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Variable
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A variable
is a characteristic of a unit being observed that may assume more than one of
a set of values to which a numerical measure or a category from a
classification can be assigned (e.g. income, age, weight, etc., and
“occupation”, “industry”, “disease”, etc.
A characteristic,
number, or quantity
that increases or decreases over time,
or takes different values in
different situations.Two basic types are (1) Independent variable: that can take different values and can cause corresponding changes in other variables, and (2) Dependent variable: that can take different values only in response to an independent variable. |
Types of Variables
A.
Control variable
An
extraneous variable that an investigator does not wish to examine in a study. Thus
the investigator controls this variable. Also called a covariate.
Criterion
variable The presumed effect in a nonexperimental study.
E.g
a control variable is the one element that must not be changed
throughout an experiment because it also affects the other independent
variables being tested, thus affecting the outcome of the experiment. For
example, in the experimental verification of Boyle's law (P = T / V),
where Pressure, Temperature, and Volume are all variables;
to test the resultant changes to any of these variables requires at least one
to be kept constant. This is in order to see comparable experimental results
in the remaining variables. Essentially, a controlled variable is what is kept
the same throughout the experiment.
Other
candidates for controlled variables might be, for example, if you are testing a
product's effects on two plants, the soil type and the pot shape may be two
controlled variables. Duration of sunlight and amount of water might be others.
B.
Dipendent and
Indipendent variables
a. Dependent variable The presumed effect in an
experimental study. The values of the dependent variable depend upon another
variable, the independent variable. Strictly speaking, “dependent variable”
should not be used when writing about nonexperimental designs.
b. Independent variable The presumed
cause in an experimental study. All other variables that may impact the
dependent variable are controlled. The values of the independent variable are under
experimenter control. Strictly speaking “independent variable” should not be
used when writing about nonexperimental designs.
c. Independent
Variables (IV) & Dependent Variables (DV)
In an experiment, the independent variable
is the variable that is varied or manipulated by the researcher, and the dependent
variable is the response that is measured.
An independent variable is the presumed
cause, whereas the dependent variable is the presumed effect.
The IV is the antecedent, whereas the DV is the
consequent.
In experiments, the IV is the variable that is
controlled and manipulated by the experimenter; whereas the DV is not
manipulated, instead the DV is observed or measured for variation as a presumed
result of the variation in the IV.
"In nonexperimental research, where there is
no experimental manipulation, the IV is the variable that 'logically' has some
effect on a DV. For example, in the research on cigarette-smoking and lung
cancer, cigarette-smoking, which has already been done by many subjects, is the
independent variable." (Kerlinger, 1986, p.32)
When reseaerchers are not able to actually
control and manipulate an IV, it is technically referred to as a status
variable (e.g., gender, ethnicity, etc.). Even though researchers do not
actually control or manipulate status variables, researchers can, and often do,
treat them as IVs (Heppner, Kivlighan & Wampold, 1999).
"The DV refers to the status of the 'effect'(or outcome) in which the researcher is interested; the independent variable refers to the status of the presumed 'cause,' changes in which lead to changes in the status of the dependent variable…any event or condition can be conceptualized as either an independent or a dependent variable. For example, it has been observed that rumor-mongering can sometimes cause a riot to erupt, but it has also been observed that riots can cause rumors to surface. Rumors are variables that can be conceived of as causes (IVs) and as effects (DVs)." (Rosenthal & Rosnow, 1991, p. 71)
d. Some
Examples of Independent and Dependent Variables
The following is a hypothesis for a study.
1. "There will be a statistically
significant difference in graduation rates of at-risk high-school seniors who
participate in an intensive study program as opposed to at-risk high-school
seniors who do not participate in the intensive study program."
(LaFountain & Bartos, 2002, p. 57)
( IV: Participation in intensive study program. DV:
Graduation rates.)
The following is a description of a study.
2. "A director of residential living on a
large university campus is concerned about the large turnover rate in resident
assistants. In recent years many resident assistants have left their positions
before completing even 1 year in their assignments. The director wants to
identify the factors that predict commitment as a resident assistant (defined
as continuing in the position a minimum of 2 years). The director decides to
assess knowledge of the position, attitude toward residential policies, and
ability to handle conflicts as predictors for commitment to the position."
(LaFountain & Bartos, 2002, p. 8)
IV: knowledge of position, attitude toward
policies, and ability to handle conflicts
DV: commitment to position (continuing in
position for 2 years or not continuing)
C.
Moderatoe and
Mediator variables
Moderating variable A variable that
influences, or moderates, the relation between two other variables and thus
produces an interaction effect.A moderator variable changes the strength of an
effect or relationship between two variables. Moderators indicate when or under
what conditions a particular effect can be expected. A moderator may increase
the strength of a relationship, decrease the strength of a relationship, or
change the direction of a relationship. In the classic case, a relationship
between two variables is significant (i.e, non-zero) under one level of the
moderator and zero under the other level of the moderator. For example, work
stress increases drinking problems for people with a highly avoidant (e.g.,
denial) coping style, but work stress is not related to drinking problems for
people who score low on avoidant coping (Cooper, Russell, & Frone, 1990).
As another example (see Fig. 1 below), negative social contacts (e.g.,
disagreeement with friend) are associated with increased drinking at home for
college students who say that they drink to cope (e.g., to forget about
problems), but negative social contacts are unrelated to drinking at home for
students who do not drink to cope ( Mohr et al., 2005). Statistically, a
moderator is revealed through a significant interaction.
Mediator
variables specify how or why a particular effect or relationship occurs.
Mediators describe the psychological process that occurs to create the
relationship, and as such are always dynamic properties of individuals (e.g.,
emotions, beliefs, behaviors). Baron and Kenny (1986) suggest that mediators
explain how external events take on internal psychological significance. For
example (see Fig. 2 below), Cooper et al. (1990) hypothesized that particular
work features such as work pressures and lack of control would increase work
distress which, in turn, would increase drinking. In this example, work
distress is a mediator that explains how work features may come to be
associated with drinking. (It should be noted that their mediation model was
not supported by the data.) Statistically, after some basic conditions are met,
mediation is indicated when the relationship between the predictor (e.g., work
pressure) and criterion (e.g., drinking) is non-significant after controlling
for the effect of the mediator.
D.
Intervening
variable
A
variable that explains a relation or provides a causal link between other
variables.
Also
called by some authors “mediating variable” or “intermediary variable.”
Example: The
statistical association between income and longevity needs
to be explained
because just having money does not make one live longer. Other
variables
intervene between money and long life. People with high incomes tend
to have better
medical care than those with low incomes. Medical care is an
intervening
variable. It mediates the relation between income and longevity.
In statistics, an intervening
variable is one that occurs between the independent and dependent variables. It
is caused by the independent variable and is itself a cause of the dependent
variable.
Examples:
A higher education (independent variable) typically
leads to a higher income (dependent variable). Occupation is an intervening
variable here between education and income because it is causally affected by
education and itself affects income. In other words, more schooling tends to
mean a better job, which in turn tends to bring a higher income.
References
Baron, R., & Kenny, D. (1986). The moderator-mediator variable
distinction in social psychological research: Conceptual, strategic, and
statistical considerations. Journal of Personality and Social Psychology, 51,
1173-1182.Cooper, M. L., Russell, M., & Frone, M. R. (1990). Work stress and alcohol effects: A test of stress-induced drinking. Journal of Health and Social Behavior, 31, 260-276.
Mohr, C. D., Armeli, S., Tennen, H., Temple, M., Todd, M., Clark, J., & Carney, M. A. (2005). Moving beyond the keg party: A daily process study of college student drinking motivations. Psychology of Addictive Behaviors, 19, 392-403.
Weiss, H. M. & Cropanzano, R. (1996). Affective events theory: A theoretical discussion of the structure, causes and consequences of affective experiences at work. In B.M. Staw and L.L. Cummings (Eds.), Research in Organizational Behavior (Vol. 19, pp. 1-74). Greenwich, CT: JAI Press.
"United Nations Glossary of Classification Terms"
prepared by the Expert Group on International Economic and Social
Classifications; unpublished on paper.
http://www.businessdictionary.com/definition/variable.html#ixzz2hwRTdupP http://en.wikipedia.org/wiki/Control_variable#Examples
http://sociology.about.com/od/I_Index/g/Intervening-Variable.htm
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