data handling

• What type of data do structured interviews primarily collect?

  Quantitative data
• What is an example of quantitative data collected in structured interviews?

  Number of 'yes' responses
• What type of data do unstructured interviews produce?

  Qualitative data only
• What type of data can structured interviews collect?

  Both quantitative and qualitative data
• What do measures of dispersion describe?

  How spread out scores are in a data set
• Why is understanding dispersion crucial?

  It helps interpret research findings and data reliability
• What are the strengths and limitations of the range as a measure of dispersion?

  Strengths:

  • Easy to calculate and understand
  • Provides a broad view of data spread

  
  

  Limitations:

  • Ignores distribution of other scores
  • Can vary significantly with different samples
• What does a low standard deviation indicate?

  Scores are tightly clustered around the mean
• What does a high standard deviation suggest?

  Scores are more spread out from the mean
• What are the steps to calculate standard deviation?

  1. Calculate the mean
  2. Subtract the mean from each score
  3. Square the deviations
  4. Sum the squared deviations
  5. Calculate variance
  6. Calculate standard deviation
• What does low dispersion indicate about scores?

  Scores are closely clustered around the central measure
• What does high dispersion indicate about scores?

  Scores are widely spread out, reflecting variation
• What is the definition of range?

  Difference between highest and lowest scores
• What are the strengths and limitations of standard deviation?

  Strengths:

  • Provides detailed insights into score distribution
  • Considers all data points for variability

  
  

  Limitations:

  • Complex and time-consuming to calculate
  • Sensitive to outliers
• What is the mean?

  Average value summarizing a data set
• What is the purpose of the mean?

  Simplifies data into a single representative value
• What are the strengths and limitations of the mean?

  Strengths:

  • Most sensitive measure of central tendency
  • Reflects small changes in data effectively

  
  

  Limitations:

  • Affected by extreme scores (outliers)
  • May not exist within the data set
• What is the definition of median?

  Middle value of a data set in order
• Why is the median useful?

  Less affected by outliers than the mean
• What are the strengths and limitations of the median?

  Strengths:

  • Resilient to outliers
  • Best for qualitative data involving rankings

  
  

  Limitations:

  • Does not consider all data points
  • Less reliable than the mean in some situations
• What is the definition of mode?

  Most frequently occurring score in a data set
• What does it mean if a data set has no mode?

  All values occur with the same frequency
• What are the strengths and limitations of the mode?

  Strengths:

  • Resilient to extreme values
  • Useful for analyzing qualitative data

  
  

  Limitations:

  • May have multiple modes
  • Considered least reliable measure of central tendency
• When is the mode particularly useful?

  When frequency is more relevant than averages
• What is a limitation of the mode in small data sets?

  It may provide an unrepresentative central measure