If you're conducting a comparative study, which involves analyzing and comparing demographics or descriptors among participants, you can use the "Participant Information" tab to specify the relevant demographic or descriptor. This feature enables you to filter and focus your findings based on the specified criterion.

A comparative study aims to examine and compare different variables or characteristics across participant groups. By utilizing the "Participant Information" tab, you can effectively narrow down your analysis by selecting specific demographics or descriptors of interest. This focused approach allows you to uncover patterns, trends, or differences among participant groups, leading to a deeper understanding of the relationships between variables in your study.

Note, some UI elements are outdated in this video.

In this example, I conducted 1x1 interviews with 3 participants, Gabe, Janice, and Mary. I'll walk through how I could mark who came from Urban vs Rural environments, so I can compare codes by location type later. 

To locate the [Participant Information] Tab, look for the drop-down menu situated at the top of the transcript interface. This menu is available on every transcript page and contains a variety of options. Among these options, you will find the [Participant Information] Tab. Simply click on it to access a section dedicated to participant details, where you can view and manage information related to the individuals involved in the transcript.

Once you have accessed the participant information page by following Step 1, scroll down to the bottom of the page. There, you will find the [Add a Descriptor] button.

Every descriptor consists of two essential components: a Descriptor Name and a set of Options. The Descriptor Name serves as the title or category that describes the specific characteristic being classified. For instance, in the case of location types, the Descriptor Name could be "Location Type."

Within each Descriptor Name, there are corresponding Options, which are the specific labels or choices available to categorize participants. Continuing with the location type example, the Options could include "Urban," "Rural," and "Suburban."

In my example below, I typed "Location Type" as the Description Name, and added "Urban" and "Rural" as options. 

This "Location Type" will now be an option I can apply to each participant in my study. 

Once you have set up the descriptor options in Step 3, you can apply them to any transcript in your study. In this example, "Location Type: Rural" was selected for the "Gabe Interview" transcript.

To apply a demographic option to a specific transcript, follow these steps:

Now, you can assign the same descriptor, such as "Location Type," to another participant. For example, click on Janice's interview transcript and assign her a corresponding "Location Type" based on her specific location. This allows for consistent categorization of participants and helps in analyzing data based on specific characteristics.

To analyze and compare data based on different descriptors, like Urban vs Rural, navigate to the Snippets tab in the left-hand menu. This feature allows you to filter and view specific snippets of text associated with different descriptors, enabling you to compare findings across various categories and gain insights into patterns and variations within different groups.

To focus specifically on data associated with a particular descriptor, like Rural, follow these steps:

Once you have selected the "Rural" option, the Snippets tab will display only the relevant snippets of text associated with participants identified as Rural.

By applying this filter, you can narrow down your analysis to examine data specifically pertaining to participants from rural locations. This allows for a focused examination of the findings within this specific subgroup, providing valuable insights and comparisons within the context of the Rural descriptor.

While already filtering by the "Location Type | Rural" descriptor, you can further refine your analysis by applying additional code filters. In this example, the focus is on the "Code | Motivation" category.

By selecting the "Code | Motivation" filter in addition to the existing "Location Type | Rural" descriptor filter, you can narrow down the snippets to those that are coded as motivation from participants classified as being in a rural area.

This combined filtering approach allows you to specifically explore snippets that are coded as motivation within the context of rural locations. By effectively utilizing both code and descriptor filters, you can gain a more precise understanding of the motivations of participants in rural areas.

To perform a side-by-side comparison of the "Motivation" code across two different location types, you can open up two browser windows and set up the filters accordingly.

By having these two separate windows, each with the desired filters, you can conveniently compare the motivations of participants from rural and urban areas side by side. This allows for a comparative analysis of the motivations across different location types, providing insights into potential differences or similarities between these two demographic groups.

To document your analysis based on the comparisons made in Step 9, navigate to the code page for the "Motivations" category. This can be done by clicking on the code tag specifically associated with "Motivations."

Take the opportunity to capture your observations, insights, and thoughts on the code page for the "Motivations" category. Record any patterns, trends, or interesting findings that emerge from your analysis. Add comments, notes, or interpretations to enhance your understanding of the motivations and their relationship with different location types. Using the code page provides a centralized location for documenting and referencing your observations, facilitating further exploration and discussion of your findings.

A: A descriptor is a label or category used to classify participants in your study, such as "Location Type," which could have options like "Urban" or "Rural." Descriptors help you organize and filter your data to focus on specific characteristics or groups.

A: First, click on the [Participant Information] tab. Next, click [Add a Descriptor], and add a Descriptor Name and corresponding Options. These will serve as categories and specific labels to classify your participants. For example, if you're studying urban versus rural populations, your Descriptor Name might be "Location Type" with Options like "Urban" and "Rural."

A: To focus specifically on data associated with a particular descriptor, navigate to the Snippets tab, and look for the relevant descriptor button. Click on this button to open a dropdown menu, and select the descriptor you're interested in. The Snippets tab will then display only the relevant snippets of text associated with this descriptor.

No, each transcript gets assigned one option per descriptor. However, you can have multiple descriptors for each transcript, allowing you to categorize and analyze the data from various angles.

Yes, you can export data based on descriptors. When you're exporting snippets to a CSV file, the system allows you to filter them by specific descriptors.

No, it's generally recommended to house all groups in one project for a single study, even if the groups are distinct. With the use of descriptors, you can effectively categorize and manage the data from each group. Descriptors allow you to analyze groups both together for comparative analysis and separately for focused analysis within the same project, offering flexible and comprehensive data examination.

No, you don't need to add a new descriptor for every transcript. Once a descriptor is created, it will automatically appear on every transcript in your project. For example, once "Location Type" has been created as a descriptor, it will be available on all transcripts. Your task then is to select an appropriate option, such as "Urban" or "Rural", for each transcript to categorize your data accurately.