VISUALIZATION

Typically a visualization is a process of displaying data/information in graphical charts, figures, and bars. Within Insight users have the ability to display data in the following manner:

  • Text
  • Table
  • Column
  • Bar
  • Line
  • Donut
  • Scatter
  • Area

DASHBOARD

An Insight Dashboard utilizes data visualizations to display a combination of institutional metrics from a wide variety of sources, such as institutional data from Core Data as well as other Campus Labs applications the institution may use.

NARRATIVE

Narratives allow users to create reports comprised of data visualizations and text. This allows for a user to create a richer context and to develop a story surrounding their data. Much like Dashboards, users can share narratives with other Insight users for them to read and review. When a user shares a narrative it will only be available to those shared in view only mode. 

 

MEASURES AND DIMENSIONS

 

Measure

In order to create a visualization, a user must first choose a Measure.  Insight defines a measure as a numeric value. We can consider a measure as an independent variable. 

Example List of Measures:

  • Sum of attempted credit hours
  • Total Enrollments - Enrolled
  • Event Count
  • Average Attendance
  • Student Count
  • Retained Student Percentage

Custom Measure(s) can be created by users with a certain permission type. Such Measures can be built from either one or multiple Course Evaluation questions or from another Campus Labs data Resource, such as Core Data, Beacon, and Engage. The process for creating a Course Evaluation custom measure and is slightly different than those from other Resources, but that is covered in the Creating and Publishing a CE Measure.

Metrics

Custome Measures can have multiple "Metrics" to form a measure's data point. Metrics are built from Core Data, Beacon, and Engage Resources. The article, Creating a Custom Measure, has an in-depth example of creating a Custom Measure with multiple Metrics.

 

Expression Builder

For Measures with multiple Metrics, a user will need to use the Expression builder to combine the two metrics together to form a single data point. You can see from the screenshot below, that you will need to type each metric's identifier into the expression builder. Metric identifiers are case sensitive, therefore if you type (a/b)*100 would return an error.

2017-07-24_10-46-54.png

 

 

Dimension

A dimension is a field that can be considered an independent variable. Dimensions within Insight are categorical/qualitative variables.

Example List of Dimensions:

  • Athletic Programs
  • Campus Student Demographics (female/male)
  • Student Organization (Greek/Non-Greek)
  • Institutional Event Check-in Location (Library, Student Union, etc...)

 

Example of Measures and Dimensions

Let's review these terms through the lens of an example using Student Academic Standing for Demo University. You can see how Measures (green box) and Dimensions (yellow box) map to the Y and X axises respectively. So here we can see we are couning the number of male and female students (meausre) as a total and then dividing it betwen the academic standing categories (dimensions).

Therefore, male/female is a numeric value on a ratio scale. Knowing the overall number of female (192) and male (82) studentsv for an institution may be helpful, but provides little value without additional context. This is where a dimension(s) will bring in additional context that comes in the form of being divdided into cateogires through the use of dimensions.

Now that we have a better understanding of measures compared to dimensions, let's round out our example and add some context to our institution's overall number of male and female students. For this example, let's add two dimensions: student sex (male/female) and Beacon alert type.

For this example, let's add the dimension of "Academic Standing" with six slices: good standing, probation-gpa, probation, warning, not specified and does not exist.

 

2017-07-24_09-34-49.png

Through the addition of dimensions we have transformed that single data point and have added much-needed context as to the impact of a student's sex has on academic standing.

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