dependent independent variables research paper

what can help with depression

They will instead make a cash settlement, which reflects the market value at the time the loss happened. This is so a prospective buyer knows a vehicle was previously written off when conducting vehicle history checks. These checks also cover whether the vehicle is stolen or has outstanding finance, too. So, what do the categories mean?

Dependent independent variables research paper our daughters ourselves stevie cameron essay

Dependent independent variables research paper

The variables should be outlined in the introduction of your paper and explained in more detail in the methods section. There are no rules about the structure and style for writing about independent or dependent variables but, as with any academic writing, clarity and being succinct is most important. After you have described the research problem and its significance in relation to prior research, explain why you have chosen to examine the problem using a method of analysis that investigates the relationships between or among independent and dependent variables.

State what it is about the research problem that lends itself to this type of analysis. For example, if you are investigating the relationship between corporate environmental sustainability efforts [the independent variable] and dependent variables associated with measuring employee satisfaction at work using a survey instrument, you would first identify each variable and then provide background information about the variables.

What is meant by "environmental sustainability"? Are you looking at a particular company [e. Why is employee satisfaction in the workplace important? How does a company make their employees aware of sustainability efforts and why would a company even care that its employees know about these efforts? Identify each variable for the reader and define each.

In the introduction, this information can be presented in a paragraph or two when you describe how you are going to study the research problem. In the methods section, you build on the literature review of prior studies about the research problem to describe in detail background about each variable, breaking each down for measurement and analysis.

For example, what activities do you examine that reflect a company's commitment to environmental sustainability? Levels of employee satisfaction can be measured by a survey that asks about things like volunteerism or a desire to stay at the company for a long time.

The structure and writing style of describing the variables and their application to analyzing the research problem should be stated and unpacked in such a way that the reader obtains a clear understanding of the relationships between the variables and why they are important. This is also important so that the study can be replicated in the future using the same variables but applied in a different way.

Search this Guide Search. Organizing Your Social Sciences Research Paper Offers detailed guidance on how to develop, organize, and write a college-level research paper in the social and behavioral sciences. The Abstract Executive Summary 4. The Introduction The C. The Discussion Limitations of the Study 9. The Conclusion Appendices Definitions Dependent Variable The variable that depends on other factors that are measured.

Identifying Dependent and Independent Variables Don't feel bad if you are confused about what is the dependent variable and what is the independent variable in social and behavioral sciences research. Specifically, it is important for these two reasons: You need to understand and be able to evaluate their application in other people's research.

In a between-subjects factorial design , all of the independent variables are manipulated between subjects. For example, all participants could be tested either while using a cell phone or while not using a cell phone and either during the day or during the night.

This would mean that each participant was tested in one and only one condition. In a within-subjects factorial design, all of the independent variables are manipulated within subjects. All participants could be tested both while using a cell phone and while not using a cell phone and both during the day and during the night.

This would mean that each participant was tested in all conditions. The advantages and disadvantages of these two approaches are the same as those discussed in Chapter 6. The between-subjects design is conceptually simpler, avoids carryover effects, and minimizes the time and effort of each participant. The within-subjects design is more efficient for the researcher and controls extraneous participant variables. It is also possible to manipulate one independent variable between subjects and another within subjects.

This is called a mixed factorial design. For example, a researcher might choose to treat cell phone use as a within-subjects factor by testing the same participants both while using a cell phone and while not using a cell phone while counterbalancing the order of these two conditions.

But he or she might choose to treat time of day as a between-subjects factor by testing each participant either during the day or during the night perhaps because this only requires them to come in for testing once. Thus each participant in this mixed design would be tested in two of the four conditions. Regardless of whether the design is between subjects, within subjects, or mixed, the actual assignment of participants to conditions or orders of conditions is typically done randomly.

In many factorial designs, one of the independent variables is a nonmanipulated independent variable. The researcher measures it but does not manipulate it. The study by Schnall and colleagues is a good example. One independent variable was disgust, which the researchers manipulated by testing participants in a clean room or a messy room.

The other was private body consciousness, a participant variable which the researchers simply measured. The manipulated independent variable was the type of word. Some were negative health-related words e. The nonmanipulated independent variable was whether participants were high or low in hypochondriasis excessive concern with ordinary bodily symptoms.

The result of this study was that the participants high in hypochondriasis were better than those low in hypochondriasis at recalling the health-related words, but they were no better at recalling the non-health-related words. Such studies are extremely common, and there are several points worth making about them. First, nonmanipulated independent variables are usually participant variables private body consciousness, hypochondriasis, self-esteem, and so on , and as such they are by definition between-subjects factors.

For example, people are either low in hypochondriasis or high in hypochondriasis; they cannot be tested in both of these conditions. Second, such studies are generally considered to be experiments as long as at least one independent variable is manipulated, regardless of how many nonmanipulated independent variables are included. Third, it is important to remember that causal conclusions can only be drawn about the manipulated independent variable.

Thus it is important to be aware of which variables in a study are manipulated and which are not. The results of factorial experiments with two independent variables can be graphed by representing one independent variable on the x -axis and representing the other by using different kinds of bars or lines. The y -axis is always reserved for the dependent variable.

Time of day day vs. It would also be possible to represent cell phone use on the x -axis and time of day as different-coloured bars. The choice comes down to which way seems to communicate the results most clearly. The bottom panel of Figure 8. This variable, psychotherapy length, is represented along the x -axis, and the other variable psychotherapy type is represented by differently formatted lines.

This is a line graph rather than a bar graph because the variable on the x-axis is quantitative with a small number of distinct levels. Line graphs are also appropriate when representing measurements made over a time interval also referred to as time series information on the x -axis.

A main effect is the statistical relationship between one independent variable and a dependent variable—averaging across the levels of the other independent variable. Thus there is one main effect to consider for each independent variable in the study. The top panel of Figure 8.

The blue bars are, on average, higher than the red bars. It also shows a main effect of time of day because driving performance was better during the day than during the night—both when participants were using cell phones and when they were not. Main effects are independent of each other in the sense that whether or not there is a main effect of one independent variable says nothing about whether or not there is a main effect of the other.

The longer the psychotherapy, the better it worked. Although this might seem complicated, you already have an intuitive understanding of interactions. It probably would not surprise you, for example, to hear that the effect of receiving psychotherapy is stronger among people who are highly motivated to change than among people who are not motivated to change. This is an interaction because the effect of one independent variable whether or not one receives psychotherapy depends on the level of another motivation to change.

If they were high in private body consciousness, then those in the messy room made harsher judgments. If they were low in private body consciousness, then whether the room was clean or messy did not matter. The effect of one independent variable can depend on the level of the other in several different ways. This is shown in Figure 8. This is like the hypothetical driving example where there was a stronger effect of using a cell phone at night than during the day.

One example of a crossover interaction comes from a study by Kathy Gilliland on the effect of caffeine on the verbal test scores of introverts and extraverts Gilliland, [2]. Introverts perform better than extraverts when they have not ingested any caffeine. But extraverts perform better than introverts when they have ingested 4 mg of caffeine per kilogram of body weight. In many studies, the primary research question is about an interaction.

The study by Brown and her colleagues was inspired by the idea that people with hypochondriasis are especially attentive to any negative health-related information. This led to the hypothesis that people high in hypochondriasis would recall negative health-related words more accurately than people low in hypochondriasis but recall non-health-related words about the same as people low in hypochondriasis. And of course this is exactly what happened in this study. In the top panel, one line remains constant while the other goes up.

THE BEST AMERICAN ESSAYS 2011 ONLINE

Variables are names that are given to the variance we wish to explain. A variable is either a result of some force or is itself the force that causes a change in another variable. In experiments, these are called dependent and independent variables respectively.

When a researcher gives an active medication to one group of people and a placebo, or inactive medication, to another group of people, the independent variable is the medication treatment. Each person's response to the active medication or placebo is called the dependent variable.

This could be many things depending upon what the medication is for, such as high blood pressure or muscle pain. Therefore, in experiments, a researcher manipulates an independent variable to determine if it causes a change in the dependent variable. As we learned earlier in a descriptive study, variables are not manipulated. They are observed as they naturally occur and then associations between variables are studied. In a way, all the variables in descriptive studies are dependent variables because they are studied in relation to all the other variables that exist in the setting where the research is taking place.

However, in descriptive studies, variables are not discussed using the terms "independent" or "dependent. For example, there is more diabetes in people of Native American heritage than people who come from Eastern Europe. In a descriptive study, the researcher would examine how diabetes a variable is related to a person's genetic heritage another variable.

Definition : A variable is either a result of some force or it is the force that causes a change in another variable. In an experimental study looking at classical music exposure and reading ability in children, the researcher divided the children into two groups Groups A and B. In Group A, the children listened to Mozart for one hour every day for one month.

In Group B, parents were instructed to refrain from playing classical music around the child for one month. At the end of the month, all children were given a reading comprehension test. Those who listened to Mozart daily Group A scored significantly higher on the reading test. In a study with a similar design as the previous example, researchers looked at the effects of nutrition on reading ability. In Group A, children ate at least three ounces of dark green vegetables every day for one month.

In Group B, children were fed their regular diet. Schnall and her colleagues, for example, observed an interaction between disgust and private body consciousness because the effect of disgust depended on whether participants were high or low in private body consciousness. As we will see, interactions are often among the most interesting results in psychological research. By far the most common approach to including multiple independent variables in an experiment is the factorial design.

In a factorial design , each level of one independent variable which can also be called a factor is combined with each level of the others to produce all possible combinations. Each combination, then, becomes a condition in the experiment. Imagine, for example, an experiment on the effect of cell phone use yes vs. This is shown in the factorial design table in Figure 8. The columns of the table represent cell phone use, and the rows represent time of day. The four cells of the table represent the four possible combinations or conditions: using a cell phone during the day, not using a cell phone during the day, using a cell phone at night, and not using a cell phone at night.

If one of the independent variables had a third level e. Notice that the number of possible conditions is the product of the numbers of levels. In principle, factorial designs can include any number of independent variables with any number of levels. For example, an experiment could include the type of psychotherapy cognitive vs. Figure 8. In practice, it is unusual for there to be more than three independent variables with more than two or three levels each.

This is for at least two reasons: For one, the number of conditions can quickly become unmanageable. For example, adding a fourth independent variable with three levels e. Second, the number of participants required to populate all of these conditions while maintaining a reasonable ability to detect a real underlying effect can render the design unfeasible for more information, see the discussion about the importance of adequate statistical power in Chapter As a result, in the remainder of this section we will focus on designs with two independent variables.

The general principles discussed here extend in a straightforward way to more complex factorial designs. Recall that in a simple between-subjects design, each participant is tested in only one condition. In a simple within-subjects design, each participant is tested in all conditions. In a factorial experiment, the decision to take the between-subjects or within-subjects approach must be made separately for each independent variable.

In a between-subjects factorial design , all of the independent variables are manipulated between subjects. For example, all participants could be tested either while using a cell phone or while not using a cell phone and either during the day or during the night. This would mean that each participant was tested in one and only one condition.

In a within-subjects factorial design, all of the independent variables are manipulated within subjects. All participants could be tested both while using a cell phone and while not using a cell phone and both during the day and during the night. This would mean that each participant was tested in all conditions.

The advantages and disadvantages of these two approaches are the same as those discussed in Chapter 6. The between-subjects design is conceptually simpler, avoids carryover effects, and minimizes the time and effort of each participant.

The within-subjects design is more efficient for the researcher and controls extraneous participant variables. It is also possible to manipulate one independent variable between subjects and another within subjects. This is called a mixed factorial design. For example, a researcher might choose to treat cell phone use as a within-subjects factor by testing the same participants both while using a cell phone and while not using a cell phone while counterbalancing the order of these two conditions.

But he or she might choose to treat time of day as a between-subjects factor by testing each participant either during the day or during the night perhaps because this only requires them to come in for testing once. Thus each participant in this mixed design would be tested in two of the four conditions. Regardless of whether the design is between subjects, within subjects, or mixed, the actual assignment of participants to conditions or orders of conditions is typically done randomly.

In many factorial designs, one of the independent variables is a nonmanipulated independent variable. The researcher measures it but does not manipulate it. The study by Schnall and colleagues is a good example. One independent variable was disgust, which the researchers manipulated by testing participants in a clean room or a messy room. The other was private body consciousness, a participant variable which the researchers simply measured.

The manipulated independent variable was the type of word. Some were negative health-related words e. The nonmanipulated independent variable was whether participants were high or low in hypochondriasis excessive concern with ordinary bodily symptoms. The result of this study was that the participants high in hypochondriasis were better than those low in hypochondriasis at recalling the health-related words, but they were no better at recalling the non-health-related words.

Such studies are extremely common, and there are several points worth making about them. First, nonmanipulated independent variables are usually participant variables private body consciousness, hypochondriasis, self-esteem, and so on , and as such they are by definition between-subjects factors.

For example, people are either low in hypochondriasis or high in hypochondriasis; they cannot be tested in both of these conditions. Second, such studies are generally considered to be experiments as long as at least one independent variable is manipulated, regardless of how many nonmanipulated independent variables are included. Third, it is important to remember that causal conclusions can only be drawn about the manipulated independent variable.

Thus it is important to be aware of which variables in a study are manipulated and which are not. The results of factorial experiments with two independent variables can be graphed by representing one independent variable on the x -axis and representing the other by using different kinds of bars or lines.

The y -axis is always reserved for the dependent variable. Time of day day vs. It would also be possible to represent cell phone use on the x -axis and time of day as different-coloured bars. The choice comes down to which way seems to communicate the results most clearly. The bottom panel of Figure 8. This variable, psychotherapy length, is represented along the x -axis, and the other variable psychotherapy type is represented by differently formatted lines. This is a line graph rather than a bar graph because the variable on the x-axis is quantitative with a small number of distinct levels.

Line graphs are also appropriate when representing measurements made over a time interval also referred to as time series information on the x -axis.

Dependent Variable The variable that depends on other factors that are measured.

Esl problem solving ghostwriters service us Expository ghostwriter for hire uk
Resume format for freshers in aviation industry Kralovec and buell end homework now
Dependent independent variables research paper 146
Lord macaulays essays and lays of ancient rome Literature term thesis

Join. free essay on revolutionary war think