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Research Variables

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❶In other words, the variation in the dependent variable depends on the variation in the independent variable. It is not possible to consider every variable in a single study.

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This article is a part of the guide:
Dependent and Independent Variables
Independent and Dependent Variables

Two questions help to identify the independent variable. First, does it come before other variable in time? Second, if the variables occur at the same time, does the researcher suggest that one variable has an impact on another variable? Independent variables affect or have an impact on other variables. When independent variable is present, the dependent variable is also present, and with each unit of increase in the independent variable, there is an increase or decrease in the dependent variable also.

In other words, the variance in dependent variable is accounted for by the independent variable. Dependent variable is also referred to as criterion variable. In statistical analysis a variable is identified by the symbol X for independent variable and by the symbol Y for the dependent variable.

In the research vocabulary different labels have been associated with the independent and dependent variables like: Research studies indicate that successful new product development has an influence on the stock market price of a company. That is, the more successful the new product turns out to be, the higher will be the stock market price of that firm. Therefore, the success of the New product is the independent variable , and stock market price the dependent variable.

The degree of perceived success of the new product developed will explain the variance in the stock market price of the company. Moderating Variables A moderating variable is one that has a strong contingent effect on the independent variable-dependent variable relationship. That is, the presence of a third variable the moderating variable modifies the original relationship between the independent and the dependent variable.

For example, a strong relationship has been observed between the quality of library facilities X and the performance of the students Y. Although this relationship is supposed to be true generally, it is nevertheless contingent on the interest and inclination of the students. It means that only those students who have the interest and inclination to use the library will show improved performance in their studies. In this relationship interest and inclination is moderating variable i.

Intervening Variables A basic causal relationship requires only independent and dependent variable. A third type of variable, the intervening variable, appears in more complex causal relationships. It comes between the independent and dependent variables and shows the link or mechanism between them.

Advances in knowledge depend not only on documenting cause and effect relationship but also on specifying the mechanisms that account for the causal relation.

In a sense, the intervening variable acts as a dependent variable with respect to independent variable and acts as an independent variable toward the dependent variable. A theory of suicide states that married people are less likely to commit suicide than single people. The assumption is that married people have greater social integration e. Hence a major cause of one type of suicide was that people lacked a sense of belonging to group family. Thus this theory can be restated as a three-variable relationship: Specifying the chain of causality makes the linkages in theory clearer and helps a researcher test complex relationships.

Look at another finding that five-day work week results in higher productivity. They might not be stated because the researcher does not have a clear idea yet on what is really going on. Confounding variables are variables with a significant effect on the dependent variable that the researcher failed to control or eliminate - sometimes because the researcher is not aware of the effect of the confounding variable. The key is to identify possible confounding variables and somehow try to eliminate or control them.

Operationalization is to take a fuzzy concept conceptual variables , such as ' helping behavior ', and try to measure it by specific observations, e. The selection of the research method is crucial for what conclusions you can make about a phenomenon.

It affects what you can say about the cause and factors influencing the phenomenon. It is also important to choose a research method which is within the limits of what the researcher can do. Time, money, feasibility, ethics and availability to measure the phenomenon correctly are examples of issues constraining the research. Choosing the scientific measurements are also crucial for getting the correct conclusion.

Some measurements might not reflect the real world, because they do not measure the phenomenon as it should. To test a hypothesis , quantitative research uses significance tests to determine which hypothesis is right. The significance test can show whether the null hypothesis is more likely correct than the research hypothesis.

Research methodology in a number of areas like social sciences depends heavily on significance tests. A significance test may even drive the research process in a whole new direction, based on the findings. The t-test also called the Student's T-Test is one of many statistical significance tests, which compares two supposedly equal sets of data to see if they really are alike or not.

The t-test helps the researcher conclude whether a hypothesis is supported or not. Drawing a conclusion is based on several factors of the research process, not just because the researcher got the expected result. It has to be based on the validity and reliability of the measurement, how good the measurement was to reflect the real world and what more could have affected the results.

Anyone should be able to check the observation and logic, to see if they also reach the same conclusions. Errors of the observations may stem from measurement-problems, misinterpretations, unlikely random events etc.

A common error is to think that correlation implies a causal relationship. This is not necessarily true. Generalization is to which extent the research and the conclusions of the research apply to the real world.

It is not always so that good research will reflect the real world, since we can only measure a small portion of the population at a time. Validity refers to what degree the research reflects the given research problem, while Reliability refers to how consistent a set of measurements are. A definition of reliability may be "Yielding the same or compatible results in different clinical experiments or statistical trials" the free dictionary.

Research methodology lacking reliability cannot be trusted. These are known as constant or controlled variables. In the ice cube experiment, one constant or controllable variable could be the size and shape of the cube.

By keeping the ice cubes' sizes and shapes the same, it's easier to measure the differences between the cubes as they melt after shifting their positions, as they all started out as the same size. A well-designed experiment eliminates as many unmeasured extraneous variables as possible.

This makes it easier to observe the relationship between the independent and dependent variables. These extraneous variables, also known as unforeseen factors, can affect the interpretation of experimental results. Lurking variables, as a subset of extraneous variables represent the unforeseen factors in the experiment.

Another type of lurking variable includes the confounding variable, which can render the results of the experiment useless or invalid. Sometimes a confounding variable could be a variable not previously considered. For example, say the surface chosen to conduct the ice-cube experiment was on a salted road, but the experimenters did not realize the salt was there and sprinkled unevenly, causing some ice cubes to melt faster.

Because the salt affected the experiment's results, it's both a lurking variable and a confounding variable.

Intervening and Moderator Variables

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An independent variable is a variable believed to affect the dependent variable. Confounding variables are defined as interference caused by another variable. Extraneous variables are defined as any variable other than the independent and dependent variable.

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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.

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are those that the researcher has control over. This "control" may involve manipulating existing variables (e.g., modifying existing methods of instruction) or introducing new variables (e.g., adopting a totally new method for some sections of a class) in the research setting. dependent variable (also referred to as outcome variable or effect variable). The independent variable is "independent of" prior causes that act on it, whereas the dependent variable "depends on" the cause.

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A variable may be situation specific; for example gender is a variable but if in a particular situation like a class of Research Methods if there are only female students, then in this situation gender will not be considered as a variable. Types of Variable 1. The key to designing any experiment is to look at what research variables could affect the outcome. There are many types of variable but the most important, for the vast majority of research methods, are the independent and dependent variables.