JASP
Comparing sample to population 👥
Continuous
Categorical
Dichotomous category
Multiple categories
Binomial
Multinomial
Checking for normality -Shapiro-Wilk Test
Non-significant --> Paramatic test
e.g. Laptop - windows/ Mac - UK population & uni students
e.g. different colour M&M counts in 5 packs
Comparing two independent groups : 🍻
Independent T test / Student T test
Assumptions not met
Homogeneity - Levene's significant
Welch adjusted t statistic
Normality - Shapiro Wilk significant or Ordinal
e.g.
Mann Witney U test
Descriptive stratics for non parametric
Median
Rain cloud / Box plot
SD/ SE - confidence interval
Comparing two related groups
Paired sample T test / 🎊 dependent T test / Repeated measures T test
Assumptions
compares means b/w two related groups
dependent variable - e.g. weight loss - continuous variable
related group - e.g. pre & post diet
Difference b/w groups
Independent variable
Dependent variable
Continuous
2 categorical / matched groups
Normally distributed
No significant outliers
Shapiro Wilk test
effect size - cohen's d
Non Parametric
Not normally distributed - significant Shapiro Wilk
Ordinal e.g. pre & post hypnotherapy anxiety scores
Wilcoxon's signed rank test
Hodges Lehmann estimate
location parameter
Rank biserial correlation
effect size
Effect
Size
the standardized mean difference
Correlation coefficient
ANOVA
T-test /Non-paramatic alternatives
Correlation
Regression
Odds ratio
2 X 2 table
Correlation Coefficient r (paramatic?)
Multiple Correlation Coefficient
Spearman's Rho & Kendall's Tau (non-paramatic)
Phi
Any table size
Cramer's V
Paramatic
Cohen's d
Non-paramatic
Rank Biserial
Eta squared
Partial Eta Squared
Omega squared
One sample T test
Significant --> Non-paramatic test
Paired sample test
e.g. Height & Weight of sample compare with population
χ 2 Chi-squared Goodness-of-fit test
Compared with expected proportion
Comparing more than two means
ANOVA - Analysis of One Sample Variation
Independent ANOVA
e.g.
3 types of Diet
Weight loss
Ho - No significant difference between the means of all the groups
2 Groups of independent variable e.g. Males & Females
Continuous dependent variable e.g. 10 weeks post diet weight loss after taking a special diet
Effect size
Cohen's d -
Inferential statitics for non paramatic
Effect size
Rank biserial correlation
Location parameter
Hodges Lehmann Estimate to decide Median difference
e.g.
e.g. Dependent variable (ordinal) Subjective pain score
e.g. Grouping variable in JASP +/- ice therapy - Independent (categorial)
Compared with equal proportion
Correlation
Types
Dependent variable - continuous
Independent variable - categorical
Contrasts - comparisons
Post Hoc tests
Priori test - 6 types
Identify which groups are different
4 Types
4 Corrections
Deviation
simple
Difference
Helmert
Repeated
Polynomial
Standard
Games - Howell
Dunnett's
Dunn
Bonferroni
Holm
Tukey
Scheffe
Sidak
Effect size
Eta squared
Partial Eta squared
Omega squared
Non parametric - Kruskal Wallis test
Parametric assumptions fail / dependent variable - ordinal
Rank based
e.g.
not specify which is significant
Dunn's Post-hoc test
Subjective pain score
treatment - compression & cryotherapy, cryotherapy, control
Dependent variable
Independent variable
Chi-squared test for independence
Purpose
Non parametric
Parametric
non standardized
standardize
Pearson's correlation coefficient
correlation coefficient
-1 or +1 : high correlation
0 : no correlation
Coefficient of determination : R square
Explained variation / total variation
0- 100%
0 : no variability around mean
100% all variabily
Rank based
Spearman Rho
Kendal Tau
Rho square
ordinal scale
small sample / ties
Determination
Features
Not showing the causation
Linear association
Synonyms
Pearson's Chi square test
Chi square test of association
Relationship b/w 2 or more categorical varaiables
Assumptions
Cross tabulation or contingency table
Ho : No association b/w two categorical
Compare
Expected if no association
Observed
two variables must be categorical
each variable - 2 or more independent categorical groups
Purpose
Association b/w 2 continuous variables
E.g.
Jump height
Leg power
Assumption violated
Normality
Variance
total population
Findings
Validity
<20% cells : < 5
No cells < 1
e.g.
Titanic survivors
Independent variable : column
dependent variable : rows
survival status
Age
sex
Class
Chi square
Chi square contingency correction
Prevent overestimation of significance in small sample size
at least 1 cell < 5
⚠ overly correct - type 2 error
Two groups
Comprarison b/w two groups
Relationship b/w two variables
t-test, ANOVA
Corelation
Likely hood ratio
Alternative to Pearson's
Maximum likely hood theory
for small small sample < 30
Nominal measures : effect size
Phi
Cramer V
2x2 table only
2 to 5
range 1 to 0
1 : complete association
0 : no association
Contingency coefficient
large table 5x5
Adjusted Phi
Odds ratio
Reciprocal of odds (1/odds)
times increase or decrease
Post-hoc
Standardized residual (version of Z score)
Z=
(Correction for df =1) Z=
Sign of Z
- Z score : observed < expected
+ Z score : Observed > expected : :
One sample t test
Ho : Medians of two samples are equal or that the difference between medians is zero
Ho : Median difference is zero
Differences not follow normal distribution
W < critical value
Regression
Predictive analysis
Predict dependent
From single independent
Multiple independent
Simple regression
Multiple regression
Linear regression model
Sample size
10 -15 data points per variable
50 + (8 x number of independent variables)
Formula : y = c + b*x + e
y = dependent variable
c = intercept
b = regression coefficient
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Multi colinearity
Outliers
< 1% of the total participants. The outliers will have a relatively small impact on the model
but keeping them means our sample may better represent the diversity of the population
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