (medical or surgical) and by the DCs. No clear association between DCs and HAIs was found. Cronbach’s alpha was 0.899, and the variance accounted for (VAF) was 52.5%. The second model included only admissions that developed HAIs; the first component was determined by the duration of the invasive devices and the ICU stay; the second component was determined by the inflammatory response, the mortality in the ICU and the HAIs. Cronbach’s alpha value was 0.855, and VAF was 46.9%. These findings highlight the role of exposure to invasive devices in the development of HAIS in patients with acute heart disease.

General Health Questionnaire-12 items: adaptation study to the Portuguese population

2008 ◽  
Vol 17 (2) ◽  
pp. 148-151 ◽  
Author(s):  
Carlos António Laranjeira
Keyword(s):  
Principal Component Analysis ◽  
Factor Structure ◽  
Convergent Validity ◽  
Principal Component ◽  
Well Being ◽  
Component Analysis ◽  
Test Reliability ◽  
Alpha Coefficient ◽  
Cronbach’S Alpha ◽  
Cronbach's Alpha

SummaryAims – To assess the validity and reliability of the GHQ-12 in Portugal. Methods – Using a standard ‘forwardbackward’ translation procedure, the English language version of the questionnaire was translated into Portuguese. To test reliability the internal consistency was assessed by Cronbach's alpha coefficient. Validity was performed using convergent validity. Finally, the factor structure of the questionnaire was extracted by performing principal component analysis using varimax rotation. Results – In all 790 undergraduates entered into the study. Reliability analysis showed satisfactory result (Cronbach's alpha coefficient = 0.91). Convergent validity indicated a significant negative correlation between the GHQ-12 and social support scores as expected (r = -0.54, P < 0.001). The principal component analysis showed that the GHQ-12 was a measure with two-factor structure. Conclusion – The Portuguese version of the GHQ-12 has a good structural characteristic for measuring psychological well being.Declaration of Interest: None.

Psychometric Evaluation of the Attitudes to Disability Scale for Use With Nurse Educators

2016 ◽  
Vol 24 (3) ◽  
pp. 465-476 ◽  
Author(s):  
Lori Lyon ◽  
Rick Houser
Keyword(s):  
Principal Component Analysis ◽  
Nurse Educators ◽  
Principal Axis ◽  
Psychometric Evaluation ◽  
Principal Component ◽  
Original Form ◽  
Cronbach’S Alpha ◽  
Cronbach's Alpha ◽  
Disability Scale ◽  
Principal Axis Factor

Background and Purpose: This article tests the construct validity of the Attitudes to Disability Scale (ADS) for use with nurse educators. The ADS was designed to assess the attitudes of people with disabilities and the general population. Methods: The responses of 126 nurse educators were analyzed using multivariate statistics. Results: Reliability analysis of the original ADS factors yielded Cronbach’s alpha of .655, .689, .781, and .621 (Inclusion, Discrimination, Gains, and Prospects, respectively). Principal component analysis confirmed factors Discrimination and Gains and identified two new factors. Principal axis factor confirmed Discrimination and Gains and defined one new factor (Opportunity) with a Cronbach’s alpha of .73. Conclusions: The original form ADS is not reliable for use with nurse educators although a modified version may prove suitable.

scholarly journals The Fama and French three-factor model in developing markets: evidence from the Chinese markets

2018 ◽  
Vol 15 (1) ◽  
pp. 46-57
Author(s):  
Man Li ◽  
Michael Dempsey
Keyword(s):  
Principal Component Analysis ◽  
Firm Size ◽  
Stock Price ◽  
Factor Model ◽  
Principal Component ◽  
Price Formation ◽  
Developing Markets ◽  
Chinese Markets ◽  
Fama And French ◽  
Three Factor

The authors study the Fama and French three-factor (FF-3F) model in relation to a developing market. To this end, they consider Chinese stock markets over the period 1995–2008, which is to say, over a period when these markets are recognized as “developing” markets influenced by speculative activity. The authors find that the model appears to be working as a form of “principal component analysis for the determinants of stock price formation with book-to-market (B/M) as the “variable of choice” on account of that it captures the earnings-to-price (E/P), cash-flow-to-price (C/P) and sales-to-price (S/P) variables while remaining largely uncorrelated with firm size (whereas E/P, C/P and S/P are themselves positively correlated with firm size). The variables, however, are unrelated to risk as represented by market exposure, volatility, or leverage.

FACTOR UNIQUENESS IN THE S&P 500 UNIVERSE: CAN PROPRIETARY FACTORS EXIST?

2013 ◽  
Vol 16 (04) ◽  
pp. 1350020
Author(s):  
SERGIO M. FOCARDI ◽  
FRANK J. FABOZZI
Keyword(s):  
Principal Component Analysis ◽  
Factor Model ◽  
Principal Component ◽  
Component Analysis ◽  
Current Theory ◽  
Factor Models ◽  
Procrustes Analysis ◽  
Dynamic Factor ◽  
Vector Autoregressive ◽  
Model Based

In this paper, we analyze factor uniqueness in the S&P 500 universe. The current theory of approximate factor models applies to infinite markets. In the limit of infinite markets, factors are unique and can be represented with principal components. If this theory would apply to realistic markets such as the S&P 500 universe, the quest for proprietary factors would be futile. We find that this is not the case: in finite markets of the size of the S&P 500 universe different factor models can indeed coexist. We compare three dynamic factor models: a factor model based on principal component analysis, a classical factor model based on industry, and a factor model based on cluster analysis. Dynamic behavior is represented by fitting vector autoregressive models to factors and using them to make forecasts. We analyze the uniqueness of factors using Procrustes analysis and correlation analysis. Forecasting performance of the factor models is analyzed by forming active portfolio strategies based on the forecasts for each model using sample data from the S&P 500 index in the 21-year period 1989–2010. We find that one or two factors which we can identify with global factors are common to all models, while the other factors for the factor models we analyzed are truly different. Models exhibit significant differences in performance with principal component analysis-based factor models appearing to behave better than the sector-based factor models.

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