Statistical and Economic Analysis

ORA staff have broad experience in econometric and statistical applications, and extensive knowledge of the practical needs of research.  ORA provides consulting and collaboration to faculty, research staff, graduate students, and industry partners in the areas of study design, statistical data analysis, and scientific communication of data. Our expertise includes:

• Statistical analysis plans and research design
• Sample size calculation
• Statistical and econometric modeling and analysis
• Post-hoc testing, adjusting for multiple comparisons
• Enterprise budget and partial budgeting analysis
• Survey design, administration, and analysis
• Data visualization, analysis and management
• Statistical advice

Current/Past Projects:

• Economics of Industrial Hemp Production in New Jersey (Sullivan, et al.) ongoing – NJAES/RCE
  • Managing all aspects of economic research. 
  • Developed economic cost model of industrial hemp production for CBD.
  • Economics of Industrial Hemp Production in New Jersey: Enterprise Budget for Hemp Grown for CBD Extract with Plasticulture Link to Paper

• Effects of Twenty-Seven Months of Rotational vs. Continuous Grazing on Horse and Pasture Condition (Williams C.A., et al.) – USDA/NIFA. 
  • Conducted analysis of experimental field research, employing mixed model ANCOVA analysis of repeated measures data to determine whether alternative grazing practices generates animal, environmental, and economic benefits. 
  • Paper submitted to Journal of Animal Science, 2020.

• Evaluating a New Oyster Cage Culture System to Solve Unique Aquaculture Issues Hampering Development of Oyster Aquaculture in Delaware Bay (Bushek et al.) – NOAA/ Saltonstall-Kennedy
  • Managed all aspects of economic research including data collection, economic analysis and report writing. 
  • Developed economic models/decision support tool used by oyster growers considering innovative production methods.

• Potential for Growing Malting Barley in New Jersey (Bamka and Komar) – NJAES/RCE
  • Analyzed field trial experimental data by employing mixed effects model (random, fixed and interaction effects). 
  • Determined statistically significant effects of nitrogen on chlorophyll levels in two barley varieties.

• Budgeting for a Corn Maze (Schilling et al.) – NJAES/RCE
  • Oversaw data collection efforts and conducted economic modeling.
  • Developed enterprise budget model/decision support tool for a corn maze agritourism enterprise. 
  • Link to Fact Sheet

• Rutgers Cranberry Grower Economics & Profitability (Segal et al.) – NJAES
  • Constructed partial-budgeting model of field renovation decision in cranberry production.
  • Developed decision making tool to help growers choose between cranberry varieties. 
  • https://cranberry.rutgers.edu/

• Enhancing Access to Preserved Farmland for Small and Medium-Sized Farmers (Schilling et al.) – USDA/AFRI. 
  • Developed ordinal logistic regression models to explain factors affecting landowners’ satisfaction with preservation; published in Society & Natural Resources, 2019.  Link to Paper
  • Created probit models to explore how preserved landowners utilize preservation funds; published in Applied Economic Perspectives & Policy, 2016.  Link to Paper
  • Developed multivariate probit models to determine landowners’ level of engagement in agriculture and conservation; published in Land Use Policy, 2015.  Link to Paper 
  • Constructed a hedonic price model of deed restricted farmland values in New Jersey using a unique database developed by ORA staff; published in the Journal of Agricultural and Resource Economics, 2013.  Link to Paper

• On-farm Performance and Nutrient Requirements of New Strawberry Varieties for the Eastern United States (Hlubik, et al.) – Walmart Foundation.
  • Conducted analysis of experimental research data. 
  • Employed mixed model ANCOVA analysis of repeated measures data to distinguish numerous attributes of several strawberry varieties. 
  • Awarded the Rutgers Team Excellence Award in 2015.

• Consumer Perceptions of Food Borne Contamination (Hallman, et al.).
  • Performed binary choice modeling to support research on effectiveness public alerts of food contamination events.

• Agritourism Development in the Northeast (Schilling, et al.) – SARE. 
  • Developed a logistic regression model to analyze/characterize agritourism operations in NJ.