Formulating a robust research question is the cornerstone of any successful study. When aiming to compare two distinct groups based on a specific outcome, a well-defined question is crucial for guiding the research process and ensuring meaningful results. This article delves into the key considerations for constructing a research question that effectively compares two groups on an outcome. We’ll illustrate these concepts using a practical example: comparing the mile run times of athletes and non-athletes.
Defining the Research Problem
The first step in formulating a research question is to clearly define the research problem. What gap in knowledge do you seek to address? In our example, the problem is determining whether athletic status influences mile run times. We observe that athletes generally have faster mile times than non-athletes. This observation leads to the core question: Is there a statistically significant difference in average mile run times between athletes and non-athletes?
Formulating the Research Question
With the problem defined, we can formulate a precise research question. This question should be:
- Comparative: It explicitly states the two groups being compared (athletes and non-athletes).
- Focused on an Outcome: It clearly identifies the outcome variable being measured (mile run time).
- Testable: It can be answered through empirical observation and statistical analysis.
A suitable research question for our example is: “Does participation in athletics significantly affect the average time taken to run a mile?”
Establishing Hypotheses
The research question leads to the formulation of hypotheses. These are testable predictions about the relationship between the groups and the outcome:
- Null Hypothesis (H0): There is no significant difference in the average mile run time between athletes and non-athletes. (µnon-athlete − µathlete = 0)
- Alternative Hypothesis (H1): There is a significant difference in the average mile run time between athletes and non-athletes. (µnon-athlete − µathlete ≠ 0)
Choosing the Right Statistical Test
Selecting the appropriate statistical test is critical. Since we are comparing the means of two independent groups (athletes and non-athletes), an Independent Samples t-test is suitable. This test determines if the observed difference in mean mile times is statistically significant or due to chance.
Before conducting the t-test, consider examining descriptive statistics and visualizing the data. For instance, a box plot can reveal differences in the distribution of mile times between the two groups, providing insights into potential variance differences.
Determining Significance Level
Prior to testing, set a significance level (alpha, α), typically 0.05. This represents the probability of rejecting the null hypothesis when it is true. A lower alpha indicates a stricter criterion for significance.
Interpreting Results
After conducting the Independent Samples t-test, analyze the results. Levene’s test for equality of variances will guide you in choosing the appropriate t-test result row (equal variances assumed or not assumed). The p-value associated with the t-test indicates the probability of observing the obtained results if there were no true difference between the groups. If the p-value is less than the chosen significance level (e.g., p < 0.05), the null hypothesis is rejected, suggesting a statistically significant difference in mile run times between athletes and non-athletes. The Confidence Interval (CI) provides a range of plausible values for the true difference in means.
Conclusion
A clearly formulated research question comparing two groups on an outcome is essential for effective research. By carefully defining the problem, crafting a focused question, establishing testable hypotheses, choosing the appropriate statistical test, and correctly interpreting the results, researchers can draw meaningful conclusions about the relationship between group membership and the outcome of interest. In our example, a significant p-value from the Independent Samples t-test would support the conclusion that athletic participation significantly influences mile run times.
