Running head: Analyzing Statistics 1

Reflection of Analyzing Statistics

Laura Babin, Rose Krohn, Talena Peterson, Preston Stamper, Ashley Walters

Learning Team C

QNT/351

September 13, 2012

Margaret Martin

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Reflection of Analyzing StatisticsStatistics is a word commonly used in daily discussion. The presence of statistics follows

society in almost every decision a person can make. For example, what college to attend based

on how well previous students have done, or betting on a particular horse during a race because

he is said to win (Lind, D. A., Marchal, W. G., … 2011).. Even on Wall Street financial experts

are constantly checking the numbers related to stocks such as the Dow Jones or Nasdaq. Reading

the numbers and percentages off of a board is the easy part; converting raw data into a numerical

order or placing a value to qualify the significance is where it gets tricky. Understanding what

statistics do to impact decision making is extremely prevalent in daily life; however, deciding

what values to assign to particular categories or what graph best suits the information provided is

where the challenge begins.

There are two types of primary research: quantitative data collection and qualitative data

collection. Quantitative data collected is based on numbers. For example collecting data related

to peoples age or income. The information collected can be analyzed after the collection using

various statistical techniques. This analysis helps the researcher create meaningful patterns and

take a deeper dive into the data. The most important use of quantitative data is in hypothesis

testing and can help researchers in reaching conclusions. The main methods used to collect

quantitative information are using questionnaires that require input of the user’s response. The

survey can be distributed using different means, such as mail, phone, and websites. Qualitative

data can be collected using personal interviews, focus groups, and observations carried out for

the purpose of research (Siddharth, 2011). Although personal interviews can help researchers

dig into greater details, they are time consuming and expensive.

Available data from the Ballard Integrated Managed Systems (BIMS) employee survey

afforded us the opportunity to quantify results and make an inference as to the possible reasons

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for the high turnover and low morale cited by management. As such, we formulated our initial

hypothesis, that poor communication was a major contributing factor. To further test our

hypothesis, our consulting firm made the recommendation to continue with the approach of using

an employee survey instrument after removing or refining any ambiguous questions. A quarterly

collection of data will allow us to reassess the data across consistent elements while attempts are

made to increase response rate, thus giving us a better sample of data to derive conclusions from.

Ordinal data is a scale of measurement in which any given class is higher or lower in rank

than the other. For example in the given questionnaire, five is better than four, four is better than

three, three is better than two, and two is better than one. Question A, C, and D represents

nominal level data. This data can be represented in any order. There is no natural order for

these. For instance, for the gender question one can either report males first or the females.

Question B is quantitative in nature and can be classed as ratio level data. In this level of data

the zero point is meaningful because the time spent being employed at BIMS cannot be a

negative number. A person could have worked for a year but not negative one year.

Team C consultants have already delivered two responses based on the survey. The first

response was the sample size was too small. BMIS need a much higher participation rate to gain

meaningful evidence of the root causes of the problem. The second response addressed the low

scores on communication. While BMIS continues administering the survey, Team C consultants

can test the hypothesis of communication being a critical problem within the company.

Team C consultants have compared the means of the answers to question nine on the

BMIS survey. Of all 78 responses only one could not be identified as food, housekeeping, or

maintenance. Of the remaining responses 32 came from food, 36 came from housekeeping, and

nine came from maintenance. The mean responses were 2.03, 2.33, and 2.33 respectively. This

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shows the food division has the lowest scores on communication, and has the most room for

improvement.

There are several different kinds of relationships between variables. Before drawing a

conclusion, you should first understand how one variable changes with the other. This means you

need to establish how the variables are related - is the relationship linear or quadratic or inverse

or logarithmic or something else? Relationships between variables need to be studied and

analyzed before drawing conclusions based on it. In natural science and engineering, this is

usually more straightforward as you can keep all parameters except one constant and study how

this one parameter affects the result under study.

One variable that must be considered and compared is the response rate among the

divisions. The overall response rate of 17% translates to each division where no division carried

a response rate above 30%. This also supports the response of needing a larger sample size. A

larger sample will help draw more accurate and complete hypotheses and conclusions.

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References

Siddharth, K. (2011). Relationship between variables. Retrieved 13 Sep. 2012 from Experiment

Resources: http://www.experiment-resources.com/relationship-between-variables.html

Lind, D. A., Marchal, W. G., & Wathen, S. A. (2011). Basic statistics for business and

economics (7th ed.). New York, NY: McGraw-Hill/Irwin.

McClave, J. T., Benson, P. G., & Sincich, T. (2011). Statistics for business and economics (11th

ed.). Boston, MA: Pearson-Prentice Hall.