SPSS Statistics is one of the most well-known and widely used programs for statistical data analysis. It is used by researchers in various fields such as social sciences, psychology, health, education, market research, as well as by government agencies analyzing large-scale data. The software offers the ability to perform many different analyses, provided that the data have been entered correctly and systematically. This process is crucial because errors in data entry can affect the entire analysis and lead to inaccurate results.

The Rule “One Person, One Row”

The fundamental principle of data entry in SPSS can be summarized by the rule “one person – one row.” This means that each unique case, that is, the subject under study, must be recorded in one single row of the data table. A case may be a person, a product, an experiment, a biological cell, or any other entity measured in the context of research. If the data of a single individual are recorded across multiple rows, this is considered an error, as it creates confusion and inconsistency. Similarly, if one row contains information for more than one person, then the entry is incorrect. Therefore, each row represents a distinct subject, and each column represents a specific characteristic or variable.

Common Tasks in Data Entry

When entering data in SPSS, the user usually needs to record variables, define separate groups, and enter repeated measures. Recording variables refers to capturing the characteristics being measured, such as height, weight, or age. Defining separate groups refers to categorizing subjects, for example, by gender, educational level, or occupational status. On the other hand, entering repeated measures is associated with recording the same variables for the same subject at different time points, such as performance across three successive tests or physiological indicators measured during different stages of an experiment.

Advanced Settings in Data Entry

Beyond the basic tasks, SPSS also allows for more complex forms of data entry. It is possible to have multiple separate groups, for instance, analyzing both gender and education level together. One can also combine separate groups with repeated measures, in order to study the evolution of a variable over time depending on gender or another categorical factor. In addition, the program enables the creation of dummy variables, which are mainly used in regression analyses when the independent variables are categorical, such as occupation or region of residence. With these advanced options, SPSS provides flexibility and adaptability to meet the requirements of any research design.

Process of Creating a New Dataset

When creating a new dataset, the first step is to define the variables. This is done through the Variable View tab. There, the name of each variable is specified, such as Age for age or Gender for gender. The type of variable is also defined, whether numeric or string, along with labels that make the presentation clearer. Value categories are then assigned, such as 1 for Male and 2 for Female, while missing values can also be defined for cases where no data are available.

Once the variables have been defined, the user moves on to the Data View tab, where values for each subject are entered. Each row corresponds to one individual, and each column to one characteristic, ensuring the structure required for proper analysis.

Adding Variables after Data Entry

Adding new variables to an existing dataset is a simple process. The researcher can go to Variable View, select an empty row, and define the name and properties of the new variable. Immediately afterward, values for all the subjects in the sample can be entered in Data View. This feature allows for the easy expansion and enrichment of the database without having to restart the data entry process from scratch.

Conclusion

Proper data entry in SPSS Statistics is the foundation of any research based on statistical analysis. The rule “one person – one row” ensures consistency and organization of the data, while the correct distinction between variables, separate groups, and repeated measures makes the dataset more reliable. Both the basic and advanced functions of SPSS allow the researcher to adapt the structure of the data to the specific needs of the study, while the ease of adding new variables makes the program flexible and practical. Through this process, SPSS becomes a valuable tool that can support every stage of research, from the initial recording of data to the final interpretation of results.