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United States Department of Transportation United States Department of Transportation

Source Guidelines and Standards for Conducting and Managing FMCSA Transportation Research Projects

Introduction

The Federal Motor Carrier Safety Administration has identified a number of resources that provide widely-accepted standard social science protocols for conducting sound research. The list below, while not exhaustive, summarizes these protocols. Any entity conducting research, analysis, or other relevant work for the Agency must adhere to widely-accepted research protocols. 

Methods for Designing Research Studies

Scientific Approaches to Transportation Research (NCHRP 20-45)[1]

Washington, S., et al. (2002)

This manual was developed as part of the National Cooperative Highway Research Program (administered by the Transportation Research Board). The research practices described in this manual are based largely on the procedures of the Transportation Research Board. This two-volume publication includes state-of-the art techniques for:
  • Problem statement development
  • Literature searching
  • Development of the research work plan
  • Execution of the experiment
  • Data collection, management, quality control, reporting of results, and
  • Evaluation of the effectiveness of the research.

Quasi Experimentation: Design and Analysis Issues for Field Settings[2]

Cook, T.D., & Campbell, D.T. (1979)

This resource addresses causal inference and the language of experimentation, study designs, the conduct of randomized experiments, validity, and statistical analysis of data.

Standards for High Quality Research and Analysis[3]

RAND Corporation (2015)

This document contains research standards developed over the past 60 years by the RAND Corporation—a nonprofit organization that specializes in research and public policy analysis. This publication provides broad guidance on the following:
  • Formulation of the problem and purpose of the study
  • Selection of study approach
  • Understanding of related studies
  • Quality of data
  • Management of assumptions
  • Substance and quality of findings
  • Structure and explanation of implications, recommendations, and caveats
  • Tone, structure, and readability of documentation and final reports.

Basic Sampling Techniques

Sampling Techniques[4]

Cochran, W.G. (1977)

This resource comprehensively discusses sampling theory as it applies to sampling surveys. The following topics are covered in detail:
  • Simple random sampling
  • Sampling proportions and percentages
  • Estimation of sample size
  • Stratified random sampling
  • Ratio and regression estimators
  • Systematic sampling
  • Cluster sampling
  • Double sampling
  • Sources of error in surveys.

Protection of Human Subjects

Policy and Guidance, Office of Human Research Protection [5]

U.S. Department of Health and Human Services (2014)

The Office of Human Research Protection provides a wide range of information on the proper procedures for conducting federally sponsored research involving the participation of human subjects. The following topics are addressed in depth:
  • Protocols for obtaining approvals
  • Guidance for institutional review boards (IRBs)
  • Informed consent requirements
  • Collection of biological materials and data
  • Investigator responsibilities
  • Working with vulnerable populations (e.g., children, prisoners, etc.).

Conducting Data Analysis

Effective Experiment Design and Data Analysis in Transportation Research (NCHRP Report 727)[6]

Lyles, R.W., et al. (2012)

This report is a companion piece to NHCRP 20-45, Scientific Approaches to Transportation Research (referenced above). In addition to describing the factors that must be considered in designing an experiment, this report provides 21 examples of typical transportation-related studies. The following techniques are discussed at length in this report:
  • Descriptive statistics
  • Fitting distributions/goodness of fit
  • Simple one- and two-sample comparison of means
  • Simple comparisons of multiple means using analysis of variance (ANOVA)
  • Factorial designs (also ANOVA)
  • Simple comparisons of means before and after some treatment
  • Complex before-and-after comparisons involving control groups
  • Trend analysis
  • Logit analysis
  • Survey design and analysis
  • Non-parametric methods.

Analyzing Naturalistic Driving Data

Feasibility of Using In-vehicle Data to Explore How to Modify Driver Behavior that Causes Nonrecurring Congestion (S2-L10-RR-1)[7]

Rakha, H., et al. (2011)

This report includes guidance on protocols and procedures for conducting video data reduction analysis, along with technical guidance on the features, technologies, and complementary data sets that researchers can consider when designing instrumented in-vehicle data collection studies.

Use of Independent Review Panels

Final Information Quality Bulletin for Peer Review[8]

Office of Management and Budget (2004)

This bulletin establishes independent peer review requirements for important scientific information intended for distribution by the Government. The purpose of the bulletin is to enhance the quality and credibility of the Government’s scientific information. Agencies are granted broad discretion to weigh the costs and benefits of using a particular peer review mechanism for a specific information product.

Guidance for Formatting Public-Use Data Sets

The Guide to Social Science Data Preparation and Archiving: Best Practice throughout the Data Life Cycle[9]

Inter-university Consortium on Political and Social Research (2012)

This guide is aimed at those engaged in the cycle of research, from applying for a research grant, through the data collection phase, and ultimately to preparation of the data for deposit in a public archive. The guide is a compilation of best practices gleaned from the experience of many archivists and investigators.

Standards and Guidelines for Statistical Surveys

Standards and Guidelines for Statistical Surveys[10]

Office of Management and Budget (2006)

This document provides 20 standards that apply to Federal censuses and surveys whose statistical purposes include the description, estimation, or analysis of the characteristics of groups, segments, activities, or geographic areas in any biological, demographic, economic, environmental, natural resource, physical, social, or other sphere of interest. The development, implementation, or maintenance of methods, technical or administrative procedures, or information resources that support such purposes are also covered by these standards. In addition, these standards apply to censuses and surveys that are used in research studies or program evaluations if the purpose of the survey meets any of the statistical purposes noted above. To the extent they are applicable, these standards also cover the compilation of statistics based on information collected from individuals or firms (such as tax returns or the financial and operating reports required by regulatory commissions), applications/registrations, or other administrative records.

 

[1] Washington, S.; Leonard, J.; Manning, D.; Roberts, C.; Williams, B.; Bacchus, A.; Devanhalli, A.; Ogle, J.; and Melcher, D. (2002). Scientific Approaches to Transportation Research (NCHRP 20-45); National Academy of Sciences, Washington, DC. Available at: http://onlinepubs.trb.org/onlinepubs/nchrp/cd-22/chapters.html.

[2] Cook, T. D., and Campbell, D. T. (1979). Quasi-experimentation: Design and analysis issues for field settings; Houghton Mifflin, Boston, MA.

[3] RAND Corporation (2015). Standards for High-Quality Research and Analysis; Santa Monica, CA. Available at: http://www.rand.org/pubs/corporate_pubs/CP413-2015-05.

[4] Cochran, W.G. (1977). Sampling Techniques (3rd ed.); John Wiley & Sons, New York, NY.

[5] U.S Department of Health and Human Services, Office of Human Research Protection (July 16, 2014). Policy and Guidance. Available at: http://www.hhs.gov/ohrp/policy/index.html.

[6] Lyles, R.W.; Siddiqui, M.A.; Buch, N.; Taylor, W.; Haider, S.W.; Gilliland, D.C.; and Pigozzi, B.W. (2012). Effective Experiment Design and Data Analysis in Transportation Research (NCHRP Report 727); National Academy of Sciences, Washington, DC. Available at: http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_rpt_727.pdf.

[7] Rakha, H.; Du, J.; Park, S.; Guo, F.; Doerzaph, Z.; Viita, D.; Golembiewski, G.; Katz, B.; Kehoe, N.; and Rigdon, H. (2011). Feasibility of Using In-vehicle Data to Explore How to Modify Driver Behavior that Causes Nonrecurring Congestion (Report S2-L10-RR-1); National Academies Press, Washington DC.

[8] Office of Management and Budget (December 16, 2004). Final Information Quality Bulletin for Peer Review; Washington, DC.

[9] Inter-university Consortium for Political and Social Research (ICPSR) (2012). Guide to Social Science Data Preparation and Archiving: Best Practice Throughout the Data Life Cycle (5th ed.); Ann Arbor, MI. Available at: http://www.icpsr.umich.edu/files/deposit/dataprep.pdf.

[10] Office of Management and Budget (September 2006). Standards and Guidelines for Statistical Surveys; Washington, DC. Available at: https://www.whitehouse.gov/sites/default/files/omb/inforeg/statpolicy/standards_stat_surveys.pdf.

Last updated: Thursday, March 10th, 2016