Czech University of Life Sciences Prague · March 2026
Data Cleaning and Statistical Analysis Strategy
Table of Contents
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1 Data Inventory and Quality Assessment
- 1.1Dataset Overview
- 1.2Temporal Mismatch Analysis
- 1.3Data Availability Matrix
- 1.4Critical Data Gaps
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2 Data Cleaning Strategy
- 2.1Mycorrhizal Data Cleaning
- 2.2Root Biomass Data Cleaning
- 2.3 High-Frequency Sensor Data Cleaning View
- 2.4 Creating Derived Variables for Integration View
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3
Visualization Strategy (Exploratory Phase)
View
- 3.1Within-Dataset Visualizations
- 3.2Cross-Dataset Visualizations
- 3.3Recommended R Packages for Visualization
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4 Statistical Analysis Strategy
- 4.1Phase 1: Exploratory Multivariate Analysis
- 4.2Phase 2: Generalised Linear Models (GLM) and LMM
- 4.3Phase 3: Structural Equation Modeling (SEM)
- 4.4Phase 4: Fungal Community Analysis
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5 Practical Recommendations and Workflow
- 5.1Recommended Timeline
- 5.2Key Analytical Decisions
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6 Fact-Checking and Verification Recommendations
- 6.1Key References by Method
- 6.2Comparable Published Studies
- 6.3Consultation Recommendations
- 6.4Assumption Verification Checklist
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7 Summary of R Packages Required