Correlational study designs aim to determine, objectively, if there is a relationship between two or more variables. Those variables can be subjective or objective; two variables must be included, and often more areincluded than that. Samples can be smaller than those in prevalence designs, there is no need for probability sampling strategies, any form of measurement can be used, and any orientation to time is possible. Results are numerical, and require some understanding of correlation coefficients, tests of difference between groups, statistical significance, and effect size. Research in HDFS relies on correlational designs to explore potentially causal relationships because we are interested in very complex phenomena that cannot easily be isolated from their environments, such that experimental designs are either impractical or impossible. However, correlational data are difficult to interpret with regard to causality.