Psychology in Tech

Learn how to calculate basic descriptive statistics (mean, standard deviation, etc.) for psychological survey data. This is the foundation of all statistical analysis.

# Step 1: Load required libraries
library(dplyr)
library(psych)

# Step 2: Load your survey data
# Make sure your CSV file has columns: anxiety_score, depression_score, stress_level
survey_data <- read.csv('psychology_survey.csv')

# Step 3: Calculate descriptive statistics
# This gives you mean, standard deviation, min, max, and more
results <- survey_data %>%
  select(anxiety_score, depression_score, stress_level) %>%
  describe() %>%
  select(mean, sd, min, max, skew, kurtosis)

# Step 4: View your results
print(results)

# What each statistic means:
# mean = average value
# sd = standard deviation (how spread out the data is)
# min = lowest value
# max = highest value
# skew = how symmetric the data is
# kurtosis = how 'peaked' the distribution is

Learn to compare two groups (e.g., control vs treatment) using an independent samples t-test. This is essential for experimental psychology research.

# Step 1: Import necessary libraries
import scipy.stats as stats
import pandas as pd
import numpy as np

# Step 2: Load your experimental data
# Your CSV should have columns: 'condition' (control/treatment) and 'score'
data = pd.read_csv('experiment_results.csv')
print(f'Total participants: {len(data)}')

# Step 3: Separate into two groups
control_group = data[data['condition'] == 'control']['score'].dropna()
treatment_group = data[data['condition'] == 'treatment']['score'].dropna()

print(f'Control group: n={len(control_group)}, mean={control_group.mean():.2f}')
print(f'Treatment group: n={len(treatment_group)}, mean={treatment_group.mean():.2f}')

# Step 4: Run independent samples t-test
# This tests if the two groups have significantly different means
t_stat, p_value = stats.ttest_ind(control_group, treatment_group)

# Step 5: Interpret results
print(f'\nT-test Results:')
print(f'T-statistic: {t_stat:.3f}')
print(f'P-value: {p_value:.4f}')

if p_value < 0.05:
    print('✓ Groups are significantly different (p < 0.05)')
else:
    print('✗ No significant difference between groups (p >= 0.05)')

# Calculate effect size (Cohen's d)
pooled_std = np.sqrt(((len(control_group)-1)*control_group.std()**2 + 
                      (len(treatment_group)-1)*treatment_group.std()**2) / 
                     (len(control_group) + len(treatment_group) - 2))
cohens_d = (treatment_group.mean() - control_group.mean()) / pooled_std
print(f'Effect size (Cohen\'s d): {cohens_d:.3f}')

Learn to compare three or more groups using one-way ANOVA. Perfect for experiments with multiple treatment conditions or comparing different populations.

# Step 1: Load required libraries
library(dplyr)
library(effectsize)

# Step 2: Load and prepare your data
# Your data should have: 'condition' (with 3+ groups) and 'score'
experiment_data <- read.csv('multi_group_experiment.csv')

# Check your groups
print(table(experiment_data$condition))

# Step 3: Run one-way ANOVA
# This tests: H0 = all groups have the same mean
#            H1 = at least one group differs
model <- aov(score ~ condition, data = experiment_data)

# Step 4: View ANOVA results
summary(model)

# Look at the p-value:
# - If p < 0.05: At least one group is significantly different
# - If p >= 0.05: No significant differences between groups

# Step 5: If ANOVA is significant, run post-hoc tests
# This tells you WHICH specific groups differ from each other
if(summary(model)[[1]][["Pr(>F)"]][1] < 0.05) {
  print('\nANOVA is significant! Running post-hoc tests...')
  posthoc <- TukeyHSD(model)
  print(posthoc)
  
  # Interpret: Look for p.adj < 0.05 to see which groups differ
}

# Step 6: Calculate effect size (eta squared)
# This tells you how much variance is explained by group membership
eta_sq <- eta_squared(model)
print(paste('Effect size (η²):', round(eta_sq$Eta2, 3)))

# Effect size interpretation:
# η² < 0.01: small effect
# 0.01 < η² < 0.06: medium effect
# η² > 0.14: large effect

Master data cleaning techniques for large psychology studies. Learn to filter participants by age, consent status, remove missing data, and handle duplicates. Essential before any analysis!

# ============================================
# DATA CLEANING WORKFLOW FOR LARGE STUDIES
# ============================================

# Step 1: Import libraries
import pandas as pd
import numpy as np

# Step 2: Load your raw dataset
# Your CSV should have columns: participant_id, age, consent, score, condition, etc.
df = pd.read_csv('psychology_study_2000.csv')
print(f'📊 STEP 1: Original data loaded')
print(f'   Total participants: {len(df)}')
print(f'   Columns: {list(df.columns)}')
print()

# Step 3: Remove participants under 18
# This is CRITICAL for ethical compliance!
print('🔍 STEP 2: Filtering by age (18+)')
under_18 = len(df[df['age'] < 18])
print(f'   Found {under_18} participants under 18 - removing...')
df = df[df['age'] >= 18]
print(f'   Remaining participants: {len(df)}')
print()

# Step 4: Filter by consent
# Only analyze data from participants who gave informed consent
print('✅ STEP 3: Filtering by consent')
no_consent = len(df[df['consent'] != 'Yes'])
print(f'   Found {no_consent} participants without consent - removing...')
df = df[df['consent'] == 'Yes']
print(f'   Remaining participants: {len(df)}')
print()

# Step 5: Check for missing data
print('🔍 STEP 4: Checking for missing data')
missing_counts = df[['age', 'score', 'condition']].isnull().sum()
print(f'   Missing values:')
for col, count in missing_counts.items():
    if count > 0:
        print(f'     - {col}: {count} missing')
print()

# Step 6: Remove rows with missing critical data
print('🧹 STEP 5: Removing rows with missing critical data')
before_drop = len(df)
df = df.dropna(subset=['age', 'score', 'condition'])
after_drop = len(df)
print(f'   Removed {before_drop - after_drop} rows with missing data')
print(f'   Remaining participants: {len(df)}')
print()

# Step 7: Remove duplicates
print('🔍 STEP 6: Checking for duplicate participants')
duplicates = df.duplicated(subset=['participant_id']).sum()
print(f'   Found {duplicates} duplicate entries')
df = df.drop_duplicates(subset=['participant_id'], keep='first')
print(f'   Remaining participants: {len(df)}')
print()

# Step 8: Final data quality check
print('✅ STEP 7: Final data quality check')
print(f'   Final dataset: {len(df)} participants')
print(f'   Age range: {df["age"].min()} - {df["age"].max()}')
print(f'   All have consent: {(df["consent"] == "Yes").all()}')
print(f'   Missing data: {df[["age", "score", "condition"]].isnull().sum().sum()}')
print()

# Step 9: Save cleaned data
print('💾 STEP 8: Saving cleaned data')
df.to_csv('cleaned_data.csv', index=False)
print(f'   ✓ Cleaned data saved to: cleaned_data.csv')
print(f'   ✓ Ready for statistical analysis!')

Put it all together! Clean your data, then run ANOVA analysis on a large dataset. This is the complete workflow from raw data to statistical results.

# ============================================
# COMPLETE ANOVA ANALYSIS WORKFLOW
# ============================================

# Step 1: Import all necessary libraries
import pandas as pd
from scipy.stats import f_oneway
import numpy as np
from scipy import stats

# Step 2: Load your CLEANED data
# (Make sure you ran the data cleaning script first!)
df = pd.read_csv('cleaned_data.csv')
print(f'📊 Analyzing {len(df)} participants')
print(f'   Conditions: {df["condition"].unique()}')
print()

# Step 3: Prepare groups for ANOVA
# Separate participants by their condition/group
control_group = df[df['condition'] == 'Control']['score'].dropna()
treatment_a = df[df['condition'] == 'Treatment A']['score'].dropna()
treatment_b = df[df['condition'] == 'Treatment B']['score'].dropna()

# Step 4: Check your groups before analysis
print('📈 Group Summary Statistics:')
print(f'   Control Group:')
print(f'     n = {len(control_group)}')
print(f'     Mean = {control_group.mean():.2f}')
print(f'     SD = {control_group.std():.2f}')
print()
print(f'   Treatment A:')
print(f'     n = {len(treatment_a)}')
print(f'     Mean = {treatment_a.mean():.2f}')
print(f'     SD = {treatment_a.std():.2f}')
print()
print(f'   Treatment B:')
print(f'     n = {len(treatment_b)}')
print(f'     Mean = {treatment_b.mean():.2f}')
print(f'     SD = {treatment_b.std():.2f}')
print()

# Step 5: Run one-way ANOVA
# This tests: Are the group means significantly different?
print('🔬 Running One-Way ANOVA...')
f_stat, p_value = f_oneway(control_group, treatment_a, treatment_b)

print(f'\n📊 ANOVA Results:')
print(f'   F-statistic: {f_stat:.3f}')
print(f'   P-value: {p_value:.6f}')
print()

# Step 6: Interpret significance
if p_value < 0.05:
    print('✅ SIGNIFICANT RESULT (p < 0.05)')
    print('   At least one group is significantly different from others')
    print('   → You should run post-hoc tests to see which groups differ')
else:
    print('❌ NOT SIGNIFICANT (p >= 0.05)')
    print('   No significant differences between groups')
print()

# Step 7: Calculate effect size (eta squared)
# This tells you HOW MUCH the groups differ (not just IF they differ)
print('📏 Calculating Effect Size (η²)...')

# Calculate sum of squares between groups
ss_between = sum([len(group) * (group.mean() - df['score'].mean())**2 
                  for group in [control_group, treatment_a, treatment_b]])

# Calculate total sum of squares
ss_total = sum((df['score'] - df['score'].mean())**2)

# Eta squared = variance explained by group membership
eta_squared = ss_between / ss_total

print(f'   Effect size (η²): {eta_squared:.3f}')

# Interpret effect size
if eta_squared < 0.01:
    effect_size = 'small'
elif eta_squared < 0.06:
    effect_size = 'medium'
else:
    effect_size = 'large'

print(f'   Effect size interpretation: {effect_size} effect')
print()

# Step 8: Summary
print('=' * 50)
print('SUMMARY')
print('=' * 50)
print(f'Total participants analyzed: {len(df)}')
print(f'Groups compared: {len([control_group, treatment_a, treatment_b])}')
print(f'ANOVA p-value: {p_value:.4f}')
print(f'Effect size: {eta_squared:.3f} ({effect_size})')
if p_value < 0.05:
    print('✓ Significant differences found - run post-hoc tests!')
else:
    print('✗ No significant differences between groups')