SP SS is a comprehensive data management software that utilizes rich built-in algorithms to perform various functions such as data analysis, data prediction, and data visualization. In addition to natural science disciplines such as engineering and medicine, SPSS has also been widely used in the field of sociology research.

Example: A company designed the following survey questionnaire to investigate the impact of five factors, including salary level, promotion opportunities, convenient transportation, vacation time, and employee benefits, on job satisfaction.

1. Enter data file

Open SPSS and the interface is shown in Figure 2. Click on File, New, Data, and create a new SPSS data file.

Figure 2: Creating a New Data File

Users who have studied mathematical statistics know that in order to conduct statistical analysis on samples, the first step is to define the sample space digitally. Simply put, it is to find numerical values for events and then use the values as variables for statistical analysis. We will assign a very dissatisfied value of 0, a dissatisfied value of 1, a basic satisfied value of 2, a satisfied value of 3, and a very satisfied value of 4, and then enter the data into the data table.

We open the variable view, as shown in Figure 3, change the variable name, define the variable as a numeric type, and then assign a value to the event in the "Value" column.

Figure 3: Setting Variable Attributes

After completing the assignment, according to the rules, enter the survey questionnaire content in the data view, as shown in Figure 4.

Enter survey questionnaire

Figure 4: Input of Survey Questionnaire

2. Analyze data files

We need to analyze which of the five factors have a significant impact on job satisfaction. Regression analysis can be introduced, as shown in Figure 5. Click on "Analysis", "Regression", and "Linear" to set job satisfaction as the dependent variable and the five factors as independent variables. Then click "OK". The analysis results are shown in Figure 6.

Figure 5 Regression analysis

Firstly, in the model summary, the R value is 1.000, indicating a highly linear relationship between the five factors and job satisfaction. At the same time, in the ANOVA scale, the significance is 0.01, which is less than 0.05, indicating that this linear relationship has statistical significance.

Under the above conditions, we examined the coefficient scale and found that the larger the t-value and standardization coefficient, the greater the contribution of factors to job satisfaction. It is not difficult to see that promotion opportunities have the greatest impact on job satisfaction, followed by salary levels. Data analysis completed.

Figure 6 Analysis Results

Reasonably assigning values to variables is the key to using SPSS for survey questionnaire analysis. Only after assigning reasonable values can survey questionnaire data be entered and analyzed through SPSS software.