Combining Parallel and Exploratory Factor Analysis in Identifying Relationship Scales in Secondary Data

Wood, Nathan D.; Gnonhosou, Djidjoho C. Akloubou; Bowling, Justin W.

doi:10.1080/01494929.2015.1059785

Cited by 87 publications

(61 citation statements)

References 24 publications

(49 reference statements)

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“…Data of the FTNS were analysed using Exploratory Factor Analysis (EFA). Scores of items 6, 7, 8 and 13 were reversed prior to the analysis (Wood et al , ). Maximum likelihood estimation method and varimax rotation were used.…”

Section: Methodsmentioning

confidence: 99%

How do processing technology and formulation influence consumers’ choice of fruit juice?

Martins

Rosenthal

Ares

et al. 2020

Int J of Food Sci Tech

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The study aimed at investigating the influence of four variables related to processing technology and formulation on consumer's choice of fruit juice, and to explore the moderating effect of food technology neophobia (FTN). A choice-conjoint task was implemented considering four variables: processing technology, added sugar, preservatives and cold pressing. Sixteen pairs of labels were presented to 369 Brazilian consumers to that indicate which of the products they would purchase. Then, they answered the FTN scales and a socio-demographic questionnaire. Data were analysed using a logit model with random parameters. Results showed that cold pressing, pressurisation and the claims 'no added sugar' and 'no preservatives' had a positive effect on consumers' choices, whereas the claim 'not pressurised' had a negative effect. The FTN moderated the effect of the variables on participants' choices of fruit juices. The main difference between groups of consumers with different level of neophobia (low, medium and high) was related to the effect of information about juice processing.

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Section: Methodsmentioning

confidence: 99%

How do processing technology and formulation influence consumers’ choice of fruit juice?

Martins

Rosenthal

Ares

et al. 2020

Int J of Food Sci Tech

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“…PA is the most recommended procedure to select the optimal number of factors in the data [65][66][67]. Specifically, this statistical technique allows to reduce over identification of factors due to sampling error [68]. Factors with eigenvalues above of 95th percentile of the eigenvalues of the parallel factor were retained.…”

Section: Resultsmentioning

confidence: 99%

The psychometric properties of the Italian adaptation of the Health Orientation Scale (HOS)

Masiero

Oliveri

Cutica

et al. 2020

Health Qual Life Outcomes

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Background: A novel approach suggested that cognitive and dispositional features may explain in depth the health behaviors adoption and the adherence to prevention programs. The Health Orientation Scale (HOS) has been extensively used to map the adoption of health and unhealthy behaviors according to cognitive and dispositional features. Coherently, the main aim of the current research was to assess the factor structure of the Italian version of the HOS using exploratory and confirmatory factor analysis and testing the construct validity of the scale by assessing differences in health orientations between tobacco cigarette smokers and nonsmokers. Method: The research protocol was organized in two studies. Study 1 evaluated the dimensionality of the HOS in a sample of Northern Italian healthy people. Three hundred and twenty-one participants were enrolled; they were 229 women (71.3%) and 92 men (28.7%). In Study 2, the factor structure and construct validity of the HOS Italian version was assessed trough confirmatory factor analysis using a tobacco cigarette smokers and nonsmokers population. Two hundred and nineteen participants were enrolled; they were 164 women (75.2%) and 55 men (24.8%). Results:In Study 1, a seven factors solution was obtained explaining 60% of cumulative variance instead of 10 factors solution of the original version of the HOS. In Study 2, the factor structure of the Italian version of the HOS was confirmed and applied to the smokers and nonsmokers; nonsmokers reported higher values than smokers in Factor 1 (MHPP) [t (208) = − 2.739 p < .007] (CI 95-4.96% to −.809), Factor 2 (HES) [t (209) = − 3.387 p < .001] (CI 95-3.93% to -. 1.03), Factor 3 (HIC) [t(213) = − 2.468 p < .014] (CI 95-2.56% to −.28) and Factor 7 (HEX) [t(217) = − 3.451 p < .001] (CI 95%-1.45 to .39). Conclusions:Results of the Italian adaptation of HOS lead to a partial redistribution of items and confirmed 7 subscales to distinguish psycho-cognitive dispositional dimensions involved in health orientation styles.

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“…[31] Parallel analysis is considered superior to reliance solely on eigenvalue scores and can minimise over-identification of factors based on sampling error. [32] Items were retained on the domain on which they had the highest loading, even if the cross-loading on a second domain was within 0.2. This was considered acceptable as the purpose of the factor analysis was to group topic areas rather than to develop a validated scale to be applied in other studies, when restrictions on cross-loading would apply.…”

Section: Resultsmentioning

confidence: 99%

Impact of intensive training on health professionals’ self-efficacy in establishing, running and maintaining a cardiac rehabilitation program

Murphy

Higgins

Grande

et al. 2019

JNEP

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We developed an intensive five-day training program for health professionals working in cardiac rehabilitation (CR). The training covers topics related to establishing, running, maintaining and evaluating a CR program. The aim of this study was to assess the impact of the training on health professionals’ self-efficacy regarding the effective delivery of CR. From 2014 to 2018, 167 health professionals participated in one of five training programs. Participants completed a 28-item pre- and post-training self-efficacy scale. For a sub-group, self-efficacy was re-assessed 4 months later. Factor analysis was used to identify self-efficacy domains. Paired sample t-tests compared pre- and post-training self-efficacy scores; repeated measures analysis of variance investigated change over the three time points. Variations in self-efficacy across profession, role in CR, and years of CR practice were investigated. Factor analysis identified three domains: Operational aspects of CR; Medical aspects of heart disease; and Psychosocial aspects of CR. Health professionals’ self-efficacy increased significantly after training participation, across the three domains and for the total score. Effects were sustained in the 4-month follow-up. Few variations in self-efficacy trajectories by participant characteristics were identified. The study demonstrates that our health professional CR training significantly improves health professionals’ confidence in a range of areas related to establishing, running, maintaining and evaluating a CR program, with immediate improvements sustained four months later. The pattern of findings was largely consistent regardless of participants’ role and experience in CR. Findings highlight the benefits of this relatively brief intensive program on enhancing the capacity of the CR workforce.

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Combining Parallel and Exploratory Factor Analysis in Identifying Relationship Scales in Secondary Data

Cited by 87 publications

References 24 publications

How do processing technology and formulation influence consumers’ choice of fruit juice?

How do processing technology and formulation influence consumers’ choice of fruit juice?

The psychometric properties of the Italian adaptation of the Health Orientation Scale (HOS)

Impact of intensive training on health professionals’ self-efficacy in establishing, running and maintaining a cardiac rehabilitation program

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