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Case Study 1: Levels of Measurement (Nominal, Ordinal, Interval, and Ratio)

Levels of measurement, also called scales of measurement, tell you how precisely variables are recorded. For example, in scientific research, a variable can take on different values across your data set (e.g., height or test scores). There are four levels of measurement:

• Nominal: The data can only be categorized
• Ordinal: The data can be categorized and ranked
• Interval: The data can be categorized, ranked, and evenly spaced
• Ratio: The data can be categorized, ranked, evenly spaced, and has a natural zero.

Depending on the variable’s scale of measurement, what you can do to analyze your data is limited. There is a hierarchy in the complexity and precision of the measurement scales, from low (nominal) to high (ratio). Going from lowest to highest, the four scales of measurement are cumulative, meaning they each take on the properties of lower levels and add new properties.

Why Are Levels of Measurement Important

The level at which you measure a variable decides how you can analyze your data. For example, the distinct levels limit the descriptive statistics you can use to get an overall summary of your data and which type of inferential statistics you can perform on your data to support or refute your hypothesis. In many cases, your variables can be measured at various levels, so you must choose the level of measurement you will use before data collection begins.

Watch

• Scales of Measurement – Nominal, Ordinal, Interval, Ratio (Part 1) – Introductory Statistics
• Scales of Measurement – Nominal, Ordinal, Interval, Ratio (Part 2) – Introductory Statistics
• Then, read Writing Strong Research Questions: Criteria & Examples. Next, select four variables, one for each measurement scale (Nominal, Ordinal, Interval, Ratio). Then, develop four research questions, including the null and alternative hypotheses, and explain how they might be used to answer each research question.

Partial Example (Grief and Loss)

Researchers have primarily investigated interpersonal loss and grief responses in samples drawn from the general populace (Andriessen et al., 2015). However, it is unknown whether death type, gender, education level, and emotional intelligence would similarly predict Persistent Complex Bereavement Disorder in a sample of active-duty police officers. Therefore, the following four research questions and hypotheses are proposed for this quantitative nonexperimental study.

Research Question 1

After experiencing a significant other’s death, will death type impact persistent complex bereavement?

• Null Hypothesis: There will be no impact of death type on persistent complex bereavement.
• Alternative Hypothesis: There will be an impact of death type on persistent complex bereavement.

Research Question 2

After experiencing a significant other’s death, will gender impact persistent complex bereavement?

• Null Hypothesis: There will be no impact of gender on persistent complex bereavement.
• Alternative Hypothesis: There will be an impact of gender on persistent complex bereavement.

Research Question 3

After experiencing a significant other’s death, will education level impact persistent complex bereavement?

• Null Hypothesis: There will be no impact of education level on persistent complex bereavement.
• Alternative Hypothesis: There will be an impact of education level on persistent complex bereavement.

Research Question 4

After experiencing a significant other’s death, will emotional intelligence impact persistent complex bereavement?

• Null Hypothesis: There will be no impact of emotional intelligence on persistent complex bereavement.
• Alternative Hypothesis: There will be an impact of emotional intelligence on persistent complex bereavement.

Requirements For Assignments/Exercises

1. Use the FNU APA Assignment/Case Study Template.