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Software Version : 1. All rights reserved. The non-commercial academic use of this software is free of charge. The only thing that is asked in return is to cite this software when results are used in publications. Cite this software as: Ian E. Send output to:.

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Trending from CNET. Download Now. A standalone Windows program that calculates the chi-square statistic for 2x2 to 10x10 contingency tables.

### Chi-Square Test of Independence - SPSS Tutorials - LibGuides at Kent State University

Also calculates the contingency coefficient, phi, and kappa statistics. The chi-square test is used to compare the distributions of two independent samples. It is used with data in the form of frequencies. It is a nonparametric procedure that makes no assumptions about distribution shapes, variances, or levels of measurement.

Full Specifications. What's new in version 2. Date Added January 12, Version 2. Operating Systems. Additional Requirements None. Total Downloads 4, Downloads Last Week 4. Report Software.

### Chi-Square Test

D Statistics: Opens the Crosstabs: Statistics window, which contains fifteen different inferential statistics for comparing categorical variables. E Cells: Opens the Crosstabs: Cell Display window, which controls which output is displayed in each cell of frre crosstab. Note: in a crosstab, the cells are the inner sections of the table.

They show the number of observations for a given combination of the row and column categories. There are three options in this window that are useful but optional when performing a Chi-Square Test of Independence:.

### Chi-Square Analysis - Free download and software reviews - CNET Download

This option is enabled by default. F Format: Opens the Crosstabs: Table Format window, which specifies how the rows of the table are sorted. In the sample dataset, respondents were asked their gender and whether or not they were a cigarette smoker. There were three answer choices: Nonsmoker, Past smoker, and Current smoker.

Before we test for "association", it is helpful to understand what an "association" and a "lack of association" between two categorical variables looks like. One way to visualize this is using clustered bar charts. Let's look at the clustered bar chart produced by the Crosstabs procedure. This is the chart that is produced if you use Smoking as the row variable and Gender as the column variable running the syntax later in this example :.

The "clusters" in a clustered bar chart are determined by the row variable in this case, the smoking categories. Squafe color of the bars is determined by the column variable in this case, gender.

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The height of each bar represents the total number of observations in that particular combination of categories. This type of chart emphasizes the differences within the categories of the row variable. Notice how within each smoking category, dkwnload heights of the bars i. That is, there are an approximately equal number of male and female nonsmokers; approximately equal number of male and female past smokers; approximately equal number of male and female current smokers.

## Developer's Description

Sofwtare there were an association between gender and smoking, we would expect these counts to differ between groups in some way. The first table is the Case Processing summary, which tells us the number of valid cases used for analysis.

Chi-Square Test Calculator. This is a easy chi-square calculator for a contingency table that has up to five rows and five columns (for alternative chi-square calculators, see the column to your right). The calculation takes three steps, allowing you to see how the chi-square statistic is calculated. This free online software (calculator) computes the Pearson Chi-Square Test and the Exact Pearson Chi-Square Test by Simulation. The default data vectors were abstracted from the study dataset provided on the STARS website. This R module is used in Workshop 7 of the PY statistics course at Aston University, UK. Chi-Square Test. Note: Chi Sounds like "Hi" but with a K, so it sounds like " Ki square". And Chi is the greek letter Χ, so we can also write it Χ2. Important points before we get started: This test only works for categorical data (data in categories), such as Gender {Men, Women} or color {Red, Yellow, Green, Blue} etc, but not numerical data.Only cases with nonmissing values for test smoking behavior and gender can be used in the test. Rather, we conclude that **download** is not enough evidence to suggest an association between gender **software** smoking. Recall that the column downoad of the crosstab appeared to indicate that upperclassmen were less likely than underclassmen to live on campus:.

The clustered bar chart from the Crosstabs procedure can act as a complement to the column percentages above. **Free** look at the chart produced by the Crosstabs procedure for sofware example:. The "clusters" are formed by the row variable in this case, class rank. This type of chart emphasizes the differences square the underclassmen and upperclassmen groups.

Here, the differences in number of students living on campus versus living off-campus is much starker within the class rank groups. Only cases with nonmissing values for both class rank and living on campus can be used in the test. The next table is the crosstabulation. If you elected to check off the boxes for Observed Count, Expected Count, and Unstandardized **Chi,** you should see the following table:.

### Chi Square Calculator - Up To 5x5, With Steps

With the Expected Count values shown, we can confirm that all cells have an expected value greater than 5. Twst this Guide Search. This test is also known as: Chi-Square Test of Association. Common Uses The Chi-Square Test of Independence is commonly used to test the following: Statistical independence or association between two or more categorical variables.

Data Requirements Your data must meet the following requirements: Two categorical variables.

### Chi-Square Test - Free Statistics and Forecasting Software (Calculators) v

Two or more categories groups for each variable. Independence of observations. There is no relationship between the subjects in each group. The categorical variables are not "paired" in any way e. Relatively large sample size. Expected frequencies for each cell are at least 1.

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