One of the methodological limitations of the studies identified in this review is the different types of economic models used, making comparability across results difficult: none of the studies compared alternative models to answer the same question.
To make comparability across the groups possible, all interactions were reduced to 30 minutes.
At the local level risk models and hazard datasets lack spatial detail required to capture the underlying drivers, and loss data lack good geospatial referencing, which makes comparability and analysis difficult over time and space (Cutter and Gall 2015).
Therefore, to make comparability possible between potential of these task types in language engagement reported in the literature and their potential in task engagement investigated in this research, the same tasks were selected to be included in the present study.
Even if most studies did provide a detailed description of the costs of the intervention programme in their country currency, data on the underlying quantities of resources used, discounting/inflation methods and the price year were often not displayed, thus making comparability difficult.
These contextual differences often make comparability of voucher costs across countries difficult to achieve.
Laboratory-dependent confounding factors include differences in sample processing and data analyses, making comparability of data difficult.
Study populations were heterogeneous, as were study designs, data collection methods, and outcomes, making comparability across studies difficult and generalizability of findings limited.
For instance, sample size estimation, age group of the study participants, and inclusion and exclusion criteria, differed considerably between studies, which makes comparability difficult.
Another source of limitations referred to as anchoring (defining a specific upper anchor that could make comparability across other preference-based instruments problematic) was also anticipated.
