Modified General Employee Well-Being Scale

2021 ◽  
pp. 261-270
Author(s):  
Bruce E. Winston
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Measurement Instrument ◽  
Principal Component ◽  
Well Being ◽  
Chi Square ◽  
Cronbach Alpha ◽  
Confirmatory Factor ◽  
Item Scale

Taylor et al. evaluated Dupuy's general employee well-being measurement instrument and pointed out two concerns: a combination of positive and negative item wording and two different measurement response methods. Taylor et al. collected new data, ran a principal component analysis, and found three of Dupuy's five reported scales. In this study, the author reworded Taylor et al.'s final 18 items so that all items were worded positively, used a common measurement response, and removed double-barreled wording, which Taylor et al. did not note. The author of this current chapter conducted two studies. The first study's analysis of the new data produced a single eight-item scale with Cronbach alpha of .96 that explained 77% of the variance. The second study used confirmatory factor analysis that showed a four-item scale with GFI = 0.98, AGFI = 0.89, RMSEA = 0.13, and Chi-square = 9.96, df = 9, p < 0.000. The four-item scale had a Cronbach alpha of 0.86.

scholarly journals Measuring adolescent attitudes towards dementia: The revalidation and refinement of the A-ADS

2020 ◽  
pp. 135910532095347
Author(s):  
Nicolas Farina ◽  
Alys W Griffiths ◽  
Laura J Hughes ◽  
Sahdia Parveen
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Concurrent Validity ◽  
Principal Component ◽  
Model Fit ◽  
Single Factor ◽  
Confirmatory Factor ◽  
Adolescent Attitudes ◽  
Good Internal Consistency

The A-ADS is one the first validated measures of attitudes of dementia in adolescents, though further validation is needed. 630 adolescents were recruited from secondary schools in England. A Principal Component Analysis was completed ( n = 230) followed by a Confirmatory Factor Analysis ( n = 400). Reducing the A-ADS into a single factor, 13-item measure (Brief A-ADS) improved the model fit of the measure (χ2 = 182.75, DF = 65, CMIN/DF = 2.81, p < 0.001, CFI = 0.90, RMSEA = 0.07). The scale demonstrated good internal consistency, good predictive and concurrent validity. Building on the validation of the A-ADS, the Brief A-ADS is suitable to capture attitudes towards dementia amongst adolescents.

Psychometric Properties of a New Scale Measuring Neglect and Abuse of Older Adults in the Community: Implications for Social Activity

2020 ◽  
Vol 41 (2) ◽  
pp. 163-172
Author(s):  
Nestor Asiamah ◽  
Kyriakos Kouveliotis ◽  
Richard Eduafo ◽  
Richard Borkey
Keyword(s):  
Older Adults ◽  
Principal Component Analysis ◽  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Psychometric Properties ◽  
Social Activity ◽  
Principal Component ◽  
Varimax Rotation ◽  
Factor Solution ◽  
Confirmatory Factor

Background Neglect and abuse of older adults are still prevalent in communities of developing countries, a situation that could discourage active and healthy behaviors in community-dwelling older adults. Methods This study used the cross-sectional and correlational approaches to construct a scale measuring neglect and abuse in older adults living in the community. The study population was all (3,211) older adults who were resident in Accra and were part of the database of the Social Security and National Insurance Trust. Participants were 515 individuals who met some inclusion criteria. Three steps were followed to determine an initial set of 11 items, and principal component analysis with varimax rotation and confirmatory factor analysis were used to select relevant items and assess the psychometric properties of the final scale. Results Principal component analysis with varimax rotation yielded a two-factor solution on all 11 items. The first factor extracted was “discrimination and exploitation,” which accounted for a variance of 53.9% out of total variance of 70.8%. The scale had a good internal consistency (Cronbach’s α = .90, factor loading ≥0.50). Confirmatory factor analysis confirmed the two-factor solution and produced satisfactory convergent and discriminant validity. Conclusion It is concluded that 11 items that make up 2 factors are potential indicators of neglect and abuse of older adults at the community level. Finally, neglect and abuse of older people in the community can contribute to an increase or decrease in social activity.

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 Evaluation of Eight-Item Vancomycin Prescribing Confidence Questionnaire Among Junior Doctors

2021 ◽  
Vol 8 ◽  
Author(s):  
Lu Liu ◽  
Zhehan Jiang ◽  
Ana Xie ◽  
Weimin Wang
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Root Mean Square ◽  
Principal Component ◽  
Component Analysis ◽  
Junior Doctors ◽  
Mean Square ◽  
Extreme Response ◽  
Confirmatory Factor

Background: Assessing the preparedness of junior doctors to use vancomycin is important in medical education. Preparedness is typically evaluated by self-reported confidence surveys.Materials and Methods: An eight-item vancomycin prescribing confidence questionnaire was developed, piloted, and evaluated. The questionnaire responses were collected from 195 junior doctors and a series of statistical techniques, such as principal component analysis and confirmatory factor analysis, and were implemented to examine the validity and reliability.Results: The principal component analysis supported a one-factor structure, which was fed into a confirmatory factor analysis model resulting in a good fit [comparative fit index (CFI) = 0.99, Tucker–Lewis index (TLI) = 0.99, root mean square error of approximation (RMSEA) = 0.08, standardized root mean square residual (SRMR) = 0.04]. Ordinal-based α was 0.95, and various ωs were all above 0.93, indicating a high reliability level. The questionnaire responses were further proved to be robust to extreme response patterns via item response tree modeling. Jonckheere–Terpstra test results (z = 6.5237, p = 3.429e−11) showed that vancomycin prescribing confidence differed based on the experience in order (i.e., four ordinal independent groups: “≤10 times,” “11–20 times,” “21–30 times,” and “≥31 times”) and therefore provided external validity evidences for the questionnaire.Conclusions: The questionnaire is valid and reliable such that teaching hospitals can consider using it to assess junior doctors' vancomycin prescribing confidence. Further investigation of the questionnaire can point to the relationship between the prescribing confidence and the actual performance.

scholarly journals Workshop Analisis Faktor untuk Data Penelitian Ilmu Sosial dan Kependidikan

2021 ◽  
Vol 5 (1) ◽  
pp. 37-46
Author(s):  
Dhoriva Urwatul Wutsqa ◽  
Kismiantini Kismiantini ◽  
Muhammad Fauzan ◽  
Rosita Kusumawati ◽  
Sahid Sahid ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Confirmatory Factor

Kegiatan PPM ini bertujuan memberikan pelatihan penggunaan program R dan SPSS dalam penelitian data ilmu sosial dan kependidikan kepada para praktisi lulusan S1, mahasiswa pasca sarjana, dan dosen di lingkungan universitas di Yogyakarta. Penelitian dalam ilmu sosial dan kependidikan seringkali melibatkan banyak variabel yang saling berkorelasi maupun mempunyai korelasi yang tinggi, misalnya dalam pengembangan suatu instrument penelitian. Penghapusan variabel yang mempunyai korelasi tinggi bisa mengakibatkan hilangnya informasi, untuk mengatasi hal tersebut dapat digunakan analisis komponen utama (Principal Component Analysis/PCA). Sedangkan dalam hal menentukan konstruk yang sesuai dari butir-butir soal yang terbentuk dapat dilakukan dengan dua pendekatan yaitu analisis faktor eksplorasi (Exploratory Factor Analysis/EFA) dan analisis faktor konfirmasi (Confirmatory Factor Analysis/CFA). Permasalahan yang terkait dengan reduksi variabel dan pembentukan konstruk pada pengembangan instrument ini merupakan hal yang sangat penting dalam penelitian ilmu sosial dan kependidikan.  Sehingga suatu metode analisis PCA, EFA, dan CFA dengan menggunakan program R dan SPSS mutlak diperlukan. Workshop yang diikuti oleh 26 peserta peserta ini dapat berjalan dengan baik. Berdasarkan pelaksanaan dan evaluasi kegiatan ini dapat disimpulkan bahwa tujuan dapat tercapai dengan baik.

scholarly journals Structural Validity of the Mini-Balance Evaluation Systems Test (Mini-BESTest) in People With Mild to Moderate Parkinson Disease

2016 ◽  
Vol 96 (11) ◽  
pp. 1799-1806 ◽  
Author(s):  
Martin Benka Wallén ◽  
Kimmo Sorjonen ◽  
Niklas Löfgren ◽  
Erika Franzén
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Parkinson Disease ◽  
Exploratory Factor Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Balance Test ◽  
Evaluation Systems ◽  
Confirmatory Factor

Abstract Background The Mini-Balance Evaluation Systems Test (Mini-BESTest) is a clinical balance test comprising 14 items assumed to reflect the unidimensional construct “dynamic balance.” Objective The study objective was to examine the dimensionality of the test and the properties of each item and their interrelationships in elderly people with mild to moderate Parkinson disease (PD). Design This was a cross-sectional study in a laboratory setting. Methods A total of 112 participants (mean age=73 years) with idiopathic PD (Hoehn and Yahr stages 1–3) were assessed by physical therapists. Local independence among items was examined with Rasch modeling. Unidimensionality was tested by running a principal component analysis on the residuals. An exploratory factor analysis was used to examine the structure of the test, and a confirmatory factor analysis was used to evaluate the fit of the derived model. Results The first residual component of the principal component analysis, with an eigenvalue of greater than 2, superseded the assumption of unidimensionality. After the omission of item 7 because of convergence problems, the exploratory factor analysis suggested that a 3-factor solution best fit the data. A confirmatory factor analysis demonstrated acceptable fit of the final model, although item 14 loaded poorly on its factor. Limitations The sample size was on the lower end of what is generally recommended. Conclusions This study could not confirm that the Mini-BESTest is unidimensional. Gait items were dispersed over all factors, indicating that they may reflect different constructs. Nonetheless, as there arguably is no clinical balance test superior to the Mini-BESTest today, we recommend using the total score for assessing gross balance in this population and individual items to identify specific weaknesses. Moreover, dual tasks should be assessed separately because they are an important aspect of balance control in people with PD, reflected in only one item of the test.

scholarly journals Validasi Big Five Inventory-2 (BFI-2) untuk Indonesia: Belum sempurna tetapi valid dan reliabel mengukur kepribadian

2021 ◽  
Author(s):  
Awaludin Ahya ◽  
Ide Bagus Siaputra
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Big Five ◽  
Factor Model ◽  
Five Factor Model ◽  
Principal Component ◽  
Big Five Inventory ◽  
Confirmatory Factor ◽  
Bahasa Indonesia

Big Five Inventory-2 (BFI-2) merupakan versi terbaru dari alat ukur kepribadian menurut kerangka teori five factor model. Penelitian ini bertujuan untuk mereplikasi pengujian validitas BFI-2 pada versi Bahasa Indonesia. Uji validitas terkait struktur internal dilakukan menggunakan Principal Component Analysis (PCA) dan Confirmatory Factor Analysis (CFA), sedangkan validitas konvergen melalui korelasi dengan skor BFI-1. Responden adalah 853 mahasiswa dari beberapa Perguruan Tinggi Negeri dan Swasta di Sumatra Utara dan Jawa Timur. Pengumpulan data secara online menggunakan Google Form. Hasil analisis PCA menunjukkan sebanyak 50 butir (83%) BFI-2 versi Indonesia memiliki muatan faktor memadai terhadap dimensi terkait. Hasil analisis CFA pada model level faset memiliki indeks fit yang lebih baik daripada level dimensi, artinya temuan ini mendukung model pengukuran faset pada BFI-2 Indonesia. Kelima dimensi BFI-2 Indonesia memiliki korelasi positif dengan dimensi BFI-1 pada tingkat moderat, sehingga temuan ini menunjukkan bahwa BFI-2 Indonesia terbukti konvergen dengan BFI-1.

scholarly journals Using principal component analysis to validate psychological scales: Bad statistical habits we should have broken yesterday II

2020 ◽  
Author(s):  
Stefan L.K. Gruijters
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Short Paper ◽  
Confirmatory Factor ◽  
Psychological Scales

In this short paper, I review some reasons why the use of PCA finds little justication in the context of validating psychological scales. I make a case for the burgeoning use of better alternatives such as confirmatory factor analysis(CFA).

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