Enter the variable information into the data analysis software selected. The statistical package for social sciences (SPSS) is used by many social researchers today. Begin by entering the name and type of data to be analyzed. Carefully consider each of the topics related to data analysis. It may be helpful to refer to research questions to determine how best to categorize or label variable information. Continue by entering the width, decimals, label and values that describe the data.
Enter the data. The most efficient method of data entry is by variable. Enter all data for each variable that will be analyzed. For instance, if variables are: gender, age, height and weight -- enter all gender, followed by all age, and so on until data entry is complete.
Review the data set once data entry is complete and accurate. Check for outliers. It is fairly easy to make mistakes when entering a large data set. It is important that all data is entered correctly, as any mistakes in data entry destroys the integrity of results. Carefully view each column of the data set to be sure that there are no missing values and that all values are in a consistent format. If unintentional missing values are found, correct the problem. If intentional missing values are found, be sure to follow procedures that will assure missing values are considered during analysis.
Analyze data relative to the research questions. To begin the process, select "analyze" and proceed selecting the appropriate method of analysis for the data set.
Review the output from SPSS to determine answers to research questions. Depending upon the data set and which statistical tests are used, data analysis may include only a reporting of the data as it appears in the output. For instance, the report of descriptive statistics such as mean, median, and standard deviation may answer research questions, without any researcher inference. However, sometimes the output for other statistical tests such as a t-test or ANOVA may necessitate researcher inference or interpretation.