Must SEM = Theoretical Framework?

In summary, the individual has constructed a theoretical framework based on theories and collected data from respondents. However, the data does not align with the initial framework and the proposed model differs from the Structural Equation Modeling (SEM) being used for analysis. The individual is questioning the acceptability of this approach in the academic research field.
  • #1
skyer1
Based on theories, I constructed a theoretical framework. After I collected data from respondents, the data seems not fit accurately with my initial theoretical framework.

I want run analysis on the data and certainly, the Model of data (i'm using Structural Equation Modeling) is different from what I proposed.

Is this acceptable in the academic research field ?
Thanks..
 
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  • #2
Excuse me, but are you saying you propose to run your data on a SEM model which does not correspond to your proposed model and then use the results to test your model? How on Earth do you expect that to tell you anything?
 
  • #3


I understand the importance of a theoretical framework in guiding research and providing a foundation for data analysis. However, it is not necessary for the theoretical framework to perfectly align with the data collected. In fact, unexpected or conflicting results can often lead to new insights and discoveries.

It is important to acknowledge any discrepancies between the data and the initial theoretical framework and to explore potential reasons for these differences. This may involve further analysis, revisiting the literature, or even modifying the theoretical framework to better fit the data.

In terms of using SEM as a model for data analysis, it is important to ensure that the model is appropriate for the data and accurately reflects the relationships between variables. If the model differs from the initial theoretical framework, it is important to discuss and justify these differences in the research findings.

In the academic research field, it is acceptable to have unexpected or conflicting results as long as they are properly addressed and discussed. It is important to be transparent and open about any discrepancies and to provide a thorough explanation for them.

Overall, it is important to remember that science is a continuous process of learning and discovery. Unexpected results can often lead to new and valuable insights, so it is important to approach them with an open mind and continue to critically evaluate and refine the theoretical framework.
 

1. What is the purpose of a theoretical framework in SEM?

A theoretical framework in SEM (Structural Equation Modeling) provides a conceptual and theoretical foundation for understanding the relationships between variables in a model. It helps to guide the selection of variables, their relationships, and the direction of the relationships within the model.

2. Is a theoretical framework necessary for SEM?

Yes, a theoretical framework is essential for SEM. It helps to ensure that the model is based on sound theoretical principles and provides a logical and coherent explanation for the relationships between variables.

3. Can I use any theoretical framework for SEM?

No, the theoretical framework used in SEM should be relevant and appropriate for the research question being addressed. It should also be supported by previous research and have a clear connection to the variables being studied.

4. How do I develop a theoretical framework for my SEM model?

The development of a theoretical framework for SEM involves a thorough review of the literature to identify relevant theories and concepts. The framework should be constructed based on the research question, with a clear explanation of the relationships between variables and their expected direction.

5. Can a theoretical framework be modified or changed during the SEM analysis?

Yes, a theoretical framework can be modified or changed during the SEM analysis if new evidence or insights emerge. However, any modifications should be supported by sound reasoning and evidence and should not deviate from the overall research question and objectives.

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