Data are obtained from establishments by means of a mail questionnaire or an electronic instrument via the Internet.
The sample for the current release is a new sample of establishments mailed starting with the first quarter 2015 survey. The sample frame for the new sample was based on the Census Bureau's 2013 Business Register with updated information from the 2012 Economic Census, and this sample frame contained approximately 190,000 manufacturing establishments and 10,000 publishing establishments. This new sample was selected on a 2012 North American Industry Classification System (NAICS) basis, which reflects any changes to industry definitions resulting from the 2012 NAICS revision. Sampling probabilities for the quarterly survey were assigned proportionate to a measure of size that approximated annual total value of shipments. Sample allocation was determined by the priority industry requirements specified by the Federal Reserve Board (FRB), which included the 94 industry groups that are used as publication levels and are comprised predominantly of 4-digit NAICS industries or combinations of NAICS industries. Each of these 94 industry groups was sampled independently to satisfy the total sample size constraint. The sampling procedure ensured that the allocated sample size for each industry group was exactly realized and yielded a sample of approximately 7,500 establishments. This sample will be supplemented by the annual birth sample process until the next new sample is selected, which will be when the results of the 2017 Economic Census are available.
The full production utilization rate for an industry is estimated based on those plants in the industry reporting both the actual value of production and the full production estimate. Simple weighted estimates of the two variables are formed by applying the plant’s sample weight to its respective values and adding these weighted values across the reporting plants. The utilization rate for a particular industry group is formed as the ratio of the actual production weighted sum to the full production weighted sum for that given industry. A similar procedure is used to estimate the national emergency production utilization rates, using the actual value of production and the national emergency production estimate.
The average plant hours per week in operation for an industry are estimated based on those plants in the industry reporting plant hours. Simple weighted estimates of the plant hours are formed by applying the plant’s sample weight to its respective values and adding these weighted values across the reporting plants. The average is formed as the ratio of the plant hours weighted sum to the sum of the weights for the reporting plants.
Comparisons between actual and full production by industry are made using various checkbox information that is collected in the survey. This information is collected to determine the primary reasons for changes in full production capability between current quarter and previous quarter, as well as the primary reasons for actual production being less than full production capability for the current quarter. Beginning with 1st-quarter 2013, these data are summarized in the form of weighted proportions for each of the checkbox data items at the 3-digit NAICS level, and historical estimates as far back as 1st-quarter 2008 are available upon request. Each proportion is formed from the QPC sample as the ratio of the weighted number of plants checking the particular checkbox data item to the weighted number of plants checking at least one of the checkbox data items for that particular question. Because plants can check more than one of the checkboxes per question, the sum of these respective weighted proportions for a given 3-digit NAICS industry does not necessarily sum to 100 percent. In addition, for any 3-digit NAICS industry with no reported checkbox data for a particular question, both the estimate and the standard error are shown as zero.
The survey estimates presented in this report are subject to both sampling and nonsampling error. Sampling error is the difference between estimates obtained from the sample and results theoretically obtainable from a comparable complete enumeration of the sampling frame. This error results because only a subset of the sampling frame is measured in a sample survey. Standard errors of the estimates shown in this publication, which are estimated measures of the sampling variability, are displayed along with the corresponding estimates. These standard errors may be used to define confidence intervals about the corresponding estimates with a desired level of confidence. Only one of many possible samples was selected. If confidence intervals were constructed for each of these possible samples, then it would be expected that the percentage of confidence intervals containing the complete coverage result would equal the percent of the level of confidence. For example, the interval defined by a margin of error of one standard error, in which the interval is constructed one standard error below the estimate to one standard error above the estimate, approximately yields a 68-percent confidence interval; the interval defined by a margin of error of two standard errors approximately yields a 95-percent confidence interval; and the interval defined by a margin of error of two and a half standard errors approximately yields a 99-percent confidence interval.
Nonsampling error encompasses all other factors that contribute to the total error of a sample survey estimate. Potential nonsampling errors include various response and operational errors, such as errors during data collection, reporting errors, transcription errors, and bias due to nonresponse. These are all types of errors that could also occur if a complete enumeration had been done under the same conditions as the sample survey. Most of the important operational errors were detected and corrected during the course of reviewing data for reasonableness and consistency. Though nonsampling error is not measured directly, quality control procedures are employed throughout the survey process to minimize this type of error.
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