Data Analysis in Quantitative and Qualitative Research Methods

Data Analysis in Research

  • Data analysis depends on research design, questions, and hypothesis.
  • Data can be quantitative (numerical), qualitative (verbal), or mixed.
  • Data analysis describes participant responses, noting typical or unusual aspects, differences, relationships, and answers to research questions.

Quantitative Data Analysis

  • Research verifies ideas and theories by gathering information to answer questions.
  • Statistical analysis is used when numbers represent information.
  • Statistics are mathematical techniques to examine data and test theories.
  • Effective statistics organize, evaluate, and analyze data to answer research problems.
  • Two classes of statistical techniques: descriptive and inferential.

Descriptive Statistics

  • Descriptive statistics summarize and describe collected data.
  • Research results are represented as percentages, proportions, ratios, and rates.
  • Proportion: P = f/n
  • Percentage: % = (f/n) \times 100
    • Where:
      • f = frequency
      • n = total number of cases
  • Ratios compare the number of cases in categories of a variable.

Measures of Central Tendency

  • Numerical values that locate the center of data: mean, median, mode, and midrange.
  • Mean ((\bar{x})): Sum of all values divided by the number of samples.
    • Formula: \bar{x} = \frac{\Sigma x}{n}
  • Median: Middle value when data is ranked.
    • Depth of median: d(x) = \frac{\text{sample size} + 1}{2}
  • Mode: Most frequent value in a data set. If no number occurs more than once, the sample has no mode.
  • Midrange: Number midway between the lowest (L) and highest (H) values.
    • Formula: \frac{L+H}{2}

Measures of Dispersion

  • Analyze data spread or variability: range, variance, and standard deviation.
  • Range: Difference between the highest (H) and lowest (L) values.
  • Sample Variance (s^2):
    • Formula: s^2 = \frac{\Sigma(x-\bar{x})^2}{n-1}
  • Standard Deviation (s):
    • Square root of the variance: s = \sqrt{s^2}

Inferential Statistics

  • Uses sample data to infer about the sampled population.
  • Inferences include estimating population parameters and testing hypotheses.
  • Null Hypothesis (H_0): Hypothesis being tested.
  • Alternative Hypothesis (H_1): Research hypothesis.
  • Decision Rule:
    • If p-value ≤ level of significance (e.g., 0.05), reject H_0.
    • If p-value > level of significance, fail to reject H_0.
  • Correlation: Measures the relationship between two variables.
    • Pearson's r: Describes the relationship between two continuous variables.

Qualitative Data Analysis

  • Based on logic and observation.
  • Qualitative data: information in words rather than numbers.
  • Focus on meanings, context, and experience.
  • Values subjectivity, analyzed with philosophical assumptions.
  • Goal is to understand meanings and how people make meaning.

Grounded Theory Analysis Tasks

  1. Researcher prepares verbatim transcripts
  2. Anonymize data
  3. Develop codes
  4. Define codes in a codebook
  5. Code data
  6. Describe
  7. Compare
  8. Categorize
  9. Conceptualize
  10. Develop theory

Data Preparation for Qualitative Analysis

  • Verbatim transcription of interviews or discussions.
  • Translation of transcripts, if needed.
  • Removal of identifiers to ensure participant anonymity.

Developing Codes

  • Codes represent issues, topics, ideas, or opinions evident in the data.
  • Inductive codes: Raised by participants.
  • Deductive codes: Prompted by the interviewer.

Making a Codebook

  • Provides a central reference for all codes.
  • Each code has a name and definition.

Interpretation of Qualitative and Quantitative Data

  • Basic research develops reliable knowledge.
  • Data should be reduced into smaller units or categories.
  • Appropriate analysis method depends on the research approach.

Interpreting Quantitative Data

  • Focuses on explaining numerical data
  • Uses a deductive method for theory and hypothesis testing.
  • Variables: nominal, interval, ordinal, and ratio.

Interpreting Qualitative Data

  • Data are non-numerical (words or pictures).
  • Data is reduced to patterns, categories, or themes.
  • Uses an inductive method to explore phenomena.
  • Thematic analysis: Segmentation, categorization, and linking of data aspects before final interpretation.