Estructural Equation Modeling (SEM)


Mind Map on Estructural Equation Modeling (SEM), created by bamezcuan on 07/13/2013.
Mind Map by bamezcuan, updated more than 1 year ago
Created by bamezcuan over 10 years ago

Resource summary

Estructural Equation Modeling (SEM)
  1. Basic concepts
    1. A tool for analyzing multivariate data that has been especially appropriate for theory testing (Bagozzi, 1980; Steenkamp and Baumgartner, 2000).
      1. * Latent variables can not be observed directly (Chin, Peterson & Brown, 2008) * Path Analysis: is a special case of SEM that only involves observed (manifest) variables (Savalei & Bentler, 2010).
        1. SEMs go beyond ordinary regression models to incorporate multiple independent and dependent variables as well as hypothetical latent constructs which may cluster several observed variables (Savalei & Bentler, 2010).
        2. Related studies
          1. SEM have become ubiquitous in all the social and behavioral sciences (e.g., MacCallum & Austin, 2000).
            1. Anderson and Gerbin (1988) conducted a review and recommended a 2 step approach: 1) A confirmatory measurement, or factor analysis, model specifies the relations of the observed measures to their posited underlying constructs, with the constructs allowed to intercorrelate freely. 2) A confirmatory structural model then specifies the causal relations of the constructs to one another.
              1. Baumgartner and Homburg (1996) conducted a review of use of SEM in marketing research.
              2. Modeling Process (Savalei & Bentler, 2010)
                1. 1) Model specification: Specify variables and constructs
                  1. 2) Model estimation: Iterative process to stimate parameters
                    1. 3) Model evaluation: There are two components to model fit: statistical fit and practical fit. Statistical fit is evaluated via a formal test of the hypothesis ( T). Practical fit is evaluated by examining various indices of fit: standardized root mean-square residual (SRMR) <.05. Goodness of Fit index (GFI) related to R2 in ordinary regression. AGFI (Adjusted GFI), is analogous to the adjusted R2 . Both GFI and AGFI take on values between 0 and 1, with values less than 0.90 often considered unacceptable. Comparative Fit Index (CFI) with values between 0 and 1. Preferable close to 0.
                      1. 4) Model modification : if the proposed model dont fit the data
                        1. 5) Parameter Testing: To interpret parameter estimates and to test for their statistical significance. Several tests used, in particular z-tests, Wald tests, and chi-square difference tests.
                        2. Rules of Thumb
                          1. Bentler & Bonett, 1980: at least three items per construct.
                            1. Bentler & Chau, 1987: Sample size of 5 to 10 subjects per item and up to 300
                              1. Baumgartner, 1996: Minimum 3 or 4 indicators per latent variable, large sample sizes 5:1, assumption of normality, model should be identified
                                1. Fornell & Larcker (1981): Standardized loading estimates should be .5 or higher, ideally .7 or higher. Average of variance Extracted (AVE) should be .5 or higher. Construct Reliability (CR) should be .7 or higher, between .6 and .7 is acceptable
                                2. The relationship between a manifest variable and a construct is expressed as being either formative or reflective. If the relationship is formative, the manifest variable (indicator) cause the construct, whereas if the relationship is reflective, the construct cause the manifest variable (Jarvis, Mckenzie, & podsakoff, 2003). Most structural equation models specify the manifest variable–construct relationship as reflective (Chin, Peterson & Brown, 2008)
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