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Activity Number:
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128
- Impact of Data Collection Modes and Data Sources
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Type:
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Contributed
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Date/Time:
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Monday, July 31, 2017 : 8:30 AM to 10:20 AM
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Sponsor:
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Survey Research Methods Section
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Abstract #322517
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View Presentation
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Title:
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Weighting Mixed Mode Data for the 2015 Residential Energy Consumption Survey (RECS)
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Author(s):
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Patrick Chen* and Shaofen Grace Deng and Neeraja Sathe and Lanting Dai and Rachel Harter and Phillip Kott
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Companies:
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RTI International and U.S. Energy Information Administration and RTI International and RTI International and RTI International and RTI
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Keywords:
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Address Based Sampling ;
Mixed Mode ;
Weight Calibration ;
Weight Adjustment ;
Eligibility Models
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Abstract:
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The 2015 Residential Energy Consumption Survey (RECS) was a stratified multistage cluster survey of housing units (HUs). RECS was designed for computer-assisted personal interviewing (CAPI) as the method of data collection. Because of difficulties experienced in the field, CAPI data collection was terminated and replaced with a Web/Mail data collection protocol. Nonrespondents and unfinished cases from CAPI were transferred to Web/Mail, and HUs in reserve replicates of sample were released to Web/Mail. This change imposed a challenge for weighting the combined CAPI and Web/Mail data. In this paper we discuss the weighting class method to adjust for bad addresses and drop points, a latent-variable technique to predict the probability of an address corresponding to an occupied HU, and logistic regression models to estimate the probability of a HU being a primary residence. We used a calibration method to adjust for unit nonresponse and to poststratify the nonresponse-adjusted weights to the estimated number of occupied HUs from the 2015 American Community Survey for specified HU characteristics.
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Authors who are presenting talks have a * after their name.
