Introduction
This paper aims to determine the most critical attributes for the buyers’ selection of a shopping area. In particular, this report demonstrates how descriptive statistics were used to determine which of the attributes, including simplicity of return and exchange, high quality of goods, low prices, variety of styles and sizes, helpfulness of staff, convenience of shopping hours, cleanliness of stores, and a high number of bargains. The analysis was based on a sample of 150 respondents who were asked to rate how important these eight factors were when selecting a shopping area on a scale from 1 (the attribute is not very important in choosing a shopping area) to 7 (the attribute is essential in choosing a shopping area). The analysis was conducted using Microsoft Excel.
Descriptive Statistics
Descriptive statistics were calculated to determine which of the eight factors were the most important for the respondents in their selection of shopping areas. The purpose of descriptive statistics is to summarize and describe the main features of a data set, providing insights into the central tendency, variability, and distribution of the data (Bluman, 2018). Five-point summaries and the means, modes, ranges, and standard deviations were calculated using Excel’s formulas. By-variable descriptive statistics are provided in Table 1 below.
Table 1. Descriptive statistics
The descriptive statistics demonstrate that all the scores for each variable varied between a minimum of 1 and a maximum of 7. Quality of products had the highest mean score of 5.73, while cleanliness had the lowest mean score of 4.75. Almost all variables had a mode of 7, while the convenience of shopping hours had a mode of 6. Low prices and high-quality products had the highest median score of 7, while helpfulness of employees, convenience of shopping hours, and cleanliness had the lowest median of 5.
In addition to descriptive statistics, the data were analyzed for outliers. The Z-score method was used to search for outliers. This method involves computing the standardized score of each data point and then comparing it to a threshold value. This method is widely used in various fields, including finance, healthcare, and social sciences (Hair et al., 2019). The z-scores for each response were calculated and compared using the formula to find outliers. Values outside the range [-3; 3] were considered outliers. Conditional formatting was used to find the outliers conveniently. The analysis demonstrated no significant outliers for any of the variables.
Drawing Conclusions
The descriptive statistics demonstrated that quality was the most critical factor for the participants when selecting a shopping area, while cleanliness was the least important factor. The conclusion was made based on the mean scores, as since there were no significant outliers, mean scores appeared to the most appropriate measure of central tendency, as there were no significant outliers. It should also be noted that low prices were also of extreme importance for the participants, as the scores for quality (5.73) and the mean score of low prices (5.7) were close.
Correlations
Correlation analysis was conducted to determine how the importance of quality for the participants was correlated with other variables, offering diverse styles and sizes, attentive staff, convenient store hours, well-maintained premises, and numerous special deals. The correlation coefficients were calculated using the CORREL function in Microsoft Excel. The analysis showed that Pearson’s correlation scores varied between 0.2 and 0.31, signifying low-to-no correlations between variables. A matrix of correlation coefficients is provided in Table 2 below.
Table 2. Correlation matrix
Conclusion
This study examined the key factors influencing buyers’ choice of a shopping location, such as ease of returns and exchanges, product quality, affordable prices, a wide range of styles and sizes, helpful staff, convenient store hours, cleanliness, and numerous deals. The analysis was conducted on a sample of 150 respondents using descriptive statistics, which showed that quality was the most important factor for participants, while cleanliness was the least important. Correlation analysis showed low-to-no correlations between quality and other variables. The study did not find any significant outliers for any of the variables.
References
Bluman, A. G. (2018). Elementary Statistics: A Step by Step Approach. McGraw-Hill Education.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.