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Educational Beliefs of Mothers and Day Care Teachers in Former East- and West-Germany
Published in Walter J. Lonner, Dale L. Dinnel, Deborah K. Forgays, Susanna A. Hayes, Merging Past, Present, and Future in Cross-Cultural Psychology, 2020
Tatjana Meischner, Wolfgang Tietze, Holger Wessels
In this paper we investigated similarities and differences within the educational beliefs of mothers and day care teachers from the former East and West Germany. For this purpose, we took the conceptual and methodological considerations discussed by Berry (1989) and Boehnke and Merkens (1994) into account. Extending these approaches, we utilized the relatively new approach of Confirmatory Factor Analysis to obtain additional information about the factor structures and to evaluate, to what extent these structures were comparable.
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.
Factor analysis, cluster analysis and structural equation modelling
Published in Louis Cohen, Lawrence Manion, Keith Morrison, Research Methods in Education, 2017
Louis Cohen, Lawrence Manion, Keith Morrison
As mentioned earlier, factor analysis can be both exploratory and confirmatory. Structural equation modelling is used in confirmatory factor analysis. Whilst the earlier discussion concerned exploratory factor analysis, confirmatory factor analysis is a feature of the group of latent variable models (models of factors rather than observed variables) which includes factor analysis, path analysis and structural equation analysis. Confirmatory factor analysis seeks to verify (to confirm or refute) the researcher’s predictions about factors and their factor loadings in data and data structures. As mentioned earlier, factors are latent, they cannot be observed as they underlie variables.
Transgender Attitudes and Beliefs Scale-Spanish (TABS-S) Version: Translation and Initial Evaluation of Psychometric Properties
Published in Journal of Homosexuality, 2023
Yasuko Kanamori, Eneritz Jiménez-Etxebarria, Jeffrey H. D. Cornelius-White, Naiara Ozamiz-Etxebarria, Kelly N. Wynne, Maitane Picaza Gorrotxategi
We then screened the data to ensure that key assumptions for Confirmatory Factor Analysis (CFA)—adequate sample size, lack of multicollinearity and outliers, and univariate and multivariate normality—were met. The sample size was adequate, and there was no evidence of multicollinearity (Tolerance > .1; VIF < 10). The data was not screened for outliers because items on the survey measured a construct that was not expected to be normally distributed and may illicit strong responses (i.e., attitudes toward transgender individuals), which could result in the presence of outliers. Thus, we instead screened the data for validity of values (e.g., random response patterns), and the data were deemed to be free of invalid values. As expected, the data failed to meet the assumptions of univariate and multivariate normality (p < .001), likely due to the large sample size (N = 605) and the nature of the constructs assessed as previously noted. Given the non-normal nature of the data, we used the robust Satorra-Bentler (MLM) estimation method in conducting the CFA.
A study of the factors affecting driving risk perception using the Bivariate Ordered Probit model
Published in International Journal of Injury Control and Safety Promotion, 2023
Sina Sahebi, Habibollah Nassiri, Hossein Naderi
Technically, there are multiple statistical and non-statistical methods of finding relations between variables. Some frequently used methods in literature are regression modelling, Structural Equation Modelling (SEM), fuzzy logic, and mutual information entropy (Carter et al., 2014; Sheykhfard & Haghighi, 2020; Čubranić-Dobrodolac et al., 2020; Yuksel & Atmaca, 2021; Peng et al., 2020). In this study, we have used Confirmatory Factor Analysis and a Bivariate Ordered Probit model, the detailed description of which is provided in this section. At the first step and before using the modelling method, Confirmatory Factor Analysis was used, which is a common statistical technique used to examine construct validation and whether a measure is invariant or unchanging across groups, populations, or time (Harrington, 2009). In this study the Confirmatory Factor Analysis was employed to assess the validity of the new version of DBQ for Iranian truck and passenger car drivers. The Confirmatory Factor Analysis used to verify the factor structure of a set of observed variables as well as test the hypothesis of the relationship between observed variables and their existing underlying latent constructs (Suhr, 2006).
Examining the Role of Body Image Instability in Young Adult Women: Conceptualization, Development, and Psychometric Evaluation of the Vacillating Body Image Scale (VBIS)
Published in Journal of Personality Assessment, 2023
Misu Kwon, Mingqi Li, Olivia D. Chang
Study 1 sought to determine the factor structure and temporal stability of the VBIS. Specifically, exploratory factor analysis (Study 1a) was conducted to identify a set of latent constructs underlying a battery of initial items; confirmatory factor analysis was conducted (Study 1b) to test whether the data fit our hypothesized measurement model; and finally, reliability testing (Study 1c) was conducted to confirm the consistency of results when the VBIS is used. Study 2 was conducted to evaluate the convergent and discriminant validities of the VBIS by examining whether the VBIS would be related to other measures of conceptually similar constructs (convergent validity), but also whether the VBIS is unrelated to measures of conceptually dissimilar constructs (discriminant validity) through bivariate analysis. Study 3 was conducted to test the concurrent criterion validity of the VBIS by examining correlation coefficients between the VBIS and relevant measures of eating disturbances and adjustment outcomes. Lastly, Study 4 was conducted to assess the predictive utility of the VBIS by examining if the VBIS would account for additional unique variance in predicting subsequent body image and eating disturbances after controlling for the role of alternative explanatory variables (i.e., self-esteem & self-concept stability), using hierarchical linear modeling.