4.4. Analyzing quantitative data

Statistical software

  • Jamovi
    • simple + free
    • highly recommended, except if you already have experience with another one
    • plugins for more advanced analyses
    • based on R, so allows for a transition to R if you need full power in the future
  • SPSS: commercial, expensive
  • Stata: commercial for advanced analyses
  • R with RStudio: very powerful but very steep learning curve

Jamovi: tutorials and guides

Descriptive statistics

  • Objective: data reduction (how to better summarize all the individual data?)
  • 2 main components:
    • central tendency
    • variation
Central tendencyVariation
If normal distribution
($N β‰₯ 20$ + normal)
Mean ($M$)Standard deviation ($\text{SD}$)
Range (min & max)
Interquartile range (IQR)…

Inferential statistics


  • Objective: determine similarity between two (typically continuous) variables
  • Correlation coefficient: Pearson’s $r$

    or Spearman’s $\rho$ (rho) if not normally distributed
    • from -1 (perfect negative correlation)… to 0 (no correlation)… to 1 (perfect correlation)

Group comparison

  • Objective: determine if experimental treatment was significantly different than control

Significance test

(β€˜Null hypothesis significance testing’, NHST)

  • $t$-test (Student) for 2 groups
  • ANOVA for multiple conditions/comparisons
Statistical tests cheat sheet (PsychDB.com)
Statistical tests cheat sheet (PsychDB.com)
  • β†’ $p$-value
    • Convention: 95% confidence
    • $p < 0.05$ = significant, $p < 0.01$ = better, $p < 0.001$ = even better
    • if $p > 0.05$, then no significant difference

Effect size

  • Beyond significance: evaluate the size of the effect
  • Measures:
    • Pearson’s $r$ (correlation)
    • Cohen’s $d$ (and Hedges’ $g$, very similar) $$d = \frac{M_\text{exp} - M_\text{control}}{\textit{SD}_\text{pooled}}$$