Name: _________________________ Date: _____________

Section: _____________________ Score: ____________

Activity Title: “Variables”

Learning Competencies:

 Be able to identify the independent and dependent variables of a study from its title or
abstract.
 Be able to define the term "extraneous variable."
 Be able to identify the features of independent and dependent variables

Learning Concept 6

A variable is a characteristic or feature that varies, or changes within a study. The opposite of
variable is constant: something that doesn't change. In math, the symbols "x" , "y" or "b"
represent variables in an equation, while "pi" is a constant. In an experimental example, if a
study is investigating the differences between males and females, gender would be a variable
(some subjects in the study would be men, and others would be women). If a study has only
female subjects, gender would not be a variable, since there would be only women. If a study
includes both males and females as subjects, but is not interested in differences between men and
women - and does not compare them, gender would not be a variable in that study.

If a study compares three different diets, but keeps all 3 diets the same in the amount of sodium,
then sodium isn't a variable in that study - it's a constant. Other features of the diets would be
variables of interest - maybe the calories or carbohydrates or fat content.

In this course, we will study independent variables, dependent variables, and confounding or
intervening variables. In this section, we will focus on how to identify and distinguish
Independent from Dependent variables, and the roles these variables play in a research study.

Independent Variables

In experimental research, an investigator manipulates one variable and measures the effect of
that manipulation on another variable. The variable that the researcher manipulates is called
the independent, or grouping variable. The independent variable is the variable that is different
between the groups compared: all the members of one group will have the same level of the
independent variable, a second group will have a different level of that same variable, and the
same for a 3rd or 4th group, if present.
For example, let's take a study in which the investigators want to determine how often an
exercise must be done to increase strength. Stop for a minute and think about how they might
organize a study so they could figure this out. There are usually several possible studies that
could be done to address a question.

These investigators decided to compare 3 groups, one group participate in a set of specific
exercises 4 times per week; a second group would do the same exercises, but only twice per
week, and a control group would participate in stretching exercises that would have no impact on
strength. The variable that differs between these 3 groups that are compared is an Independent
Variable. This particular independent variable has 3 LEVELS of the SINGLE independent
variable - in this example: type of exercise.

Some non-experimental studies also have independent variables, but they may not be determined
or manipulated by the investigators. For example, a study may compare test performance
between men and women; so gender would be the independent variable. However, since
investigators didn't determine or specify which individuals would be men and which would be
women (!), it is not considered to be an active independent variable. Because gender does define
the variable used for comparison, it is still an independent variable, even though it has lost some
of its power. We'll look at this in more detail in the next chapter.

Dependent Variables

The outcome variable measured in each subject, which may be influenced by manipulation of

Example: study title: Effects of a new tooth paste (YummyTooth) on incidence of caries in 1st
grade children. The intervention group was given YummyTooth toothpaste, while the control
group was given an identical toothpaste that did not contain the secret ingredient in
YummyTooth. Subjects were observed brushing their teeth 3x per day with the assigned
toothpaste (by teacher or parent). 6 months later, dental appointments were scheduled, and the
number of dental caries present in each child was reported.

In this study, the toothpaste was the independent variable; it was different between the
two groups: one level was the YummyTooth toothpaste itself, and the second level (a control
group) was the identical non-YummyTooth toothpaste (a placebo). The outcome measure
(dependent variable) - that "depended" upon the type of toothpaste, was the number of dental
caries.

Frequently a single research study may have many dependent variables. However, since most
analyses only consider one dependent variable at a time (called univariate analyses), each
dependent variable analysis is considered a separate study for the purposes of statistical analysis.
Independent Variables in Observational Studies and Some Quasi-Experimental Studies:
When Independent Variables are not Manipulated

Observational and some quasi-experimental studies lack active interventions - their independent
variables are not specifically imposed by the investigators. They may study variables that cannot
physically impose the intervention (e.g., gender, country of birth, family history of heart disease)
or cannot manipulate it ethically (smoking, exposure to risk factors). While these studies cannot
tell us whether one variable causes changes, they can tell us how strong a relationship exists
between variables.

Identifying the Independent variables in these studies is a bit trickier than in true experiments,
where the investigators control them. Observational studies may collect all of the data from a
single questionnaire or set of medical records, so all information comes from a single
assessment. Since they don't impose a change, they cannot tell us what would happen if we
changed something. They tell us about relationships among variables in populations. In many
cases, a single set of data can be analyzed in several ways, so it is important to determine exactly
how the particular study probed the data: what questions did they ask?

In these studies, independent variables are still the grouping variables, so key in on statements
that indicate comparisons. In a tooth-brushing study, the investigators might ask the parents how
frequently the children brushed their teeth (check 0, 1, 2, 3), and collect the caries data from
dental records from the schools. In this case, the investigators are not imposing a tooth-brushing
regime, but are simply inquiring about existing habits, and then comparing those groups to
determine the strength of the relationship. Here, as before, the independent variable is tooth-
brushing, but now it is the comparison of groups of children in each category (#times brushed
per day). The dependent (outcome measure) variable, is still the number of caries.

Another example from a study title: 

The independent variable is smoking status (undoubtedly not imposed, not active)- could be
reporting just smoking/non-smoking/quit categories. The dependent variable would be long-term
mortality.

Confounding or Extraneous Variables

In the best circumstances, the only consistent feature that differs between the intervention and
control groups is the intervention level itself. The groups that are compared should be similar in
every other way, and only differ in the independent variable level. In the YummyTooth
toothpaste example above, this would mean that the groups receiving the two types of toothpaste
should be similar. If children with a history of many more caries were systematically put into the
control group, this would introduce bias. When the two groups start out the same (have the same
incidence of prior caries), then introduce a single intervention difference, any difference in later
number of caries reflects only the influence of the intervention. If there are other differences
between the two groups of children, such as a bias that put children with more caries in the
control group, then we can no longer have that confidence. In this situation, even if the
YummyTooth group of children have significantly fewer caries, we won't be able to tell whether
it was the toothpaste, or the history of caries, or some combination, that caused the different
number of caries between the groups. These biasing variables are called confounding or
extraneous variables.

The confounding variables are differences between groups other than the independent variables.
That means that most members of a group are alike on a variable, but different from the other
group, e.g., if the control group was mostly smokers and the experimental group mostly non-
smokers. These variables interfere with assessment of the effects of the independent variable
because they, in addition to the independent variable, potentially affect the dependent variable.
Since they cannot be separated from the independent variable, they are said to be confounding
variables. These variables produce differences between groups that cannot be attributed to the
independent variable. In these situations,the independent variable is not the only difference that
exists between the groups. Therefore, there may be many other variables contributing to the
differences observed between the groups compared. Thus, we cannot conclude that the
independent variable is the cause of the difference or change seen. These other factors that may
influence the dependent variable are termed "extraneous", "intervening" or "confounding"
variables. Usually this type of confounding variable is avoided by randomly assigning subjects to
groups, so not all of one kind of subject goes into one group.

Activity: Group Discussion (30mins)

Identify the dependent and independent variables of each group’s study.