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Enhancing Agricultural Services with Ontario Machinery Ring: A Research-Based Approach

This document outlines the Ontario Machinery Ring (OMR), a non-profit, member-owned cooperative that facilitates matchmaking services between farmers, replacing traditional agricultural "custom work." It discusses the organization's research design objectives, focusing on effective questionnaire design for data collection from farmers across Ontario. Key considerations include relevance, accuracy, simplicity, and avoiding complexity in questions. The ultimate goal is to acquire funds from the Ontario Ministry of Agriculture and Food to expand OMR’s services province-wide, ensuring reliable and valuable input for stakeholders.

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Enhancing Agricultural Services with Ontario Machinery Ring: A Research-Based Approach

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  1. Ontario Machinery Ring – Research Design Dominic Beaulieu MaykNazaryan Chris Foran

  2. Case (A) Overview Non-profit, Member owned cooperative Provides matchmaking services between Farmers Replacing agricultural “Custom Work”

  3. Case (B) Oerview Expand to Province Wide Acquire Funds from Ontario Ministry of Agriculture and Food Research Design Plan Research Objectives

  4. Questionnaire Design • Relevance and Accuracy • No unnecessary information • Information must be reliable and valid • Simple Language • Avoid leading and loaded question • Open-Ended versus Fixed-Alternative • Questions may lead to problems • Time and Money • Overall Ease

  5. Types of Fixed-Alternative • Simple-Dichotomy • Ex: Yes or No • Determinant-Choice • Ex: Multiple Choice • Frequency-Determination • Ex: How many times… • Checklist • Ex: Multiple Answers

  6. Questionnaire Design • Question Sequence • Capture Interest • Easy to Answer • Attractiveness of the Questionnaire • Encourages Response • Pre-Testing • Cultural Factors • Translation

  7. Questionnaire Design • Avoid: • Complexity • Ambiguity (Be Specific) • Double-Barrelled Items (Several Issues) • Making Assumptions • Burdensome Questions

  8. Data Collection • Collecting Data in the Field • Training Fieldworkers • Raw Data Conversion • Edited and Coded for Analysis

  9. Develop Questionnaire for OMR Question Examples: • What is your level of satisfaction with Custom Work? (Pivot Question) • Very High • High • Neutral • Low • Very Low • What are the main reasons behind your answer? (Open-Ended Question)

  10. Develop Questionnaire for OMR • Have you heard of OMR? (Fixed Alternative) • Yes • No What NOT to Ask: • Do you like the concept of OMR and would you use it if it were available in your area? (Double-Barrelled Question)

  11. Thank you Questions?

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