Analysis in Social Research

Explanations > Social Research > Analysis in Social Research

Social Research seeks to develop conclusions about human behavior. It starts with hypothesis and, after data collection, can require significant analysis before any generalized laws or even specific conclusions can be asserted.

• Principles:
  • Principles in analysis: some very basic principles.
  • The test statistic: An underlying comparison.
  • Correlation: How connected two variables are.
  • Parametric vs. non-parametric tests: Two very different types.
• Tests for correlation:
  • Choosing a correlation test: Which to use.
  • Pearson correlation: For interval or ratio data.
  • Spearman correlation: For ranked, ordinal data.
  • Kendall correlation: For ranked, ordinal data.
  • Multiple regression: Comparing multiple measures.
• Parametric data: Normal distribution, homogeneous variance.
  • Choosing parametric test: Which to use.
  • t-test: Basic comparison of means.
  • Independent t-test: Equality of means of separate data sets.
  • Matched-pair t-test: Equality of means of pairs of data.
  • Z-test: Equality of means where variance is known.
  • ANOVA: Equality of means of multiple data sets.
  • F-ratio: Basic ANOVA test statistic.
  • Sphericity: Equality of variances of variables.
• Tests for Normal distribution: Bell-shaped.
  • Kolmogorov-Smirnov test: A common test.
  • Shapiro-Wilk test: An original alternative.
• Tests for variance homogeneity: Similar sample variance.
  • Variance homogeneity test: Using a table.
  • Levene's test: testing for similar variance.
• Non-parametric tests:
  • Choosing a non-parametric test: Which to use.
  • Chi-square test: For frequency data.
  • Cramer's V: Post-test for strength of association.
  • Phi Correlation: Post-test for 2x2 tables.
  • McNemar's Q: For dichotomous data.
• Other analysis and tests:
  • Factor Analysis: Finding combination measures.
  • Multiple regression: Comparing multiple measures.
• Participant/observer: for qualitative, observer, participative research.
  • Ethnographic principles: key differences with quantitative research.
  • Content analysis: Extracting information from documents.
  • Ethnographic coding: Extracting themes and core principles from the data.