3.1.3 Data Analysis Techniques

• What is the purpose of data analysis in language investigation?

  Identify patterns, trends, insights
• What type of results does qualitative analysis provide?

  Subjective, interpretive insights
• Match the type of data set with its description:

  Textual data ↔️ Documents, emails, social media posts

  Audio data ↔️ Speech recordings, podcasts

  Video data ↔️ Visual and auditory elements

  Mixed media data ↔️ Combines different formats
• The ethical considerations of data privacy are irrelevant in language investigation.

  False
• Percentages express the absolute frequency of linguistic features.

  False
• Relevance is a key criterion for selecting data sets.


  True
• Steps to identify appropriate data sets for language investigation:

  1️⃣ Identify types of data sets

  2️⃣ Consider selection criteria

  3️⃣ Evaluate ethical considerations
• Percentages express the relative frequency of linguistic features.


  True
• Lexical analysis considers word frequency, word associations, and semantic fields
• Data visualizations enhance understanding by making complex data more accessible.


  True
• Match the data visualization technique with its purpose:

  Charts ↔️ Display quantitative data

  Graphs ↔️ Represent relationships

  Tables ↔️ Organize numerical data
• Effective data visualization techniques can improve the impact and understanding of research findings
• Tables are used to organize and display numerical data in a structured format.


  True
• Percentages show the relative significance and distribution of linguistic features.


  True
• Combining quantitative and qualitative insights leads to more nuanced and impactful conclusions.
• Quantitative analysis is useful for identifying frequencies, trends, and correlations.


  True
• Relevance is a key criterion for selecting appropriate data sets.
• Lexical analysis examines word associations and semantic fields.
• Charts are used to graphically display quantitative data
• Visualizations make complex data more accessible and easier to understand
• What should you match the visualization type to when creating effective data visualizations?

  Data and research questions
• What do frequency tables reveal in quantitative analysis?

  Most and least common features
• Combining quantitative and qualitative insights provides a holistic perspective in language investigation.

  True
• Lexical analysis is useful for understanding context, meanings, and nuances
• Quantitative analysis focuses on descriptive observations.

  False
• Qualitative analysis employs techniques like close reading.


  True
• What does textual data include?

  Novels, emails
• What does a frequency table show in quantitative analysis?

  Number of times a feature appears
• What is included in textual data for language investigation?

  Documents, emails, social media posts
• Match the data set type with its relevant aspect:

  Textual ↔️ Lexical usage

  Audio ↔️ Phonetics

  Video ↔️ Body language

  Mixed Media ↔️ Multiple modes of communication
• A frequency table shows the numerical count of linguistic features
• Steps to perform quantitative analysis using frequency tables and percentages:

  1️⃣ Collect data

  2️⃣ Create frequency table

  3️⃣ Calculate percentages

  4️⃣ Interpret results
• Lexical analysis examines vocabulary and word choices in language data.

  True
• What are three methods for effective data presentation?

  Charts, graphs, tables
• Data visualizations can persuade audiences by supporting arguments
• Consistent formatting and colors enhance readability in data visualizations.


  True
• Graphs visually represent relationships between variables.
• Frequency tables reveal the most and least common linguistic features.
• Lexical analysis examines vocabulary, word associations, and semantic fields.
• Qualitative analysis provides subjective, interpretive insights.