Chapter
In-Depth Understanding of Research Methods for Solutions to Problems in Agricultural Sciences
2.1. Classifications of Research
2.2. Research Based on Purpose
2.2.1. Basic vs. Applied Research
2.2.4. Research and Development
2.3. Research Based on Method
2.3.1. Descriptive Research
2.3.2. Analytical Research
2.3.3. Historical Research
2.4. Research Based on Statistical Analysis
2.4.1. Qualitative vs. Quantitative
2.4.2. Qualitative Research Methods
2.4.2.1. Methods of Qualitative Research
2.4.3. Quantitative Research Methods
2.4.3.1. Methods of Quantitative Research
(b) Instrumentation and Materials
2.5.1. Methods for Data Collection
2.6. Components of a Survey Method
2.6.2. Population and Sample
2.6.2.1. Cluster Sampling
2.6.2.2. Stratified Sampling
2.6.5. Data Analysis and Interpretation
Research Problems and Hypotheses
Observations of Data from Sampling to Establish Knowledge of Population
3.2. Specification of Research Problem
3.3.1. Basic Concept Concerning Hypothesis Testing
3.3.1.1. Null Hypothesis and Alternative Hypothesis
3.3.1.2. Level of Significance
3.3.1.5. One-Tailed versus Two-Tailed Tests
3.4. General Procedure for Hypothesis Testing
3.5. Type of Hypothesis Tests
3.5.1.1. Testing of Significance for Single Mean
3.5.1.2. Testing of Significance for Difference of Means
3.5.1.3. Testing of Significance for Proportion
3.5.2.1. t-Test for the Mean of a Random Sample
3.5.2.2 .t-Test for Difference of Means of Two Independent Samples
3.5.2.3. t-Test for Difference of Means for Two Dependent Samples
3.5.4. Chi-Square Test (χ2 Test)
Process to Synthesize Data to Obtain the
Answer for a Specific Key Evaluation of the Research Question
4.1. Measures of Central Tendency (Mode, Median and Mean)
4.1.3.1. Weighted Average
4.1.4. Mode vs. Median vs. Mean
4.2. Measures of Dispersion
4.2.2. Semi-Interquartile Range
4.2.3. Variance and Standard Deviation
4.2.3.2. Variance of a Population
4.2.3.3. Variance of a Sample
4.2.3.4. Standard Deviation
4.2.4. Standard Error of the Mean
4.2.5 Coefficient of Variation
4.5. Probability Distribution (Discrete and Continuous Distributions)
4.5.1. Discrete Distribution
4.5.1.1. Binomial Distribution
4.5.1.2. Poisson Distribution
4.5.2. Continuous Distribution
4.5.2.1. Uniform Distribution
4.5.2.2. Normal Distribution
4.5.2.3. Weibull Distribution
4.6. Statistical Inference (Sampling Distributions and Variability)
4.6.1. Sampling Distribution
4.6.2. Sampling-Variability
Bivariate and Multivariate Data Analysis
Statistical Inference for Conclusions on a Population using Data from a Subset, or Sample
5.1. Correlation Analysis
5.1.2. Correlation Coefficient
5.1.3. Pearson’s Correlation Coefficient
5.1.4. Interpretation of r
5.1.5. Significance Test for Correlation
5.2.1. Simple Linear Regression Model
5.2.2. Least-Squares Estimation of Parameters
5.2.2.1. Least Squares Criterion
5.3. Explained and Unexplained Variation
5.4. Coefficient of Determination, R2
5.5. Error in Regression Equation
5.5.1. Standard Error of Estimate
5.5.2. Standard Error of Regression Slope
5.6. Inference about the Slope
5.6.1. Hypothesis, t-Test
5.6.2. Confidence Interval for the Slope
5.8. Principal Component Analyses (PCA)
5.9. Multiple Linear Regression
Categorical Data Analysis to Measure the Attributes or Quality of a System
6.2. Test for Goodness of Fit
6.2.1. State the Hypothesis
6.2.2. Formulate an Analysis Plan
6.2.3. Analyze Sample Data
6.3. Test for Independence
Systematic Approach for Problem Solving during Data Collection Stage for Conclusion
7.1.5. Factorial Experiments
7.2. Design of Experiments
7.3.1. Completely Randomized Design (CRD)
7.3.2. Randomized Complete Block Design (RCBD)
ANOVA for No. of Grains per Panicle
Comparison of Means of Genotypes
Construction of CRD ANOVA from above data for grains per panicle
7.3.2.1. Split-Plot in RCBD
Sample Field Layout for Split-Plot
ANOVA for Split-Plot in RCBD
7.3.2.2. Two Factorial in RCBD
Sample Field layout for 2-Factorial in RCBD
ANOVA for Two Factorial in RCBD
7.3.3. Latin Square Designs
Survey Questions and Pilot Testing are a Critical Part of the Planning of a Research Process
8.1. Some Concepts Related to Sampling
8.1.1. Population and Sample
8.1.2. Sampling with Replacement and without Replacement
8.2.1. Probability Sampling
8.2.1.1. Simple Random Sampling (SRS)
8.2.1.2. Systematic Sampling
8.2.1.3. Stratified Sampling
8.2.1.4. Cluster Sampling
Single-Stage Cluster Sampling
Two-Stage Cluster Sampling
Multi-stage Cluster Sampling
8.2.2. Non-Probability Sampling
8.2.2.1. Purposive Sampling
8.2.2.2. Snowball Sampling
8.2.2.4. Reliance on Available Subjects
8.3. The Logic of Probability Sampling
8.6.1. Defining Sample Size/Determination of Sample Size
8.6.2. Sample Size: Formula
Sample Size Formula – Proportion
8.6.3. Defining Sample Size Based on Practical Aspects
8.6.5. Other Methods of Sample Size Determination
8.6.5.1. Arbitrary “Percentage Rule of Thumb” Sample Size
8.6.5.2. Conventional Sample Size Specification
8.6.5.3. Statistical Analysis Requirements of Sample Size Specification
8.6.5.4. Cost Basis of Sample Size Specification
8.7. Confidence Interval (CI): Method of Determining
8.7.1. CI-Proportion Variability
8.7.2. CI, Variability and Sample Error
8.8. Central Limit Theorem (CLT)
8.9.1. Non-Sampling Error
8.9.1.2. Systematic Errors
8.10.1.1. Self-Administered Questionnaires
8.10.1.2. Interview Surveys (Face-to-Face)
8.10.1.3. Telephone Surveys
8.12. Research Questions for Survey Research
8.13. Strengths of Survey Research
8.14. Written Instruments
8.15. Choosing the Format of Your Questions
8.15.1. Fixed Alternative
8.15.6. To Obtain Best Results (Mistakes to Avoid)
8.15.7. Sequencing Questions
8.15.8. Final Touches on Survey Instrument
Publication Ethics to Sharing Data to
9.1. Background vs. Research Ethics
9.1.1. Background of Research Problems
9.1.2. Identification of Research Problem
9.1.3. Purpose and Scope toward a Solution to the Problem
9.1.4. Hypothesis and Predictions
9.1.5. Setting Objectives
9.2. Methodology vs. Research Ethics
9.2.2. Intellectual Property and Copyright
9.2.3. Confirmation of Reproducibility of Data
9.3. Results vs. Research Ethics
9.3.1. Data Protection or Confidentiality
9.3.2. Responsible Publications
9.4. Objectivity and Conflict of Interest
Data to Knowledge: Interdisciplinary Research Methodologies for Agricultural Sciences
( Research Methodology and Data Analysis )
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