*      Variable: Any characteristic that can take on more than one value

           Examples: speed, level of hostility, accuracy of feedback, reaction time

    *      Research is the study of the relationship between variables Therefore, there must be at least two variables in a research study (or there is no relationship to  study) Measuring Variables in Researchh

    *      Measurement: A process by which we assign numbers to indicate the amount of some variable present

    *      Sometimes the number assignment is easy to understand (e.g., speed measured in number of seconds)

    *      Sometimes it is more arbitrary (e.g., 1 for male and 2 for female Scales of Measurement

    *      Based on how closely the scale matches the real number system

    *      Scales of Measurement (as proposed by Stevens)

          O Nominal

          O Ordinal

          O Interval

          O Ratio

Nominal Scales

     *      A naming scale

          O Each number reflects an arbitrary category label rather than an amount of a variable

          O Examples: diagnostic categories, political affiliations, preference for consumer products

 produces nominal or categorical data

    *     Has mathematical property of identity  A scale that indicates rank ordering           o Reflects the order, but not the amount of a variable

          o Examples: order of finish in a race, class rankings

  *Produces ordered data

     *Has a mathematical properties of identity and magnitude Interval Scales

    * A scale that has equal intervals

          O The scale indicates amount, but there is no zero point on the scale

          O Examples: temperature on the Celsius scale, most psychological tests

     *Produces score data

    *    Has the mathematical properties of identity, magnitude, and equal intervals

atio Scales

A scale that fits the number system well

          O The scale has a true zero and equal intervals, just like the real number system

          O Examples: time, distance, number correct, weight, frequency of behavior

     *    Produces score data   has the mathematical properties of identity, magnitude, equal intervals, and a true zero

 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 as either "checked" or "unchecked", attributes that are mutually exclusive.