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Bureau of Labor Statistics > Office of Survey Methods Research > Home

ASA/NSF/BLS Senior Research Fellow Program

General Information

See the main Fellowship page for more information on eligibility and application requirements. Applicants may also be interested in topics in Economics or Behavioral Science.

Statistical Methodology and Data Science

Proposals in this area should be for research projects generally applicable to the collection, processing, or analysis of BLS data.

Statistical Graphics and Data Visualization

BLS publishes large volumes of economic data on employment, wages, and prices. Research is needed in the following areas:

Improved ways of graphically presenting cross-sectional and time series data that are normally published in tables
Graphical methods to detect outliers in high-dimensional and massive data sets
Approaches that visually convey the uncertainty in predictions and estimates
Graphical techniques to dynamically explore BLS data spatially
Tools that allow data users to do interactive analyses and create custom visualizations

Statistical Methods for Data Analysis

Inference based upon data from complex sample surveys is a broad area of research. Examples are:

Analysis of longitudinally linked data sets from continuing surveys
Graphical analysis techniques
Analysis of sample and administrative data sets
Complex survey applications of nonparametric regression
Computational statistical inference including applications to Bayesian adaptive or responsive survey design and Markov chain Monte Carlo simulation and bootstrapping to improve statistical inference
Area-based statistics and spatial data analysis
Combining multiple survey data sources and observational data to produce more efficient survey estimator
Statistical analysis of unstructured text in survey records
Linking survey data with administrative records, using probabilistic or other linkage methods that can be scaled to a production level
Evaluation of the quality of record linkage and development of error modeling for statistics estimates from linked data
Methods for augmenting survey data with data from alternative sources

Statistical Quality Control

BLS is continuing to strengthen our quality control program. Of special interest is the development of additional techniques for assessing the quality of data obtained from establishment surveys. Some areas for research include:

Integrated overall error models, with provisions for estimating magnitudes and interrelationships of various error sources
Methods of applying adaptive design techniques to BLS operations
Analysis of survey paradata to improve data collection and processing
Effective patterns of quality control feedback, training, and incentives applicable to establishment surveys
The redesign of management information systems to incorporate process performance and data quality monitors
Development and measurement of proxy variables for estimating components of nonsampling error
Model-based outlier detection for quality control of survey inputs

Item Imputation

Diversity in the types of BLS surveys provides a wide variety of missing-data problems. Some examples are:

Longitudinal imputation techniques for establishment surveys where units remain in the sample for 4 to 5 years
Multiple imputation techniques to improve key survey estimates such as employment or wages
Multivariate imputation techniques for income and assets in the Consumer Expenditure Survey, based on household demographics and spending information

Small Domain Estimation

Demand is continuously growing in survey programs for estimates for domains where the corresponding sample size is insufficient to produce standard design-based estimates of sufficient accuracy. Consequently, model-based or model-assisted estimators are needed.

Multiple BLS programs, including the National Compensation Survey and the Current Employment Statistics program are interested in producing small domain estimates Specific projects might include:

Scalable Bayesian hierarchical estimation for respondent-level and domain-level estimation and imputation
Spatio-temporal-industry forecasting for domains indexed by area and industry

Time Series Methods

Recent years have seen broad advances in the field of time series modeling, but BLS is interested in further development of these methods and their application. BLS has a broad array of monthly and quarterly time series for study and testing. Topics of particular interest include:

Application of model-based seasonal adjustment methods
Estimation of standard errors for seasonally adjusted estimates, particularly from complex surveys
Treatment of outliers in time series modeling and seasonal adjustment
Multivariate time series modeling
Survey estimation incorporating time series methods

Disclosure Limitation / Confidentiality Protection

BLS is also interested in a wide range of methods for disclosure limitation and confidentiality protection. Examples include:

Masking of microdata in public-use datasets
Construction of partially or fully synthetic datasets
Formally private disclosure limitation methods for use on establishment survey data
General evaluation of disclosure risk and/or data utility in disclosure-limitation settings
Application of differential privacy to establishment surveys

Information Dissemination

BLS provides public access to micro-, macro-, and metadata via the Internet. Research is needed on:

Improving the ‘user friendliness’ of BLS data-dissemination web sites
Explaining simple and complex statistical concepts to the diverse audiences that visit BLS data-dissemination web sites
Improving the presentation of statistical information through the use of graphics
Providing tools that facilitate data analysis
Development of user friendly tools to interact with BLS data, creating custom tables and visualizations

Analysis of Text Data

BLS collects a great deal of text data. Examples of research with these data are:

Statistical analysis of unstructured text in survey records
Unsupervised cluster and topic modeling
Automated text classification

Entity Resolution and Alternative Data Sources

BLS is increasingly using alternative data sources to complement data collected in traditional formats. Research topics include:

Linking survey data with administrative records, using probabilistic or other linkage methods that can be scaled to a production level
Evaluation of the quality of record linkage and development of error modeling for statistics estimates from linked data
Methods for augmenting survey data with data from alternative sources

Last Modified Date: September 11, 2023