SPSS (Statistical Package for the Social Sciences) is one of the most popular and powerful software applications for statistical data analysis. It is widely used in research centers, universities, market research organizations, and government agencies. Its main function is to allow the organization, processing, and analysis of data in flexible ways. However, in order to perform any type of analysis, the correct input of data is essential. This process may seem simple, but following specific rules and understanding the program’s capabilities are fundamental elements for the success of any research project.

The Rule “One Person, One Row”

The basic principle of data entry in SPSS is that each case, that is, each unit of analysis, must occupy one and only one row in the data sheet. Typically, the case represents a person, but it may also be a product, a biological cell, or any other object being measured. For example, if we are examining the height and weight of one hundred individuals, then we will have one hundred rows of data, with each row corresponding to one person. If the data of a single individual are recorded in two or more rows, then there is a mistake in the structure of the dataset. Similarly, data from two different individuals must not be entered in the same row. Adhering to this rule ensures the accuracy and reliability of the analysis.

Basic Tasks in Data Entry

When entering data into SPSS, there are three main tasks that are most frequently performed. The first concerns the entry of variables, meaning the characteristics we measure for each case, such as height, weight, or age. Each variable is represented by a column in the data sheet. The second task is the definition of distinct groups. Researchers often need to compare different groups, such as males and females or individuals with different educational levels. In SPSS, these groups are recorded as separate categories of a variable. The third task is the entry of repeated measures. When we measure the same individuals at different time points, for example in a longitudinal study, we record the data in a way that allows us to analyze changes within the same person over time.

Advanced Settings and Extensions

Beyond the basic tasks, SPSS allows more complex configurations. In many cases, it is useful to define multiple distinct groups, such as simultaneously studying the effect of gender and educational level. There is also the possibility of combining separate groups with repeated measures, in order to analyze how two or more groups change over time. Another advanced feature is the creation of dummy variables, which are mainly used in regression analyses when the independent variables are categorical. Dummy variables help encode qualitative characteristics so that they can be used in quantitative models, allowing for more complex and precise analyses.

Creating a New Dataset

When creating a new dataset in SPSS, the typical practice is to begin by defining the variables. This is done in the Variable View, where we enter the variable name, its type (numeric, string, date, etc.), the labels for easier interpretation, and the values in the case of categorical variables, such as 1 for male and 2 for female. After defining the variables, we proceed to the Data View, where we input the actual values for each case. In this way, the dataset is organized and ready for analysis.

Adding New Variables

In many situations, after the initial data entry, it becomes necessary to add new variables. This process is not particularly difficult. We simply go to the Variable View, select an empty row, and define the properties of the new variable. Then, we can return to the Data View to enter its values. This feature offers flexibility and makes data management more functional, allowing the researcher to enrich the dataset according to the needs of the analysis.

Conclusion

Correct data entry in SPSS is the foundation of any statistical analysis. The rule “one person, one row” ensures that data have a clear and organized structure. The basic tasks, such as entering variables, defining groups, and recording repeated measures, combined with advanced features like dummy variables, provide the researcher with both flexibility and accuracy. In this way, SPSS becomes a powerful tool that facilitates the process of scientific research and contributes to evidence-based decision making.