Question

Voters. A February 2007 Gallup Poll question asked, "In politics, as of today, do you consider yourself a Republican, a Democrat, or an Independent?" The possible responses were "Democrat", "Republican", "Independent", "Other", and "No Response". What kind of variable is the response?

Short answer

The response variable is a nominal categorical variable.

Step-by-step solution

Define the Variable Type

In order to determine the type of variable, we need to understand what information the responses provide. The options "Democrat", "Republican", "Independent", "Other", and "No Response" indicate categories; thus, the variable captures political affiliation or preference.

Identify Known Variable Types

There are generally two main types of variables: quantitative and qualitative. Qualitative variables categorize or describe attributes and characteristics, while quantitative variables measure numerical values or amounts.

Determine If Variable Is Categorical

Since the variable groups individuals based on political affiliation without any intrinsic numerical value or order, it is qualitative. More specifically, this is a categorical variable because it places individuals into distinct, non-numeric categories based on their response choice.

Recognize the Level of Measurement

The responses do not have a natural ordering or ranking; one category is not inherently "more" or "less" than another. Therefore, this variable is a nominal categorical variable, meaning it categorizes data without a quantitative value or order.

Key concepts

Categorical Variable

A categorical variable, as the name suggests, places data into categories. These categories represent different groups or classes. In the context of our original exercise, where the survey question asks for political affiliation, each response corresponds to a distinct category. These include "Democrat", "Republican", "Independent", "Other", and "No Response". The key point is that categorical variables do not hold numerical values, nor do they imply any kind of order or ranking among them.

In research or data analysis, categorical variables are used to group observations, organize results, and draw distinctions between different types of subjects or responses. They are particularly helpful in survey analysis, which often involves questions leading to answers that naturally fall into specific categories.

In summary, categorical variables segment data into groups that help researchers to identify patterns and trends by the differences in these groups. Once categorized, it becomes easier to perform a comparative analysis.

Nominal Variable

Nominal variables are a specific type of categorical variable that have no inherent order. In the exercise, the responses such as "Democrat", "Republican", and "Independent" represent nominal variables since they categorize political affiliations without any sequential or hierarchical value.

The main feature of nominal variables is that they merely label different groups. For example:

• "Red", "Green", "Blue" to categorize car colors.
• "Circle", "Square", "Triangle" to differentiate between shapes.

Nominal variables allow us to distinguish between different categories, but they do not inform us about the scale or rank among these categories.

It's important to note that nominal variables are not comparable numerically. You can't say one response is "more" something than another. They are simply different, which is a key distinction from other types of categorical variables, like ordinal variables, which do have an order.

Data Types

Data types in statistics refer to the classification of data based on its inherent characteristics, making it easier to analyze and interpret the information. Generally, data types can be split into two broad categories: qualitative and quantitative.

Qualitative data, like the responses in the exercise, are all about categories and descriptions. They explain the 'what' of data, focusing on names, labels, or qualities. In our scenario, qualitative data manifest as categorical and nominal variables.

Quantitative data, on the other hand, relate to numbers and measurements. They tell us about 'how much' or 'how many'. Some common examples include weights, heights, or temperatures.

Understanding the type of data you're dealing with is crucial because it guides how you can process and analyze the information. For example, while you might calculate averages with quantitative data, you would use qualitative data to determine frequency or percentages.

Survey Analysis

Survey analysis involves gathering, evaluating, and interpreting data from questionnaires or polls, like the Gallup Poll exercise in question. It is crucial in understanding and interpreting the opinions and preferences of a particular group.

For effective survey analysis, consider these steps:

• Designing the Survey: Ensure questions are clear and objective to gather meaningful responses.
• Data Collection: Collect responses systematically for reliability.
• Categorizing Data: Use variables such as categorical and nominal to differentiate responses.
• Statistical Analysis: Determine the frequency, patterns, and trends from the categorized responses.

Survey analysis offers valuable insights into societal trends, consumer preferences, and more. The key is ensuring that the data collected is both reliable and valid, so that sound conclusions can be drawn.