Sample representativeness

Definition

Sample representativeness refers to how closely a sample reflects the characteristics of the larger population from which it is drawn. A representative sample ensures that the results of a study can be generalized to the broader population, making the findings more credible and reliable. In the context of mobile research methods, achieving sample representativeness can be particularly challenging due to varying access to technology, differences in demographic groups using mobile devices, and potential biases introduced by self-selection in survey responses.

5 Must Know Facts For Your Next Test

1. Mobile research methods often attract specific demographic groups, which can impact sample representativeness if not properly accounted for.
2. Achieving sample representativeness is crucial for ensuring that research findings are valid and can inform real-world applications.
3. Self-selection bias is a common issue in mobile surveys, where individuals who choose to participate may not accurately reflect the entire population.
4. Using stratified sampling can improve sample representativeness by ensuring that key demographic groups are included in the research.
5. Non-response bias can also threaten sample representativeness if certain participants decline to participate or fail to complete surveys on mobile platforms.

Review Questions

• How does sample representativeness impact the validity of research findings obtained through mobile research methods?
  • Sample representativeness significantly impacts the validity of research findings because an unrepresentative sample can lead to misleading conclusions. When participants in a mobile survey do not reflect the broader population's characteristics, the results may not be applicable outside the sample group. This undermines the credibility of any recommendations made based on the findings, making it essential to ensure representativeness for accurate insights.
• Discuss the challenges faced in achieving sample representativeness when using mobile research methods and suggest potential solutions.
  • Achieving sample representativeness in mobile research methods faces challenges such as access disparities among different demographic groups and self-selection bias. For example, individuals without smartphones or those who are less tech-savvy may be underrepresented. To address these issues, researchers can use stratified sampling techniques to ensure diverse representation or implement incentives to encourage participation from underrepresented groups.
• Evaluate the implications of non-response bias on sample representativeness in mobile surveys and its effect on generalizability.
  • Non-response bias occurs when certain individuals choose not to participate in a survey, leading to gaps in data that can skew results. This bias can severely affect sample representativeness because it may mean that key demographics are missing from the findings. As a result, generalizability is compromised since the outcomes may not accurately reflect the views or behaviors of the entire population, limiting the applicability of the research in real-world contexts.

Related terms

Sampling bias: A type of error that occurs when certain members of a population are more likely to be selected for a sample than others, leading to an unrepresentative sample.

Generalizability: The extent to which research findings can be applied to settings, people, or situations beyond the specific conditions of the study.

Stratified sampling: A sampling method that involves dividing the population into subgroups (strata) and randomly selecting samples from each subgroup to ensure representation.