Week 6 Discussion
Applying Variables, Values, and Levels of Measurement to a Research Scenario
The nature of the values of a variable determines its level of measurement. A common framework for classifying level of measurement was presented by Stanley Stevens, in 1946, and included: nominal, ordinal, interval, and ratio. Stevens’s framework is controversial, and its utility is rejected by the vast majority of statisticians (see, e.g., Borgatta & Bohrnstedt, 1980, in this week’s Optional Resources). Nonetheless, its ubiquity demands knowing the distinctions between the four levels.
For example, the psychology, health sciences, and information technology values of academic discipline are nominal level—often referred to as categorical—because there is no inherent hierarchical order. Age values are ratio level because they do have inherent order, equal distance between values (the distance between 22 and 23 is the same as between 50 and 51), and an absolute zero, which simply means it makes sense to say someone 50 years old is twice as old as someone 25 years old.
In 1932, Rensis Likert developed a technique to measure attitudes on a 5-point response scale of 1 (strongly disapprove), 2 (disapprove), 3 (undecided), 4 (approve), and 5 (strongly approve). Its more familiar form—strongly disagree, disagree, undecided, agree, strongly agree—was introduced by Likert, Roslow, and Murphy in 1934. Since then, a host of Likert-type labelling and varying number of response options have been used. According to Stevens’s levels of measurement, Likert-type values are ordinal but it is common to treat them as interval for statistical analysis purposes (see, e.g., Norman, 2010, in this week’s Optional Resources).
The level of measurement of a variable determines an appropriate visual display. For example, academic discipline could be displayed using a pie chart or bar chart but not a histogram. Age could be displayed using a histogram.
In a particular research context, a variable might be referred to as independent or dependent. Often the values of the independent variable are thought of as causing or influencing the values of the dependent variable. That is, the dependent variable “depends” on the independent variable. For example, the critical thinking skills of graduate students may depend on their academic discipline.
In this Discussion, you will build on your quantitative scenario adding a description of your variables to include their range of measured values, level of measurement (nominal, ordinal, interval, or ratio), and identification as either an independent or dependent variable.
Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 22(140), 5–55.
Likert, R., Roslow, S., & Murphy, G. (1934). A simple and reliable method of scoring the Thurstone attitude scales. Journal of Social Psychology, 5, 228–238.
Stevens, S. S. (1946). On the theory of scales of measurement. Science, 103, 677–680.
Review the “Levels of Measurement” Skill Builder. Pay particular attention to definitions, examples, and differences between and among levels of measurement (nominal, ordinal, interval, and ratio).
Review the “Visual Displays for Categorical Variables” and “Visual Displays for Continuous Variables” Skill Builders,as well as the video, Visual Displays of Data. Think about how the level of measurement of a variable determines an appropriate visual display.
Review the “Independent and Dependent Variables” Skill Builder. Consider the differences between independent and dependent variables.
Think about a social problem or phenomenon that could be researched using quantitative methodology, perhaps something you are considering as a dissertation topic. Consider the independent and dependent variables you would include in a study about the social problem or phenomenon and their respective levels of measurement (nominal, ordinal, interval, and ratio).
Alignment of scenario elements is important. See the Examples of Aligned and Misaligned Scenarios document, which can be downloaded from the Week 6 Learning Resources area of the classroom.
Discussion posts are pass/fail but have minimum criteria to pass. See the Discussion Rubric to ensure you understand the pass/fail criteria.
By Day 3
This week is only about the quantitative scenario. Repost, or build on or refine as needed, your quantitative scenario using the following headings and according to the italicized instructions given for each element:
Program of Study: Identify your specific program of study and, if applicable, your concentration area.
Social Problem: Briefly describe the social problem or phenomenon of interest. Typically, this can be done in 3 or fewer sentences.
Quantitative Research Problem: Complete the following sentence: The scholarly community does not know…
Quantitative Research Purpose: Typically, this is a 1-sentence statement addressed by completing the following sentence: The purpose of this quantitative study is…
Quantitative Research Question: Typically, this is a 1-sentence question unless you have more than one research question.
Theory or Conceptual Framework: Identify a specific psychological or sociological theory or specific aspects of a conceptual framework that guides the scenario. Briefly describe how the specific theory or conceptual framework guides your research question and will aid in interpretation of results.
Quantitative Research Design: Identify a specific quantitative research design. Do not use broad terms, such as survey design, cohort design, longitudinal design, causal-comparative design, cross-sectional design, and so on. Briefly describe how the selected design fits your scenario.
Quantitative Sampling Strategy: Be specific.
Quantitative Data Collection Method: Be specific.
Variables: Briefly describe each of your variables to include their range of measured values, level of measurement (nominal, ordinal, interval, or ratio), and identification as either an independent or dependent variable.
Note: Use proper APA format. If helpful, support your postings and responses with specific references to the Learning Resources.
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