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Exploratory Factor Analysis
Published in Douglas D. Gunzler, Adam T. Perzynski, Adam C. Carle, Structural Equation Modeling for Health and Medicine, 2021
Douglas D. Gunzler, Adam T. Perzynski, Adam C. Carle
Consider the varimax rotation. This is an orthogonal rotation technique that specifies uncorrelated factors and rotates the axes in order to maximize the variance of the squared loadings on each factor, hence its name (maximum variance). In orthogonal rotations, the original factor space is rotated while independence (perpendicularity) is preserved. In many situations it may be more reasonable or theoretically justified to assume that the factors will be correlated and select a different rotation.
Internet and World Wide Web Usage in an Institution of Higher Learning
Published in Cleborne D. Maddux, D. LaMont Johnson, The Web in Higher Education: Assessing the Impact and Fulfilling the Potential, 2021
In spring 1995, Sherry (1997) also conducted a survey of 73 members of the I.T. Division to ascertain their use of e-mail and the Internet for instructional purposes. Participants were asked about their role in the division (student, faculty, staff), access to technology that would support the use of Internet tools, patterns of use, reasons for use, facilitators and barriers, and rankings on eight proposed supports for training and performance using telecommunications. A factor analysis of the reasons for use, with Varimax rotation, resulted in four factors: share/disseminate information and communicate (41.5% of variance), find/organize information (11.7% of variance), collaborate (8.7% of variance), and consult with advisor (7.8% of variance). Responses to eleven challenges to use grouped into three factors: clear benefit and value (32.5% of variance), self-efficacy (17.2% of variance), and finding a voice and having something to say (10.4% of variance). These were three of the five factors that emerged from the focus groups with students and the interviews with faculty.
Kiasuism across Cultures: Singapore and Australia
Published in J.-C. Lasry, J. Adair, K. Dion, Latest Contributions to Cross-Cultural Psychology, 2020
Shee Wai Ho, Don Munro, Stuart C. Carr
Two separate principal components analyses were conducted on the two sets of data using varimax rotation extracting six components. Again all of the 49 items had significant loadings of .30 or above on at least one of the components. When only those items with a loading of .50 and above were considered, it resulted in 23 items for the Singaporean sample (accounting for 43.7% of the variance) and 21 items for the Australian sample (41.0% of the variance). All items had high loadings on the main component and relatively low loadings on others.
Clustering pedestrians’ perceptions towards road infrastructure and traffic characteristics
Published in International Journal of Injury Control and Safety Promotion, 2023
Aditya Saxena, Ankit Kumar Yadav
After the collection of samples, data analysis was undertaken using SPSS version 22. The data obtained for the selected 14 parameters were then subjected to factor analysis (a dimension reduction method) using the principal component analysis technique with varimax rotation. Varimax rotation involves using a mathematical algorithm that maximizes high- and low-value factor loadings and minimizes mid-value factor loadings. Using factor analysis, factor loadings were obtained which were used to derive the number of factors to be considered for dimension reduction. Factors with an eigenvalue above 1 were only retained. Four factors were acquired from component variances. The correlation between factors and parameters was found from component loading and only those parameters were retained whose correlation value was above 0.5 (Jolliffe & Cadima, 2016; Maskey et al., 2018; Venkatramanan et al., 2019). The following equation was used for computing covariance (Mishra et al., 2017):
A Student-Centered Approach for Assessing Sexuality Education in the Classroom
Published in American Journal of Sexuality Education, 2023
Paula Tavrow, Lauren Schenker, Meredith R. Johnson
In analyzing the data, we first performed univariate analysis of the socio-demographic survey responses. Next, we conducted factor analysis of the 22 CHYA standards using Varimax rotation. We used Varimax rotation because it maximizes factor loadings and provides the clearest defined factor structures (Kimberlin & Winterstein, 2008). This yielded five factors, which we labeled as: Contraception and Consent (six items), HIV Misconceptions (four items), Gender and Sexual Orientation Stereotypes (three items), Sexual Health Services and Rights (four items) and Sexual Harassment, Rape and Trafficking (five items). We then conducted factor analysis of the eight classroom environment questions, which yielded two factors, which we labeled as: Interesting (three items: engaging, dealt with social issues, and allowed anonymous questions) and Feel at Ease (five items: not rushed, a safe space, and teacher was comfortable, unbiased, and confidential). Lastly, we conducted Chi-square tests of various categories (such as gender of the teacher, sexual orientation of students, ethnicity of the students, students’ grade in school) for validity testing to assess if there seemed to be any systematic biases in student responses.
Handball and movement screening – can non-contact injuries be predicted in adolescent elite handball players? A 1-year prospective cohort study
Published in Physiotherapy Theory and Practice, 2021
Jens Karlsson, Annette Heijne, Philip von Rosen
Factor analysis using the principal component analysis was used to examine the psychometric properties of the 9SB and thereby determine the factor structure of the 9SB (Williams, Brown, and Onsman, 2010). Varimax rotation was utilized to enhance the interpretation of the factors and to ensure the resulting factors are independent of each other. To assess the suitability of the respondent data, Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett’s test of sphericity were conducted. Based on the anti-image correlation matrix, the test “diagonal lift” was excluded from the principal component analysis since the item was not considered to share common variance with the other items (KMO < 0.4). Factors were extracted based on eigenvalues (>1) and the scree plot analysis (factors above the break in the curve), resulting in that three factors were retained. ROC curves were generated for the three factors and for the 9SB and corresponding area under the curve (AUC) values were determined. The cutoff values determined to be optimal for injury screening were calculated using the formula: (1-sensitivity)2 + (1-specificity)2, where the cutoff score with the lowest value was chosen (Perkins and Schisterman, 2006).