Question

Example 1.5 studied a variant of the gene \(A C T N 3\) which inhibits fast- twitch muscles and seems to be less prevalent in sprinters. A separate study \(^{9}\) indicated ethnic differences: Approximately \(20 \%\) of a sample of Caucasians, approximately \(25 \%\) of a sample of Asians, and approximately \(1 \%\) of a sample of Africans had the gene variant. What are the variables in this study? Classify each as categorical or quantitative.

Short answer

The two variables in the study are 'Ethnicity' and 'Presence of the ACTN3 gene variant'. 'Ethnicity' is a categorical variable and 'Presence of the ACTN3 gene variant' is a quantitative variable.

Step-by-step solution

Identify the Variables

The variables in this study are: the 'ethnicity' of the individuals in the sample (Caucasian, Asian, African), and the 'presence of the ACTN3 gene variant' in these individuals. Ethnicity is described by names (Caucasian, Asian, African) while the presence of the ACTN3 variant is noted as a percentage (\(20\%\), \(25\%\), \(1\%\)).

Classify Each Variable

'Ethnicity' is a categorical variable because it describes a quality or characteristic (Caucasian, Asian, African) and these cannot be measured numerically. The 'presence of the ACTN3 gene variant' is a quantitative variable, because it is something that can be measured numerically, as percentages in this case.

Key concepts

Categorical Variables

In statistics, when we refer to categorical variables, we're talking about a type of variable that can take on one of a limited, and usually fixed, number of possible values. These values represent different categories or groups that the subjects of the study can belong to. For instance, in the gene variant and ethnicity study, 'ethnicity' is a categorical variable because it classifies individuals into groups such as Caucasian, Asian, or African.

Categorical variables are often further classified as either nominal or ordinal. Nominal variables have categories with no intrinsic order, like countries or brands. In our case, ethnicity is a nominal categorical variable since the categories do not have a natural order. Ordinal variables, on the other hand, do have a meaningful order or ranking, such as stages of cancer or ratings on a survey.

Quantitative Variables

Quantitative variables are at the heart of data analysis—they represent measurements or counts that are expressed as numbers. These variables can be of two types: discrete or continuous. Discrete quantitative variables have a countable number of values, like the number of students in a class. In the context of the gene variant and ethnicity study, the 'presence of the ACTN3 gene variant' is a discrete quantitative variable, as it tells us the percentage of individuals within each ethnic group having the gene variant.

On the other side of the spectrum of quantitative variables lies continuous variables, which can take on any value within a range. Think about the continuous fluctuation of the stock market or temperature readings. In both cases, while the data may be presented in a rounded-off form, there is an understanding that finer measurements could always be taken.

Variables in Statistics

In the broad field of statistics, 'variables' are the characteristics, numbers, or quantities that can be measured or counted. They are called 'variables' because the value they take on can vary from one observation to the next. Each variable in a study has a specific role and helps to provide insights into the questions researchers aim to answer.

How we analyze data depends heavily on whether the variable is categorical or quantitative. For the gene variant and ethnicity study, recognizing that 'ethnicity' is a categorical variable and that 'presence of the ACTN3 gene variant' is a quantitative variable informs scientists how to approach the statistical analysis. While categorical variables are key for classification and qualitative comparison, quantitative variables allow for a broad spectrum of numerical analysis, including calculating averages, variances, and correlations among others. Understanding the types and functions of variables is crucial in any scientific inquiry as it dictates the types of statistical methods that will be most effective for analyzing the data.