Application

Identifying appropriate variables for a study begins with a clear understanding of the research problem. The research problem serves as the foundation for determining which variables will be most relevant and impactful in addressing the questions posed by the study. Researchers should start by thoroughly analyzing the research problem to identify the key concepts and relationships that will be explored. From this analysis, they can derive the variables that will help measure these concepts and test the proposed hypotheses. For example, if the research problem involves understanding the factors that influence student engagement in online courses, potential variables might include the frequency of online interactions, types of instructional methods used, and levels of student engagement measured through surveys. It is crucial that these variables align with the theoretical framework guiding the research, ensuring that the study is coherent and that the variables can effectively explain the phenomenon under investigation. By grounding variable selection in the research problem, researchers can ensure that their study is both focused and relevant, providing meaningful insights that contribute to the broader field of knowledge.

Describing Variables

Describing variables should be a structured activity. You should identify the variable's name, how the variable was measured, and the variable level of measurement. The variable name should be used consistently and without variation within the research document and should be consistent with the naming convention described by the instrument, theoretical framework, and literature. A recommended approach is first to list the variable’s characteristics and then describe them in a narrative sentence. The benefit of using this approach is that it easily fits into the written narrative and serves as a reminder of the variable characteristics when preparing for testing of the assumptions for a statistical analysis. The narrative format also fits easily into the research report when describing the research questions and hypotheses.

Example 1: 

• Variable Name: Student Engagement
• Measurement: Student Engagement in Schools Questionnaire (SESQ)
• Level of Measurement: Interval
• Narrative: Student engagement, as measured by the Student Engagement in Schools Questionnaire (SESQ), an interval level of measurement variable.

Example 2:

• Variable Name: Exam Scores
• Measurement: Scores from a final exam
• Level of Measurement: Ratio
• Narrative: Exam scores, as measured by the final exam scores in <course>, are a ratio level of measurement.

Example 3:

• Variable Name: Participation Frequency
• Measurement: Number of times students participate in online discussions
• Level of Measurement: Ratio
• Narrative: Participation frequency, as measured by the number of times students participate in online discussions, a ratio level of measurement.

Descriptive Statistics Calculation

Accurately describing the data provides insights into the study sample. How researchers summarize the data depends on the variable level of measurement.

 For nominal variables, such as academic major, researchers can report frequency counts and the mode to identify the most common category. For example, if the study aims to understand the distribution of academic majors among students, frequency counts can show how many students are in each major, while the mode can highlight the most popular major.

 For ordinal variables, such as faculty member end-of-course rating items, researchers can report the median and percentiles. These measures help in understanding the central point and the spread of students' faculty member ratings. For instance, the median satisfaction score can indicate the typical faculty member rating level, while percentiles can show the rating distribution across different levels.

 Interval and ratio variables, like test scores, allow for more sophisticated analyses. Researchers can calculate the mean and standard deviation to understand the average performance and the variability around the mean. Additionally, the range can provide insights into the spread between the highest and lowest scores. For example, in a study measuring exam scores, the mean score gives a sense of the overall performance, while the standard deviation indicates how consistently students performed relative to the mean.

 By using descriptive statistics, researchers can better understand their sample, identify patterns, and make informed decisions about further analyses or interventions. Descriptive statistics provide a foundation for interpreting data and drawing meaningful conclusions, which is essential for effective research.