Nongeneralizable

Nongeneralizable describes findings, theories, models, or conclusions that cannot be readily extended beyond the specific conditions under which they were obtained. In research, generalizability is the extent to which results hold across populations, settings, times, and measurements. Nongeneralizable results are limited in their applicability and are not easily transported to different contexts.

Nongeneralizability can arise from several factors. Nonrandom or convenience sampling, small sample sizes, and studies conducted in a single location or with a highly specific population reduce the likelihood that results apply elsewhere. Contextual factors such as culture, environment, or particular interventions can render findings dependent on unique circumstances. Measurement issues, methodological flaws, or overfitting in data analysis can also produce results that do not transfer beyond the original study. Publication bias and selective reporting may exaggerate the perceived generalizability of restricted findings.

Implications of nongeneralizability include limited usefulness for informing policy, practice, or theory beyond the studied setting. Researchers and practitioners should acknowledge the scope of applicability and exercise caution in extrapolating results.

To enhance generalizability or to clearly delineate its limits, researchers can design studies with diverse, representative samples; conduct multi-site or cross-context replication; preregister methods and analyses; use robust statistical techniques and cross-validation; and employ meta-analytic approaches to synthesize across studies. Clear reporting of population, setting, time, and measurement boundaries helps readers assess generalizability.

See also: external validity, ecological validity, transportability, generalizability.