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Effect of Eco-Literacy, Consumer Effectiveness and Perceived Seriousness on Consumer Environmental Attitude: A Confirmatory Factor Analysis (CFA) Approach
Published in K. M. Baharul Islam, Zafar Mahfooz Nomani, Environment Impact Assessment, 2021
Confirmatory factor analysis (CFA): In this paper, confirmatory factor analysis is run through IBM-AMOS software. Confirmatory factor analysis (CFA) allows the researcher to test the hypothesis that the relationship between observed variable and underlying latest construct exists. Confirmatory factor analysis (CFA) is a measurement model that estimates latent variables based on observed indicator variables and also checks the reliability of the model. CFA is the technique to find out the exact relationship between the common factors and theitems used to measure them as well as the linkages among the factor with reliability (Salisbury et. al., 2001; Joreskog and Sorbom, 1989). The table 5 represents the convergent and discriminant validity measures of four extracted constructs taken together in CFA. As shown in the results composite reliability (CR) is more than 0.7 as well as greater than average variance extracted (AVE). This ensures the existence of convergent validity in the instrument. In addition to this, average variance extracted of each construct is greater than MSV and ASV statistics for the constructs which ensures the existence of discriminant validity.
Overview on Structural Equation Modeling
Published in Sergey V. Samoilenko, Kweku-Muata Osei-Bryson, Creating Theoretical Research Frameworks Using Multiple Methods, 2017
Sergey V. Samoilenko, Kweku-Muata Osei-Bryson
There are two types of FA—exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The basic difference between the two approaches is their purpose. EFA aims to explore whether the number of items (variables) in the data set could be reduced to a smaller number of meaningful factors—latent constructs. In this sense, EFA is a data analytic tool allowing for discovering of common themes in the data set. While EFA is not performed with the preconceived structure to be discovered in mind, an investigator may interpret the extracted factors in the light of a theory or a framework. CFA, on the other hand, is driven by the goal of testing a hypothesis of the existence of relationships between the items and the factors that is in accordance with the established framework or a theory.
Pilot Selection Methods
Published in Pamela S. Tsang, Michael A. Vidulich, Principles and Practice of Aviation Psychology, 2002
Thomas R. Carretta, Malcolm James Ree
Confirmatory factor analysis (CFA; Jöreskog & Sörbom, 1996; Kim & Mueller, 1988) is a statistical technique that allows investigators to specify and test hypotheses about the relations among a set of variables. Recent CFAs (Carretta & Ree, 1996a) have found that the AFOQT has a hierarchical structure similar to other multiple aptitude tests (Jensen, 1994; Ree & Carretta, 1994b; Vernon, 1969). See Figure 10.2. The higher order factor was identified as general cognitive ability (g). All 16 tests contributed to the measurement of g The proportion of common variance due to g was 67%. The remaining common variance (33%) in the residualized (Schmid & Leiman, 1957) lower order factors was 11 %4 for verbal, 9% for aviation interest/aptitude, 4% for perceptual speed, 4% for spatial, and 4% for math. These proportions are similar to that found in other multiple-aptitude batteries (Jensen, 1980). Most of the predictive utility of the AFOQT against pilot training performance can be attributed to its measurement of g and aviation interest/aptitude (Olea & Ree, 1994; Ree et al., 1995).
Pursuing supply chain ecosystem health under environmental turbulence: a supply chain learning approach
Published in International Journal of Production Research, 2023
Liukai Wang, Xinyi Kong, Weiqing Wang, Yu Gong
The measurement model demonstrates that the latent constructs fundamental for testing the proposed structural model were assessed effectively based on the indicator variable (Paul and Maiti 2008). Confirmatory factor analysis (CFA) is a method to analyse the measurement model. The second-order model was applied for the purpose of structural model simplification. In order to verify the applicability of the second-order structure of SCE Health, we used the CFA of the first order and the second order, respectively. The target coefficient was calculated by comparing the CFA of the first order and the second order of SCE Health, which was very close to 1. The results imply that the second-order CFA could replace the first-order CFA and that the second-order model was suitable for the next stage of the research (Hair et al. 2014; Marsh and Hocevar 1985). According to the suggestion of Paul and Maiti (2008), a minimum of four tests of model fit should be satisfied with the acceptability and compatibility of the model. The CFA of the measurement model was performed, and the outcome is illustrated in Table 6. Six fit indices were accepted, indicating that the model fits the data very well.
The impact of organisational conflict on green supplier integration: the moderating role of governance mechanism
Published in International Journal of Logistics Research and Applications, 2022
Junya Cai, Jia Cheng, Haiqing Shi, Taiwen Feng
The fit indices of confirmatory factor analysis (CFA) suggest that the CFA model is satisfactory (χ2(303) = 484.78, RMSEA = 0.051, NNFI = 0.97, CFI = 0.97, SRMR = 0.0473) (Hair et al. 2010). All factor loadings are greater than 0.65, and each scale has significant factor loading on its intended construct (p < 0.001) (Anderson and Gerbing 1988). As shown in Table 2, the average variance extracted (AVE) value of each construct is greater than 0.50. Thus, convergent validity is ensured. For each construct, we compared its square root of AVE with its correlations with other variables. Results in Table 3 reveal that the square root of AVE of each construct is greater than its correlations. Therefore, we can conclude satisfactory discriminant validity.
Distributor Opportunism Toward the Supplier: A Social Network Perspective
Published in Journal of Computer Information Systems, 2023
Priyanka Sharma, Angappa Gunasekaran, Girish Subramanian
Confirmatory factor analysis (CFA) indicated that the measurement model fits the data satisfactorily, , χ2/df = 1.615; comparative fit index [CFI] = 0.947; root mean square error of approximation [RMSEA] = 0.051; goodness-of-fit index [GFI] = 0.859.84 All standardized factor loadings were statistically significant; high composite reliability (CR) (0.81–0.90) and Cronbach’s α (0.71–0.89) confirmed convergent validity (Refer Table A3 for construct measures and metrics and Table A4 for correlation matrix).85 The resulting fit indices and the residuals indicate unidimensionality. The AVE for each construct (50%–80%) was more than its highest shared variance with other constructs.85