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Introduction and Brief History of Structural Equation Modeling for Health and Medical Research
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
One way SEM deals with continuous latent variables in practice is through confirmatory factor analysis (CFA). Factor analyses are approaches to represent the relationships among multiple observed variables in terms of a smaller number of hypothesized latent variables. In the context of factor analysis, latent variables are commonly also referred to as factors. Exploratory factor analysis (EFA) is a data-driven method used to help identify the underlying latent variable or variables from a set of observed variables. CFA is used to verify hypothesized relationships between the latent variables and the set of observed variables. EFA can also be viewed as a special case of CFA and vice versa. Both EFA and CFA employ the same common mathematical model under different constraints (Chapter 8).
Correlational-based methods
Published in Claudio Violato, Assessing Competence in Medicine and Other Health Professions, 2018
We begin with a large number of items reflecting a theoretical “guess” about the items or variables that are most meaningful. The variables are given to candidates and factors are derived. As a result of the first “exploratory” factor analysis, variables are added and deleted, a second test is devised, and that test is given to other participants. The process continues until a test is developed with numerous items forming several factors that represent the area to be measured.
Health Economics and Outcomes Research in Precision Medicine
Published in Demissie Alemayehu, Joseph C. Cappelleri, Birol Emir, Kelly H. Zou, Statistical Topics in Health Economics and Outcomes Research, 2017
Demissie Alemayehu, Joseph C. Cappelleri, Birol Emir, Josephine Sollano
Two approaches of (construct) validity assessment are exploratory factor analysis and confirmatory factor analysis. In exploratory factor analysis, there is initial uncertainty as to the number of factors being measured, as well as regarding which items are representing those factors. As such, the technique is suitable for generating hypotheses about the structure of distinct concepts and which items represent a particular concept. In contrast, confirmatory factor analysis is a hypothesis-confirming technique that relies on a researcher’s hypothesis, and that requires prespecification of all aspects of the factor model. While exploratory factor analysis explores the patterns in the correlations of items, confirmatory factor analysis tests whether the correlations conform to an anticipated or expected scale structure given in a particular research hypothesis.
Measuring an understudied factor in medical education – development and validation of the medical curiosity scale
Published in Medical Education Online, 2023
Till Johannes Bugaj, Tim Alexander Schwarz, Valentin Terhoeven, Ede Nagy, Anna Cranz, Hans-Christoph Friederich, Christoph Nikendei
The data were analyzed using IBM SPSS Statistics for Macintosh, version 25.0. The data analysis was performed in two parts: Factor analysis of the preliminary MCS (25 items): Before factor analysis, skewness and kurtosis scores were calculated to determine whether items needed to be removed. In addition, a Little’s MCAR test was performed to assess the distribution of missing responses. Next, the Kaiser-Meyer-Olkin criterion was calculated to confirm that the data were suited for factor analysis. Then, an exploratory factor analysis (EFA) with oblique (promax) rotation was performed. Finally, the internal consistency of the scales was determined using Cronbach’s alpha.Study of convergent and discriminant validity of the MCS: Analysis of the correlations with the other scales in use (4 scales, 32 items).Results
Turkish validity and reliability study for the perceived control of asthma questionnaire
Published in Journal of Asthma, 2023
Ziya Koçak, Huri Seval Gönderen Çakmak
As seen in Table 3, the scale aiming to measure perceived control of asthma comprises two subscales. These dimensions are called “F1” and “F2”. Within this framework, exploratory factor analysis was used to determine the factor pattern. Before performing exploratory factor analysis, the Kaiser-Meyer-Olkin test (KMO) was applied with the aim of testing the suitability of the sample size for factorization. As a result of the analysis, the KMO value was determined to be 0.880. In line with this information, it was concluded that the sample size was “perfectly adequate” to perform factor analysis (18). Additionally, when the Bartlett sphericity test results are investigated, the obtained chi-square values appear to be meaningful (χ2(78) = 550.230; p < 0.01). In line with this, the data were accepted as having multivariate normal distribution.
Turkish Validity and Reliability of the Self-care of Hypertension Inventory (SC-HI) among Older Adults
Published in Journal of Community Health Nursing, 2023
Zehra Gok Metin, Merve Gulbahar Eren, Cemile Ozsurekci, Mustafa Cankurtaran
The Keizer-Mayer-Olkin (KMO) index was used to calculate the sample size, and Bartlett’s test of sphericity was used for suitability of factor analysis and to investigate the correlation of variables. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were used to assess scales’ factor structure (Black et al., 2017). Exploratory factor analysis aims to decrease the number of variables and reveal new structures based on the relation between the variables. The eigenvalue is the sum of squares of factor loads of each factor, and an increase in this value also increases the variance explained by that factor (Black et al., 2017). To rate the model compatibility in the CFA and structural equation model, fit indices were classified as those based on residuals, independent models, root mean square error of approximation (RMSEA), data criterion, and those based on relation criteria (Black et al., 2017). When analyzing model fit, X2/SD was determined together with fit indices such as SRMR, GFI, AGFI, CFI, and RMSEA.