# 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

## Descriptive statistics

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

## Inferential statistics

### Correlation

• 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

(‘Null hypothesis significance testing’, NHST)

• $t$-test (Student) for 2 groups
• ANOVA for multiple conditions/comparisons
• → $p$-value
• $p < 0.05$ = significant, $p < 0.01$ = better, $p < 0.001$ = even better
• if $p > 0.05$, then no significant difference
• Pearson’s $r$ (correlation)
• Cohen’s $d$ (and Hedges’ $g$, very similar) $$d = \frac{M_\text{exp} - M_\text{control}}{\textit{SD}_\text{pooled}}$$