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# \$25.00Types of Variables

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A researcher wishes to operationalize her study variables in the following
manner. How might she go about doing so? Summarize your responses in tabular
form (see example below), and supplement your table with a supporting
narrative.

Age (continuous)

Gender (dichotomous)

Ethnicity (categorical)

Education (ordinal)

Income (continuous)

Marital status (categorical)

Weight (continuous)

Blood pressure (continuous)

Hypertension (dichotomous)

Body temperature (continuous)

Health insurance status (categorical)

Smoking status (dichotomous)

Cancer Stage (ordinal)

EXAMPLE:

 Variable: Name Type How operationalized How measured Age   Etc. continuous Age in years How old were you on your last birthday?

Narrative: When age is operationalized in this manner, one would merely need
to query the respondent to determine the age she turned on her last birthday.
Alternatively, the researcher could ask the respondent to provide his birth date
and year, and then calculate age at a later time.

Length: 2-3 pages typed, double-spaced apa with, cite your references .

# Variables

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You won't be able to do very much in research unless you know how to talk about variables. A variable is any entity that can take on different values. OK, so what does that mean? Anything that can vary can be considered a variable. For instance, age can be considered a variable because age can take different values for different people or for the same person at different times. Similarly, country can be considered a variable because a person's country can be assigned a value.

Variables aren't always 'quantitative' or numerical. The variable 'gender' consists of two text values: 'male' and 'female'. We can, if it is useful, assign quantitative values instead of (or in place of) the text values, but we don't have to assign numbers in order for something to be a variable. It's also important to realize that variables aren't only things that we measure in the traditional sense. For instance, in much social research and in program evaluation, we consider the treatment or program to be made up of one or more variables (i.e., the 'cause' can be considered a variable). An educational program can have varying amounts of 'time on task', 'classroom settings', 'student-teacher ratios', and so on. So even the program can be considered a variable (which can be made up of a number of sub-variables).

An attribute is a specific value on a variable. For instance, the variable sex or gender has two attributes: male and female. Or, the variable agreement might be defined as having five attributes:

• 1 = strongly disagree
• 2 = disagree
• 3 = neutral
• 4 = agree
• 5 = strongly agree

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. In addition to being exhaustive, the attributes of a variable should be mutually exclusive, no respondent should be able to have two attributes simultaneously. While this might seem obvious, it is often rather tricky in practice. For instance, you might be tempted to represent the variable "Employment Status" with the two attributes "employed" and "unemployed." But these attributes are not necessarily mutually exclusive -- a person who is looking for a second job while employed would be able to check both attributes! But don't we often use questions on surveys that ask the respondent to "check all that apply" and then list a series of categories? Yes, we do, but technically speaking, each of the categories in a question like that is its own variable and is treated dichotomously

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Variable Data Types and How to operationalize or measure them
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