An attribute is a specific value on a variable. For instance, the variable sex or gender has two attributes: Or, the variable agreement might be defined as having five attributes: Another important distinction having to do with the term 'variable' is the distinction between an independent and dependent variable.
This distinction is particularly relevant when you are investigating cause-effect relationships. It took me the longest time to learn this distinction. Of course, I'm someone who gets confused about the signs for 'arrivals' and 'departures' at airports -- do I go to arrivals because I'm arriving at the airport or does the person I'm picking up go to arrivals because they're arriving on the plane!
I originally thought that an independent variable was one that would be free to vary or respond to some program or treatment, and that a dependent variable must be one that depends on my efforts that is, it's the treatment. But this is entirely backwards! In fact the independent variable is what you or nature manipulates -- a treatment or program or cause. The dependent variable is what is affected by the independent variable -- your effects or outcomes.
For example, if you are studying the effects of a new educational program on student achievement, the program is the independent variable and your measures of achievement are the dependent ones. Finally, there are two traits of variables that should always be achieved. Each variable should be exhaustive , it should include all possible answerable responses.
For instance, if the variable is "religion" and the only options are "Protestant", "Jewish", and "Muslim", there are quite a few religions I can think of that haven't been included. The list does not exhaust all possibilities. On the other hand, if you exhaust all the possibilities with some variables -- religion being one of them -- you would simply have too many responses. The way to deal with this is to explicitly list the most common attributes and then use a general category like "Other" to account for all remaining ones.
The key to designing any experiment is to look at what research variables could affect the outcome. There are many types of variable but the most important, for the vast majority of research methods , are the independent and dependent variables. The independent variable is the core of the experiment and is isolated and manipulated by the researcher. The dependent variable is the measurable outcome of this manipulation, the results of the experimental design. For many physical experiments , isolating the independent variable and measuring the dependent is generally easy.
If you designed an experiment to determine how quickly a cup of coffee cools, the manipulated independent variable is time and the dependent measured variable is temperature. In other fields of science, the variables are often more difficult to determine and an experiment needs a robust design.
Operationalization is a useful tool to measure fuzzy concepts which do not have one obvious variable. In biology , social science and geography, for example, isolating a single independent variable is more difficult and any experimental design must consider this. For example, in a social research setting, you might wish to compare the effect of different foods upon hyperactivity in children.
The initial research and inductive reasoning leads you to postulate that certain foods and additives are a contributor to increased hyperactivity. You decide to create a hypothesis and design an experiment , to establish if there is solid evidence behind the claim. The type of food is an independent variable, as is the amount eaten, the period of time and the gender and age of the child. All of these factors must be accounted for during the experimental design stage.
Randomization and controls are generally used to ensure that only one independent variable is manipulated. To eradicate some of these research variables and isolate the process, it is essential to use various scientific measurements to nullify or negate them.
For example, if you wanted to isolate the different types of food as the manipulated variable, you should use children of the same age and gender. The test groups should eat the same amount of the food at the same times and the children should be randomly assigned to groups. This will minimize the physiological differences between children. A control group , acting as a buffer against unknown research variables, might involve some children eating a food type with no known links to hyperactivity.
In this experiment, the dependent variable is the level of hyperactivity, with the resulting statistical tests easily highlighting any correlation. Depending upon the results , you could try to measure a different variable, such as gender, in a follow up experiment. Ensuring that certain research variables are controlled increases the reliability and validity of the experiment, by ensuring that other causal effects are eliminated.
This safeguard makes it easier for other researchers to repeat the experiment and comprehensively test the results. What you are trying to do, in your scientific design, is to change most of the variables into constants, isolating the independent variable.
Any scientific research does contain an element of compromise and inbuilt error , but eliminating other variables will ensure that the results are robust and valid. Check out our quiz-page with tests about:.
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Research Variables: Dependent, Independent, Control, Extraneous & Moderator of the dependent and independent variables in research ; Dependent, Independent, Control, Extraneous & .
The dependent variable is what is affected by the independent variable-- your effects or outcomes. For example, if you are studying the effects of a new educational program on student achievement, the program is the independent variable and your measures of achievement are the dependent ones.
Unlike extraneous variables, moderator variables are measured and taken into consideration. Typical moderator variables in TESL and language acquisition research (when they are not the major focus of the study) include the sex, age, culture, or language proficiency of the subjects. The key to designing any experiment is to look at what research variables could affect the outcome. There are many types of variable but the most important, for the vast majority of research methods, are the independent and dependent variables.
What are Examples of Variables in Research? October 22, Regoniel, Patrick A. Comments In the course of writing your thesis, one of the first terms that you encounter is the word variable. Lecture notes Research Methodology devidas 7 August Variables And Types Of Variables-Research Methods-Handouts, Lecture notes for Research Methodology. (e.g. amount of education). The second type of concept and measures of the concept are variables. A variable is defined as anything that varies or changes in value. Variables take on.