Data Log Activity

Overview of Data Collection and Documentation for Data Log Assignments

Introduction

  • Focus of the week: data collection and formatting for upcoming data log assignments

  • This session aims to provide clarity on:

    • Data collection processes

    • Documentation practices

Goals of Today's Session

  • Equip students with a clear understanding of what to do for:

    • Data collection

    • Documentation

  • Engage in in-class group activities to facilitate learning and collaboration

  • Achieve a preliminary entry for data collection and documentation by the end of the class

Data Collection Overview

Assignments Overview

  • The assignments span approximately two months, not just a couple of weeks.

  • First submission includes:

    • A preliminary entry for data collection (one row in a spreadsheet)

    • A one-page documentation that outlines initial thoughts and methods regarding the collected data

Data Collection Process

  • Importance of considering the type of data and the format for organization in your collection:

    • Suitable formats may differ depending on the nature of the data (e.g., text, geographical data like GeoJSON, numerical data, etc.)

  • A boring but effective format (e.g., a simple spreadsheet) is often sufficient, especially for data involving multiple variables over time.

Documentation Practices

  • Limited exploration of documentation theory as it does not closely relate to the class’s focus. However, awareness of best practices is necessary for scholarly inquiry.

  • Future discussions will address documentation more comprehensively alongside data analysis.

Preliminary Documentation Expectations

  • Aim for a one- to two-page documentation file covering:

    • Data source

    • Data collection process

    • Any transformations made on the data

    • The questions you are trying to address with the collected data

Group Activities and Discussions

  • Students will be segmented into groups based on common types of data of interest:

    • Health data

    • Social media metrics

    • Video content

    • Publication metrics

    • Viral content (memes and slang)

  • Each group encourages collaboration and discussion on the following:

    1. Individual data sources and methods

    2. The specific questions each person hopes to answer with their data

    3. Limitations of their chosen data sets

Categories of Collectible Data

  • Common themes in data that students may collect:

    • Health-related metrics

    • Social media performance and trends (e.g., virality of content shared)

    • Video and publication rankings across platforms such as Netflix or Goodreads

    • Trends in music popularity through charts (e.g., Billboard Hot 100)

  • Group members will present their data collection methods to inform each other about their approaches and facilitate feedback for documentation preparation.

Investigation of Data Sources

  • During group discussions, students will briefly explain their chosen data sources, ideas for data collection, and reasons behind their choices.

  • Each individual’s method should be documented, including:

    • The specific variables being measured

    • Any anticipated challenges or gaps in the data

Final Tasks

  • The class will conclude with a switch in group members to encourage cross-pollination of ideas and feedback on individual projects.

  • At the end of the session, each group member will share what they expect the documentation should include based on their discussions and findings.

Documentation Checklist

  1. Data sources: Name of the source and type of content

  2. Description of the data collection process: How data is recorded and organized

  3. Transformations: Any manipulations made to the data to analyze it

  4. Questions: Specific inquiries that guide data usage and analysis

  5. Data dictionary: Definitions of variable names and descriptions of measurement units

  6. Summary labels: Considerations of data format (numeric, text, etc.)

Closing Remarks

  • Validation of data tracking; examples should acknowledge the flavor of data tracking, such as tracking cultural phenomena or metrics over time.

  • Exploration of software tools (like Google Sheets, Excel) for data analysis is vital.

  • Encourage ethical considerations in data collection and usage regarding privacy and consent before moving forward with said data.