Data Preparation Before calculating savings, the Evaluator cleaned and prepared the AMI data provided by E1. The received AMI data contained consumption data aggregated on a monthly basis. The pre-program data cover the 12-month period pri...
AI summary The Evaluator cleaned AMI data from E1, removing outliers, duplicates, and inactive accounts, while retaining opted-out customers to avoid bias. Pre-program data spanned May 2023–April 2024, and post-program data covered May–December 2024. Attrition rates were 5.6% for customers and 0.6% for observations.
Data Collection Elements - › Pretest the survey to ensure that: - › Questions are correctly understood - › Questions provide results that are valid and reliable - › Skips work as they should - › Questions and sections flow well - › To impr...
AI summary The text outlines best practices for data collection in energy efficiency programs, emphasizing pretesting surveys, improving response rates through branding and incentives, conducting fieldwork promptly to minimize recall bias, and using qualified interviewers. It also notes considerations for spillover effects and the importance of timing in survey design.
Table 368: Overview of NTGR Survey and Question Design Best Practices Survey Design Question Design Question Order Data Collection › Identify key decision maker › Use simple words › Make early questions easy and pleasant › Pre-test the sur...
AI summary Table 368 outlines best practices for designing surveys and questionnaires, focusing on survey design, question design, question order, and data collection. It emphasizes clarity, simplicity, and consistency to improve data quality and participant engagement.