The first step in determining which statistical analysis to use is identifying your data type. Different analyses will be appropriate depending on the type of independent variable (IV) and dependent variable (DV) you're working with. Here are some standard analyses:
T-test: Use this analysis for a categorical IV with two levels and a continuous DV. This test determines if there's a significant difference between the two groups on the DV (r-code).
ANOVA: Use ANOVA when you have a categorical IV with two or more levels and a continuous DV (r-code).
Pearson Correlation: Use this test to measure the relationship between a continuous IV and a continuous DV (r-code).
Spearman Correlation: Use this test to measure the relationship between an ordinal IV and an ordinal DV (r-code).
Chi-Square: Use this test to measure the relationship between two categorical variables (r-code).
Regression: Use this predictive analysis to measure the effect of a continuous IV on a continuous DV (r-code).
Binary Logistic Regression: Use this predictive analysis to measure the effect of a continuous or categorical IV on a categorical dependent variable with two levels (r-code).
Recommended Books
Discovering Statistics Using IBM SPSS Statistics by Andy Field Principles and Practice of Structural Equation Modeling by Rex B. Kline Introduction to Mediation, Moderation, and Conditional Process Analysis By Andrew F Hayes Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences By Cohen, Cohen, West, and Aiken Discovering Statistics Using R By Andy Field, Jermey Miles, and ZOE Field Using Multivariate Statistics by Barbara G. Tabachnick and Linda S. Fidell Statistics for the Behavioral Sciences by Frederick J. Gravetter and Larry B. Wallnau