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Module 1 Several Key Points

BUSN 7278 Data Analysis Professor Bassell

Page |1

The process for data preparation and analysis. The value of marketing research is its ability to provide accurate decision-making information to the user. To accomplish this, the data must be converted into usable information or knowledge. After collecting data through the appropriate method, the task becomes one of ensuring that the data provides meaning and value. Data preparation is the first part of the process of transforming data into useful knowledge. This process involves several steps:
(1) data validation,
(2) editing and coding,
(3) data entry,
(4) error detection, and
(5) data tabulation.
Data analysis follows data preparation and facilitates proper interpretation of the findings.

Validation, editing, and coding of survey data. Data validation attempts to determine whether surveys, interviews, or observations were conducted correctly and are free from fraud. In recontacting selected respondents, the researcher asks whether the interview
(1) was falsified,
(2) was conducted with a qualified respondent,
(3) took place in the proper procedural setting,
(4) was completed correctly and accurately, and
(5) was accomplished in a courteous manner.
The editing process involves scanning of interviews or questionnaire responses to determine whether the proper questions were asked, the answers were recorded according to the instructions given, and the screening questions were executed properly, as well as whether open-ended questions were recorded accurately. Once edited, the questionnaires are coded by assigning numerical values to all responses. Coding is the process of providing numeric labels to the data so they can be entered into a computer for subsequent statistical analysis.
Data entry procedures and how to detect errors. There are several methods for entering coded data into a computer. First is the PC keyboard. Data also can be entered through terminals having touch-screen capabilities, or through the use of a handheld electronic pointer or light pen. Finally, data can be entered through a scanner using optical character recognition. Data entry errors can be detected through the use of error edit routines in the data entry software. Another approach is to visually scan the actual data after it has been entered.

Module 1 Several Key Points

BUSN 7278 Data Analysis Professor Bassell

Page |2

Data tabulation and analysis approaches. Two common forms of data tabulation are used in marketing research. A one-way tabulation indicates the number of respondents who gave each possible answer to each question on a questionnaire. Cross-tabulation provides categorization of respondents by treating two or more variables simultaneously. Categorization is based on the number of respondents who have responded to two or more consecutive questions.
The process of data validation and the issues of fraud, screening, procedure, completeness, and courtesy. Data validation is something akin to a system of “checks and balances” which allows the research team to determine if interviews and observations were conducted with accuracy and precision. It’s important to for the team to audit the data, with a keen eye to the following five issues:
(1) Respondents were treated with courtesy during the interview process;
(2) Respondents completed all questions on the survey instrument;
(3) Respondents offered up their views in accord with the procedures articulated in the research design;
(4) Respondents were screened for their alignment with the target population;
(5) Respondents were actually interviewed, not fraudulently represented by responses packaged up and provided by the interviewers themselves.
The differences between data validation, data editing, and data coding. The process of validating data is all about ensuring the interviews or observations were conducted correctly and free of fraud on the part of interviewers. Data editing and data coding, unlike data validation, approaches the data captured from respondents in a “what we see is what we get” fashion. In other words, the focus of error hovering above the editing and coding process has more to do with administrative error than slips in “procedure” and “character”.
Data editing involves checking survey instruments for mistakes which may have occurred during the process of data collection. As an example, when a questionnaire is reviewed responses suggest the data is being provided by a person who is employed. However, in an early question the interviewer checked “Unemployed” by mistake. This is an administrative error, not indicative of the kind of underhanded issues of character which hover around practices such as “curbstoning.” Data coding involves grouping and assigning values to various responses from the survey instrument.
The differences between establishing codes for open-ended questions and for closed-ended questions. Open-ended questions are useful to researchers and clients since they allow us to probe answers more deeply for clarity and specifics. The downside of establishing codes for open-ended questions is the process of analysis is far more protracted. As well, open-ended responses are always open to “interpretation” on the part of the team analyzing the data. Ultimately, the analysis of an open-ended question ends up in the same spot as close-ended questions; namely, with the research assigning a numerical value as a code and attaching a coded value to each response. The challenge is taking all the many and varied responses provided by respondents to open-ended questions (sometimes illegible, often downright nasty – even foul,

Module 1 Several Key Points

BUSN 7278 Data Analysis Professor Bassell

Page |3

most of the time left blank) generating a master list of potential responses and assigning values to the responses. When developing a survey instrument class participants are often enticed by the opportunity to include three or four open-ended questions in their instrument. However, once confronted with the data captured by a set of open-ended questions, it is a painful and time consuming process.
The process of data entry and how changes in technology have simplified this procedure.
Data entry has often been characterized as “grunt work” in the marketing research industry. It is quite likely class participants will approach this topic (and actually doing it) with similar perceptions. However, changes in technology such as
 touch-screen capability,
 hand-held electronic pointers,
 hand-helds (e.g. iPads) and
 optical scanners have done a lot to alter this unsavory (and quite unjustified) image of data entry. As well, great leaps have been made in the online arena by such companies as Mercator Corporation. Their “Snap” software provides a seamless transition between data entry on the part of respondents and analysis on the part of the research team, effectively removing the task of data entry by “grunts” a thing of the past. Data entry includes all those tasks involved with people directly inputting coded data into a software package


Minitab, SAS, and SPSS.

This essential and important step in the research process allows an analyst to manipulate and transform raw data into useful information.
The three approaches to error detection and the data/column list procedure. The three approaches to error detection are: 1) Determine if the software used for data entry and tabulation will allow a user to perform error edit routines – “expert systems” something like a “Clippit” in Microsoft XP which pops up when the wrong type of data is entered; 2) Scan the actual data that has been entered; 3) Produce a data/column list procedure to audit the data which has been entered. The beauty of utilizing a data/column list procedure is that an analyst can scan rows and columns and determine if any inappropriate codes have been entered into data fields. Using the rows as a reference, the analyst can go back and “pluck” the corresponding questionnaire and rectify the error as needed.
The purpose of a simple one-way tabulation and a one-way frequency table. One-way tabulations can be used to: 1) determine the degree of nonresponse to each question in the survey (nonresponse can suggest either a “bad” question, or that the survey was given to a respondent not aligned with the target population);
2) ferret out simple slip-ups in the data entry process; and 3) profile a host of summary statistics of interest to the research team.

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