Explain the following terms with examples.
a) Continuous Variable
b) Categorical Variable
c) Independent Variable
d) Dependent Variable
e) Co-Variation
- Course: Introduction to Educational Statistics (8614)
- Level: B.Ed (1.5 Years)
Answer:
A variable is a quantity that
has a changing value; the value can vary from one example to the next. A
continuous variable is a variable that
has an infinite number of possible values. In other words, any value is
possible for the variable. A continuous
variable is the opposite of a discrete variable, which can only take on a
certain number of values. A continuous
variable doesn’t have to have every possible number (like -infinity to +infinity), it can also be continuous
between two numbers, like 1 and 2. For example, discrete variables could be 1,2
while the continuous variables could be 1,2 and everything in between: 1.00,
1.01, 1.001, 1.0001…
What is a Continuous Variable? Examples of Continuous Data
A few examples of continuous variables/data:
•
Time it takes a computer to complete a task. You might think you
can count it, but time is often rounded up to convenient intervals, like
seconds or milliseconds. Time is actually a continuum: it could take 1.3
seconds or it could take 1.333333333333333… seconds.
• A person’s
weight. Someone could weigh 180 pounds, they could weigh 180.10 pounds or
they could weigh 180.1110 pounds. The number of possibilities for weight is limitless.
_______________________________________________________________________________
b) Categorical Variable
Answer:
In statistics, a categorical
variable is a variable that can take on one of a limited, and usually fixed,
number of possible values, assigning each individual or other unit of
observation to a particular group or nominal category based on some
qualitative property.[1] In computer science and some branches of mathematics,
categorical variables are referred to as enumerations or enumerated types.
Commonly (though not in this article), each of the possible values of a
categorical variable is referred to as a level. The probability distribution associated
with a random categorical variable is called a categorical distribution.
Categorical data is the
statistical data type consisting of categorical variables or data that has
been converted into that form, for example as grouped data. More specifically, categorical
data may derive from observations made of qualitative data that are summarised as
counts or cross-tabulations, or from observations of quantitative data grouped
within given intervals. Often, purely categorical data are summarised in the
form of a contingency table.
However, particularly when
considering data analysis, it is common to use the term "categorical
data" to apply to data sets that, while containing some categorical
variables, may also contain non-categorical variables.
______________________________________________________________________________
c) Independent Variable
Answer:
INDEPENDENT VARIABLE DEFINITION
An independent variable is defined as a variable that is changed or controlled in a scientific experiment.
It represents the cause or reason for an outcome. Independent variables are the variables that
the experimenter changes to test their dependent variable. A change in the independent variable directly
causes a change in the dependent variable. The effect on the dependent variable
is measured and recorded.
Common Misspellings: independent
variable
INDEPENDENT VARIABLE EXAMPLES
•
A scientist is testing the effect of light and dark on the behavior of
moths by turning a light on and off. The independent variable is the amount of
light and the moth's reaction is the dependent variable.
•
In a study to determine the effect of temperature on plant pigmentation,
the independent variable (cause) is the temperature, while the amount of
pigment or color is the dependent variable (the effect).
GRAPHING THE INDEPENDENT VARIABLE
When graphing data for an
experiment, the independent variable is plotted on the x-axis, while the
dependent variable is recorded on the y-axis. An easy way to keep the two
variables straight is to use the acronym DRY MIX, which stands for:
• The dependent variable that Responds to change goes on the Y-axis
•
Manipulated or Independent variable goes on the X-axis
____________________________________________________________________________
d) Dependent Variable
Answer:
The two main variables in an
experiment are the independent and dependent variables. An independent variable
is a variable that is changed or controlled in a scientific experiment to
test the effects on the dependent variable.
A dependent variable is the variable being tested and measured in a
scientific experiment. The dependent
variable is 'dependent' on the independent variable. As the experimenter
changes the independent variable, the
effect on the dependent variable is observed and recorded.
For example, a scientist wants to
see if the brightness of light has any effect on a moth being attracted to the
light. The brightness of the light is controlled by the scientist. This would
be the independent variable. How the moth reacts to the different light levels
(distance to the light source) would be the dependent variable.
The independent and dependent
variables may be viewed in terms of cause and effect. If the independent
variable is changed, then an effect is seen in the dependent variable.
Remember, the values of both variables may change in an experiment and are
recorded. The difference is that the value of the independent variable is
controlled by the experimenter, while the value of the dependent variable only
changes in response to the independent variable.
When results are plotted in
graphs, the convention is to use the independent variable as the x-axis and the
dependent variable as the y-axis.
_______________________________________________________________________________
e) Co-Variation
Answer:
When explaining other people’s
behaviors, we look for similarities (covariation) across a range of situations
to help us narrow down specific attributions. There are three particular types
of information we look for to help us decide, each of which can be high or low:
•
Consensus: how similarly other people act, given the same stimulus, as
the person in question.
•
Distinctiveness: how similarly the person acts in different situations,
towards other stimuli.
•
Consistency: how often the same stimulus and response in the same
situation are perceived.
People tend to make internal
attributions when consensus and distinctiveness are low but consistency is
high. They will make external attributions when consensus and distinctiveness are
both high and consistency is still high. When consistency is low, they will make
situational attributions.
People are often less sensitive
to consensus information.
Related Topics
Chi-Square, and independent test.
Measures of Dispersion
What is measure of difference? Explain different types of test
Concept of Reliability, Types and methods of Reliability
Level of Measurement
Types of Variable in Stats
Measures of Central Tedency and Dispersion,
Role of Normal Distribution, and also note on Skewness and Kurtosis.
Methods of Effective Presentation
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