The sampling methods for secondary research, more accurately described as secondary sampling designs, are applied to existing data or information to refine models or decisions. Unlike primary research, where data is collected for the first time, secondary sampling focuses on further analysis and optimization of data that has already been obtained.
Understanding Secondary Sampling Designs
Secondary sampling designs come into play after initial data or other relevant information has already been acquired. The primary objective of these designs is to further refine the model or the decision in some very specific way. This means that researchers are not collecting new raw data but rather applying specific strategies to delve deeper into, or re-evaluate, previously collected datasets.
Core Types of Secondary Sampling Designs
According to the provided reference, secondary sampling designs can be categorized into two main types based on their foundation:
1. Point (Sample) Based Designs
- Focus: These designs are centered around specific data points or existing samples within the larger dataset.
- Application: In a point-based secondary sampling design, the focus is on re-evaluating or refining insights derived from individual data points or specific subsets that have already been collected. This might involve selecting a subset of an existing sample for more intensive analysis or validation.
2. Model (Geospatial Model) Based Designs
- Focus: These designs leverage existing models, often geospatial, to guide the refinement process.
- Application: For instance, if a geospatial model has already been developed based on initial data, a model-based secondary sampling design might involve using that model to identify areas where more precise information is needed or where existing data points need to be re-assessed in relation to the model's predictions. The goal is to enhance the model's accuracy or applicability.
Here's a quick overview of these two design types:
| Design Type | Description | Objective |
|---|---|---|
| Point (Sample) Based | Applied to specific data points or subsets of already obtained samples. | To refine understanding or decisions based on individual data units. |
| Model (Geospatial Model) Based | Applied using existing models (e.g., geospatial) to guide further refinement. | To enhance or validate the accuracy and utility of an existing model. |
Objective of Secondary Sampling
Regardless of the specific type, the overarching goal of any secondary sampling design is to enhance or confirm the insights derived from pre-existing data. This strategic approach ensures that resources are efficiently allocated to areas requiring more precision or validation, building upon the foundation of already available information.
When are Secondary Sampling Designs Applied?
Secondary sampling designs are unique in that they are applied after some data or other information has already been obtained. They serve as a subsequent step in the research process, allowing for a more focused and refined analysis of existing datasets, models, or decisions.
