Determining the Number of Factors to Retain in EFA: Using the SPSS R-Menu v2 0 to Make More Judicious Estimations

Courtney, Matthew

doi:10.7275/9cf5-2m72

Cited by 45 publications

(14 citation statements)

References 25 publications

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“…From the EFA, validation of the classification of digital activities was performed with a Kaiser-Meyer-Olkin test [ 47 ], with a sample adequacy of 0.929 [ 48 ], and a Bartlett test of sphericity, which was statistically significant ( χ 2 253 =1260.1, P <.001) [ 49 ]. Based on the parallel analysis, four factors were determined [ 50 ]. Due to the nonnormally distributed data, principal axis factoring was used as an appropriate extraction method [ 51 ], and oblimin rotation was used as an appropriate oblique rotation method [ 52 ].…”

Section: Resultsmentioning

confidence: 99%

The Sociodemographic Digital Divide in Mobile Health App Use Among Clients at Outpatient Departments in Inner Mongolia, China: Cross-sectional Survey Study

Cao¹,

Chongsuvivatwong²,

McNeil³

2022

JMIR Hum Factors

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Background Mobile health (mHealth) apps have become part of the infrastructure for access to health care in hospitals, especially during the COVID-19 pandemic. However, little is known about the effects of sociodemographic characteristics on the digital divide regarding the use of hospital-based mHealth apps and their benefits to patients and caregivers. Objective The aim of this study was to document the cascade of potential influences from digital access to digital use and then to mHealth use, as well as the potential influence of sociodemographic variables on elements of the cascade. Methods A cross-sectional survey was conducted from January to February 2021 among adult clients at outpatient departments in 12 tertiary hospitals of Inner Mongolia, China. Structural equation modeling was conducted after the construct comprising digital access, digital use, and mHealth use was validated. Results Of 2115 participants, the β coefficients (95% CI) of potential influence of digital access on digital use, and potential influence of digital use on mHealth use, were 0.28 (95% CI 0.22-0.34) and 0.51 (95% CI 0.38-0.64), respectively. Older adults were disadvantaged with regard to mHealth access and use (β=–0.38 and β=–0.41), as were less educated subgroups (β=–0.24 and β=–0.27), and these two factors had nonsignificant direct effects on mHealth use. Conclusions To overcome the mHealth use divide, it is important to improve digital access and digital use among older adults and less educated groups.

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Section: Resultsmentioning

confidence: 99%

The Sociodemographic Digital Divide in Mobile Health App Use Among Clients at Outpatient Departments in Inner Mongolia, China: Cross-sectional Survey Study

Cao¹,

Chongsuvivatwong²,

McNeil³

2022

JMIR Hum Factors

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“…We conducted sensitivity analyses to determine if the findings differed using alternate methods. First, we sought to confirm the factor structure using Horn's parallel analysis for principal component factors using polychoric correlations, mean eigenvalue estimates, and scree plots, which is a method often recommended for use with ordinal variables (Courtney & Gordon, 2013;Yang & Xia, 2015); these analyses were conducted in Stata 16 (College Station, TX). We also repeated all analyses using an alternate Everyday Ageism Score created by summing item response Z-scores, which are sometimes used to account for differing item responses options.…”

Section: Methodsmentioning

confidence: 99%

The Everyday Ageism Scale: Development and Evaluation

et al. 2021

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Objectives Older adults regularly encounter age-based discrimination and stereotyping in their day-to-day lives. Whether this type of routine ageism negatively affects their health and well-being is unclear, in part due to the absence of validated scales that comprehensively measure this phenomenon and distinguish it from other sources of everyday discrimination. Methods This study describes the development of a novel scale, the Everyday Ageism Scale, and its psychometric evaluation using a nationally representative sample of US adults age 50–80 from the December 2019 National Poll on Healthy Aging ( N = 2012). Results Exploratory factor analysis indicated a 3-factor structure comprised of ageist messages, ageism in interpersonal interactions, and internalized ageism. The ten-item scale was psychometrically sound and demonstrated good internal reliability. Discussion Everyday ageism is a multidimensional construct. Preliminary evaluation of the Everyday Ageism Scale suggests its utility in future studies examining the prevalence of everyday ageism and its relationships with health.

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“…Nesse sentido, de imediato, deveria descartar-se o SPSS, pois até hoje ele utiliza apenas matriz de Pearson. Uma solução para empregar matrizes policóricas/tetracóricas seria a utilização de syntaxes de alguns autores desenvolvidas para o SPSS, tal como TETRA-COM (Lorenzo-Seva & Ferrando, 2012) e POLYCOR-C (Lorenzo-Seva & Ferrando, 2015) ou plug-ins do R no SPSS, tal como R-Factor v2.4.3 (Courtney & Gordon, 2013). No entanto, esse procedimento pode não ser familiar para o usuário comum, inclusive depende de constantes atualizações dos pacotes do software R, do próprio plug-in e das versões do SPSS, gerando diversas incompatibilidades.…”

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Melhores Práticas para sua Análise Fatorial Exploratória: Tutorial no Factor

Rogers

2022

Rev. adm. contemp.

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RESUMO Contexto: a análise fatorial exploratória (AFE) é um dos métodos estatísticos mais utilizados em administração. No entanto, sua prática corrente coexiste com regras de bolso e heurísticas proferidas há meio século. Objetivo: o propósito deste artigo é apresentar as melhores práticas e recomendações recentes para uma AFE típica em administração através de uma solução prática acessível aos pesquisadores. Métodos: nesse sentido, além de serem discutidas as práticas correntes versus as práticas recomendadas, ilustra-se um tutorial com dados reais no Factor, um software ainda pouco conhecido na área de administração, porém freeware, fácil de usar (point and click) e poderoso. O passo a passo ilustrado no artigo, além das discussões levantadas e de um exemplo adicional, também é disponibilizado no formato de vídeos tutoriais. Conclusão: através da metodologia didática proposta (artigo-tutorial + vídeo-tutorial), incentivamos os pesquisadores/metodologistas que dominam alguma técnica particular a fazerem o mesmo. Especificamente sobre a AFE, esperamos que a apresentação do software Factor, como uma primeira solução, possa transcender as regras de bolso e heurísticas correntes ultrapassadas, ao tornar acessíveis as melhores práticas para os pesquisadores da administração.

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Determining the Number of Factors to Retain in EFA: Using the SPSS R-Menu v2 0 to Make More Judicious Estimations

Cited by 45 publications

References 25 publications

The Sociodemographic Digital Divide in Mobile Health App Use Among Clients at Outpatient Departments in Inner Mongolia, China: Cross-sectional Survey Study

The Sociodemographic Digital Divide in Mobile Health App Use Among Clients at Outpatient Departments in Inner Mongolia, China: Cross-sectional Survey Study

The Everyday Ageism Scale: Development and Evaluation

Melhores Práticas para sua Análise Fatorial Exploratória: Tutorial no Factor

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