Variables
Variables are anything that can be observed or measured during a study to test a hypothesis.
Categorical Variables: Variables that are qualitative groups. For example:
Categorical variables are with nominal or ordinal.
Nominal variables can not be ranked or ordered into a logical hierarchy. For example:
Ordinal variables can be ranked or ordered into a logical hierarchy. For example:
Categorical Variables: Variables that are qualitative groups. For example:
- Leaves on a tree might be categorized into groups by color: red leaves, green leaves, yellow leaves, brown leaves...
- Trees in a forest might be categories into groups by species: Douglas Firs, Western Red Cedars, Mountain Hemlocks...
Categorical variables are with nominal or ordinal.
Nominal variables can not be ranked or ordered into a logical hierarchy. For example:
- Color (red, orange, yellow)
- Chemical property (polar, non-polar, charged)
- Type (oak, maple, cedar, fir)
Ordinal variables can be ranked or ordered into a logical hierarchy. For example:
- pH (acidic, neutral, basic)
- Life stage (egg, embryo, baby, child, teenager, adult)
Numerical Variables: Variables that are quantitative (numbers). Numerical variables can be discrete or continuous.
- Discrete: can not be divided into smaller increments. For example: counting the number of individuals in a population.
- Continuous: can be divided into smaller increments if a more precise tool was used. For example: temperature might be measured to a whole number (37° C) with one thermometer, but with a better thermometer the temperature could be measured to the tenths place (37.4° C).
The responding variable is what the investigator measures (or counts or records). It is what the investigator thinks will vary during the experiment. For example, she may want to study peanut plant growth. One possible responding variable is the height of the peanut plants. There are different responding variables possible in an experiment. The investigator can choose the one she thinks is most important, or she can choose to measure more than one responding variable.
The manipulated variable is what the investigator deliberately varies during the experiment. It is chosen because the investigator thinks it might affect the responding variable. In many cases, the investigator does not change the manipulated variable directly. She collects data and uses the data to evaluate the hypothesis, rather than doing a direct experiment. The investigator can measure as many responding variables as she thinks are important indicators of peanut growth. By contrast she must choose only one manipulated variable to investigate in an experiment. For example, if the scientist wants to investigate the effect that the amount of fertilizer has on peanut growth, she will use different amounts of fertilizer on different plants; the manipulated variable is the amount of fertilizer.
Time is frequently used as the manipulated variable. The investigator hypothesizes that the responding variable will change over the course of time. For example, he may want to study the diversity of soil bacteria found during different months of the year. However, the units of time used may be anywhere from seconds to years, depending upon the system being studied.
A controlled experiment should have only ONE manipulated variable. However, there may be multiple "treatment levels" of the manipulated variable. Levels refer to the different variations or dosages of the manipulated variable (treatment) being tested, essentially representing the different amounts or intensities of the manipulation applied to the experimental groups; for example, if testing the effect of different fertilizer amounts on plant growth, each fertilizer amount would be considered a "level of treatment."
Time is frequently used as the manipulated variable. The investigator hypothesizes that the responding variable will change over the course of time. For example, he may want to study the diversity of soil bacteria found during different months of the year. However, the units of time used may be anywhere from seconds to years, depending upon the system being studied.
A controlled experiment should have only ONE manipulated variable. However, there may be multiple "treatment levels" of the manipulated variable. Levels refer to the different variations or dosages of the manipulated variable (treatment) being tested, essentially representing the different amounts or intensities of the manipulation applied to the experimental groups; for example, if testing the effect of different fertilizer amounts on plant growth, each fertilizer amount would be considered a "level of treatment."
Another type of variable is the controlled variable. Controlled variables are factors that are kept equal in all treatments, so that any changes in the responding variable can be attributed to the changes the investigator made in the manipulated variable. Since the investigator's purpose is to study the effect of one particular manipulated variable, he must try to eliminate the possibility that other variables are influencing the outcome. This is accomplished by keeping the other variables at constant levels, in other words, by standardizing these variables. For example, if the scientist has chosen the amount of fertilizer as the manipulated variable, he wants to be sure that there are no differences in the type of fertilizer used. He would use the same formulation and same brand of fertilizer throughout the experiment. Scientists must measure each of the variables they claim to be controlled.
Materials used and measurement techniques are NOT controlled variables (they are validity measures). While materials and techniques must be consistent, a true variable is something that could directly influence the responding variable, not just how it is measured.
Materials used and measurement techniques are NOT controlled variables (they are validity measures). While materials and techniques must be consistent, a true variable is something that could directly influence the responding variable, not just how it is measured.
