Mistakes to Avoid While Choosing Research Variable in Descriptive Research Study?

The nature of variables is the most important aspect to be considered while choosing your research variable. The selection of the right type of variables is critical in a descriptive research study. If you are not careful enough while choosing your research variable, it might result in problems while analyzing data which may even lead to wrong results. So, it is important to know about the different types of mistakes that researchers usually make while choosing their research variables and how they can avoid those mistakes.


Not understanding the nature of variables:

A variable is a measurement that can be classified into groups on the basis of its characteristics. It’s a characteristic of something, and it can take any value. For example, your age may be an independent variable in a study because it has many possible values (the number of years you have been alive). A research variable is any testable entity that you are measuring to help you answer your research question(s). This can include people, objects, or even processes. If you’re unable to identify the nature of the variable, then you need to start from the basics. It is considered one of the major mistakes in a descriptive research study.

Lack of population and sampling knowledge.

Population and sampling are two important concepts that every researcher should be aware of while conducting a descriptive research study. A population is the complete group of people or things you want to study, while a sample is a small subset of the population. When conducting research, it’s important to understand how to select a representative sample that accurately reflects your target population. If you don’t have this information and don’t know how the variables relate to each other—or if they even do—your study results could end up being meaningless.

Ignoring the zero-significance level.

There are two types of errors that you can make when conducting a descriptive research study: Type I, or a false positive; and Type II, or a false negative. The significance level is the probability of making a Type I error. It’s usually set at 0.05 (5%) or 0.01 (1%).

The significance level defines how many people need to be tested in order for the results of your experiment to be considered statistically significant in terms of inferential statistics. That is why it’s important that you understand how this works so you can choose your sample size appropriately before conducting your experiment!

Poor interpretation of statistical significance results.

It is tempting to interpret statistical significance as a measure of practical significance. The problem with this approach is that statistical significance is a measure of how likely it is that the results are due to chance. In contrast, practical significance refers to the degree to which a result impacts your life.

To illustrate this distinction, imagine that you have tested two groups and found that Group A had 20% lower scores on average than Group B in all three measures: math test scores (20%), science test scores (20%), and social studies test scores (20%). If you use traditional methods for calculating p-values and find that they were all .05 or less (which means there was less than a 5% chance of getting these results by chance alone), most researchers will consider these findings statistically significant because each p-value was below .05. However, practically speaking; do any of these findings mean anything? Probably not!


Variability in data should not be ignored.

Variability in data is the degree of dispersion of data points around the mean. The greater the variability, the less confidence you can have in your results; and conversely, if there is little variability, then you should be able to have more confidence in your results. In order for this to happen, you need to know why there is variability in your data. For example:

  • Was it due to human error? If so what steps did you take to minimize it?
  • Was it due to chance events occurring during testing/measurement? If so was a control group used?

Choose a good statistical technique.

To avoid this common mistake, you should use the right statistical technique or methods for your research problem. The goal of this article is to help you choose proper statistical techniques before starting your research study; after reading this article, if you still feel confused about how to choose a good statistical technique or method then contact PhD dissertation help.

Be careful while choosing your sample size.

Choosing the right sample size is very important in descriptive research. The sample size should be large enough to represent the population. If a study is conducted on a very small sample (n<30), researchers will not be able to generalize their findings and make them applicable to many people.

On the other hand, if you select too many participants than required then your analysis will cost you more money and time. So, it is also important to keep in mind that selecting an appropriate number of participants requires careful consideration and planning as well as proper sampling techniques.

Most importantly, remember that your results will not be affected by sampling error if you choose a large enough sample size that is proportional with respect to the design effect.

Understanding the nature of variables.

When you are choosing a research variable, it is important to understand the nature of the variables in your study.

For example, suppose that you wish to study the reason for people not going to school and the factors that influence them from doing so. The variable here would be the willingness or unwillingness of students, who do not attend school because they want a break, or they have no money. If someone wants a break, then there is a high likelihood that they will not go back to school until the next term starts anew; if someone has no money then he/she might try hard as possible but duly fail because of lack of funds. Therefore, this variable can be identified as being categorical in nature because it has two groups which are ‘willingness’ and ‘unwillingness’ which can only be either true or false (i.e., yes/no).


We hope that this article has given you a better understanding of how to choose research variables in your descriptive research study. If you want to ensure that your research is successful and accurate, then it is important that you consider all the factors described in this article. We also suggest taking a training course on statistical significance testing and analysis if possible before beginning any new research project.

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