# Independent and dependent variables in research paper

Be able to identify the independent and dependent variables of a study from its title or abstract.

- 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.
- Another example from a study title.
- While these studies cannot tell us whether one variable causes changes, they can tell us how strong a relationship exists between variables. Be able to identify the independent and dependent variables of a study from its title or abstract.
- If a study has only female subjects, gender would not be a variable, since there would be only women.

Be able to define the term "extraneous variable. A variable is a characteristic or feature that varies, or changes within a study.

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The opposite of variable is constant: 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.

### Using Independent and Dependent Variables

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 and dependent variables in research paper variable is the variable that is different between the groups compared: 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. 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.

### Graphing Dependent and Independent Variables

However, since investigators didn't determine or specify which individuals would be men and which would be women! 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. In experimental studies, where the independent variables are imposed and manipulated, the dependent variable independent and dependent variables in research paper the variable thought to be changed or influenced by the independent variable.

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. In this study, the toothpaste was the independent variable; it was different between the two groups: 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 analyseseach dependent variable analysis is considered a separate study for the purposes of statistical analysis. 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.

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.

## Independent and Dependent Variable Examples

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: 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, 3and 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 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.

- 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;
- We would assume that in the study, some balls were kicked intervention or experimental group , and others were not kicked; or had something else done to them; so there were at least 2 levels of the independent variable;
- Generally speaking, in any given model or equation, there are two types of variables;
- They may study variables that cannot physically impose the intervention e;
- These other factors that may influence the dependent variable are termed "extraneous", "intervening" or "confounding" variables.

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 cariesthen 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. 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. The variable thought to influence the distance, the independent variable, would be the kicking. We would assume that in the study, some balls were kicked intervention or experimental groupand others were not kicked; or had something else done to them; so there were at least 2 levels of the independent variable.

You can use this typical form to determine the independent and dependent variables from the title of the study. If the study title is in the form "The effects of X on Y in Z". X is the independent variable and Y is the dependent variable - the outcome, and Z is the type of subjects represented.

A simple example would be: The effects of tomatoes on risk of prostate cancer in Scandinavian men. The "tomatoes" is in the X position, so it is the independent variable - it is the variable being compared between groups and the variable possibly manipulated - it also implies that there's another level - a comparison group of some sort. The Y position is "risk of prostate cancer" - that's the dependent variable, which is measured as the outcome. Scandinavian men is the sample in which the study was done - however, the results may be more generalizable.

A randomized trial independent and dependent variables in research paper breast cancer risk counseling: From this title, you can tell that the independent variable is type of counseling with 2 or more levels, risk counseling and no counseling or standard care.

The dependent variable is self-reported mammography use.