# Predicting Missing Values in Survey Data Using Prompt Engineering for Addressing Item Non-Response
## 4 Experiments
### Results and Discussion  
Table 2 presents the performance of our methods compared to various baselines across different survey questions. From these results, several key findings emerge:  
1. Significant Performance Improvement: The Full Context Method consistently outperforms the baseline models across most survey questions. Specifically, GPT4-turbo outperformed TabTransformer on SATIS questions by a margin of 0.36. As such, GPT4o-mini on DRLEAD and ECON1B achieved F1-Scores of 0.73 and 0.60, respectively, which is a significant gap from the baseline. The Non-Row Selection Method also performed well. GPT4-turbo achieved the highest F1-Scores of 0.91 and 0.65 on the POL1 JB question.
2. Robustness Across Different Question Types: The proposed method demonstrated robustness across various question types, particularly in more complex scenarios. For example, in predicting responses to the DRLEAD question, which requires a deeper understanding of leadership dynamics, GPT4o-mini achieved an F1-score of 0.73, outperforming both the Non-Row Selection Method and all baseline models. This result highlights the method's ability to handle complex and nuanced survey questions effectively.
3. Practical Implications and Scalability: The results also underscore the practical benefits of using our approach in survey data analysis. A key advantage is its ability to consistently outperform traditional baselines without requiring complex additional tuning, which can be time-consuming and resource-intensive, particularly in large-scale survey analysis. Unlike MICE and Missforest, which suffer from high computational complexity \(O(knp^{3}\min(n,p))\) and require significant time to process large datasets, our method--especially when leveraging LLMs--demonstrates a much more efficient workflow. For instance, MICE and Missforest can take between 4 h 18 min and 5 h 54 min to complete imputation. TabTransformer, on the other hand, requires approximately 4 h of training time per column to achieve the performance observed in our experiments. In stark contrast, our method significantly reduced the time required for predictions. Both LLMA3 and GPT-based models take only about 4 to 6 min to predict responses for a single question. This substantial reduction in processing time not only makes our approach more practical for real-time or large-scale survey analysis but also highlights its scalability across various survey sizes and complexities.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & **SATIS** & **POL1 JB** & **POL10 * & **DRLEAD * & **PERSFNCB * & **ECON1B * \\ \hline \hline \multicolumn{8}{c}{Baselines} \\ \hline MICE & 0.39 & 0.86 & 0.40 & 0.53 & 0.43 & 0.48 \\ Missforest & 0.36 & 0.38 & 0.24 & 0.23 & 0.31 & 0.36 \\ TabTransformer & 0.48 & 0.85 & **0.78** & 0.54 & 0.44 & 0.42 \\ (NP) LLAMA38\({}_{B}\) & 0.47 & 0.77 & 0.21 & 0.47 & 0.24 & 0.42 \\ (NP) GPT4o-mini & 0.65 & 0.55 & 0.36 & 0.65 & 0.42 & 0.35 \\ (NP) GPT4-turbo & 0.47 & 0.89 & 0.41 & 0.53 & 0.55 & 0.51 \\ \hline \multicolumn{8}{c}{Our method} \\ \hline \hline \multicolumn{8}{c}{Our method} \\ \hline \hline \end{tabular}
\end{table}
Table 2: F1-scores of various methods across six different survey questions in Table 1. **Bold** numbers indicate the best performance achieved by each method. Asterisks (*) denote challenging questions with three options that may impact the model’s performance. The table provides a comparative overview of traditional baselines, Naive prompt methods (NP), and our Full Context (FC) and Non-Row Selection (NRS) Methods across multiple tasks.