Standardization and decentralization of SPSS data
Publish: 2021-05-19 14:20:01
1. In fact, when describing statistics, check the Save option to get a standardized variable. The space is limited. You can take a look at this tutorial. http://jingyan..com/article/9f7e7ec04ee5c56f28155416.html
2. 1. The mediating effect analysis does not need data centralization and standardization
2. Forced centralization or centralization, only the non standardization coefficient is different, the standardization system is the same
(provided by Nanxin)
2. Forced centralization or centralization, only the non standardization coefficient is different, the standardization system is the same
(provided by Nanxin)
3. Standardize the data, find out the mean and variance
analysis description statistics description, and then select "save standardized score as a variable" and confirm to get the processed standardized data, and then cluster, factor and regression analysis can be carried out
analysis description statistics description, and then select "save standardized score as a variable" and confirm to get the processed standardized data, and then cluster, factor and regression analysis can be carried out
4.
1. Open SPSS and switch the interface to variable view. Create observation index and type in editing column. The example creates two indicators, one as the independent variable and the other as the dependent variable, namely GPD and urbanization, representing the per capita GDP and urbanization level
5. 1. Input data
2. Menu analysis - description statistics - Description
3. In the pop-up dialog box, select the variable to be standardized into the variable box on the right
4. There is a check box to save the variable as the standardized score, check
5 and OK to get the calculation result, return to the data inspection and get the new standard score variable
2. Menu analysis - description statistics - Description
3. In the pop-up dialog box, select the variable to be standardized into the variable box on the right
4. There is a check box to save the variable as the standardized score, check
5 and OK to get the calculation result, return to the data inspection and get the new standard score variable
6. The common Standardization (x-mu) / sqrt (delta)
centralization (x-mu)
Mu is the mean value and delta is the variance
centralization (x-mu)
Mu is the mean value and delta is the variance
7. Analyze --- descriptive
statistics --- descriptions
select variables into the box on the right and select "save as variable" in the lower left corner to standardize
statistics --- descriptions
select variables into the box on the right and select "save as variable" in the lower left corner to standardize
8.
The main purpose of data standardization is to eliminate the influence of different dimensions (units). For example, an indicator is in 10000 yuan, and an indicator is in yuan. The two indexes can not be compared directly, so it is necessary to eliminate the influence of dimension
standardization can be processed with one key of [generate variable] in online spssau
9. SPSS statistical analysis software is my earliest data analysis tool. My blog will introce the relevant content of SPSS statistical analysis software one after another. This kind of articles will be organized in the form of SPSS case analysis + number in the title or the first paragraph of the text, which is convenient for readers to quickly query and collect. Today is the first article, namely SPSS case analysis 1, It will not be explained later< br />
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in multivariate statistical analysis, we often need to collect data of different dimensions, such as total sales (10000 yuan), profit margin (percentage). This is reflected in the difference of the variable in the order of magnitude and unit of measurement, which makes each variable not comprehensive. However, most of the multivariate analysis methods have special requirements for variables, such as normal distribution or comparability between variables. At this time, we must use some method to standardize the values of the variables, or called dimensionless processing, to solve the problem that the values are not comprehensive
SPSS provides a convenient method for data standardization. Here we only introce the Z standardization method. That is, the difference between the value of each variable and its average divided by the standard deviation of the variable. After dimensionless, the average value of each variable is 0 and the standard deviation is 1, thus eliminating the influence of dimension and order of magnitude. This method is the most widely used method in multivariate comprehensive analysis. In the case of normal distribution of the original data, it is reasonable to use this method for dimensionless data processing
implementation steps of SPSS: Legend
[1] analysis - description statistics - Description
[2] the dialog box of description statistics will pop up. First, move the variables to be standardized into the variable group. At this time, the most important step is to check "dou" to save the standardized score as the variable place, and finally click OK
[3] return to the data view of SPSS and add a new variable starting with Z at the end of the original variable, which is the standardized variable. Other analysis can be done based on this field.
--------------------------------------------------------------->
in multivariate statistical analysis, we often need to collect data of different dimensions, such as total sales (10000 yuan), profit margin (percentage). This is reflected in the difference of the variable in the order of magnitude and unit of measurement, which makes each variable not comprehensive. However, most of the multivariate analysis methods have special requirements for variables, such as normal distribution or comparability between variables. At this time, we must use some method to standardize the values of the variables, or called dimensionless processing, to solve the problem that the values are not comprehensive
SPSS provides a convenient method for data standardization. Here we only introce the Z standardization method. That is, the difference between the value of each variable and its average divided by the standard deviation of the variable. After dimensionless, the average value of each variable is 0 and the standard deviation is 1, thus eliminating the influence of dimension and order of magnitude. This method is the most widely used method in multivariate comprehensive analysis. In the case of normal distribution of the original data, it is reasonable to use this method for dimensionless data processing
implementation steps of SPSS: Legend
[1] analysis - description statistics - Description
[2] the dialog box of description statistics will pop up. First, move the variables to be standardized into the variable group. At this time, the most important step is to check "dou" to save the standardized score as the variable place, and finally click OK
[3] return to the data view of SPSS and add a new variable starting with Z at the end of the original variable, which is the standardized variable. Other analysis can be done based on this field.
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