Matching goals with variables. When designing a statistical study, it is important to understand and match your goals with each variable’s measurement type: nominal, ordinal, interval, and ratio. Each type contains distinct characteristics and provides differing benefits. Nominal Nominal variables are also qualitative which represent data usually assuming categorical values, such as a person’s ethnicity…

Understanding a Variable’s Function Variables, whether quantitative or qualitative, serve a specific function depending on the type of study being performed and it is important to understand each when designing the study. Independent Variable When designing the study, note those variables which are changing during the scenario, they are considered independent (IV). For example, if…

Identifying the Best Method to Create a Sample As discussed in Population versus Sample, sampling provides a more cost and time-effective means of mining data versus attempting to gather an entire population. Building the sample requires deciding which sampling methods best suites your statistical needs: Simple Random Sampling: This represents the best method as it…

Mining data cost-effectively All statistical analysis requires gathering data. Data may either be gathered for the entire target population, or only a portion or “sample.” In most cases, gathering all data (population) is unrealistic in terms of cost and time. For example, if desiring to study the effects of an environmental cause on patients in…

Classifying Variables as Quantitative or Qualitative Data in samples represent variables which are used to statistically analyze patterns. For example, a sample of clinic patients would contain a list of their weight values. In this case weight is the variable. Quantitative or Qualitative All variables are either quantitative or qualitative based on the type of…