In immersive analytics, observation via walking is known to be time-efficient but physically demanding. Prior work has primarily contrasted the extremes of walking versus full manipulation (scaling, rotation, translation), without systematically examining intermediate techniques that retain the benefits of walking while reducing its physical load. Furthermore, analyst characteristics, such as spatial ability, may influence the relative benefits of these methods, necessitating evaluations that account for individual differences. We introduce four interaction techniques that incrementally add scaling, rotation, and translation to walking. We evaluated these techniques in a within-subjects study with 24 participants performing two 3D scatterplot tasks while we measured their individual characteristics. Our results show that augmenting walking with scaling preserves its benefits, reduces physical demand, and improves time efficiency for exploration-intensive tasks. While analyst characteristics influenced effect sizes, they did not alter the overall ranking of the techniques. These findings clarify the design trade-offs between walking and manipulation and provide concrete guidance for interaction design in immersive analytics.