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JASP ordered classification variable kendall coefficient is what, many novices are not very clear about this, in order to help you solve this problem, the following editor will explain in detail for you, people with this need can come to learn, I hope you can gain something.

In correlation analysis, it is most common to examine the correlation between two consecutive data, such as advertising expenses and product sales. However, in practice, it is inevitable to encounter the relationship between the two classification variables, such as some researchers want to investigate the correlation between different academic qualifications and income levels in an industry.

These two data are not ordinary, not ordinary disordered classification variables, but ordered classification variables, so we can not use Pearson correlation coefficient to explore at will, the appropriate choice is kendall's tau-b correlation coefficient.

Original data record

Suppose we get the original data record, one case by one line, and the two columns are income inc and education edu. Some of the data are shown as follows:

The menu operation of JASP is actually very simple. Select [Correlation Matrix] in [Regression]. First of all, we throw the data that needs to be analyzed into the target variable box, which is to tell the software that we have to analyze both of them.

Then tell the software directly, I ask you to calculate the kendall's tau-b coefficient for me immediately, just check the parameter option.

The results on the right will be presented immediately. Let's take a look:

The three-wire table is good, but the format is a little ugly and there are extra cells.

Check [Display pairwise table], change the table form, and then check [Report sig] to mark the significant level for more statistical sense. The results are as follows:

The range of correlation coefficient is [- 1], negative sign indicates negative correlation, positive number indicates positive correlation, the greater the absolute value of coefficient, the stronger the correlation.

In this case, Kendall's tau-b=0.719, significance test P < 0.001, indicating that there is a positive correlation between educational background and income, that is, the higher the educational background, the higher the income, which is statistically significant.

If you do the significance test of the correlation coefficient (P > 0.05), it means that there is no statistical correlation between the two variables.

Crosstab frequency data

Suppose what we get is not the original data, but the frequency table data after statistical summary, and the relevant analysis can be easily completed in JASP. At this point, it is time to use contingency table analysis.

Row variable education edu, column variable inc, plus a frequency data, this is the crosstab frequency data.

By selecting [Contingency Tables] from the [Frequencies] menu at the top, you can use the education edu as the row variable (for grouping), the income inc as the column variable (the target of interest), and then weighted by the frequency data. Weighting and crossover tables are in the same menu, which is much more convenient than SPSS.

Then we need to tell the software what I want. Cross-table chi-square test is not omnipotent, it is suitable for unordered classification variables, for ordered classification variables, we do not have to output chi-square test results.

Since the goal of the analysis is clear, is to examine the correlation between education and income, then crisp command software technology [Kendall's tau-b]. Forget about the other parameters.

Let's see the results.

Just take a look at the crosstab table. What is valuable is the synchronous output of "theoretical frequency", which is the principle of chi-square test. Look directly at the relevance. Kendall's tau-b=0.719, significance test P < 0.001, indicating that there is a positive correlation between educational background and income, that is, the higher the educational background, the higher the income, which is statistically significant. There is no doubt that the results are exactly the same as the previous original data records. Is it helpful for you to read the above content? If you want to know more about the relevant knowledge or read more related articles, please follow the industry information channel, thank you for your support.

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