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What you need to know  Before selecting the correct statistical analysis, you need to

Testable hypotheses  A hypothesis is the researcher's prediction, derived from theory or speculation, about how two or more measured variables are related to one another. Testable hypotheses often state or H1: There is a significant relationship between (continuous variable) and (continuous variable). These are the two most basic types of hypotheses. Review quantitative research studies for examples of many more types of testable hypotheses. 
Operationalized variables  Operationalizing your variable means that you know how you are going to measure it. For example, we operationalize 'gender' by asking a person if they are male or female. 
Variable level  Is your variable Nominal level (one or more mutually exclusive categories) Ordinal level (ranked data where the differences in value are not equal  categories are mutually exclusive and exhaustive) Interval level (there are meaningful amounts of differences between the data values but there is no absolute zero) Ratio (equal distances between data points (e.g., between 1 and 2, 2 and 3, etc.) and there is an absolute zero 
Categorical/Continuous  Nominal level variables are always categorical level Ordinal level variables are usually considered categorical, but if you sum a series of Likerttype ordinal level questions, you will end up with a continuous variable Interval/ratio level variables are always continuous variables 
Steps to choosing your statistical analysis  Follow this link to a presentation that will ask you a series of questions. Each answer will lead you closer to a recommended data analysis. Before you begin, you must know

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by Diane M. Dusick, Ph.D.
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