Metode Statistika | Konsep Dasar Statistika | Part 3 | Data, Teknik Pengumpulan Data dan Big Data

Introduction

This tutorial provides a comprehensive overview of basic statistical concepts, focusing on variables, data types, data collection techniques, and the definition of Big Data. It aims to equip learners with foundational knowledge essential for understanding statistics and data analysis, particularly for students in the Statistics and Data Science Department at IPB University.

Step 1: Understanding Variables

• Definition of Variables: Variables, or "peubah," are characteristics or properties that can take on different values. They are fundamental units of analysis in statistics.
• Types of Variables:
  • Qualitative Variables: These are categorical and describe characteristics (e.g., gender, color).
  • Quantitative Variables: These can be measured numerically and can further be divided into:
    • Discrete Variables: Countable values (e.g., number of students).
    • Continuous Variables: Measurable quantities that can take any value within a range (e.g., height, temperature).

Step 2: Measurement Scales

• Levels of Measurement:
  • Nominal Scale: Categorizes data without a specific order (e.g., types of fruits).
  • Ordinal Scale: Categorizes data with a meaningful order but no consistent difference between levels (e.g., race rankings).
  • Interval Scale: Measures data with meaningful intervals, but no true zero point (e.g., temperature in Celsius).
  • Ratio Scale: Similar to interval scale, but with a true zero point (e.g., weight, height).

Step 3: Types of Data

• Primary Data: Collected firsthand for a specific purpose (e.g., surveys, experiments).
• Secondary Data: Data that has already been collected and is being reused (e.g., census data).
• Qualitative Data: Non-numerical information that describes qualities or characteristics.
• Quantitative Data: Numerical information that can be used for statistical analysis.

Step 4: Techniques for Data Collection

• Surveys: Gather information from a sample of individuals using questionnaires.
• Interviews: Conduct one-on-one discussions to collect detailed responses.
• Experiments: Test hypotheses by manipulating variables and observing outcomes.
• Observations: Collect data by watching subjects in their natural environment.

Step 5: Introduction to Big Data

• Definition of Big Data: Refers to extremely large datasets that may be analyzed computationally to reveal patterns, trends, and associations.
• Characteristics of Big Data:
  • Volume: The amount of data generated.
  • Velocity: The speed at which data is generated and processed.
  • Variety: Different types of data (structured, unstructured).
  • Veracity: The reliability and accuracy of the data.

Conclusion

This tutorial has outlined the essential concepts of variables, measurement scales, types of data, data collection techniques, and the foundational understanding of Big Data. Equipped with this knowledge, you can better navigate the statistical landscape, whether for academic purposes or practical applications in data science. For further learning, consider exploring more advanced topics in statistics or practical applications of data analysis tools.