Compelling Questions

A descriptive question aims to summarize specific characteristics of data without interpreting the results.

For example: "How many students in the 10th grade have a GPA above 3.5?"

An exploratory question examines data for patterns or relationships between variables to generate potential hypotheses, rather than directly testing them.

For example: "Do students who participate in extracurricular activities have different attendance rates than those who do not?"

An inferential question takes findings from one data set and analyzes a new dataset to determine if an observed association is applicable to a broader population, such as all students in a school.

For example: "If a reading intervention program has been successful in Grade 6, to what extent would it be successful in other grade levels?"

An predictive question looks to find correlations between data that would allow the data from one set to predict the data in another set, without understanding the causal mechanism.

For example: "To what extent are the social/emotional survey results predictive of student absences from school?"

An causal question looks to establish a cause and effect relationship between two different sets of data. Usually this can only be established through randomized experiments.

For example: "Does implementing a daily 30-minute reading program improve students' reading comprehension scores over a semester, compared to students who don't participate in the program?"

A mechanistic question delves into the underlying processes or mechanisms through which one factor affects another, seeking to explain the "how" or "why" behind observed relationships.

For example: "How does a school-based mindfulness program reduce stress and anxiety levels in students?"