Stepping Your Way through Effective Quantitative Data Analysis

1. Data management – This involves familiarizing yourself with appropriate software; systematically logging in and screening your data: entering the data into a program; and finally, ‘cleaning’ your data.

2. Understanding variable types – Different data types demand discrete treatment, so it has important to be able to distinguish variables by both cause and effect (dependent or independent), and their measurement scales (nominal, ordinal, interval, and ratio).

3. Run descriptive statistics – These are used to summarize the basic features of a data set through measures of central tendency (mean, mode, and median), dispersion (range, quartiles, variance, and standard deviation), and distribution (skewness and kurtosis).

4. Run appropriate inferential statistics – This allows researchers to assess their ability to draw conclusions that extend beyond the immediate data. For example, if a sample represents the population; if there are differences between two or more groups; if there are changes over time; or if there is a relationship between two or more variables.

5. Make sure you selecting the right statistical test – This relies on knowing the nature of your variables; their scale of measurement; their distribution shape; and the types of question you want to ask.

6. Look for statistical significance – This is generally captured through a ‘p-value’, which assesses the probability that your findings are more than coincidence. The lower the p-value, the more confident researchers can be that findings are genuine.