Part 1 SPSS Recode Likelihood To Recommend Follow This Proce

Part 1spss Recode Likelihood To Recommendfollow This Procedure To T

Recoding variables and creating visualizations in SPSS is essential for analyzing data efficiently and accurately. This assignment involves transforming the 'Likelihood to Recommend' variable from a 10-point scale into a dichotomous variable representing customer loyalty, creating corresponding labels, and visualizing the results with a pie chart. Additionally, it requires recoding another quantitative variable into a binary form and visualizing this as well.

First, you will recode the 'Likelihood to Recommend' variable from its original 10-point rating into two categories: 'loyal' and 'not loyal.' Follow the specified steps to perform the recode, which include selecting the transformation menu, defining old and new values, and assigning labels such as 'not loyal' for ratings 0-6 and 'loyal' for ratings 7-10. After recoding, create value labels corresponding to these categories and generate a pie chart to visualize the distribution of customer loyalty.

Next, choose another quantitative variable from the dataset, recode it into two categories, and produce a pie chart showing the distribution of the recoded variable. This process entails similar steps: defining ranges, assigning new values, and labeling categories for interpretability.

Paper For Above instruction

In the realm of customer satisfaction and loyalty research, effective data analysis techniques such as variable recoding and visualization are fundamental. This paper demonstrates the process of transforming a 10-point Likert scale variable, 'Likelihood to Recommend,' into a dichotomous indicator of customer loyalty, and subsequently visualizing its distribution through a pie chart. Additionally, the paper discusses recoding a secondary quantitative variable and visualizing its distribution, emphasizing the importance of these techniques in understanding customer behavior and patterns within the context of the Avery Fitness Center dataset.

Recoding 'Likelihood to Recommend' from a 10-point scale to a dichotomous variable

The initial step involves recoding the 'Likelihood to Recommend' variable, which ranges from 0 to 10, into two categories: 'not loyal' and 'loyal.' This transformation simplifies the data, making it easier to interpret customer loyalty levels and perform further analyses. Using SPSS, the recoding process begins by selecting the TRANSFORM menu and choosing RECODE into DIFFERENT VARIABLES. The original variable, termed 'recom,' is moved into the output variable box, with a new variable name, 'rrecom,' and label, 'loyalty.'

In the 'OLD AND NEW VALUES' dialog box, the user specifies ranges to group ratings. Ratings from 0 to 6 are recoded as '1,' representing 'not loyal,' while ratings from 7 to 10 are recoded as '2,' representing 'loyal.' Properly assigning value labels enhances data interpretability, with '1' labeled as 'not loyal' and '2' as 'loyal.' These settings ensure a clear dichotomous classification that reflects customer loyalty accurately.

After completing the recode, a pie chart is generated to visualize the proportions of loyal versus not loyal customers. The visualization illuminates the overall customer loyalty within the dataset, offering immediate insights into customer satisfaction levels. The pie chart's segments, displaying percentages, facilitate easy comparison and aid in strategic decision-making for the fitness center’s management.

Second, a similar process is undertaken with another quantitative variable from the dataset. This variable is recoded into two categories based on its distribution, and a pie chart is created to visualize the distribution. This approach demonstrates the versatility of recoding techniques in simplifying complex data and enhancing interpretability across different variables.

In conclusion, recoding and visualizing variables in SPSS are essential skills for data analysts working with customer satisfaction data. They facilitate clearer insights into customer loyalty and behavior, support strategic decision-making, and enable efficient presentation of findings. Mastery of these techniques allows researchers and practitioners to better understand patterns and relationships within their data, ultimately contributing to improved service quality and customer retention in organizations like the Avery Fitness Center.

References

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  • Glen, S. (2019). How to Recod Variables in SPSS. Social Science Statistics. https://www.socscistatistics.com/articles/recode/

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