### 1. Diagnosing and Correcting Violations of Classical Model Assumptions
In real-world datasets, classical assumptions such as normality, homoscedasticity, and independence are often violated. This section explores advanced diagnostic tools in SPSS, including residual plots, normal probability plots, and influence statistics. More importantly, it focuses on practical correction strategies such as data transformation, robust standard errors, and alternative modeling approaches. Understanding when and how to adjust models is critical for producing valid and publishable results.
### 2. Implementing Generalized Linear Models (GLM) for Non-Normal Outcomes
Many clinical and social science outcomes are binary, count-based, or skewed, making linear regression inappropriate. This heading dives into the use of Generalized Linear Models in SPSS, including logistic, Poisson, and negative binomial regression. It emphasizes correct link function selection, interpretation of model coefficients, and assessment of model fit. Researchers gain insight into handling complex outcome distributions without compromising statistical rigor.
### 3. Advanced Use of Interaction Terms and Effect Modification
Beyond main effects, interaction terms allow researchers to explore how relationships change across subgroups. This section explains how to correctly specify, test, and interpret interaction effects in SPSS models. It also highlights common pitfalls such as multicollinearity and misinterpretation of coefficients. Proper visualization and stratified interpretation are discussed to ensure results are both statistically sound and clinically meaningful.
### 4. Multilevel and Mixed-Effects Modeling in SPSS
Hierarchical data structures—such as patients nested within hospitals or students within schools—require specialized analytical techniques. This heading focuses on multilevel and mixed-effects models using SPSS, explaining fixed versus random effects in a practical manner. It discusses intra-class correlation, model building strategies, and variance component interpretation. These methods are essential for handling clustered data accurately.
### 5. Model Validation, Sensitivity Analysis, and Reproducibility
High-quality research extends beyond running a single model. This final section addresses internal validation techniques, sensitivity analyses, and robustness checks within SPSS. It emphasizes transparency, reproducibility, and reporting standards expected by high-impact journals. By incorporating these practices, researchers can confidently defend their findings under peer review and enhance the credibility of their work.
