Week 2 Learning Team A Paper Parts 4 & 5

Levels of Measurement for Each Variable in the Study In the survey that Debbie Horner, the Human Resource manager, distributed to the company, there are different levels of data available for analysis. The question seeking information about the respondent’s gender is a nominal-level question. “The nominal level of measurement observations of a qualitative variable can only be classified and counted” (Lind, Marchal, & Wathen, p. 10, 2011). For analytical purposes the order in which the data is displayed makes no difference. The question regarding the division of work for the respondent, and the question regarding if the respondent is a member of management or supervision is also nominal. Also, the question pertaining to length is service is nominal because it only seeks one answer. The ten questions relating to how each respondent feels is interval-level data. The ten questions ask each respondent to rate his or her individual feelings on a scale of one to five. One is considered very negative and five is considered very positive. This is known as a Likert Scale. The Likert Scale is the most popular form of survey data collection because it is easy to assemble, the scale is more reliable, and produce more interval-level data (Cooper & Schindler, 2011).
Data Coding After collecting the data, Sally, one the office support staff, began the task of coding the data. While some values were pre-determined other values needed coding. In addition to the questions there were also filter question asked such as gender, length of service, if the respondent worked as a supervisor or in management, and in which division each person works.
Questions 1-10
The questions numbered one through ten were pre-coded with assigned values one through five. A response of one indicates the respondent has very negative feelings, whereas a five shows very positive feelings. The team established that a response of two indicated the respondent’s feelings were negative. A response of three shows the respondent feels neutral. If the respondent chose the number four his or her feelings are considered positive. Sally coded the questions where respondents failed to answer as a zero. For the team’s analytic purposes all zeroes are coded no response.
Questions A, B, C, and D Question A asked each respondent the division he or she worked in. For coding, a number one is the food division. A response of two indicates the respondent works in the housekeeping division. When the respondent chose the number three that indicates he or she works in the maintenance division. Question B asked each person how long he or she has worked at BIMS. The survey asked each person to answer with years and months. To facilitate easier handling of the data Sally converted all years and months into total months. Question C requested that the individual indicate his or her gender. The answers were assigned the number one when the respondent indicates female, and the number two indicates a male. Sally also assigned the number zero to show where respondents did not wish to reveal his or her gender. The team has coded the number zero as no response, here, as well. Question D wants to know if the respondent works in management or supervision. The question only yields a yes, which is assigned the number one, and no, which is assigned the number two. Once again, some respondents chose not to answer this question and Sally coded those responses for the team as a zero. The team uses the zeroes in analysis as a no response again.
Cleaning the data When the surveys were completed and turned in Debbie had requested that Sally input the data for the team to analyze. The data input helped the team tremendously and saved the team time when analyzing the data. However, Sally admitted that she sometimes inputs data incorrectly. She stated that there were times that she may have input the number six when she meant to input a five. The team found, ironically, six instances where Sally input the number six instead of the number five.
References
Cooper, D., & Schindler, P. (2011). Business research methods (11th ed.). New York, NY: McGraw-Hill/Irwin.
Lind, D. A., Marchal, W. G., & Wathen, S. A. (2011). Basic statistics for business and economics (7th ed.). New York, NY: McGraw-Hill/Irwin.