1.2 Types of Data and Measurement Scales

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

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• 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