Quantitative data analysis

This document discusses various quantitative data analysis techniques for research. It covers describing and summarizing data, identifying relationships between variables, comparing variables, and forecasting outcomes. The five most important methods are identified as mean, standard deviation, regression, sample size determination, and hypothesis testing. Parametric and non-parametric techniques are also discussed. Four levels of data measurement are defined: nominal, ordinal, interval, and ratio data. Examples are provided for coding nominal/ordinal data and visualizing data through graphs and charts. Statistical tests like the t-test, ANOVA, and chi-square are also summarized.