Hierarchical Linear Modeling (HLM) is a statistical technique used to analyze nested data, which means that data are grouped into subunits (for example, students nested within classrooms). Your request appears to involve a complex model with multiple independent variables, a mediator, and a moderator, so this will require a solid understanding of HLM principles.

However, before proceeding, please note that SPSS is not commonly used for hierarchical linear modeling. While SPSS does have a 'MIXED' procedure for multilevel models, it is less intuitive and less flexible than other software specifically designed for multilevel modeling such as Stata, or R.

If you are really committed to using SPSS, here is a general outline:

Ensure Data is Structured Correctly: your data should be structured in long format, meaning each row represents a single observation. If you have Level-1 units (e.g., individuals) nested within Level-2 units (e.g., teams), individual-level data should be on the same row as the group to which they belong.

Center Your Variables: this is a key step in HLM. Grand mean centering is often used for Level-1 predictors, while group mean centering is used for Level-2 predictors. You'll need to create these centered variables before running your analysis.

Run Null Model: with no predictors, just the outcome variable. This will provide the intraclass correlation coefficient (ICC), which tells you how much of the variance in the outcome variable is due to group-level differences. You can run this model in SPSS using the 'MIXED' procedure.

Add Level-1 Predictors: after running the null model, you can start adding predictors. First, add Level-1 predictors to the model and observe how this impacts the ICC.

Add Level-2 Predictors: finally, add Level-2 predictors to the model.

Consider Cross-Level Interactions: occur when a variable at one level interacts with a variable at another level. If you have reason to suspect such interactions in your data, you would add these to your model as well.

Please read :

Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (Vol. 1). Sage.

Heck, R. H., Thomas, S. L., & Tabata, L. N. (2013). Multilevel and Longitudinal Modeling with IBM SPSS. Routledge.

Dear Sebastien Keiff

Thanks for providing me fruitful information.

Few things to mentions here that I don't have any other software to perform HLM analysis, that is why I am using SPSS.

Secondly R is a programming tool which requires programming skills and I am lacking these programming skills.

Thirdly ICC1 and ICC2 values are as per the threshold.

Let me follow your suggestions and will get back to you.

Once again thanks for the guidance

Hello! Performing Hierarchical Linear Modeling (HLM) analysis in SPSS v27 involves a multistep process. First, you need to organize your data in a hierarchical structure, with individuals nested within groups. Then, you can use the Mixed Models procedure in SPSS to conduct the analysis. You would specify your dependent variable (Project Success) and include your independent variables (Project Governance, HPWPs, and Transformational Leadership) as fixed effects. For mediation, you can use the PROCESS macro in SPSS, which allows you to test the indirect effects. For moderation, you would include the interaction term between the independent variable (Organizational Culture) and the moderator (Project Success) in your model. To gain a deeper understanding of HLM and its implementation in SPSS, I recommend consulting the following resources: 1) "Multilevel and Longitudinal Modeling with IBM SPSS" by Heck and Thomas; 2) "Applied Multilevel Analysis: A Practical Guide for Medical Researchers" by Twisk; 3) SPSS tutorials and online forums specific to HLM analysis, such as the SPSS Community forums or websites like ResearchGate and Stack Exchange. Good luck with your analysis!