Comparison-Focused Sampling

The Starting Theoretical Framework

In particular, CFS investigates significant similarities and/or divergences in depth in order to effectively detect the main insightful factors supporting the interpretation of those significant differences. For an outlier sampling analysis in a program’s evaluation, for instance, CFS works by comparing successes (high outcomes) with failures (low outcomes and dropouts).

Likewise, the study of top achievers’ behaviors, excellent organizations and modes in actions, and/or outstanding communities compared to great mistakes and disasters manifesting significant ineffectiveness in similar conditions (e.g., frictions, distortions, disturbances, misalignments, misunderstandings) could be another example of CFS. In general, for CFS, it is preferable to choose a small sample size of groups in order to better conduct an in-depth case analysis.

Comparison-Focused Sampling Versus Other Types of Samplings

Although CFS is a novel technique, there are many other sampling strategies existing in the literature, such as the following:

• Single significant case. The single significant case represents one sample in-depth case (where n is equal to 1) that contributes to rich and deep understanding [Page 241]of the main subject and provides advanced distinctive insights of its own standout features. (i.e., exemplar of a special thing of interest).
• Group characteristics sampling. The group characteristics sampling strategy provides case selection in order to establish a specific enriched information group that might disclose and clarify principal or key patterns (i.e., illuminating patterns in a group).
• Theory-focused and concept sampling. Theory-focused and concept sampling includes cases that are exemplars of the main argument or concept pertaining to the focus question in order to enlighten the theoretical hypothesis of interest (i.e., deductive theoretical sampling).
• Instrumental-use multiple case sampling. This sampling techniques captures more than one case of a circumstance with the aim to generate standard outputs that might be used to change some practice, program, or policy (i.e., utilization-focused sampling).
• Sequential and emergence-driven sampling. With sequential and emergence-driven sampling, one case after another are investigated in sequence to build the sample fieldwork. Then, new findings can emerge during this research after analyzing further leads (i.e., snowball sampling).
• Analytically focused sampling. This sampling technique involves selecting cases in order to facilitate a deeper qualitative analysis and interpretation of patterns among the defined cases. It also establishes a form of emergent not expected sampling during the analysis (i.e., emergent sampling).
• Mixed, stratified, and nested sampling. With mixed, stratified, and nested sampling, multiple cases are investigated through focused in-depth analysis, creating relative bounds for emerging relevance and credibility (i.e., mixed methods).

CFS includes some features of all the aforementioned sampling techniques and as such can address well-known difficulties in analysis, such as predicting and estimating future trends.

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