How do you analyze data with multiple responses?
The three general steps are:
- Define a set of two more responses (you cannot do step 2 without doing this step first)
- Obtain multiple response frequencies (or cross-tabs) of the set you created – this will provide frequencies and percentages of each response option by total number of responses and by cases.
How do you determine which variables are statistically significant?
The level of statistical significance is often expressed as a p-value between 0 and 1. The smaller the p-value, the stronger the evidence that you should reject the null hypothesis. A p-value less than 0.05 (typically ≤ 0.05) is statistically significant.
How do you analyze multiple variables in SPSS?
Creating and Using a Multiple-Response Set in SPSS
- Open the Apples and Oranges.
- Choose Analyze→Multiple Response→Define Variable Sets.
- In the Set Definition list, select each variable you want to include in your new multiple dataset, and then click the arrow to move the selections to the Variables in Set list.
What are multiple response variables?
Overview. Multiple response variables, also called multi-punch questions or MRVs, are questions for which respondents can select more than one answer. Many dataset formats represent the original options as individual variables instead of the “select all that apply” format in which the question was presented.
How do you specify a variable distribution?
The probability distribution for a random variable describes how the probabilities are distributed over the values of the random variable. For a discrete random variable, x, the probability distribution is defined by a probability mass function, denoted by f(x).
What are the 4 ideas that should be addressed when asked to describe a distribution?
When describing distributions on the AP® Statistics exam, there are 4 key concepts that you need to touch on every time: center, shape, spread, and outliers. Below is a preview of the main elements you will use to describe each of these concepts.