## Independent Samples T-Test

The independent-samples t-test is used to determine if a difference exists between the means of two independent groups on a continuous dependent variable. More specifically, it [...]

## Regression Analysis

A simple linear regression assesses the linear relationship between two continuous variables to predict the value of a dependent variable based on the value of an [...]

## Binomial Logistic Regression

A binomial logistic regression attempts to predict the probability that an observation falls into one of two categories of a dichotomous dependent variable based on one [...]

## One-Way ANOVA

If you want to determine whether there are any statistically significant differences between the means of two or more independent groups, you can use a one-way [...]

## Correlation Analysis

The Pearson product-moment correlation is used to determine the strength and direction of a linear relationship between two continuous variables. More specifically, the test generates a [...]

## Paired-Samples T-Test

The paired-samples t-test is used to determine whether the mean difference between paired observations is statistically significantly different from zero. The participants are either the same [...]

## One-Way MANOVA

The one-way multivariate analysis of variance (MANOVA) is an extension of the one-way ANOVA to incorporate two or more dependent variables (i.e., the one-way ANOVA investigates just one [...]

## Multiple Regression Analysis

A multiple regression is used to predict a continuous dependent variable based on multiple independent variables. As such, it extends simple linear regression, which is used when [...]

## HMR

Like standard multiple regression, hierarchical multiple regression (also known as sequential multiple regression) allows you to predict a dependent variable based on multiple independent variables. However, the procedure [...]

## Chi-Square Test

The chi-square test can be used to test a variety of sizes of contingency tables, as well as more than one type of null and alternative [...]

## Two-Way ANCOVA

The two-way ANCOVA is used to determine whether there is an interaction effect between two independent variables on a continuous dependent variable (i.e., if a two-way [...]

## One-Way ANCOVA

The analysis of covariance (ANCOVA) can be thought of as an extension of the one-way ANOVA to incorporate a covariate variable. This covariate is linearly related to the [...]

## Three-Way RM ANOVA

The three-way repeated measures ANOVA is used to determine if there is a statistically significant interaction effect between three within-subjects factors on a continuous dependent variable [...]

## Two-Way ANOVA

The two-way ANOVA is used to determine whether there is an interaction effect between two independent variables on a continuous dependent variable (i.e., if a two-way [...]

## Kaplan-Meier analysis

The Kaplan-Meier method (Kaplan & Meier, 1958) (also known as the "product-limit method") is a nonparametric method used to estimate the probability of survival past given [...]

## Two-Way RM ANOVA

The two-way repeated measures ANOVA is used to determine if there is a statistically significant interaction effect between two within-subjects factors on a continuous dependent variable [...]

## PCA

Principal components analysis (PCA, for short) is a variable-reduction technique that shares many similarities to exploratory factor analysis. Its aim is to reduce a larger set [...]

## One-Way MANCOVA

The one-way multivariate analysis of covariance (one-way MANCOVA) can be thought of as an extension of the one-way MANOVA to incorporate a continuous covariate or an extension of [...]

## Two-Way MANOVA

The two-way multivariate analysis of variance (two-way MANOVA) is often considered as an extension of the two-way ANOVA for situations where there are two or more dependent variables. [...]

## One-Way RM ANOVA

The one-way repeated measures analysis of variance (ANOVA) is an extension of the paired-samples t-test and is used to determine whether there are any statistically significant differences between [...]