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General steps for spss data analysis

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2019-06-22 09:49:2010282browse

SPSS is the abbreviation of Social Statistical Science Software Package, and its official full name is IBM SPSS Statistics.

General steps for spss data analysis

The SPSS software package was originally launched by SPSS Inc. in 1968 and was acquired by IBM in 2009. It is mainly used for data management and statistical analysis in various fields. As the world's standard for social science data analysis, SPSS has an extremely user-friendly interface, a beautiful result output interface, and a very detailed user manual. (Recommended learning: PHP video tutorial)

The main steps of SPSS data analysis

The key to using SPSS for data analysis is to follow the rules of data analysis General steps, but the aspects involved will be relatively few. Mainly focus on the following stages.

1. SPSS data preparation stage

In this stage, SPSS data files should be prepared according to the requirements of SPSS and using the functions provided by SPSS. This includes defining the structure of SPSS data in the data editing window, entering and modifying SPSS data, etc.

2. SPSS data processing and sorting stage

This stage mainly performs necessary preprocessing on the data in the data editing window.

3. SPSS data analysis stage

Choosing the correct statistical analysis method to analyze and model the data in the data editing window is the core task of this stage. Because SPSS can automatically complete mathematical calculations in the modeling process and automatically provide calculation results, it effectively blocks many mathematical formulas that are very obscure to ordinary users. Analysts do not need to memorize mathematical formulas, which undoubtedly improves statistical analysis methods. and SPSS paved the way for widespread application.

4. Reading and interpretation of SPSS analysis results

The main task of this stage is to understand the analysis results in the SPSS output editing window, clarify its statistical meaning, and make practical and reasonable explanations based on application background knowledge .

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