Chapter 1B Chapter 1 continued... Fall 2000

Chapter 1B II. Sources of Data You can use data that has either been collected by yourself for a specific research question (primary) or by previously collected by someone else for other purposes (secondary). Fall 2000

A. Existing Sources Also called secondary data, always look for data that will help your research and is already available. 1. Collected or recorded within the firm. Ex. Payroll, production, inventory, sales 2. Outside organizations hired to collect data. Ex. A.C. Nielson, Dun and Bradstreet 3. Government Ex. Census, FED, BLS, Commerce

Chapter 1B B. Statistical Studies Also called primary sources of data. If you are unable to find readily available data from existing sources you need to obtain them yourself. Fall 2000

1. Experimental Study Ask a question, identify the relevant variables, control one or more factors and analyze the results. Ex. How would you develop an experiment to answer a question about consumer preferences for one cola over another?

2. Observational study This kind of study makes no attempt to control or influence variables. Examples are surveys, questionnaires, customer satisfaction cards, etc.

C. Data Acquisition Errors Many researchers would argue that “bad data is worse than no data”. What do you suppose this means? Data can be “bad” if you have missing responses in the middle of a survey. You can have a complete survey but it is incorrectly entered into a database. What about values that are extremely above or below the vast majority of values (outliers)?

Examples of “bad data” A trucking company reports millions of dollars in annual sales but the value for “# of employees” is blank. What would you do to this observation? A company reports total revenues of $1 million, total costs of $500,000 and net income (TR-TC) of $500. What would you do with this observation?

III. Descriptive Statistics Descriptive statistics are a summary of your data. These summaries can be tabular, graphical or numerical. An example of a tabular summary is seen below.

An example of a graphical summary is below.

An Example of a Numerical Summary is Below At this point you’re not expected to understand all of these measures, but you’ll soon be able to interpret this entire chart.

IV. Statistical Inference A population of data is the set of all elements of interest in a particular study. (i.e. all television viewers) A sample is a subset of the population. (i.e. the 200 viewers randomly called in a survey of viewing habits.) Statistical inference is the process of making observations for a sample and assuming them to hold true for the larger population.

If we wanted a sample of automobile owners, how would we possibly collect one?