What is it about?
We did a study to see how well a certain method (called full information maximum-likelihood, or FIML for short) works in analyzing data with missing pieces and in situations where data points within groups are more or less similar to each other. This method is used in complex analyses where data is organized at different levels, like students within schools. We looked at how missing data and the similarity of data within groups (measured by something called intra-class correlations, or ICCs) affect the accuracy of our analysis in various ways, such as whether our model correctly represents reality, how close our estimates are to the true values, and our confidence in these estimates. We found that this FIML method is pretty good at handling situations where data is missing randomly or completely by chance, especially when it comes to the outcomes we’re interested in or data missing from variables that affect the whole group (like school-wide policies). The method did better in terms of less error in estimates and more reliable error measurements when no data was missing from group-level variables and when the data points within groups were more alike (high ICC) rather than less alike (low ICC). This study suggests that while the FIML method is reliable in many cases, researchers should pay attention to how and where data is missing and how similar data points within groups are. Further research is encouraged to look into other factors that might affect the accuracy of this method, like how data is distributed, how complex the group-level analysis is, and the impact of missing data on variables that describe differences between groups.
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Why is it important?
This work stands out and arrives at an important time for a couple of reasons. Firstly, it delves into a challenge that many researchers face: dealing with missing data. When we try to analyze data from real-world situations, like understanding how different teaching methods affect students' learning across various schools, it's common to find that not all the information we hoped to gather is available. Some schools might not report all the data, or there might be incomplete surveys. This study offers insights into how to accurately analyze such incomplete data sets, ensuring that the conclusions drawn are as reliable as possible. Secondly, the research is timely because it tackles the complexity of data that's structured in layers, such as students within classrooms within schools. This kind of data is becoming increasingly common in many fields, from education to healthcare, because it can provide a more nuanced understanding of how individual outcomes are influenced by group contexts. The study's focus on how similarity within groups (high or low ICC) affects analysis is crucial. In an era where personalized and context-sensitive approaches are becoming the norm, understanding these dynamics can significantly improve how we interpret data and make decisions based on it. In summary, this research provides a valuable tool for tackling two major challenges in data analysis today: dealing with missing information and understanding the impact of group dynamics on individual outcomes. Its findings can help researchers across various fields make more accurate and meaningful conclusions from their data, even when it's incomplete or complex.
Perspectives
As a university professor and scholar specializing in psychometrics and quantitative methods, I find this publication particularly compelling for several reasons. First and foremost, it directly addresses some of the most pressing challenges in our field: handling missing data and accurately analyzing multilevel structures. These issues are not just academic concerns; they have practical implications for the quality and integrity of our research outcomes, which in turn influence policy and practice in education, psychology, and beyond. From my perspective, the study’s exploration of the full information maximum-likelihood (FIML) method as a robust solution for these challenges is both timely and critical. The FIML method offers a sophisticated approach that can enhance the accuracy of our analyses, ensuring that we make the most out of the data available to us, even when it's incomplete. This is particularly relevant in today’s research environment, where we often deal with large datasets from varied sources, each with its own pattern of missingness. Moreover, the focus on intra-class correlations (ICCs) and their impact on the analysis in a multilevel SEM context is a valuable contribution to our understanding of how group similarities affect statistical estimates. This insight is crucial for my work and that of my colleagues, as it helps us better design our studies and interpret our findings, especially when dealing with hierarchical or nested data structures common in educational and psychological research. What stands out to me about this publication is its practical implications for improving research methods and outcomes in psychometrics and related fields. It reinforces the importance of carefully considering how we handle missing data and group effects in our analyses, urging a more nuanced and sophisticated approach to dealing with these ubiquitous challenges. The findings from this study will undoubtedly influence my own research and teaching, offering a solid foundation for advancing our methods and ensuring that our work remains at the cutting edge of quantitative analysis.
Chunling Niu
University of the Incarnate Word
Read the Original
This page is a summary of: Impact of missing data and ICC on full information maximum-likelihood estimation in multilevel SEMs, Model Assisted Statistics and Applications, March 2024, IOS Press,
DOI: 10.3233/mas-231444.
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