Question

Mood. A January 2007 Gallup Poll question asked, "In general, do you think things have gotten better or gotten worse in this country in the last five years?" Possible answers were "Better", "Worse", "No Change", "Don't Know", and "No Response". What kind of variable is the response?

Short answer

The variable is qualitative and nominal.

Step-by-step solution

Identify the Nature of the Variable

Begin by examining the possible responses to the survey question: "Better", "Worse", "No Change", "Don't Know", and "No Response". These responses are categories that describe different states of opinion that cannot be quantitatively measured.

Determine If the Variable is Quantitative or Qualitative

Quantitative variables are numeric and allow arithmetic operations. The responses here are categories, not numbers, so the variable is qualitative.

Classify the Qualitative Variable

Qualitative variables can be either nominal or ordinal. Nominal variables have no order, while ordinal variables have a clear ordering of categories.

Evaluate Ordering of Responses

Consider the responses: "Better", "Worse", "No Change", "Don't Know", and "No Response". While "Better" and "Worse" might seem ordered in logical progression, "No Change" and "Don't Know" do not fit neatly into an ordered sequence. Additionally, there's no inherent ranking in these categories, thus making it a nominal variable.

Key concepts

Nominal Variables

When we discuss nominal variables, we're referring to variables that represent categories without any specific order or ranking. These categories serve purely to label different kinds of data qualitatively. For instance, in the context of a survey like the one conducted by Gallup, responses such as "Better," "Worse," "No Change," "Don't Know," and "No Response" fit this description perfectly.

A few characteristics that define nominal variables include:

• No numerical or intrinsic order in the categories.
• Categories are mutually exclusive, meaning each response fits into one and only one category without overlap.
• Appropriate for qualitative data that cannot logically be ranked.

Understanding that these variables only serve as labels is crucial, as it impacts how we analyze our data. For example, assigning arbitrary numbers to these categories, like labeling "Better" as 1 and "Worse" as 2, doesn't imply any mathematical or ranking relationship between them.

Nominal variables are most prevalent in survey data and are commonly analyzed using frequencies and mode, which is the most encountered value in data distribution. They help in understanding proportions or predominant opinions within a given dataset without implying any numerical sense.

Survey Data Analysis

In survey data analysis, understanding the type of variable one is working with is essential to choosing the correct analytical method. Surveys often ask a variety of questions eliciting both quantitative and qualitative responses, which require different treatments in analysis.

For qualitative responses like the one in the Gallup survey, you focus on categorizing the results and looking at frequency distributions. Here's how you might approach such an analysis:

• First, tally the number of responses for each category, such as how many people said "Better" or "Worse."
• Next, calculate the proportion of each response category out of the total responses to understand their relative prevalence.
• Finally, use graphical representations, like pie charts or bar charts, to visualize these proportions for easier interpretation.

Qualitative data from surveys broadly informs trends, opinions, and attitudes among a population. Techniques like coding responses can help distill thousands of open-ended text responses into measurable insights.

The key in survey data analysis is matching analytical techniques to data types. For instance, qualitative nominal data requires descriptive statistics rather than means or medians, which would not apply here.

Qualitative vs Quantitative

To understand how to approach survey questions like the Galup Poll query, it's important to differentiate between qualitative and quantitative variables. These two classification types are fundamental to selecting the right approach for data analysis.

**Qualitative Variables**

• Non-numeric in nature. Examples include categories such as colors, names, and labels.
• Used to describe characteristics or qualities that can't be counted or measured directly.

**Quantitative Variables**

• Numeric values, which can be measured or counted. Examples are height, weight, or time.
• Allow for mathematical operations and can be divided further into discrete and continuous variables.

An illustrative way to remember the difference is: qualitative data shows "what type," while quantitative data shows "how many." Maintaining clarity between these categories aids in the selection of suitable tools and techniques for data analysis, ensuring that the results are as accurate and insightful as possible. In practice, combining insights from both qualitative and quantitative data often yields the most robust interpretations.