Independent Variable : The number of hours of sleep a person receives Dependent Variable : If they will do well on an exam or not Experimental Group : The group that was woken up at various times throughout the night Control Group : The group allowed to sleep throughout the night Participants took part in a study to determine the number of ...
Sep 07, 2014 · What are the two main types of variables in psychological research.docx tut 1 ... as a dependent variable it might be changed . Subject variable : " while doing research ; occupation or gender difference is an example of subject variable . " Individual relatedness . b) ... Course Hero is not sponsored or endorsed by any college or university. ...
Dependent Variable: The dependent variable of the experiment would be done before the experiment starts due. The participants will walk the straight line with no beers in their system and see how they do. This will be used to observe the effects. Experimental Group: All the participants that drink beers and walk the line will be the experimental group. Each will have a different …
Local Variables A variable defined within a block or method or constructor is called a local variable. These variables are created when the block is entered, or the function is called and destroyed after exiting from the block or when the call returns from the function. The scope of these variables exists only within the block in which the ...
Data unit | A person |
---|---|
Numeric variable | “How much do you earn?” |
Quantitative data | $60,000 p.a. |
Categorical variable | “What is your occupation?” |
Qualitative data | Photographer |
Some examples of variables that can be measured on a ratio scale include: Height: Can be measured in centimeters, inches, feet, etc. and cannot have a value below zero. Weight: Can be measured in kilograms, pounds, etc. and cannot have a value below zero.
Interval. The next type of measurement scale that we can use to label variables is an interval scale . Interval scale: A scale used to label variables that have a natural order and a quantifiable difference between values, but no “true zero” value. These variables have a natural order.
The next type of measurement scale that we can use to label variables is an ordinal scale. Ordinal scale: A scale used to label variables that have a natural order, but no quantifiable difference between values. Some examples of variables that can be measured on an ordinal scale include:
For variables on an interval scale, though, we know that the difference between a credit score of 850 and 800 is the exact same as the difference between 800 and 750. These variables have no “true zero” value. For example, it’s impossible to have a credit score of zero. It’s also impossible to have an SAT score of zero.
Variables can be broadly classified into one of two types: Quantitative. Categorical. Below we define these two main types of variables and provide further sub-classifications for each type. Categorical variables take category or label values, and place an individual into one of several groups. Categorical variables are often further classified as ...
In our example of medical records, there are several variables of each type: Age, Weight, and Height are quantitative variables. Race, Gender, and Smoking are categorical variables.
Common examples would be gender, eye color, or ethnicity. Ordinal, when there is a natural order among the categories, such as, ranking scales or letter grades. However, ordinal variables are still categorical and do not provide precise measurements.
However, ordinal variables are still categorical and do not provide precise measurements. Differences are not precisely meaningful, for example, if one student scores an A and another a B on an assignment, we cannot say precisely the difference in their scores, only that an A is larger than a B.
Quantitative variables are often further classified as either: Discrete, when the variable takes on a countable number of values. Most often these variables indeed represent some kind of count such as the number of prescriptions an individual takes daily. Continuous, when the variable can take on any value in some range of values.
They have no arithmetic meaning (i.e., it does not make sense to add, subtract, multiply, divide, or compare the magnitude of such values). Usually, if such a coding is used, all categorical variables will be coded and we will tend to do this type of coding for datasets in this course.
Quantitative variables take numerical values, and represent some kind of measurement. Discrete, when the variable takes on a countable number of values. Most often these variables indeed represent some kind of count such as the number of prescriptions an individual takes daily.