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Research Variables

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❶A theory of suicide states that married people are less likely to commit suicide than single people. In other words, the variance in dependent variable is accounted for by the independent variable.
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The temperature varies according to other variable and factors. You can measure different temperature inside and outside. If it is a sunny day, chances are that the temperature will be higher than if it's cloudy. Another thing that can make the temperature change is whether something has been done to manipulate the temperature, like lighting a fire in the chimney. In research, you typically define variables according to what you're measuring.

The independent variable is the variable which the researcher would like to measure the cause , while the dependent variable is the effect or assumed effect , dependent on the independent variable.

These variables are often stated in experimental research , in a hypothesis , e. In explorative research methodology, e. 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. 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. What is the process of moving from the independent variable to the dependent variable? What exactly is that factor which theoretically affects the observed phenomenon but cannot be seen?

Its effects must be inferred from the effects of independent variable on the dependent variable. In this work-week hypothesis, one might docsity. To rephrase the statement it could be: Extraneous Variables An almost infinite number of extraneous variables EV exist that might conceivably affect a given relationship.

Some can be treated as independent or moderating variables, but most must either be assumed or excluded from the study. Such variables have to be identified by the researcher. In order to identify the true relationship between the independent and the dependent variable, the effect of the extraneous variables may have to be controlled.

This is necessary if we are conducting an experiment where the effect of the confounding factors has to be controlled. Confounding factors is another name used for extraneous variables.

Relationship among Variables Once the variables relevant to the topic of research have been identified, then the researcher is interested in the relationship among them. A statement containing the variable is called a proposition. It may contain one or more than one variable. The proposition having one variable in it may be called as univariate proposition, those with two variables as bivariate proposition, and then of course multivariate containing three or more variables.

Prior to the formulation of a proposition the researcher has to develop strong logical arguments which could help in establishing the relationship. For example, age at marriage and education are the two variables that could lead to a proposition: What could be the logic to reach this conclusion? All relationships have to be explained with strong logical arguments.

If the relationship refers to an observable reality, then the proposition can be put to test, and any testable proposition is hypothesis. If you don't receive any email, please check your Junk Mail box. If it is not there too, then contact us to info docsity.

If even this does not goes as it should, we need to start praying! Search in the document preview. Access your Docsity account. The independent variable is the core of the experiment and is isolated and manipulated by the researcher. The dependent variable is the measurable outcome of this manipulation, the results of the experimental design.

For many physical experiments , isolating the independent variable and measuring the dependent is generally easy. If you designed an experiment to determine how quickly a cup of coffee cools, the manipulated independent variable is time and the dependent measured variable is temperature. In other fields of science, the variables are often more difficult to determine and an experiment needs a robust design.

Operationalization is a useful tool to measure fuzzy concepts which do not have one obvious variable. In biology , social science and geography, for example, isolating a single independent variable is more difficult and any experimental design must consider this.

For example, in a social research setting, you might wish to compare the effect of different foods upon hyperactivity in children. The initial research and inductive reasoning leads you to postulate that certain foods and additives are a contributor to increased hyperactivity. You decide to create a hypothesis and design an experiment , to establish if there is solid evidence behind the claim. The type of food is an independent variable, as is the amount eaten, the period of time and the gender and age of the child.

All of these factors must be accounted for during the experimental design stage. Randomization and controls are generally used to ensure that only one independent variable is manipulated.

To eradicate some of these research variables and isolate the process, it is essential to use various scientific measurements to nullify or negate them.

For example, if you wanted to isolate the different types of food as the manipulated variable, you should use children of the same age and gender. The test groups should eat the same amount of the food at the same times and the children should be randomly assigned to groups.

This will minimize the physiological differences between children. A control group , acting as a buffer against unknown research variables, might involve some children eating a food type with no known links to hyperactivity. In this experiment, the dependent variable is the level of hyperactivity, with the resulting statistical tests easily highlighting any correlation.

Extraneous variables are defined as any variable other than the independent and dependent variable. Control variables are variables that are kept the same in each trial. Lastly, the moderator variables are variables that increase or decrease the relationship between the independent and dependent variable.

Types of research methods can be broadly divided into two quantitative and qualitative categories. Quantitative research “describes, infers, and resolves problems using numbers. Emphasis is placed on the collection of numerical data, the summary of those data and the drawing of inferences from the data” [2].

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. 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.

Variables And Types Of Variables-Research Methods-Handouts, Lecture notes for Research Methodology. Ambedkar University, Delhi. Ambedkar University, Delhi. Research Methodology, Management. PDF ( KB) 3 pages. 50 Number of download. + Number of visits. % on 7 votes Number of votes. Unlike extraneous variables, moderator variables are measured and taken into consideration. Typical moderator variables in TESL and language acquisition research (when they are not the major focus of the study) include the sex, age, culture, or language proficiency of the subjects.