Comparison of Independent, Dependent, and Extraneous Variables
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Comparison of Independent, Dependent, and Extraneous Variables
Variables are factors used in research to investigate the topic of interest. Variables are important in elaborating the relationship between two given parameters. Some of the variables may include independent, dependent, and extraneous variables. Therefore, the paper is centered on comparing the independent variables, dependent variables, and extraneous variables. The piece will also examine two ways researchers attempt to control extraneous variables. Peer-reviewed articles would also get used to support the paper's claims and discussion.
Comparison of the Variables
Independent Variables:
Independent variables are those used by researchers to examine the impacts of certain factors on the dependent variables. These independent variables are selected by the researcher and manipulated so that researchers can make observations of their effects on the research outcomes (Tincani et al., 2022). The independent variable manipulation is carried out to examine whether changes in the variables lead to changes in the dependent variable.
For instance, if the researcher wants to examine the effects of caffeine on alertness, they will assign the independent variable as the amount of caffeine consumed by a given number of participants. These independent variables would be manipulated by designing multiple conditions where participants would be given different amounts of caffeine. Using this independent variable, the researchers can investigate the influence of caffeine consumption on alertness.
Other factors do not influence the independent variables in the study, and they are isolated to ensure their impacts on dependent variables are not interfered with. Researchers can conclude the relationship between these two variables by varying the dependent variable and measuring their effects.
Dependent Variables:
Dependent variables contribute to research by representing the outcomes or variables of interest researchers desire to investigate. These dependent variables are manipulated to determine the influence of the independent variables on the study.
For instance, if the researcher wants to examine the influence of caffeine on alertness, they will take the dependent variable as the level of alertness of participants. The level of alertness indicates the results the researcher desires to examine and the dependent variables that influence it. Through the dependent variable in this example, the researcher would be able to determine whether changes in caffeine intake cause changes in participants' levels of alertness.
The dependent variables are expected to change as the independent variable changes. They are also in line with the hypothesis or the research question. Researchers should also ensure that they choose dependent variables that are measurable and are directly influenced by the independent variable.
Extraneous variables:
Extraneous variables are not of interest in the research but may influence the relationship between the dependent and independent variables. These variables sometimes influence the research outcomes and may not be controlled intentionally in the study (Cook et al., 2022). If researchers fail to account for these extraneous variables, the study will be biased, and the result interpretation will be confounded.
The extraneous variables, such as environmental conditions and individual characteristics, may be in various forms. For instance, if the researcher examines the relationship between exercise and mood, the extraneous variables would be fitness level, stress, and participant age. These extraneous variables influence the mood of the participants and are not directly measured as the independent or dependent variables. They influence the participant's spirit directly and thus impact the dependent variables, which affect the research outcomes.
Researchers use various strategies to minimize the influence of extraneous variables on the research outcomes. These strategies can isolate the independent and dependent variables from other external factors. Hence, the two ways attempted by researchers to control extraneous variables are randomization and experimental design.
Two Ways Attempted By Researchers To Control Extraneous Variables
Randomization:
Randomization is the process where researchers assign participants to different conditions and groups, helping distribute extraneous variables. This assignment of participants to different groups minimizes the possibility of extraneous variables influencing one group more than the other group (Li et al., 2022). As a result, the researchers attribute differences in the groups as the results of the independent variable. The process of randomization assists in ensuring that the extraneous variables are distributed equally in all the groups and conditions, strengthening the study's validity.
Experimental design:
Experimental design is another way research controls extraneous variables using various techniques. One of the experimental designs that researchers may use to control extraneous variables is the matched–pairs design (Miočević et al., 2022). The design pairs participants with similar characteristics and then assigns them to different groups. This type of design accounts for the extraneous variables by ensuring the variables are equally distributed between the groups.
Another experimental design the researcher uses is the counterbalancing approach, where participants experience conditions at different levels to control extraneous variables. That enables the researchers to minimize the influence of extraneous variables by controlling the sequence of conditions. Therefore, by controlling these experiments, researchers can reduce the influence of extraneous variables.
In conclusion, understanding the independent, dependent, and extraneous variables is important in research since its assists in conducting comprehensive research. The variables serve as building blocks, enabling researchers to study and analyze various phenomena.
References
Cook, B. G., Wong, V. C., Fleming, J. I., & Solari, E. J. (2022). Preregistration of Randomized Controlled Trials. Research on Social Work Practice, 10497315221121117.
Li, S. (2022). Quantitative research methods in ISLA. Instructed Second Language Acquisition Research Methods, 3, 31.
Miočević, M., Moeyaert, M., Mayer, A., & Montoya, A. K. (2022). Causal Mediation Analysis in Single Case Experimental Designs: Introduction to the Special Issue. Evaluation & the health professions, 45(1), 3-7.
Tincani, M., & Travers, J. (2022). Questionable research practices in single-case experimental designs: Examples and possible solutions. In Avoiding questionable research practices in applied psychology (pp. 269-285). Cham: Springer International Publishing.