BIMS

Examine the data before settling on a specific analytical method. The nature of the data affect, and may even limit, the type of analysis you can conduct. Consider both the dependent variable (also known as the outcome variable) and the independent variable(s). Are these data categorical, ordinal or interval? Categorical data, in which the values of the data represent categories (such as gender, ethnicity, political affiliation), are the simplest. Attitude scales such as the Likert scale are examples of ordinal data. A Likert-type scale asks you to answer a question by making a choice along a range of answers numbered from, say, 1 through 5. With interval level data, the values tell you something about the difference between given values. Test score data, for example, are often interval.

Consider the design of your study. The gold standard in most research is the randomized experiment, in which subjects are randomly assigned to the experimental and control groups. Many clinical trials of medications use this design. For various practical and ethical reasons, most social science research uses quasi-experimental research designs, in which the experimental and control groups differ on key characteristics. This is an important consideration for the analyst, who must control for these differences when analyzing the data. Often, quasi-experimental studies require more complex analytical techniques.

Preliminary Recommendations
1. BIMS response rate of 17.2% is considerably low. Team A believes drawing conclusions based on the limited data collected may be premature. BIMS should encourage remaining employees to participate in the surveys. Team A recommends, an experience survey company to administer the survey in the future. This will provide a sense of confidentiality to employees. Once other surveys are administered, the new analysis should be repeat.
2. Focus groups shall be conducted, as a follow-up randomly with chosen employees, using outside facilitators.