scholarly journals Residential Energy Consumption Survey: Quality Profile

1996 ◽  
Author(s):  
Keyword(s):  
Energy Consumption ◽  
Residential Energy ◽  
Residential Energy Consumption ◽  
Survey Quality ◽  
Quality Profile ◽  
Residential Energy Consumption Survey ◽  
Consumption Survey

Estimating Net Coverage of Segments in ABS Frames

Field Methods ◽  
2020 ◽  
Vol 33 (1) ◽  
pp. 68-84
Author(s):  
Rachel Harter ◽  
Katherine B. Morton ◽  
Ashley Amaya ◽  
Derick Brown
Keyword(s):  
Energy Consumption ◽  
Standard Method ◽  
Residential Energy ◽  
Residential Energy Consumption ◽  
Residential Energy Consumption Survey ◽  
Consumption Survey

The literature has no standard method for estimating the coverage of area probability segments in address-based frames. Versatility is desirable for different study needs, but standardization improves comparability. Many segment estimates are simple ratios of counts of frame addresses to control totals, or net coverage ratios. Challenges to segment ratios include geocoding error, outdated control totals, errors in the address frame, and systematic exclusion of types of addresses. We tested various net coverage ratios on segments selected for the 2015 Residential Energy Consumption Survey, and we share our results and recommendations for using net coverage ratios for estimating coverage for segments.

scholarly journals Constructing Strata of Primary Sampling Units for the Residential Energy Consumption Survey

2017 ◽  
Author(s):  
Rachel M. Harter ◽  
Pinliang (Patrick) Chen ◽  
Joseph P. McMichael ◽  
Edgardo S. Cureg ◽  
Samson A. Adeshiyan ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Energy Consumption ◽  
Decision Trees ◽  
Principal Components ◽  
Survey Design ◽  
Outcome Data ◽  
Residential Energy ◽  
Residential Energy Consumption ◽  
Residential Energy Consumption Survey ◽  
Consumption Survey

The 2015 Residential Energy Consumption Survey design called for stratification of primary sampling units to improve estimation. Two methods of defining strata from multiple stratification variables were proposed, leading to this investigation. All stratification methods use stratification variables available for the entire frame. We reviewed textbook guidance on the general principles and desirable properties of stratification variables and the assumptions on which the two methods were based. Using principal components combined with cluster analysis on the stratification variables to define strata focuses on relationships among stratification variables. Decision trees, regressions, and correlation approaches focus more on relationships between the stratification variables and prior outcome data, which may be available for just a sample of units. Using both principal components/cluster analysis and decision trees, we stratified primary sampling units for the 2009 Residential Energy Consumption Survey and compared the resulting strata.

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