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Using your understanding of the data preparation process, explain what categories of variables the researcher must code and combine, and diagnose what types of problematic responses are present in this dataset.
Case context: A researcher is studying the impact of study break frequency on test scores. The raw dataset contains age and gender data, the number of study breaks taken (independent variable), test scores (dependent variable), and a question checking if participants paid attention to instructions. When reviewing the dataset, the researcher notices some empty cells, an age entry of '-5', and a participant who completed the test in 1 second.
Question: Using your understanding of the data preparation process, explain what categories of variables the researcher must code and combine, and diagnose what types of problematic responses are present in this dataset.
Sample answer: The researcher must code and combine the demographics (age and gender), the independent and dependent variables (study breaks and test scores), and the manipulation checks (attention question). The researcher must also address these problematic responses: the empty cells represent missing responses, the age of '-5' represents an incorrect response, and the 1-second test completion represents a suspicious response.
Key points:
- Identifying demographics, independent/dependent variables, and manipulation checks that require coding and combining.
- Diagnosing empty cells as missing responses.
- Diagnosing the negative age as an incorrect response.
- Diagnosing the extremely rapid completion as a suspicious response.
Rubric: To receive full credit, the student must explain that age/gender are demographics, study breaks/test scores are independent/dependent variables, and the attention question is a manipulation check. They must also correctly diagnose empty cells as missing responses, the negative age as an incorrect response, and the 1-second completion as a suspicious response.
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Research Methods in Psychology - 4th American Edition @ KPU
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