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Type of data in this project.

There are two broad categories of data classification namely qualitative and quantitative data. Qualitative data is information that is not collected in terms of numerical figures such as 1, 2 and 11 up to n but can be qualified using descriptive words such as the type of colors, for example, red or blue. Quantitative data is the data that can be expressed in terms of numbers either discrete numbers or continuous figures (Lee, 2014). Examples include scores in an examination or the weight of a gas cylinder. In this research we will be dealing with both qualitative and quantitative data because by using the questionnaires, we will be collecting information regarding the gender and the level of education from the various respondents.

When collecting qualitative data, most times the respondents with the same qualities are grouped together for analyzing even though they are the sources of the primary data we are collecting. Respondents will give us the quantitative figures which are the main reasons for this research. Therefore, the people who will share the same or almost similar levels of education, or same gender will be grouped together. Comparisons of the data they filled in the questionnaires will be carried out. The type of data, we will be mainly looking at will be the quantitative data from the questionnaires.

From the side A of the questionnaires, we will expect to record the age of the respondents, the numbers of the respondents in the research, the level of salaries which are all quantitative measures. From side B, we will purely collect quantitative data which will be the figures of the people visiting the Certus Bank in Easley for either

  1. i) Consultations regarding the products the bank offers
  2. ii) The number of people who take the time to look for information from the digital signage media.

iii) Customer satisfaction is a qualitative measure. Since it will be difficult to categorize the responses, we will assign values to a scale of up to 10 where respondents will tick appropriately according to their experiences with the introduction of the digital signage media.

The methods of data analysis.

For this research, we are handling both of the types of data. Therefore, more data analysis techniques will be employed for accurate results as there are many variables such as the different quantities stated in the data to be collected. Once the data is collected, there comes the need to draw conclusions from it and to make sense of the responses we got.

The initial phase will be to consolidate and classify the data relevantly according to the different categories we will have made and summarize the data in a way that will represent the most significant features. Recording of frequencies and variables will be the next step. We will be dealing with a lot of variables which will be giving different frequencies which we must record down either in tabulation or a tally. Differences in the variables will be calculated to determine the variances and, therefore, become closer to the exact numbers we are looking for in order to nullify or support our null hypothesis.

The principal aim of data analysis is to prove that there exists some relationship between the findings and remove the suspicions that the occurrences may have happened by chance (Schmidt, 2014). To analyze our results, we will use the four broad levels of measurement which are the ordinal analysis, interval measurement, nominal measurement and ratio measurements. Nominal size will be used for analyzing the gender since we are only dealing with two types which are the male and the female gender. Ordinal analysis will be assigned to the qualitative data such as the level of education, for example, high school and college. Interval measurements will be used to tell the gap between the time interval taken before the bank gets another visitor. Ratios will come in very handy in trying to analyze the rates for guests visiting the banks before and after the use of digital signage media.
















Lee, H. (2014). Foundations of applied statistical methods.

Schmidt, J. W. (2014). Applied Statistical Methods. Elsevier Science.


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