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Statistics

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Apr 3, 2024

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Questionnaire Questions Variable Type MRO 1 Ratio 1,2 2 Ratio 1,2 3 Nominal 1,2 4 Ratio 1,2 5 Interval/Graphic 1,2 6 Ratio 1,2 7 Nominal 1,2 8 Nominal 1,2 9 Open-Response 2 10 Nominal 3 11 Nominal 3 12 Open-Response 3 Research objectives: 1. To identify the main reasons for the decline in customer numbers and sales at the McNamara Shopping Centre, including the impact of surrounding shopping centres and shift to online retailers 2. To identify the primary reasons for why customers are choosing to shop at other nearby shopping centres and their motivations for shopping online instead of shopping at McNamara Shopping Centre 3. To explore potential marketing strategies that can be implemented to promote McNamara Shopping Centre as a desirable shopping destination and increase customer numbers and sales

1. What is your current age (in years)? (correlation) 2. How often do you visit McNamara Shopping Centre? (in times a month) (correlation) (VAR 2) 3. Which of the following stores at McNamara Shopping Centre do you frequent the most? 4. On average, how much money do you spend during each visit to McNamara Shopping Centre? (independent t test) 5. Are you satisfied with the variety of stores offered at McNamara Shopping Centre? 6. Have you ever visited any other shopping centres in the area? 7. What are your perceptions of McNamara Shopping Centre compared to other shopping centres in the area? (frequencies) 8. Do you shop online for products that are also available at McNamara Shopping Centre? 9. If yes, why do you prefer to shop online rather than shopping at the physical store? 10. Would you be interested if McNamara Shopping Centre releases a loyalty/rewards programme? (frequencies + suggestions) 11. Would you be interested in attending events or activities at McNamara Shopping Centre, such as food festivals, live music or workshops? (frequencies + suggestions) 12. What is one thing you would like to see added or changed at McNamara Shopping Centre to make it a more attractive shopping destination?

5. You are going to use SPSS - but here you should explain what tests you will do and with what variables - that you identified in the section above. The data you are using is constructed from you and the variables you are analysing are also constructed by you - trying to give you some freedom to really think about the task here. Correlation, Frequency, independent sample t test here is a fictional dataset with 50 samples and 5 variables: Variable 1: Age Variable 2: Gender (1 = male, 2 = female) Variable 3: Frequency of visits to McNamara Shopping Centre (1 = once a week, 2 = 2-3 times a month, 3 = once a month, 4 = less than once a month) Variable 4: Reasons for shopping at McNamara Shopping Centre (1 = convenient location, 2 = good variety of stores, 3 = competitive prices, 4 = enjoyable shopping experience) Variable 5: Perception of McNamara Shopping Centre compared to other shopping centres in the area (1 = better than other shopping centres, 2 = similar to other shopping centres, 3 = worse than other shopping centres)

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Correlation – Customer satisfaction to shop at McNamara shopping centre & shifting retail spending to online retailers (Pearson correlation coefficient) Introduction: The purpose of this study is to investigate the relationship between customer motivation to shop at McNamara shopping centre and shifting retail spending to online retailers. With the increasing popularity of online shopping, it is important to understand how customer motivation to shop at physical stores may influence their willingness to shift their spending to online retailers. Methods: We collected data from 50 participants who regularly shop at McNamara shopping centre. We asked participants to rate their motivation to shop at McNamara shopping centre on a scale of 1 to 10 (with 1 being not at all motivated and 10 being very motivated) and their likelihood of shifting their retail spending to online retailers on a scale of 1 to 10 (with 1 being not at all likely and 10 being very likely). Results: The mean motivation score for shopping at McNamara shopping centre was 7.2 (SD = 1.8), and the mean likelihood score of shifting retail spending to online retailers was 5.8 (SD = 2.1). To investigate the relationship between these two variables, we conducted a Pearson correlation analysis. The correlation coefficient was r = -0.48, indicating a moderate negative correlation between customer motivation to shop at McNamara shopping centre and likelihood of shifting retail spending to online retailers (p < 0.05). Discussion: The negative correlation between customer motivation to shop at McNamara shopping centre and likelihood of shifting retail spending to online retailers suggests that customers who are highly motivated to shop at physical stores may be less likely to shift their spending to online retailers. This finding may have implications for retailers who operate physical stores, as they may need to focus on enhancing the in-store shopping experience to retain customers and prevent them from shifting their spending to online retailers. Limitations: One limitation of this study is that it was conducted with a relatively small sample size (n = 50). A larger sample size may be needed to generalize the results to a larger population. Additionally, this study only focused on customers who shop at McNamara shopping centre, and the results may not be applicable to customers who shop at other physical stores. Conclusion: Overall, the results of this study suggest a negative correlation between customer motivation to shop at McNamara shopping centre and likelihood of shifting retail spending to online retailers. Retailers who operate physical stores may need to focus on enhancing the in-store shopping experience to retain customers and prevent them from shifting their spending to online retailers.

Based on the information provided, this type of coding appears to be descriptive coding. Descriptive coding involves assigning descriptive labels or codes to different aspects of the data, such as themes or patterns, without necessarily trying to fit them into a preconceived framework or theory. Descriptive coding is a type of inductive coding, which involves developing categories or themes from the data itself rather than using a pre-existing framework or theory. In vivo coding is a type of coding where the exact words or phrases used by the participants are used as the codes or labels for the data. Process coding is a type of coding that focuses on the steps or stages involved in a particular process or phenomenon. Structural coding involves organizing the data based on the relationships or structures that exist within it, such as cause and effect or hierarchy. Value coding involves identifying and coding data based on the values, beliefs, or attitudes expressed by the participants. Note: In this example, we assumed that the data was normally distributed and had equal variances. These assumptions should be checked before conducting a t-test. Additionally, a larger sample size would improve the accuracy and reliability of the results. Limitations: Normality histogram

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