How does SPSS do correlation analysis?

Correlation analysis is used to study the relationship between quantitative data, including whether there is a relationship and how closely the relationship is. For example, study the relationship between employee salary and employee seniority; The relationship between product sales and product after-sales service.

Correlation analysis has a wide range of applications, and theoretically speaking, any investigation of the correlation between two variables can be called correlation analysis. Correlation analysis studies are quantitative and quantitative data, and ANOVA is used for qualitative and quantitative data, and crossover (chi-square) is used for qualitative and quantitative data.

In the "General Methods" module, select the "Relevant" method, put the quantitative variables in the analysis box, and click "Start Analysis".

Here are the results: <>

As can be seen from the above table, the correlation analysis is used to study the correlation between company satisfaction and interpersonal relationships, opportunity perception, turnover propensity, and working conditions, and the Pearson correlation coefficient is used to represent the strength of the correlation relationship.

The above table shows the mean standard deviation and correlation coefficient of each variable, for example, the mean of company satisfaction is , the standard deviation is, the mean of interpersonal relationships is standard deviation, the mean of opportunity perception is and the standard deviation is, and so on.

Additional note: For correlation analysis, the ** format of the general specification is: p values are represented by an * sign (identified in the upper right corner of the correlation coefficient), and p< is represented by 2 * signs; p< is indicated by a 1 * sign.

SPSSAU also provides a result with a specific p-value**.

First of all, look 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 Xiaoyou guess 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 touching indicating a negative correlation.

ssps, the results after correlation analysis are naturalized, and he is still very accurate, and you can judge some factors through it.

The results of the analysis include the mean and standard deviation, as well as the correlation coefficient and p-value.

The first two columns are the mean and standard deviation of each variable, and the third column starts with the correlation coefficient between the two variables.

The asterisk in the upper right corner of the value represents the p-value. For correlation analysis, the general specification is that p values are represented by an * sign and p < is represented by 2 * signsp< is indicated by a 1 * sign.

3.There is a high positive correlation between the average number of daily ** and the search volume of related Weibo4The average number of daily searches and the number of related Weibo searches can be subtracted by the error ratio of (r squared).

The actual significance level of the Pearson correlation coefficient between the average daily number of ** and the related Weibo search volume calculated from the 9 sample data is less than the theoretical significance level, indicating that the value of the correlation coefficient is not caused by accidental factors, and is close to 1, indicating that there is a high linear positive correlation between the average daily ** number and the related Weibo search volume.

The Pearson correlation coefficient between the length of entry and the current annual salary is , indicating that there is a negative weak correlation between them, and the significant P-value is greater than that, so it is considered that there is no correlation between the current annual salary and the length of employment.

The Pearson correlation coefficient between years of education and current stickiness is, and the significance test P value is less than, so the number of years of education is positively correlated with the current annual salary. This is why most of the current undergraduate graduates do not choose direct employment, but continue their studies and study for a doctorate.

SPSS correlation** Each cell has three rows of data, one is the Pearson correlation coefficient value, which represents the size of the correlation coefficient, one is the sample size, which represents how many subjects you have in this set of data, and the last one is the significance test result, i.e., SIG (two-sided), which can be used to indicate whether the correlation analysis results you get are statistically significant.

If it is not significant, even if the correlation coefficient is large, it does not mean that the correlation is meaningful, and the correlation may be caused by sampling error, but at this time, you can consider increasing the sample size and then analyze it. The asterisk after the correlation coefficient value also reflects significance, with an * indicating a level of significant and ** indicating a level of significance.

SPSS (Statistical Product and Service Solutions) software. Originally, the full name of the software was "Solutions Statistical Package for the Social Sciences", but with the expansion of SPSS products and services and the increase in service depth, SPSS officially changed the full English name to "Statistical Products and Services Solutions" in 2000, which marked a major adjustment in the strategic direction of SPSS. SPSS is the general name of a series of software products and related services launched by IBM for statistical analysis operations, data mining, analysis and decision support tasks, including Windows and Mac OS X.

SPSS is the world's first statistical software to adopt a graphical menu-driven interface, and its most prominent feature is that the operation interface is extremely user-friendly and the output results are beautiful and beautiful. It unifies almost all of its functions.

First, the standardized interface is displayed, using the windows window mode to display the functions of various management and analysis data methods, and the dialog box shows various function options. As long as the user has certain Windows operation skills and is proficient in the principles of statistical analysis, he can use the software to serve specific scientific research work. SPSS uses Excel-like method to input and manage data, and the data interface is more general, which can easily read data from other databases.

Its statistical process includes commonly used and relatively mature statistical processes, which can fully meet the work needs of non-statistical professionals. The output is very beautiful, and it is stored in a dedicated SPO format, which can be converted to HTML format and text format. For users who are familiar with the old version of the program, SPSS also has a specially designed syntax generation window, which allows users to automatically generate standard SPSS programs by simply selecting each option in the menu and pressing the "Paste" button.

It is very convenient for intermediate and advanced users.

SPSS correlation analysis operation steps, 1, open the SPSS software, select factors and correlation in the analysis menu, or select correlation in the data menu, 2, click relevance, select the variable to be studied in the pop-up dialog box, click the OK button, 3, click the finish button, you can click the finish multiple times to add variables, 4, click the calculation button on the right side, start the calculation, 5, click the OK button, get the operation result, 6, in the entity output window, display the result, 7, In another output window, you can view more details, including factor correlation coefficients in the analysis, etc.

Correlation analysis is a type of statistical analysis, and its main function is to test the relationship between two variables, which can be two numerical variables, a numerical variable and a subtype variable.

Through the calculation of the data, the correlation prudence coefficient can be obtained, so as to determine whether there is a positive correlation, negative correlation or no correlation between the two variables.

In SPSS, in order to perform correlation analysis more easily and quickly, it provides a wealth of power and disadvantages.

The relationship between multiple variables can be tested by trying non-stupid analysis models, such as multiple linear regression analysis, stepwise regression analysis, etc., so as to draw effective conclusions.

At the same time, there are many other analysis tools in SPSS, such as multivariate statistical analysis, centralized trend analysis, etc., which can help to identify and analyze the relationship between variables more comprehensively and accurately.