Rewrite this and use the paper attached to describe it better:
In psychology research, the ceiling and floor effects refer to the phenomena where a significant number of subjects achieve the highest (ceiling) or lowest (floor) possible scores on a measurement scale, leading to a clustering of scores at these extremes. This clustering results in a lack of sensitivity in the measurement instrument, making it difficult to detect differences among subjects or over time. Ceiling and floor effects can profoundly impact the statistical analysis of data, affecting the mean, variance, and other distributional properties. According to Šimkovic and Träuble (2019), these effects can introduce bias in the estimates of group differences and effect sizes, such as Cohen’s d, and can influence the outcomes of hypothesis testing procedures like t-tests and ANOVAs. The study highlighted that bias and uncertainty in statistical inferences tend to increase with the magnitude of the ceiling or floor effect. To mitigate these issues, the authors recommend using data transformations, such as logarithmic or rank-based transformations, and robust statistical methods that account for these effects. For instance, log transformations work well with Gamma and Beta prime distributions, while logit transformations are effective with Beta distributions. The study also underscores the importance of measurement validation and calibration studies to understand and address the impact of ceiling and floor effects on statistical analyses, ensuring more accurate and reliable results in psychological research (Šimkovic & Träuble, 2019).
Reference:
Šimkovic, M., & Träuble, B. (2019). Robustness of statistical methods when measure is affected by ceiling and/or floor effect. PLoS ONE, 14(8), e0220889. https://doi.org/10.1371/journal.pone.0220889
