Many of the most important studies in the social sciences are about relationships between variables. For example, think of how different states voted in the 2020 election, which unfolded over the COVID-19 pandemic. You may have heard that Trump’s lacking response to the pandemic has cost him his reelection. But is this backed by the data? To find out, we may test if states with more COVID-19 cases voted more heavily for Joe Biden.

Hypothesis: States with more COVID-19 cases voted more heavily for Joe Biden.

To test this hypothesis, we would look at the relationship between two variables: the number of COVID-19 cases and the percentage of votes for Joe Biden.

More than just studying the relationship between two variables, this study is also posing a hypothesis about how one variable affects the other. We want to test whether COVID infections affect voting for Biden. This means that the number of COVID-19 cases are the independent variable, and the percentage of votes for Joe Biden are the dependent variable.

In any hypothesis that poses a causal relationship between two variables, there will be an independent variable and a dependent variable. By definition, the independent variable affects the dependent variable. In other words, the independent variable is the cause, and the dependent variable is the effect.

To identify the dependent and independent variables in a hypothesis, we must ask: according to this hypothesis, what is affecting what? Let’s work through an expanded set of examples:

dv and iv table 2.png