HORTICULTURAL RESEARCH II

COURSE CONTENT

There are 7 lessons in this module as follows:

1. Identifying research issues and determining research priorities

2. Acquisition of technical information

3. Specialised research techniques

4. Research planning and designing

5. Statistics

6. Conducting research

7. Writing reports.

WHAT to RESEARCH?

Research can be valuable, contributing to our understanding of what factors are influencing observed outcomes, which need changing, and what specific changes may be needed. On the other hand, irrelevant or needless research, no matter how well done or how detailed, can waste time, energy and money that could have been much better applied elsewhere.

The second step is to arrive at a specific topic for research, one that clearly articulates the aim of the research, and defines the focus for the research. It defines clearly the goals: what are we doing the research for?

The third step is to consider whether the proposed research is realistic. This is a necessary step on the analysis as it will help determining the strategies, how we will approach and study the problem. Can it be done in a realistic time frame? Has it already been thoroughly researched by someone else? Are there still important questions to be asked? Is there enough information? Steps two and three may need to be repeated several times before the final research topic is identified.

Aims

• Determine areas where there is a valid need to research processes relevant to horticultural research in today's social, economic, political and environmental context.
• Acquire and demonstrate skills in locating and reviewing scientific and technical information.
• Develop and explain alternative research and observational techniques for a particular Horticultural research study.
• To design a quality and focused research project addressing a social, technological, environmental and/or economic issues that impact on Horticulture today. The research component must allocate resources needed (time, financial and human resources).
• Demonstrate and explain basic statistical knowledge used for research with emphasis on your ability to present and monitor given data.
• Conduct a quality and focused research project addressing a social, technological, environmental and/or economic issue that impact on Horticulture today.
• Demonstrate skills in report writing

ominal duration: 100 hrs.

Lesson Structure

There are 7 lessons in this course:

1. Identifying Research Issues and Determining Research Priorities
   • Introduction: first, second, third steps
   • Finding research ideas
   • Brainstorming
   • Steps to brainstorming
   • Mind maps
   • How to mind map
   • Concept mapping
   • Determining research priorities
   • Beginning your research
   • Formulating a research topic
   • Is the reseach feasible
   • Formulating a hgypothesis
   • Terminology
2. Acquisition of Technical Information
   • Literature review
   • Research methods
   • Basic methods of collecting information: experimental, correlation, questionnaires, surveys, tests, document review
   • Naturalistic observation
   • Focus groups
   • Case studies
3. Specialised Research Techniques
   • Selecting a research method
   • Fishbone diagrams
   • Applications for cause and effect diagrams
   • Lateral thinking
   • Lateral thinking techniques
   • Pareto analysis
   • Observations
   • Root cause analysis
4. Research Planning and Designing
   • Project planning
   • Defining the problem, possible solutions and objectives
   • Problem tree analysis tool
   • SWOT analysis
   • Prioritise objectives and define activities
   • Allocate resources
   • Results and assessment
5. Statistics
   • Introduction
   • Data presentation
   • Measures of central tendancy
   • Distributions
6. Conducting Research
   • Collecting and logging data
   • Developing a data base structure
   • Data transformations
   • Analyzing data
   • Managing data
   • Analytical procedure
7. Writing Reports
   • Reporting results
   • Report structure
   • Contents of a research report (example)
   • Pitfalls to avoid

If you had unlimited resources and unchecked control circumstances, you would always be able to do the best quality research. Unfortunately, this seldom occurs and researchers are quite often forced to look for the best trade offs they can find in order to get the rigour they want.

There are also several practical considerations that need to be considered when deciding of the feasibility of a research project:

• First you need to take into consideration how long the research will take to accomplish. 
• You then need to consider whether there are any ethical constraints.
• Thirdly, can you achieve the needed cooperation to take the project to a successful conclusion?; and finally, 
• How significant are the costs of conducting the research. 

Failure to consider any of these factors could lead to the cessation of the project at any point before having good results.

You will find the following terminology in research that you need to use in order to express adequately in written and oral communications.

Formulating a Hypothesis
A hypothesis is a conjecture or an unproved model.  It often includes a prediction about what will happen and a possible explanation for why it will happen. Hypotheses can be tested by doing experiments or making observations.

Under the scientific method, the definition of hypothesis is: a tentative explanation that accounts for a set of facts and can be tested by further investigation.

Hypothesis testing is the keystone of most statistical applications. Every acceptance sampling test, designed experiment, and control chart is a statistical hypothesis test.

Statistical tests separate significant effects from mere luck or random chance.

All hypothesis tests have unavoidable, but quantifiable, risks of making the wrong conclusion.  Statistical tests always involve Type I (producer's or alpha) and Type II (consumer's or beta) risks. The Type I risk is the chance of deciding that a significant effect is present when it isn't.  The Type II risk is the chance of not detecting a significant effect when one exists.

Null and Alternate Hypothesis
Every statistical test tests the null hypothesis H0 against the alternate hypothesis H1. Null means "nothing," and the null hypothesis is that nothing is present.

The process change or treatment makes no difference, or the process is operating properly. The null hypothesis is like presumption of innocence. 

"Accepting the null hypothesis" is like acquitting a defendant. It does NOT prove that the null hypothesis is true, or that the defendant is innocent. It means there is a reasonable doubt about the defendant's guilt. In statistical testing, the significance level, Type I risk, or alpha risk is the "reasonable doubt."  It is the chance of wrongly rejecting the null hypothesis when it is true.  In acceptance sampling, it is the producer's risk, or risk of wrongly rejecting something that meets requirements.

The alternate hypothesis is that the process change or treatment has an effect, or something is wrong with the process. The Type II risk is the chance of accepting the null hypothesis when it is false. The "consumer's risk" is the Type II risk for an acceptance sampling plan. It is the chance of passing something that does not meet the requirements. If the Type I risk is the chance of crying wolf, the Type II risk is the chance of not seeing a real wolf.