The correlation index of leukocytosis was analyzed

Me too, it should be that the immunity is a bit low. Try some spirulina. After I saw it, the doctor prescribed it. It's okay to eat. Personal words. Ask your doctor. Satisfied.

Analysis-Dimensionality Reduction-Factor Analysis, then pull all the variables in the correlation matrix you want to generate into "Variables", click "Description", and in the "Correlation Matrix" box below, select "Coefficient", "Significance", "Determinant", and "Determinant", and click "OK" and "OK".

Landlord, do you have an answer? Ask for advice.

This requires data entry with SPSS first, and then data analysis.

As we can see from the table, the correlation coefficient between EDI and EDI is 1 (which is obvious, self is related to definite linearity), and similarly, the matrix diagonal position is 1. The correlation coefficient of the other two variables is between -1 and 1, such as the correlation coefficient between EDI and HP. The number in the second subline of each row of each column of the matrix is the value of the two-sided test, known from the note below, divided into two levels of significance, and .

n should be the number of observations.

The correlation coefficient ranges from -1 to 1, with the stronger the correlation and the smaller the p-value will be, the more the absolute value tends to be 1. If the p-value is less than , the correlation coefficient is meaningful.

The same goes for the indicator below.

Table 3 uses ANOVA (analysis of variance) to analyze significance.

p=> not significant.

You can calculate the average of the dimensions, combine multiple items into a single dimension, and then perform correlation analysis.

For questionnaire data, several questions represent one dimension at the same time. For example, if you want to combine the two questions "I can get a sense of accomplishment at work" and "I can use my personal talents at work" into one dimension (influencing factors), you can use the [Generate Variables] function of SPSSAU to calculate the mean value and generate new variables for subsequent analysis.

Steps: 1. Select all the items you want to merge; 2. Add the name of the variable; 3. Confirm the processing.

Generate variables. Correlation analysis.

You don't need to do the validity first, and then make a dimension correlation.

Adding the total score directly is the easiest.

1.For the scores of each dimension, you can use the scores under each dimension.

Addition, you can also use the addition to find the average, and the general psychological scale is averaged. 2.Scale A and Scale B cannot be summed together, so there is no point in 3

Regarding the average score of scale A and scale B, it is generally added by adding the average score of each dimension and then dividing by the number of dimensions In addition, general psychological scales have dimensional explanations of scales, as well as calculation methods, so it is recommended that you look for the scale you use and see if there are corresponding instructions and explanations. Hope to adopt, thank you.

Hello, how do you do the correlation analysis between the above 7 questions? I want to categorize the options within the questions in the questionnaire and see the correlation between the options.

You have to do not only correlation analysis, but also SEM, which is very complicated for you and almost impossible to complete.

I do a lot of this kind of statistical analysis for others.

Use SPSS to do dimensionality reduction processing, the step is to upload**, click the conversion tab, then click on the calculation variable, fill in the target variable, calculate the mean of the scale variable represented by many problems, and then directly use the mean to represent this variable, and the problem is basically not needed.

Let's start by looking at the significance value.

That is, the sig value or p value, it is to judge the r value, that is, whether the correlation coefficient is statistically significant, the judgment standard is generally, as can be seen from the table, the correlation coefficient between the two variables r = , and its p value is", so the correlation coefficient is not statistically significant.

Regardless of the size of the r-value, it indicates that there is no correlation between the two, and if the p-value is <, then it indicates that there is a correlation between the two.

Then look at the r-value, |r|The higher the value, the better the correlation, with a positive number indicating a positive correlation and a negative number indicating a negative correlation.

First, look at the significance value, which is the SIG value or p-value.

It is to determine whether the r-value, i.e., the correlation coefficient, is statistically significant.

The judging criteria are generally as follows:

As can be seen from the table, the correlation coefficient between the two variables is r=, and its p value is ", so the correlation coefficient is not statistically significant.

Regardless of the size of the r-value, it indicates that there is no correlation between the two.

If the p-value is <, then there is a correlation between the two.

Then look at the r-value, |r|The higher the value, the better the correlation, with a positive number indicating a positive correlation and a negative number indicating a negative correlation.

r|When it is greater than or equal to less than, the two variables are considered to be moderately correlated.

r|When it is greater than or equal to less than, it is considered that the two variables are poorly correlated or weakly correlated, |r|Less than indicates that the degree of correlation is very weak or not correlated.

Therefore, to judge the correlation, first look at the p-value to see if there is a correlation.

Then look at the r-value to see if the correlation is strong or weak.

r is the value of relevance, and p is the value of significance.

Are you asking how to get it by hand?

First, the Pearson correlation coefficient is calculated.

The correlation coefficient is calculated by the formula, which is then based on significance.

The z-test formula calculates the z-statistic (both formulas can be found on the Internet, you can search for the Pearson correlation coefficient calculation formula in it, and you should be able to find it), and then look up the z-value table to get the probability value p

1. After entering the data on the main interface of the copy SPSS, click on the relevant samples in the non-parametric test by analyzing there.

2. At this time, come to a new window, set the test pair and select Wilcoxen.

3. If there is no problem in the next step, it will be determined directly.

4. In this way, detailed data results will be generated, and SPSS can be used for correlation analysis.

Analyze the correlation. Partial correlation.

e Select one or more numeric control variables.

The following options are also available:

Significance test. You can choose between a two-tailed probability or a single-tailed probability. If you know the direction of the association beforehand, please choose it.

Choose a single tail. Otherwise, choose Twin Tails.

Shows the actual level of significance. By default, the probability and degrees of freedom for each correlation coefficient are displayed. If.

If this option is deselected, a single asterisk is used to identify a factor with a significance level of , and two asterisks are used.

Identifies a factor with a significance level of , without showing degrees of freedom. This setting also affects the partial correlation matrix.

and zero-order correlation matrices.

Partial Relevance: Option.

Statistics. You can choose one or both of the following options:

Mean and standard deviation. Displayed for each variable. The number of cases with non-missing values is also displayed.

Zero-order correlation coefficient. Displays a matrix of simple correlations between all variables, including control variables.

Missing values. You can choose one of the following options:

Exclude cases by list. Any of its variables, including control variables, with missing values will be excluded from all calculations.

of cases. Click on the right to exclude cases. For calculations of zero-order correlation on which partial correlation is based, do not use either a pair of variables or one of them.

Cases where the variable has a missing value. Press Pair Delete to make full use of the data. However, the number of cases may vary with the coefficient.

Different and different. If the pair deletion is valid, then the degrees of freedom of a particular partial correlation coefficient are based on any.

The minimum number of cases used in zero-order correlation calculations.