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Statistical Tests Cheat Sheet (DRAFT) by

Overview of statistical tests an when to use them

This is a draft cheat sheet. It is a work in progress and is not finished yet.

1 Dependent Variable & 0 IVs (1 Popula­tion)

DV
Test
interval & normal
One-sample t-test
 
tests if a sample mean differs sig. from a hypoth­esized value
ordinal or interval
One-sample median test
 
tests if a sample median differs sig. from a hypoth­esized value
categorial (2 catego­ries)
Binominal Test
 
tests if the proportion of successes on a two-level categorial dependent variable differs sig. from a hypoth­esized value
categorial
Chi-square goodne­ss-­of-fit
 
tests if the observed propor­tions for a categorial variable differ from hypoth­esized propor­tions

1 DV & 1IV with 2 levels (indep­endent groups)

DV
Test
interval & normal
2 indepe­ndent sample t-test
 
compares the means of a normally distri­buted interval DV for two indepe­ndent groups
ordinal or interval
Wilcox­on-Mann Whitney test
 
is a non-pa­ram­etric analog to the indepe­ndent samples t-test
 
used, when you do not assume that the DV is a normally distri­buted interval variable
categorial
Chi-square test
 
to see if there is a relati­onship between 2 categorial varibales
 
assumes that each cell has an expected frequency of 5 or more
categorial
Fischer`s exact test
 
same as Chi-square test, but can be used regardless of the expected frequency (expected frequency of 5 or less)

1 DV & 1IV with 2 or more levels (indep. groups)

DV
Test
interval & normal
One-Way ANOVA
 
test for differ­ences in the means of the DV broken down by the levels of the IV
 
used when categorial IV (with one or more catego­ries) an normally distri­buted interval DV
ordinal or interval
Kruskal Wallis test
 
is non-pa­ram­etric version of ANOVA and a genera­lized form of the Mann-W­hitney test since it permits two or more groups
categorial
Chi-square test

1 DV & 1IV with 2 or more levels (indep. groups)

DV
Test
interval & normal
One-Way ANOVA
 
test for differ­ences in the means of the DV broken down by the levels of the IV
 
used when categorial IV (with one or more catego­ries) an normally distri­buted interval DV
ordinal or interval
Kruskal Wallis test
 
is non-pa­ram­etric version of ANOVA and a genera­lized form of the Mann-W­hitney test since it permits two or more groups
categorial
Chi-square test
 

1 DV & 1IV with 2 (depen­den­t/m­atched groups)

DV
Test
interval & normal
Paired t-test
 
used when you have two related observ­ations and want to see if the means on these two normally distri­buted interval variables differ from one another
ordinal or interval
Wilcoxon signed rank sum test
 
is non-pa­ram­etric version of a paired sample t-test
 
used, when you do not wish to assume that the difference between the two variables is the interval and normally distri­buted
categorial
McNemar test
 
use if interested in the marginal freque­ncies of two binary outcomes

1 DV & 1 IV with 2 or m. lev. (dep./­matched g.)

DV
Test
interval & normal
One-Way repeated measures ANOVA
 
is the equivalent of paired t-test, but allows for 2 or more levels of the categorial variable
ordinal or interval
Friedman test
 
use when you have one within­-su­bjects IV with 2 or more levels and a DV that is not interval or normally distri­buted
categorial (2 catego­ries)
Repeated measures logistic regression
 
use if you have a binary outcome measured repeatedly for each subject and wish to run a logistic regression that accounts for the effects of multiple measures from a single subject

1 DV & 2 or more IVs (indepen. groups)

DV
Test
interval & normal
factorial ANOVA
 
use if you have 2 or more categorial IV and a single normally distri­buted interval DV
ordinal or interval
Ordered logistic regression
 
used, when the DV is ordered, but not continuous
categorial (2 categories
Factorial logistic regression
 
used, when you have 2 or more categorial IV but a dichot­omous DV

1 DV & 1 interval IV

DV
Test
interval & normal
Correl­ation
 
used, when you want to see the relati­onship between two (or more) normally distri­buted interval variables
interval & normal
Simple linear regression
 
allows us to look at the linear relati­onship between one normally distri­buted interval IV and one normally distri­buted interval DV
ordinal or interval
Non-pa­ram­etric correl­ation (Spearman)
 
used, when one or both of the variables are not assumed to be normally distri­buted and interval
 
the values of the variables are converted in ranks and then correlated
categorial
Simple logistic regression
 
assumes that the outcome variable is binary

1 DV & 1 or m. interval IV/ 1 or m. categ. IVs

DV
Test
interval & normal
Multiple Regression
 
similar to simple regres­sion, except that in multiple regression you have more that one IV in the equation
interval & normal
Analysis of Covariance
 
like ANOVA, except in addition to the categorial IV you also have continuous IV
categorial
Multiple logistic regression
 
like simple regres­sion, except that there are 2 or more IV
 
IV can be dummy or interval variables, but cannot be categorial variables (if, should be coded into 1 or more dummy variables)
categorial
Discri­minant analysis
 
used, when you have one or more normally distri­buted interval IV and a categorial DV
 
is a multiv­ariate technique that considers the latent dimensions in the IV for predicting group membership in the categorial DV
 

2+ DV & 1 IV with 2 or more levels (indep. groups)

DV
Test
interval & normal
One-way MANOVA
 
like ANOVA, except that there are 2 or more DV.
 
there is one categorial IV and two or more DV
interval & normal
Multiv­ariate multiple linear regression
 
used, when you have two or more DV that are to be predicted from two or more IV
interval & normal
Factor analysis
 
is a form of explor­atory multiv­ariate analysis that is used to either reduce the number of variables in a model or to detect relati­onships amongst variables
 
all variabales need to be interval and assumed to be normally distri­buted
 
goal is to try to identify factors which underlie the variables

2 sets of 2+ DV & 0 IV

DV
Test
interval & normal
Canonical correl­ation
 
is a multiv­ariate technique used to examine the relati­onship between two groups of variables
 
for each set of variables, it creates latent variables and looks at the relati­onship among the latent variables
 
assumes that all variables in the model are interval and normally distri­buted