Why is spss26 bigger than other versions?

spss26 has improved the overall opening speed compared to other older versions i.e. spss24 version, which is well optimized for win10, and experienced that the unique feature ROC analysis will be more suitable than version 24.

SPSS is the world's first graphical menu-driven interface statistical software, its most prominent feature is the extremely friendly interface, the output results beautiful and beautiful. It presents almost all functions in a unified and standardized interface.

How to design a restaurant satisfaction questionnaire?

1. Convenient parking and attractive appearance

2. Modern facilities

3. Sensory appeal of the restaurant's facilities

4. Clear menus and a unique appearance in line with the restaurant's image

5. Facilities matching the service provided by the wait staff

6. 6. The dining area is comfortable and open

7. The dining area and restrooms are clean and tidy

8. The service staff is dressed appropriately, clean and tidy, and reliable:

9. The restaurant is able to fulfill its promises to the customer in a timely manner

lO. The service staff has the enthusiasm to help the customer solve the problem when the customer encounters trouble

11. The restaurant provides accurate and perfect service to the customer the first time

12. The restaurant is able to complete the service within the agreed upon time

13. The restaurant's wait staff accurately records the service requested by the customer

14. The wait staff is able to provide the correct bill responsiveness:

15. The restaurant's wait staff is able to inform the customer of the 16．The wait staff is able to accommodate special requests

17．The wait staff provides prompt service

18．The wait staff responds to customer requests in a timely manner, even when they are busy

19．The wait staff is trustworthy

20．The wait staff is happy to explain the menu items and the cooking methods

21． p>21. The staff is always courteous to customers

22. Customers feel safe in the restaurant

23. The staff is well-trained and experienced in empathy:

24. The staff does not comply with the company's rules and regulations to the detriment of the customer's individual needs

25. The restaurant is able to adjust the service time according to the different needs of the customers

26.

26．The restaurant pays special attention to the customers

27．The restaurant has the customers' interests at heart

28．The service staff has mastered some of the customers' preferences for remedial service:

29．The service staff has a very good attitude and strong communication skills in the face of mistakes

30．The restaurant staff who performs remedial service is able to solve the problem once and for all

31． The restaurant was able to solve the problem in one go

31. The restaurant was able to respond quickly with information about service remediation

32. The restaurant used reasonable service remediation methods

33. The restaurant staff clearly and briefly explained the quality of the product for the reason for the error:

34. The meal was unique and reflected the restaurant's specialties

35. Meals with the right nutritional mix

36. meal hygiene, clean plates without gaps

37. meal quality of the unified standard then, satisfaction is divided into five levels: very satisfied, satisfied, average, dissatisfied, very dissatisfied.

Scoring, and then use the professional analysis software spss to analyze can be. I hope it is useful to you.

Which version of spss is good?

Spss software 26.0 is still good, depending on which you need to generally choose the newer and more stable.

Spss26 how to import questionnaire data?

SPSS26 can import questionnaire data in a variety of ways, including .sav files produced with SPSS, Excel files, external databases and so on. Here are the steps to import questionnaire data with SPSS26:

1. Prepare your questionnaire data file, and choose the file type according to the specific situation, such as .sav file made with SPSS or Excel file.

2. Open SPSS software and create a new data set. You can create a new dataset by selecting the "New Dataset" option on the start page.

3. In the New Dataset window, select the type and path of the data file you want to import. Then click the Open button.

4. In the Data Import Wizard, you need to specify the data variable type, variable name, variable order and other information. You can set them as appropriate and make sure that the data variables are assigned correctly.

5. In the Variable View, you can check the variable names and variable attributes after the questionnaire data has been imported. You can also add, adjust, or delete variables to meet the needs of data analysis.

6. If you need to analyze the questionnaire data, you will also need to perform processes such as data cleaning and statistical analysis. Select the commands in SPSS to complete data cleaning and analysis as appropriate.

It should be noted that before importing the questionnaire data, you should make sure that the format and content of the data file is correct, and perform the necessary data cleaning and preprocessing work on the data to ensure the quality and accuracy of the data imported into SPSS.

spss perform principal component analysis graphic full tutorial?

spss for principal component analysis graphic complete tutorial

1, the data will be entered into excel or spss

2, data standardization: open the data and select the analysis ?ú Descriptive Statistics ?ú Description, standardization of data, selected standardized scores will be saved as a variable:

3, principal component analysis: select the analysis ?ú Dimensionality Reduction ?ú Factor analysis,

4, set the descriptive, extraction, score and options:

5, view the principal component analysis and analysis: the correlation matrix shows that the indicators have a strong correlation between. For example, the correlation coefficient between the total indicator GDP and the fiscal revenue, total investment in fixed assets, value added of the secondary industry, value added of the tertiary industry, value added of industry is large. This indicates that there is an overlap between the information of indicators between them, which is suitable for principal component analysis. (The following table is not a complete presentation)

6. It can be seen from the TotalVarianceExplained (principal component eigenroot and contribution rate) that the eigenroot λ1=9.092 and the eigenroot λ2=1.150 of the first two principal components have a cumulative variance contribution rate of 93.107%, which means that most of the information is covered. This indicates that the first two principal components can represent the initial 11 indicators to analyze the development level of the comprehensive economic strength of each city in Henan, so it is sufficient to extract the first two indicators. The principal components, respectively, are denoted as F1 and F2.

7. Indicators X1, X2, X3, X4, X5, X6, X7, X8, X9, and X10 have high loadings on the first principal component, and have strong correlation. The first principal component concentrates on the overall economic output.X11 has high loading on the second principal component with strong correlation. The second principal component reflects the level of economic volume per capita. But note: this principal component loading matrix is not the eigenvectors of the principal components, that is to say, it is not the coefficients of principal component 1 and principal component 2. The coefficients of the principal components are obtained by dividing the vector of the loadings of each principal component by the arithmetic square root of the eigenvalues of each principal component.

8, component score coefficient matrix (factor score coefficients) lists the strong two eigenroots corresponding to the eigenvectors, i.e., the coefficient vectors of the standardized variables in the main components of the analytic expression. Therefore, the analytic expressions of each major component are as follows: F1=0.32ZX11+0.33ZX12+0.31ZX13+0.31ZX14+0.32ZX15+0.32ZX16+0.32ZX17+0.32ZX18+0.32ZX19+0.21ZX110+0.15ZX111F2=8.46ZX21+ 0.02ZX22-0.02ZX23-0.20ZX24-0.23Z25-0.04ZX26-0.15ZX27-0.02ZX28+0.10ZX29+0.47ZX210+0.78ZX211

9, the score of the principal component is the arithmetic square root of the corresponding factor score times the corresponding variance. That is: principal component 1 score = factor 1 score multiplied by the arithmetic square root of 9.092 principal component 2 score = factor 2 score multiplied by the arithmetic square root of 1.150 For example, Zhengzhou: principal component factor = FAC1_1 * arithmetic square root of 9.092 = 3.59386 * arithmetic square root of 9.092 = 10.83, the standardization of the indicators of the data into the expression of the analysis of a principal component In addition, 2 principal component scores (F1, F2) were calculated, and then the contribution of the principal components of the whole book on the principal component scores for the weighted average, i.e.: H = (82.672*F1+10.497*F2)/93.124, to obtain the composite score of the principal components.

Expanded Information:

Principal Component Analysis is a method of multivariate statistical analysis that reduces multiple indicators into a few uncorrelated composite indicators and classifies the composite indicators in accordance with certain rules A multivariate statistical analysis method. This analysis method can reduce the dimension of the indicators, condense the information of the indicators, simplify the complex problems, and thus make the problem analysis more intuitive and effective. At present, this method has been widely used in the economy and other fields, and selected data can use spss to carry out principal component analysis.