Although wikis have gained considerable attention in higher education, students are often reluctant to use wikis in formal learning processes. Unlike company employees, students are not often rewarded for their participation in wiki-based assignments. Therefore, students seem to be opportunistic and decide to adopt wikis if they fit their current situation and preferences. This paper adapts the Decomposed Theory of Planned Behaviour to examine the situation in higher education classrooms.
System Requirements Consequences of Measurement Error Application Researchers in marketing and the social and behavioral sciences often work with theories that posit relationships between concepts. In Amos, the concepts are latent variables that are measured by one or more indicators using structural equation modeling SEM.
The measures are often imperfect and contain measurement error.
Consequences of measurement error include: A perhaps surprising consequence of measurement error is that error in any variable can adversely affect estimated parameters throughout the model. In practice, researchers use reliable multiple indicators to try to overcome these problems.
The following example illustrates how multiple indicators can help remedy measurement error. No provision for measurement error In a study of coupon usage, Richard Bagozzi considers a model in which attitudes affect behavior both directly and by way of intentions.
He compares models in which single and multiple indicators are used. The model with no provision for measurement error appears in the following figure. This path diagram illustrates the use of a single indicator to represent each latent variable: X1 is an indicator of attitudes Y1 is an indicator of intentions Y3 is an indicator of behavior This model makes no provision for measurement error.
The indicators are treated as perfect measures of their respective latent variables. Amos produces the following regression estimates.
Click to View Larger Image Note that these coefficients are un-standardized. Amos produces the following variance estimates.
Click to View Larger Image Note that to estimate the model you must set certain regression weights to 1 and certain variances to 0. These values appear in the Amos input model graphic shown above.
Here is Amos graphic output for the above model. Measurement error taken into account in attitudes This model appears in the following figure. In this model, there are now three indicators of attitudes, namely, X1, X2, and X3.
To make the model identified, you must fix the scale of attitudes. You can either set the variance of attitudes to a constant, or you can constrain one of the regression weights from attitudes to its indicators to 1.Amos is a structural equation modeling (SEM) software solution from SPSS that extends standard multivariate analysis methods, including: regression, factor analysis, correlation, and analysis of variance.
A DISSERTATION SUBMITTED TO THE FACULTY OF THE UNIVERSITY OF MINNESOTA BY Benjamin D. Aronson from PharmD to PhD including Kristin Janke, Tim Stratton, and Keri Hager.
each DSMB was tested using structural equation modeling. Results. AN EXPERIMENTAL STUDY USING STRUCTURAL EQUATION MODELING by AN EXPERIMENTAL STUDY USING STRUCTURAL EQUATION MODELING by John C.
Bernard, Ph.D. Professor in charge of thesis on behalf of the Advisory Committee Approved: _____ Titus O. Awokuse, Ph.D.
Chair of the Department of Applied Economics and Statistics.
In the present study, I aim to use structural equation modeling techniques to construct and validate a model of public library activities using large-scale publicly- available data.
Mustafa Degerli - - Master of Science Thesis - Identifying Factors Influencing the Acceptance of Processes: An Empirical Investigation Using the Structural Equation Modeling Approach 1.
Structural Equation Modeling Structural Equation Modelling (SEM) is a statistical model used to test the causal relationships between latent variables (unobserved variables).
There are two main parts in SEM: (1) Measurement model(s) and (2) Full model.