Types Of Statistical Tests.docx

TYPES OF STATISTICAL TESTS In testing your hypothesis, you have to choose the appropriate statistical test. How do you know? Types of statistical tests: There is a wide range of statistical tests. The decision of which statistical test to use depends on the research design, the distribution of the data, and the type of variable. In general, if the data is normally distributed, you will choose from parametric tests. If the data is non-normal, you will choose from the set of non-parametric tests. Below is a table listing just a few common statistical tests and their use

Type of Test

Use

Correlational: These tests look for an association between variables Pearson correlation

Tests for the strength of the association between two continuous variables and collected from the same respondents.

Spearman correlation

Tests for the strength of the association between two ordinal variables (does not rely on the assumption of normally distributed data) and collected from the same respondents.

Chi-square

Tests for the strength of the association between two categorical variables.

Comparison of Means: look for the difference between the means of variables Tests for the difference between two related variables on one Paired t-test outcome variable Tests for the difference between two independent variables using Independent categorical/ordinal data on one outcome (Dependent) variable t-test using continuous data. One-Way ANOVA

Tests the difference between 3 or more independent group means using categorical/ordinal data on one outcome (Dependent) variable.

Two-Way ANOVA

Tests the difference between 3 or more independent group means using categorical/ordinal data on two or more outcome (Dependent) variables.

Regression: assess if change in one variable predicts change in another variable Simple regression

Tests how change in the predictor variable predicts the level of change in the outcome variable.

Multiple regression

Tests how change in the combination of two or more predictor variables predict the level of change in the outcome variable.