Statistics and Data Management

Data Collection and Management

• Data collection and management is a topic in mathematics.
• A statistician is an expert in collecting, organizing, analyzing, interpreting, and presenting data.
• Statisticians must have at least a master's degree in mathematics or statistics or be a trained professional in a related field.
• A statistic is a single measure (number) used to summarize a sample dataset.

What is Statistics?

• Statistics is the science of collecting, organizing, analyzing, interpreting, and presenting data.
• Example of a statistic: the average height of students in a class.

Descriptive vs. Inferential Statistics

• Descriptive statistics describes data without making predictions or offering conclusions.
• Inferential statistics draws conclusions from data to predict or generalize results for a larger population.

Data vs. Information

• Data is raw, unorganized facts. Data can be simple, random, and useless until organized.
• Information is processed, organized, structured, or presented data. Information is given context to be useful.

Types of Data/Levels of Measurement

• Data types are categorized and measured in different ways.
• Categorical Data: divided into subcategories.
  • Nominal: data with no meaningful order, like gender or eye color.
  • Ordinal: data with a meaningful order but no equal intervals, like school grades or Likert-type questions.
• Numerical Data: data that can be measured or counted.
  • Interval: data with equal intervals but no true zero point, like temperature in Celsius.
  • Ratio: data with equal intervals and a true zero point, like weight or height.

Levels of Measurement Summary

• Nominal: Categorizes and labels variables. (e.g., gender, eye color)
• Ordinal: Ranks categories in order. (e.g., school grades, socioeconomic status)
• Interval: Equal intervals, but no true zero. (e.g., temperature in Celsius, test scores)
• Ratio: Equal intervals, and a true zero. (e.g., weight, height, time)

Data Collection Methods

• Qualitative vs. Quantitative:
  • Qualitative: Research setting not as important. Non-standardized, narrative.
  • Quantitative: Controlled to the degree possible. Standardized, numerical.

Data Collection Methods (continued)

• Survey Methods: used to collect data on opinions, behaviors, experiences, and characteristics by directly asking people.
  • Questionnaires: Common in quantitative research, may be distributed online, by phone, mail, or in person. Usually offer closed questions with limited options. Allow data collection from many people.
  • Interviews: More common in qualitative research. Researcher conducts the interview in person, by phone, or online. Participants answer in their own words leading to exploring deeper ideas.
• Observation Methods: Studying characteristics, behaviors, or social interactions without relying on self-reporting. May be conducted in real-time, taking notes, or making audiovisual recordings for later analysis.
  • Quantitative: Measuring, systematically counting, using categories and criteria.
  • Qualitative: Taking detailed notes, writing rich descriptions, and recording all relevant observations.

Data Collection Methods (continued)

• Field-Specific Methods: Data collection methods appropriate to various fields:
  • Media & Communication: Collecting text samples (speeches, articles).
  • Psychology: Neuroimaging, eye-tracking, computer-based tasks.
  • Education: Tests and assignments.
  • Physical Sciences: Scientific instruments (weight, blood pressure).

Data Collection Methods (continued)

• Adopted, Adapted, Researcher-made Instruments:
  • Adopted: Research instruments taken directly from a standardized source without modifications.
  • Adapted: Pre-existing research instruments modified to fit a study's needs.
  • Researcher-made: Instruments created by the researcher to collect data for their specific study.

Ways of Presenting Data

• Textual Presentation: Data in narrative or descriptive format. Useful for detailed explanation, trends, and insights.
• Tabular Presentation: Data organized in rows and columns. Useful for easy comparison across multiple categories.
• Graphical Presentation: Using visual elements like charts, graphs, or plots to present data. Highlights patterns, trends, and relationships. Good for quickly grasping insights.