Final answer:
Sophie should use a missing values code in SPSS for participants who did not answer the income question. This preserves data integrity and allows for proper analysis and investigation.
Step-by-step explanation:
When dealing with missing data in questionnaires, like the question about annual income that some participants did not answer, the recommended approach is to specify and use a missing values code in SPSS to indicate instances where the question has been left unanswered (option c). This method is preferred over fabricating data (option a) or using a random number generator (option b) because it allows the researcher to maintain the integrity of the data and avoids introducing bias or inaccuracies into the analysis. Making up data or using randomly generated numbers could compromise the validity of the study and the reliability of the results.
In research, the handling of missing data is crucial. If the missing values are not handled correctly, they can lead to erroneous conclusions. SPSS offers various features to manage missing data effectively, and researchers must be well-acquainted with these tools. Instead of making assumptions about the missing income figures or artificially filling in the gaps, coding the missing responses allows for accurate representation and acknowledgment of the data as it is, which can later be addressed through statistical techniques designed for dealing with missing values, such as data imputation or sensitivity analysis, if necessary.
It is also important to recognize that missing data can provide insights into the characteristics of the sample or the survey process itself. For instance, if a significant proportion of respondents skipped the income question, this may indicate sensitivity around the topic or a design flaw in the questionnaire. Thus, properly coded missing values not only preserve the authenticity of the dataset but also offer an opportunity for further investigation.