About: This article describes the Analyze subtab in the Analysis tab.
Table of Contents
Introduction
The Analyze subtab allows users to perform means, frequency, or profiling analyses on their dataset.
Means Analysis
The Means tab performs means analysis on any continuous variable. This is important for instances where you may want to isolate specific variables and compare and contrast average values as well as other basic information like: a count of the number of observations; and maximum value, and minimum value for each category or subclass.
Variables (numeric)
From the variable list, select a variable to examine the mean of by selecting the variable and clicking the arrow to move it to the Selected Variable window. You may select more than one variable to examine the means of in this step.
Variable (binary and categorical)
Select one or more variables from the Variables (binary and categorical) list by selecting the variable names, then clicking the arrow to move them to the By (optional) field. This section will take the numeric variables from the Selected Variables window, and further collate them by the binary and/or categorical variables selected. Select Analyze to view the results of the means analysis.
Frequency Analysis
The Frequency tab looks at the frequency of occurrence for binary and categorical variables.
Single Variable Category Frequencies
Users may select one or more variables from the list by using the arrow buttons. Selecting the Analyze button displays the counts and percentages for each variable selected.
Selecting a By (optional) category further breaks down these variable frequencies by a variable category. There are four views for each comparison of two variables:
- Raw Numbers
- Row Percentages
- Column Percentages
- Total Percentages
Profiling Analysis
The Profiling tab compares two groups of a binary variable to determine how statistically different they are.
- Select a Binary Variable to Profile: Lists all of the binary variables in a dataset. Select a single variable to run a profile analysis. Only one variable can be selected at a time.
- P-Value: The P-Value drop-down menu gives the ability to change the p-value according to how statistically significant the relationship between the X-variables and Y-variable should be. The lower the p-value, the more significant the relation.
- Profile: Generates a chart that compares the categories of the binary variable chosen to variables for which the two categories have statistically significantly different means or proportions.
The upper portion of the chart compares each category to each of the continuous variables in a dataset by showing the means of each variable for both categories of the binary variable.
The lower portion of the chart compares each category to each of the binary and categorical variables in the dataset for which the two categories have statistically significantly different means or proportions.
Z-Scores are also included in the table. A Z-score (measure of how many standard deviations an observation is above or below a mean. The larger the Z-score, the further away each observation is from the mean.
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