EN_ACD_L4_Bivariate analyses - Copy (1)

Bivariate Analysis Overview

  • Bivariate analysis involves examining relationships between two variables.

  • It aims to explain results of one variable (dependent) using another (independent).

  • Hypothesis testing is used to ensure that observed links are statistically significant.

Hypothesis Testing

  • Validates relationships are not random, using a calculated value compared to a critical value.

  • Expressed as a p-value; p-value < 0.05 indicates a significant relationship.

  • General threshold in Social Sciences is p=0.05; lower in medical research.

Types of Variable Relationships

  • Qualitative-Qualitative: Analyzed through Chi-square test.

  • Numeric-Numeric: Correlation coefficient (r).

  • Qualitative-Numeric: ANOVA or T-test, with T-test used when qualitative has two modalities; ANOVA for more than two modalities.

Chi-square Test

  • Decision rule: p-value ≤ 0.05 suggests a relationship; p-value > 0.05 indicates independence.

Normal Distribution for Pearson's r

  • When t > |1.96|, a relationship exists; if t is between -1.96 and +1.96, no relationship.

ANOVA

  • Measures relationships between numeric and qualitative variables.

  • Compares means, with significance identified if p-value < 0.05.

Statistical Analysis Parameters

  • Report specific values depending on the test used (Chi-square, r-value, F statistic).

  • Statistical tests are critical for inferential statistics in social sciences.

Reporting Analysis Results

  • Chi-square Report: Significant relationship with Chi-square = 9.90, p-value < .01.

  • Correlation Report: Strong positive correlation of r = 0.58, p-value < .05.

  • ANOVA Report: Significant gender differences in car maintenance time: F = 49.89, p-value < .01; Men: M = 3.34, Women: M = 0.76.