We may define a study variable as an attribute of an object of study. Suppose you wish to establish a sound experimental design. In that case, selecting variables to measure is exceedingly crucial.
We may define a study variable as an attribute of an object of a study. Suppose you wish to establish a sound experimental design. In that case, selecting variables to measure is exceedingly crucial. Imagine you want to test the yield of a specific wheat breed. The researcher must define the study variables affecting the yield, such as the amount of fertilizer, the average weather temperature, the amount of rainfall, and the tillage regimen. You must know the types of variables you handle beforehand to select the appropriate statistical tests and interpret your research results. Inquiring about two issues can help you identify two variables. These include the variable's data type and the part of the experiment the variable represents.
Quantitative data are about amounts, while categorical data are about groups. As the name suggests, a variable possessing quantitative data is quantitative. In contrast, a variable with categorical data is categorical. When a variable is discrete, it includes counts of individual items or values. The number of patients in a hospital on a given day and the number of books at a specific library are discrete variables. Discrete variables must be integers. They cannot have decimal numbers.
Ratio variables or continuous variables involve measurements of continuous or infinite values. Temperature, volume, distance, or age are examples.
When you consider categorical variables, you must remember that some kinds of grouping should be present. They may include numbers, but the numbers represent categories instead of actual amounts of things. Categorical variablescomprise binary, nominal, and ordinal variables. Binary variablesinvolve yes or no outcomes. Tossing a coin or winning or losing in a soccer game includes binary or dichotomous variables. Nominal variables mean that they have groups with no meaningful rank or order between them. Colors, brands, gender, and microorganism species could be examples. Nonetheless, the ordinal variable may include a type of order, even if it may be a loose one. The position of basketball players in a team and rating scale responses in a survey such as Beck's depression scale are some examples.
We usually design experiments to explore the effect of one or more variables on another variable, such as the effect of varied fiber concentration on yogurt texture. This circumstance suggests that we manipulate the independent variable (y variable) by changing the levels of the independent variable (x variable). Therefore, the cause-and-effect relationship is evident. The researcher would also have variables held constant throughout the experiment. Independent variables (manipulated) are the ones the researcher controls to see the effect on dependent variables. In the above example, the researcher manipulates the fiber amount to explore the changes in yogurt texture. Suppose that the researcher keeps the room temperature constant during the study. Hence, room temperature becomes the control variable.
The "dependent" and "independent" terms are not meaningful in correlational research. The reason for that lies in the fact that the researcher does not try to build a cause-and-effect relationship, in other words, causation. Nevertheless, one variable may precede the other as the fertilizer application must be before any irrigation treatment in agricultural farming. Should it be the case, you may call the preceding variable the predictor variable.
Once you have chosen your study variables, you are ready to select the proper statistical analysis. In interpreting your results, some other variables are also relevant. Confounding variables are critical as they may block you from seeing the actual effect of your treatment. It would be best to control your confounding variables by holding them constant. Imagine your research in a greenhouse. The temperature inside the greenhouse may prevent you from seeing the reel effect of fertilizer type on fruit yield. Thus, keeping the inside temperature constant is beyond critical. Latent variables can also affect your results as they cannot be determined directly. Finally, you may form composite variables by combining the measured variables in your experiment.
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