Double sampling is probably the least used of all sampling designs in organizational research. D

step 1 So your text lists five reasons for sampling and says that A is that the sample may be adequate.

Double sampling is probably the least used of all sampling...

Key Concepts

Double Sampling

Double sampling, also known as two-phase sampling, is a research design where an initial large sample is used to collect basic information, and a subsample from this group is later used to gather more detailed or expensive measurements. This method is particularly useful when it is costly or impractical to collect detailed data from the entire population, but it also introduces trade-offs in terms of potential measurement error and issues related to sample representativeness between the phases.

Sampling Designs

Sampling designs refer to the various strategies researchers employ to select representative subsets from a larger population for data analysis. These designs, which include simple random sampling, stratified sampling, cluster sampling, and others, are critical in ensuring data accuracy, reducing bias, and enhancing the generalizability of research findings. The choice of design affects the reliability, validity, and overall success of research studies.

Organizational Research

Organizational research involves studying structures, behaviors, processes, and performance within organizations. In this context, selecting an appropriate sampling design is crucial to understanding organizational dynamics accurately. Researchers must consider factors like cost, accessibility, and response rates when designing samples to capture the complexity and diversity of organizational settings.

Methodological Trade-offs

Methodological trade-offs in sampling designs, such as those seen in double sampling, involve balancing cost, accuracy, and efficiency. While double sampling can lower costs and manage extensive data collection by focusing resources on a subsample, it may be less frequently used in organizational research due to its complexity, potential for increased error, and the challenges of maintaining representativeness across two phases.

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