Researchers measure moderator variables and take them into consideration during the experiment. Sometimes certain characteristics of the objects under scrutiny are deliberately left unchanged. 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.
Mariecor Agravante earned a Bachelor of Science in biology from Gonzaga University and has completed graduate work in Organizational Leadership.
By Mariecor Agravante; Updated April 13, References Arizona State University: Designing an Experiment Iowa State University: Researchers are often seeking to measure variables. The variable can be a number, a name, or anything where the value can change. An example of a variable is temperature. 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. 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. Replication studies are a way to test reliability. Both validity and reliability are important aspects of the research methodology to get better explanations of the world. Logically, there are two types of errors when drawing conclusions in research:.
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.
In scientific research, scientists, technicians and researchers utilize a variety of methods and variables when conducting their experiments.
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. 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.
Dependent Variable The variable that depends on other factors that are measured. These variables are expected to change as a result of an experimental manipulation of the independent variable or variables. It is the presumed effect. Independent Variable The variable that is stable and unaffected by the other variables you are trying to measure. Intervening variables, like extraneous variables, can alter the results of our research. These variables, however, are much more difficult to control for. Intervening variables include motivation, tiredness, boredom, and any other factor that arises during the course of research.