Excluding Participant Data

A researcher has just finished collecting survey data from 200 participants who rated their stress levels on a 1-to-10 scale. Before running any statistical analyses, the researcher reviews the raw dataset and notices that one participant recorded a value of 15. What does this example best illustrate?

A researcher is reviewing the raw data from a survey on academic stress. Match each specific issue found in the dataset with the correct category of raw data error.

A researcher asks participants to rate their anxiety before an exam on a scale from 1 to 7. While reviewing the completed surveys, she notices that one participant circled '0' and another left their rating completely blank. She decides to include both responses in her dataset and proceeds directly to statistical analysis. This is an appropriate way to handle the raw data review stage.

A psychology researcher has just exported their survey results and is reviewing the records for integrity. Arrange the following steps in the logical order necessary to analyze the usability of the dataset.

While reviewing a raw dataset, a researcher finds a response of '12' on a stress scale that only ranges from 1 to 10. By choosing to exclude this participant's data rather than guessing their intended response, the researcher is making a methodological judgment to prioritize the ______ of the study's conclusions over the total number of participants.

You are designing a 'Data Quality Protocol' for a laboratory conducting psychology surveys that use a $1$-to-$10$ rating scale. Which of the following sets of instructions would you construct to most effectively guide research assistants through the process of checking raw data before it is used for statistical analysis?

Once data is secured, researchers should review the ______ data for completeness and accuracy to determine if it is usable for statistical analysis.

True or False: Once data is secured, a researcher can bypass checking the raw data and immediately proceed to statistical analysis, as modern statistical software is designed to automatically detect and correct participant misunderstandings or missing responses.

A researcher is auditing completed surveys from a psychology study that used a 1-to-10 stress rating scale. For each data anomaly discovered in the dataset, match it to the specific analytical consequence that makes the entry problematic for statistical analysis.

A researcher has exported survey data and notices that one participant's record contains a mix of missing items, an out-of-range value, and a smudged illegible response. Order the following steps from first (1) to last (5) to best evaluate whether this participant's data should be retained or excluded from statistical analysis.

Once data is secured, what two primary qualities should a researcher review the raw data for, and what are the three specific types of response issues they should look for to determine data usability?

Diagnose the specific raw data issues represented by these three anomalous surveys, and explain why identifying these issues at this stage is crucial for the instructor's subsequent statistical analysis.

Suppose you have just secured survey data where participants rated their stress on a $1$-to-$10$ scale, and you discover a response of $12$. Apply the rules of checking raw data to explain what this specific response represents and what decision you must make because of it.