Marketing Research Ch. 22

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Across
  1. 3. An indicator of the average squared discrepancy between the model-implied covariance matrix and the observed covariance matrix, providing a measure of how well the model fits the data.
  2. 5. A conventional statistical approach used in SEM to produce parameter estimates that are interpreted relative to each other and lack an absolute scale.
  3. 7. A model parameter for which the value is specified by the analyst or the software and is not estimated.
  4. 8. An unobservable theoretical concept that is represented by multiple measured variables or indicators.
  5. 10. A model parameter whose value is estimated by the SEM software.
  6. 12. A latent variable within a structural model that is equivalent to an independent variable and is presumed to influence other latent variables.
  7. 15. A unique piece of information derived from the covariance matrix that serves as input for a SEM model.
  8. 16. An index that indicates how well a proposed model fits the data compared to a baseline model that assumes no relationships among the variables.
  9. 17. A structured arrangement containing all the information among a set of variables, where each element indicates the shared variance between two variables, and the diagonal elements represent the variance of each individual variable.
  10. 18. The difference between the observed covariance matrix and the covariance matrix estimated by the SEM model.
  11. 19. An interdependence technique used within SEM to test a specific measurement theory by examining the relationships between measured variables and underlying latent constructs.
  12. 21. An estimate in CFA that indicates the strength and direction of the relationship between an indicator and its underlying latent construct.
  13. 22. An assessment of the consistency and stability of a measure representing a latent construct, often reported in CFA instead of Cronbach's alpha.
Down
  1. 1. A latent variable within a structural model that is equivalent to a dependent variable and is expected to be influenced by other latent variables in the model.
  2. 2. A measure of how two variables change together; it portrays the relationships between pairs of variables.
  3. 4. The number of unique pieces of information from the covariance matrix available for analysis, minus the number of parameters estimated in the model.
  4. 6. An estimated value in SEM that represents the hypothesized direct relationship between two latent constructs, functioning similarly to a regression coefficient.
  5. 9. A statistical framework used by researchers to estimate relationships among concepts and between these concepts and the items that represent them, often employed for testing theories involving multiple equation processes.
  6. 11. Evidence that different latent constructs are distinct from one another, often assessed by comparing the Average Variance Extracted (AVE) for each construct with the squared correlations between constructs.
  7. 13. The part of a SEM model that specifies the hypothesized relationships between different latent constructs, often testing a theoretical explanation of how these constructs influence each other
  8. 14. The extent to which a SEM model accurately explains the observed relationships within the data, specifically the covariance matrix.
  9. 20. A measured variable that is used to represent a latent construct in a SEM model.