How can researchers reduce p-hacking while maintaining flexibility to explore emergent LGBTQ+ phenomena?
P-hacking is a serious problem for scientists who study sexuality, especially those who conduct quantitative analyses of survey data. It occurs when researchers start with a hypothesis and then search through their data until they find statistical significance. This can lead to false positives, which can mislead future investigations and harm society.
Some scientists argue that rigid adherence to pre-registered hypotheses limits exploration of new ideas and can miss important insights. In this essay, I will explain how researchers can avoid both errors. First, they should formulate clear, falsifiable hypotheses before collecting data. Next, they should pre-register their analysis plan online so other scholars can verify it.
They should collect as much data as possible to increase power and avoid spurious results. These strategies promote scientific integrity without stifling creativity or innovation.
The first step in reducing p-hacking is to create clear, testable hypotheses. Researchers must specify what variables they want to measure and how they expect them to relate to each other.
If they want to investigate gender identity, they could ask respondents whether they identify as male, female, nonbinary, agender, or another category. If they suspect transgender people may experience more mental health problems than cisgender ones, they might predict higher scores on the Depression Anxiety Stress Scale (DASS) for trans women and men. They can use these predictions to guide data collection and determine if the sample is large enough for reliable analysis. By keeping their hypotheses simple and specific, researchers can reduce the likelihood of false positives and focus their attention on meaningful patterns.
Researchers should pre-register their analysis plans. This involves describing their proposed methods, including the number of tests they intend to run and the statistical significance level they will accept. Pre-registration makes experiments more transparent, improves replicability, and helps ensure that researchers do not change their methods after seeing unexpected results. There are several online platforms where scientists can pre-register their studies, such as AsPredicted.org and Ecological Validity Checklist (EVC). By sharing their plan before data collection, researchers can make it harder to manipulate results after the fact and increase confidence in their findings.
Pre-registration allows others to critique their design and suggest modifications. This can improve scientific integrity while still allowing creativity and flexibility.
Researchers should collect as much data as possible. Collecting more samples increases power and reduces the risk of spurious results.
Suppose a study has 100 participants per group, but only 50 of them report relevant variables. The resulting p value will be less powerful than if all 100 participants contributed data.
Researchers must balance sample size with cost and effort. Collecting too many observations may impose an unreasonable burden on respondents or require excessive resources. Therefore, they should seek advice from experts to determine how large a sample is necessary for reliable analysis. By balancing these considerations, researchers can reduce both p-hacking and the risk of missing important insights.
Researchers can avoid p-hacking and maintain flexibility by following three steps: forming clear hypotheses, pre-registering their plans, and collecting sufficient data. These strategies promote scientific integrity without stifling exploration or innovation. By taking these steps, we can create more reliable knowledge about sexuality and minimize false positives that mislead future research.
How can researchers reduce p-hacking while maintaining flexibility to explore emergent LGBTQ+ phenomena?
One way to address this challenge is by using statistical software that incorporates multiple testing corrections such as the Benjamini-Hochberg procedure (BH). This approach involves setting an alpha level at 0. 05/n, where n is the number of tests performed, which helps control for false positives.