Data types and measurement scales are crucial for effective business analysis. Understanding the difference between qualitative and quantitative data helps choose appropriate collection methods and analysis techniques.

Measurement scales - nominal, ordinal, interval, and ratio - determine how data can be analyzed and interpreted. Matching the right scale to business scenarios ensures accurate insights and informed decision-making.

Types of Data

Qualitative vs quantitative data

• Qualitative data represents attributes, characteristics, or categories that cannot be measured numerically
  • Collected through observations, interviews, or open-ended survey questions (colors, emotions, opinions)
• Quantitative data represents numerical values or quantities that can be measured and expressed using numbers
  • Collected through experiments, surveys with closed-ended questions, or observations (height, weight, temperature, sales figures)

Types of measurement scales

• Nominal scale categorizes data into mutually exclusive groups without any order or hierarchy
  • Gender (male, female), marital status (single, married, divorced), eye color (blue, brown, green)
• Ordinal scale categorizes data into ordered groups, but the differences between categories are not necessarily equal
  • Education level (high school, bachelor's, master's, doctorate), customer satisfaction (very dissatisfied, dissatisfied, neutral, satisfied, very satisfied)
• Interval scale measures data on a scale with equal intervals between values, but lacks a true zero point
  • Temperature (Celsius or Fahrenheit), dates, IQ scores
• Ratio scale measures data on a scale with equal intervals and a true zero point, allowing for meaningful ratios between values
  • Height, weight, income, sales revenue, time

Measurement Scales and Data Analysis

Data selection for business scenarios

• Customer preferences and opinions use qualitative data with nominal or ordinal scales
  • Surveys with open-ended or multiple-choice questions
• Sales performance and financial metrics use quantitative data with ratio scales
  • Collecting numerical data on revenue, costs, and profits
• Employee satisfaction and engagement use qualitative data with ordinal scales
  • Surveys with Likert-type questions (strongly disagree to strongly agree)
• Product defect rates and quality control use quantitative data with ratio scales
  • Measuring the number of defects per unit or percentage of defective products

Impact of data types on analysis

• Qualitative data is analyzed using frequency distributions, cross-tabulations, and chi-square tests
  • Visualized using bar charts, pie charts, and word clouds
• Quantitative data is analyzed using measures of central tendency (mean, median, mode) and dispersion (range, variance, standard deviation)
  • Visualized using histograms, box plots, and scatter plots
• Nominal and ordinal scales are limited to non-parametric statistical tests
  • Chi-square, Mann-Whitney U, and Kruskal-Wallis H tests
• Interval and ratio scales allow for parametric statistical tests in addition to non-parametric tests
  • T-tests, ANOVA, and regression analysis