After the research problem has been defined and a suitable approach developed , an appropriate research design formulated ,and the fieldwork conducted the researcher can move on to data preparation and analysis, the fifth step of the marketing research process. Before the raw data contained in the questionnaires can be subjected to statistical analysis, they must be converted into a form suitable for analysis. The quality of statistical results depends on the care exercised in the data preparation phase. Paying inadequate attention to data preparation can seriously compromise statistical results, leading to biased findings and incorrect interpretation.
This chapter describes the data-collection process, which begins with checking the questionnaires for completeness. Then, we discuss the editing of data and provide guidelines for handling Illegible, Incomplete, inconsistent, ambiguous, or otherwise unsatisfactory responses. We also describe coding, transcribing, and data cleaning, emphasizing the treatment of missing responses and statistical adjustment of data. We discuss the selection of a data analysis strategy and classify statistical techniques. The intracultural, pancultural, and cross-cultural approaches to data analysis in international marketing research are explained. The ethical issues related to data processing are identified with emphasis on the discarding of unsatisfactory responses, violation of the assumptions underlying the data analysis techniques, and evaluation and interpretation of results.
Finally, we diSCUSSusing computers in data preparation and analysis. Help for running the SPSSand SAS Learning Edition programs used in this chapter is provided in four ways: (1) detailed step-by-step instructions are given later in the chapter, (2) you can download (from the Web site for this book) computerized demonstration movies illustrating these step-by-step instructions, (3)you can download screen captures with notes illustrating these step-by-step instructions, and (4)you can refer to the Study Guide and Technology Manual, a supplement that accompanies this book.
In the department store project, Ihe data were obtained Ihrough in-home personal interviews, TIle supervisors edited the questionnaires as the interviewers turned Ihem in. TIle questionnaires were checked for incomplete, inconsistent, and ambiguous responses. Questionnaires with unsatisfactory responses were returned to the field and the interviewers were asked to recontact the respondents 10 obtain the required information, Nine questionnaires were discarded because the proportion of unsatisfactory responses was large. This resulted in a final sample size of 271
A codebook was developed for coding the questionnaires. Coding was relatively simple because there were no open-ended questions. The data were transcribed onto a computer tape via keypunching. About 25 percent of the data were verified for keypunching errors. The data were cleaned by identifying , out-of-range and logically inconsistent responses. Most of the rating information was obtained using 6-point scales, so responses of 0, 7, and 8 were considered out of range and a code of 9 was assigned 10 missing responses.
Any missing responses were treated by casewise delelion, in which respondents with any missing values were dropped from the analysis. Casewise deletion was selected because the (respondents) with missing values was small and the sample size was sufficiently large. In statisticullv adjusting the data, dummy variables were created for the categorical variables. New variables composites of original variables were also created. For example. the familiarity ratings of the stores were summed to o create a familiarity index. Finally, a data analysis strategy was developed.