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Valid Extreme Outlier Example: Lifetime Sexual Partners
In a large survey of university students (Brown & Sinclair, 1999), most reported having fewer than 15 lifetime sexual partners, but a few reported extreme scores of 60 or 70. Although these could be errors or intentional exaggerations, they may also represent honest, accurate estimates of highly atypical behavior. Such valid extreme outliers require researchers to carefully consider how to handle them analytically (e.g., by using resistant statistics or comparing analyses with and without the outliers) rather than automatically discarding them.
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Research Methods in Psychology - 4th American Edition @ KPU
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Valid Extreme Outlier Example: Lifetime Sexual Partners
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When encountering an extreme outlier in a dataset, such as a participant reporting a highly atypical number of lifetime sexual partners, what is the most appropriate perspective for a researcher to take?
In a research study where most participants report studying for 10 hours per week, one participant reports studying for 90 hours per week. If the researcher determines that this score is an honest and accurate reflection of that individual's highly atypical behavior, they should automatically discard the data point as a measurement error.
A researcher is studying 'daily smartphone screen time' (in hours) among teenagers. The sample mean is 4 hours per day. Match each participant's reported data point with the most appropriate classification based on the principle of identifying valid extreme outliers.
A researcher studying 'weekly volunteer hours' among college students (mean = 2 hours) finds a participant reporting 60 hours. Arrange the steps in the correct logical sequence to determine if this score is a valid extreme outlier rather than a measurement error.
You are formulating a data-management protocol for a new study on 'weekly reading habits' among college students (mean = hours). Which of the following strategies should you construct to best implement the principle of valid extreme outliers when handling a report of hours?
In psychological research, extreme outliers in a dataset are definitive indicators of measurement errors or participant misunderstandings and should be automatically discarded.
Match each data-analysis concept with its correct description or rationale under the principle of identifying valid extreme outliers in psychological research.
In a study of university students' behavior, a participant reports having 70 lifetime sexual partners while the sample average is 4. If a researcher determines this score is an honest and accurate report of the individual's history, they should evaluate the data point as a(n) _____ extreme outlier rather than automatically discarding it as a measurement error.
A researcher replicating the design from Brown & Sinclair (1999) finds that one participant reports 80 lifetime sexual partners while the rest of the sample reports fewer than 15. To analytically distinguish this extreme score from a data artifact, the researcher must evaluate whether the score is _____, rather than the product of deliberate exaggeration or a recording error.
A research team studying 'hours of exercise per week' among college students finds that one participant reports 50 hours while the rest report 2–10 hours. The team must construct and justify their analytical decision about this extreme score for peer reviewers. Rank the following steps in the order that produces the most rigorous and defensible justification, from first to last.
Do extreme outliers in a dataset always indicate measurement errors or participant misunderstandings? Recall the findings and example from the Brown & Sinclair (1999) study to explain what extreme outliers can represent and how researchers should approach them.
Based on your understanding of outlier validity, explain the conceptual error in the analyst's recommendation. What alternative explanation must the team consider, and how should they address these data points?
Suppose you are running a replication of the Brown & Sinclair (1999) study on student behavior. A participant reports a score of while others report fewer than . Apply the principle of handling valid extreme outliers to formulate a brief strategy for how you will handle this participant's data in your analysis.