9: Transforming and Recoding Data

9.1 Introduction to Data Transformation and Recoding

In applied research, it’s often necessary to transform or recode data to make it suitable for analysis. This process involves changing the structure or values of a variable to simplify, categorize, or standardize the data, making it easier to interpret and use in statistical tests. Transformation generally refers to applying mathematical functions to data (such as log transformations or scaling). In contrast, recoding refers to converting data from one form to another (such as categorizing a continuous variable).

Jamovi provides a simple and intuitive interface for transforming and recoding data, making it an essential tool for researchers. This chapter will cover common types of transformations, including creating new variables, recoding continuous variables into categories, and calculating z-scores.

9.2 Recoding Variables

Recoding is the process of transforming data values into new categories or levels. This can be particularly useful when working with continuous variables that must be grouped into specific categories for analysis.

To recode variables in Jamovi, follow these steps:

1. Select the Variable: In the Data Pane, choose the variable you want to recode. For example, if you have a Test Score variable, you might want to group scores into categories such as Low, Medium, and High.
2. Go to the Transform Tab: Right-click on the selected variable, and choose Transform to open the Compute dialog box.
3. Set Recoding Conditions: In the Compute dialog, you can define rules for recoding. For example:
   • If the Test Score is less than 50, label it as Low.
   • If the Test Score is between 50 and 75, label it as Medium.
   • If the Test Score is greater than 75, label it as High.

You can do this by using IF statements to assign new categories based on the values of the original variable.

1. Apply the Recoding: After entering the recoding conditions, click OK. This will create a new column in the Data Pane with the recoded categories for each participant.

Recoding is useful when you simplify a continuous variable into distinct categories, which can be analyzed in more categorical terms (e.g., using Chi-Square tests or ANOVA).

9.3 Transforming Continuous Variables into Categories

Sometimes, it’s helpful to transform continuous data into categorical data. For example, you might want to categorize participants based on their age, income, or test scores. This allows for easier group comparisons and statistical analysis.

To transform continuous variables into categories in Jamovi, follow these steps:

Below is an example of the results generated when the steps are correctly followed.

IMAGE [INSERT NAME OF DATASET]

1. Select the Variable: Choose the continuous variable (e.g., Test Scores) you want to categorize.
2. Go to the Transform Tab: Right-click the selected variable, and choose Transform to open the Compute dialog box.
3. Define Categories: Use logical conditions to categorize the data. For example, if you want to categorize Test Scores into Low, Medium, and High groups, you can use conditions based on score ranges (e.g., scores below 50 are Low, scores between 50 and 75 are Medium, and scores above 75 are High).
4. Apply the Transformation: After defining your categories, click OK, and a new column with the categorized values will be created.

This method of transforming continuous variables into categories helps simplify the data. It can be helpful when analyzing differences between groups or creating variables for use in ANOVA or Chi-Square tests.

9.4 Creating a Mean Variable

In many cases, multiple variables must be combined to create a mean variable (e.g., averaging test scores or combining multiple survey questions into a single score). This is particularly helpful when creating a single summary measure for a set of related variables.

To compute a mean variable in Jamovi:

Below is an example of the results generated when the steps are correctly followed.

IMAGE [INSERT NAME OF DATASET]

1. Select the Variables: Choose the variables you want to average (e.g., Test Scores 1, Test Scores 2, and Test Scores 3).
2. Go to the Transform Tab: Right-click on any of the selected variables, and choose Transform to open the Compute dialog box.
3. Create a New Computed Variable: In the Compute window, enter the formula for calculating the mean. For example, sum the selected variables and divide by the number of variables.
4. Name the New Variable: Give the new mean variable a name (e.g., Mean Test Score) and click OK. Jamovi will create a new column with the average values for each case.

This process allows you to create a variable that summarizes the average value of multiple variables, making it easier to perform analyses on a single summary measure rather than multiple individual variables.

9.5 Creating Z-Scores

A z-score standardizes a data point by telling you how many standard deviations it is from the mean of the dataset. Z-scores help compare values across different distributions or for identifying outliers.

Below is an example of the results generated when the steps are correctly followed.

IMAGE [INSERT NAME OF DATASET]

To create a z-score in Jamovi, follow these steps:

1. Select the Variable: In the Data Pane, choose the variable you want to standardize (e.g., Test Score).
2. Go to the Transform Tab: Right-click on the selected variable, and choose Transform to open the Compute dialog box.
3. Create a New Computed Variable: In the Compute window, enter the formula for calculating the z-score. This formula involves subtracting the variable’s mean from the value of the data point and dividing by the variable’s standard deviation.
4. Name the New Variable: Name the new variable (e.g., Z-Score Variable) and click OK. A new column will be created in the Data Pane with the computed z-scores.

Z-scores allow for standardization, making comparing data points from different scales or distributions easier.