Polling Methodology & Procedures

1. Overview

USPolling.com operates on the principle of absolute transparency. We believe that public opinion data is only as reliable as the methodology used to collect and analyze it. This document outlines the standard operating procedures for our national and state-level tracking polls.

Our data collection relies primarily on high-quality probability-based panels and rigorous RDD (Random Digit Dialing) methods, supplemented by carefully calibrated text-to-web outreach. We do not utilize opt-in online panels for our headline metrics.

2. Sampling Frame

The sampling frame is constructed to represent the non-institutionalized adult population of the United States. To ensure adequate coverage, we employ a stratified samplingA method of sampling that involves the division of a population into smaller sub-groups known as strata. In stratified random sampling, the strata are formed based on members' shared attributes or characteristics. design, dividing the population into mutually exclusive subgroups based on geographic region, urbanicity, and historical voter turnout models.

Phone numbers (both landline and cellular) are drawn proportionally from active blocks. For text-to-web panels, lists are matched against national voter files to verify registration status where the target population is "Registered Voters" (RV) or "Likely Voters" (LV).

3. Weighting Procedures

Raw survey data is rarely perfectly representative of the target population. To correct for differential non-response rates among various demographic groups, we apply iterative proportional fitting, commonly known as rakingA statistical method for weighting data. It adjusts the sample weights so that the marginal totals of the adjusted weights match the known population totals for specified demographic variables..

The data is weighted to match the most recent parameters from the U.S. Census Bureau's American Community Survey (ACS) and the Current Population Survey (CPS) Voting and Registration Supplement.

4. Margin of Error

The theoretical margin of sampling error for a full sample of 1,200 respondents is ±2.8 percentage points at the 95% confidence level. However, because our data is weighted to population parameters, we report an adjusted margin of error that incorporates the design effectA measure of the variance of an estimate compared to the variance that would have been obtained from a simple random sample of the same size. Weighting increases the design effect, effectively reducing the effective sample size. (DEFF).

5. Scripts

For replication and auditing purposes, we provide the core weighting logic utilized in our production environment. The following is a simplified excerpt of the R script utilizing the anesrake package to balance demographic targets.

# Define targets based on latest ACS data
targets <- list(
  age = c("18-34" = 0.291, "35-64" = 0.494, "65+" = 0.215),
  educ = c("HS or less" = 0.389, "Some College" = 0.274, "College Grad+" = 0.337),
  race = c("White" = 0.616, "Black" = 0.124, "Hispanic" = 0.187, "Other" = 0.073)
)

# Apply iterative proportional fitting (raking)
raked_data <- anesrake(
  inputter = targets,
  dataframe = survey_raw,
  caseid = survey_raw$id,
  choosemethod = "total",
  type = "pctlim",
  pctlim = 0.05
)

# Append weights to main dataframe
survey_raw$weight <- raked_data$weightvec

# Calculate Design Effect (DEFF)
deff <- sum(survey_raw$weight)^2 / (sum(survey_raw$weight^2) * length(survey_raw$weight))
print(paste("Design Effect:", deff))