Data Preparation for Analysis

Data Preparation for Analysis Chapter 11, Student Edition MR/Brown & Suter

Learning Objectives • Explain the purpose of the editing process • Define what coding is • Describe the kinds of information contained in a codebook • Describe common methods for cleaning the data file • Discuss options for dealing with missing data in analyses MR/Brown & Suter

Learning Objective 1 • Editing – the inspection and correction of the data received from each element of the sample (or census) • During the editing process, it must be decided what to do about cases with incomplete answers, obviously wrong answers, and answers that reflect a lack of interest MR/Brown & Suter

Learning Objective 2 • Coding – the process of transforming raw data into symbols (usually numbers) that can be utilized for analysis • Coding Closed-ended Items • Examples • 1 = Female, 2 = Male • 1 = Unfavorable to 7 = Favorable • Coding Open-ended Items • Factual open-ended items are highly structured and easy to code • Exploratory open-ended items are less structured and allow for multiple responses making it more difficult to code MR/Brown & Suter

SPORTING GOODS SURVEY • Please answer the following questions about buying sporting goods over the internet: • During the past year, what percentage of the sporting goods you purchased were ordered through the internet? • ________ percent • 2. How willing are you to purchase merchandise offered through the Avery Sporting Goods web site? • Not at all willing Somewhat willing Very willing • 3. Please provide some reasons why someone might not want to purchase sporting goods over the internet: Learning Objective 3 MR/Brown & Suter

Learning Objective 3 MR/Brown & Suter

Learning Objective 4 • Blunder • An error that arises during editing, coding or data entry • Blunders are usually due to researcher carelessness • Blunders Can Be Located By • Examining frequency distributions on all variables • Checking a sample of questionnaires against the data file • Double-entry of data in which data are entered into two separate data files and then compared for discrepancies (preferred) • Optical scanning can be used to “read” responses • Nonresponse/Missing Data Should Also Be Investigated MR/Brown & Suter

Learning Objective 5 • Eliminate the Case with the Missing Items from All Further Analyses • Eliminate the Case with the Missing Items from Analyses Using the Variable • Substitute Values for the Missing Items • Contact the Respondent Again MR/Brown & Suter

Data Preparation for Analysis