This thesis develops diagnostic and visualisation methods to support the explo ration, modelling, and evaluation of country-level panel data in global development research. Chapter 3 introduces wdiexplorer, an R package that provides an end to-end workflow for sourcing World Development Indicators data, computing group aware diagnostic indices, and visualising temporal dynamics across countries and predefined group structures. The package enables identification of patterns, hetero geneity, and outliers in longitudinal indicators, illustrated through an application to global air pollution data. Building on this exploratory foundation, Chapter 4 presents principled visual isations for the comparison of Bayesian hierarchical model structures in the data space and the evaluation of multiple models simultaneously. These visual tools are designed to complement conventional numerical summaries by revealing how alternative hierarchical specifications influence parameter distributions and uncer tainty. Through a case study modelling cross national mathematics trends from the PISA (Programme for International Student Assessment) database, the proposed approach demonstrates how visual comparison can support more transparent and informed model selection. The methods from Chapter 3 and 4 are then adapted and applied in Chapter 5 in a substantive analysis of global family planning indicators. Using group-aware diagnostic indices and visual comparisons, Chapter 5 evaluates the alignment be tween observed survey data and model-based estimates from a Bayesian hierarchical time-series framework used for international monitoring of family planning indica tors. By jointly examining observed data, modelled trajectories, and diagnostic measures across countries and sub-regions, the analysis highlights where model es timates capture underlying trends and where discrepancies emerge. Together, the contributions show how integrating structured diagnostics with carefully designed visualisations enhance the understanding of country-level panel data, improve evaluation of hierarchical models, and support more transparent in terpretation of global development indicators.