This paper analyzes a department store customer satisfaction survey, examining the research techniques used to collect and interpret data across interval, ordinal, and categorical variable types. The paper distinguishes between qualitative and quantitative research methods, explains the role of descriptive statistics, and presents frequency distribution findings for variables such as age, gender, department preference, payment type, and shopping habits. It also applies inferential statistics — specifically the t-test — to evaluate whether men travel longer distances to stores than women, and whether regular customers hold neutral price opinions. The analysis draws on Trochim's Knowledge Base framework for survey research and statistical methodology.
The study analyzed in this paper represents a survey conducted by department stores to determine customer satisfaction levels. Regarding respondent characteristics, the researchers gathered data on gender, age, reasons for purchase, preferred departments, payment type, preferred store type, available services, service quality, and other factors relevant to the shopping experience.
Qualitative research methods focus on understanding and describing the characteristics of variables involved in a study, as well as identifying similarities and contrasts between variables or groups used to determine behaviors at the population level. Quantitative research methods are used to control, measure, and evaluate phenomena of interest to researchers (Jacobsen, 2010). Descriptive statistics are used to identify the characteristics relevant to these research methods (Trochim, 2006). This study represents a survey research design, which is one of the most important areas of measurement (Trochim, 2006).
The interval level variables in the data are: agecat, referring to age categories, and dist_cat, referring to the distance between customers' homes and the stores they purchase from.
The ordinal level variables in the data are: dept referring to the most favored department; payment referring to the most preferred payment type; price referring to customers' opinions on prices in these stores; numitems referring to the number of items customers usually purchase; org referring to customers' opinions on store organization; service referring to the services provided by the stores; quality referring to the quality of products and services; and overall referring to overall customer satisfaction.
The categorical level variables in the data are: gender referring to the gender of customers; regular referring to the number of shopping sessions in these stores; reason1 referring to the most important reason customers prefer these stores; reason2 referring to other reasons customers shop there; purchase referring to whether customers purchase products when they visit; followup referring to whether customers want to receive newsletters and the channel they prefer; store referring to which store customers prefer; and contact referring to whether customers prefer to be contacted by the store.
The frequency distribution for the interval variable agecat is as follows:
Age Category — Percent of Respondents
18–24: 5%
25–34: 25%
35–49: 30%
50–64: 25%
64+: 15%
As these figures show, the largest groups of respondents fall in the 35–49 and 50–64 age categories. The smallest group is the 18–24 age bracket, representing only 5% of respondents.
Frequency distribution results for ordinal variables show that the clothing department is preferred by 25% of respondents, followed by the electronics department at 18%. Regarding payment preferences, 35% of customers prefer to pay with a credit card, while 52% prefer to pay with cash. With respect to store prices, 26% of respondents hold a somewhat negative opinion, 18% hold a strongly negative opinion, and 42% hold a neutral opinion.
"Gender, shopping frequency, and purchase reason findings"
"T-test hypotheses on gender, distance, and price opinion"
The t-test results reveal that although a small percentage of men is more likely to travel a longer distance to go to a store compared with women, the difference is not statistically significant. Additionally, the hypothesis that regular customers tend to hold a neutral opinion on store pricing was examined through inferential analysis. Together, the descriptive and inferential findings from this survey provide a comprehensive picture of customer satisfaction patterns and shopping behavior across the department stores studied.
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