Below is a brief introduction to each of the commonly used statistical designs with examples of each type. After eliminating any issues based on exploratory data analysis and reducing the likelihood of committing type I and type II errors, a statistical test can be chosen. For example, parametric tests, including some discussed below (t-tests, analysis of variance (ANOVA), correlation, and regression), require the data to have a normal distribution and that the variances within each group are similar. Furthermore, investigators should ensure appropriate statistical assumptions. Importantly, before deciding on a statistical test, individuals should perform exploratory data analysis to ensure there are no issues with the data and consider type I, type II errors, and power analysis. With this overview of the types of variables provided, we will present commonly used statistical designs for different scales of measurement. The following examples are ordinal variables: continuous: if the variable has more than ten options, it can be treated as a continuous variable. A general guideline for determining if a variable is ordinal vs. For example, on a 20-item scale with each item ranging from 1 to 5, the item itself can be an ordinal variable, whereas if you add up all items, it could result in a range from 20 to 100. Likert items can serve as ordinal variables, but the Likert scale, the result of adding all the times, can be treated as a continuous variable. Ordinal data (also sometimes referred to as discrete) provide ranks and thus levels of degree between the measurement. Multiple types of variables determine the appropriate design. To determine which statistical design is appropriate for the data and research plan, one must first examine the scales of each measurement. By understanding the types of variables and choosing tests that are appropriate to the data, individuals can draw appropriate conclusions and promote their work for an application. This decision could lead to work being rejected for publication or (worse) lead to erroneous clinical decision-making, resulting in unsafe practice. Individuals who attempt to conduct research and choose an inappropriate design could select a faulty test and make flawed conclusions.
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