Research SAE
SAE Research Project Rubric
SAE Research Experimental
Project Rubric
You must ask a question that is well defined, measurable, and controllable.
Why is grass green? What is the cause of AIDS? Why does skin wrinkle with age?
You must develop a hypothesis, or a possible explanation to answer your question.
A hypothesis is a "tentative explanation" for what we observe (Campbell, 1993).
Research- As agriculture has become more scientific, there is a need to conduct research to discover new knowledge. There are two major kinds of Research SAE programs.
Experimental - The student plans and conducts a major agricultural experiment using the scientific process. The purpose of the experiment is to provide students “hands-on” experience in verifying, learning or demonstrating scientific principles in agriculture, discovering new knowledge, and using the scientific process. In an experimental SAE, there is a hypothesis, a control group, and variables, which are manipulated. Examples of experimental SAE activities
include: comparing the effect of various planting media on plant growth,
determining the impact of different levels of protein on fish growth, comparing
three rooting hormones on root development or analyzing the effectiveness of
different display methods on plant sales in a garden center.
Non-Experimental (analytical) – Students choose an agricultural problem that
is not amenable to experimentation and design a plan to investigate and analyze
the problem. The student will gather and evaluate data from a variety of sources
and then produce some type of finished product. The product could be a
marketing display or marketing plan for an agricultural commodity, a series of
newspaper articles, a land use plan for a farm, a detailed landscape design for a
community facility, an advertising campaign for an agribusiness, and so forth. An
analytical SAE is flexible enough so that it could be used in any type of
3. Design Your Experiment or Research Procedure
Components of an Experimental Protocol
1. Purpose: This is a formal statement which encompasses your hypothesis. It is a statement of what question you are trying to answer and what hypothesis you wish to test.
2. Materials: List all major items needed to carry out your experiment. This list need not be lengthy if the materials are already published, but it should include the essentials.
3. Methods: How will you set up your experiment? How many experimental groups will you have? How will you measure the effect you wish to study? How long will the experiment last? These and any other methods should be explicitly stated or referenced so that a reader has all the information they need to know to be able to repeat your experiment and verify your results.
Outline Procedure
You must find a procedure or method to measure the dependent variable.
Procedures are developed by:
a. reading articles published by other scientists;
b. talking to other scientists at universities/industries or at scientific meetings;
c. one's own novel and creative ideas.
In the process of outlining a procedure, the following must also be determined:
a. The levels of treatment or the appropriate values to use for the independent variable;
b. the numbers of replications or how many times the experiment will be repeated to ensure that the results are consistent;
c. The control* treatments.
* A control is a "treatment" in which the independent variable is either eliminated or set at a standard value. It has nothing to do with the Controlled Variable(s).
4. Controls: Identify the relevant control(s) treatment. Think about the variable(s) you and your group are manipulating. Your control needs to be held under natural, or unmanipulated conditions, not affected by the tested variable.
Define the Variables
There should be three categories of variables in every experiment: dependent, independent, and controlled.
Dependent -- is what will be measured; it's what the investigator thinks will be affected during the experiment.
For example, the investigator may want to study coffee bean growth. Possible dependent variables include: number of beans, weight of the plant, leaf surface area, time to maturation, height of stem.
Independent -- is what is varied during the experiment; it is what the investigator thinks will affect the dependent variable.
In our coffee bean example, possible independent variables include: amount of fertilizer, type of fertilizer, temperature, amount of H2O, day length, all of these may affect the number of beans, weight of the plant, leaf area, etc.
Key : Since you need to know which factor is affecting the dependent variable(s), there may be only one independent variable. The investigator must choose the one that he/she thinks is most important. But the scientist can measure as many dependent variables as he/she thinks are important indicators of coffee bean growth.
Controlled -- the variables held constant. Since the investigator wants to study the effect of one particular independent variable, the possibility that other factors are affecting the outcome must be eliminated.
For example, the above scientist must ascertain that no differences in the type of fertilizer used exists, or amount of H2O, variations of temperature, or day length exist.
5. Data Interpretation: What will be done with the data once it is collected? Data must be organized and summarized so that the scientist himself, and other researchers can determine if the hypothesis has been supported or negated. Results are usually shown in tables and graphs (figures). Statistic analyses are often made to compare experimented and controlled populations.
6. References: Any published works (journals, books, websites) that you cite in your protocol should be listed in the reference section so that anyone reading your protocol can look that work up if they desire.
Putting this all together, the scientist will be able to write a scientific paper once his/ her data is collected. For these laboratories it should be possible to write a good protocol in less than a page. A sample protocol format has been written for your reference. Remember do not write "fluff," i.e., extraneous information and/or overly descriptive text that is not relevant to the experiment. The reader of a protocol is interested in being informed concisely and accurately!!
4. Collect Data
Data Evaluations
A well-designed experiment will produce mounds of data. This data is like the forensic evidence at a murder trial, and your role is like both the prosecution and the defense. You must weigh the evidence. Evaluate the data to determine whether or not it supports your hypothesis. Sort through it all, and organize it into a meaningful presentation of tables and/or figures. Sometimes statistical tests or other calculations are required to evaluate the significance of the results.
5. Report Your Findings
Conclusions
If the data supports your hypothesis, you are on to something meaningful. If not, consider why, based on your knowledge of the system. Then it is back to the drawing board-time to make another hypothesis. Perhaps you cannot trust your results due to some questionable procedures, and you should repeat the experiment. Maybe you find you do not have enough information; collect more data from the same experiment, or design a new one to test the same hypothesis. Or maybe your results bring up many exciting new questions that require you to do scores of new experiments to answer them!!!
You should get the idea that whatever your conclusion is, it is never final. You should always end up with new questions, with some components yet unresolved. The more we know, the more we know how much more we do not know.