Study Guides (390,000)
CA (150,000)
Ryerson (10,000)
MKT (500)
MKT 500 (40)

MKT 500 Study Guide - Data Analysis, Frequency Distribution, Central Tendency


Department
Marketing
Course Code
MKT 500
Professor
Tina West

This preview shows half of the first page. to view the full 2 pages of the document.
MKT500 Marketing Research
CHAPTER 10 Data Collection and Basic Descriptive Statistics
ERRORS ENCOUNTERED IN THE DATA COLLECTION STAGE
Nonsampling errors: errors in research process involving anything except the sample size
Fieldworkers: individuals hired to administer the survey to respondents
o Intentional fieldwork errors: interviewers deliberately falsify their work by, for example, submitting bogus
completed questionnaires
o Unintentional fieldwork errors: interviewers make mistakes such as those caused by fatigue, or lack of
understanding of how to administer the questions
Respondent errors: errors committed by respondents when answering the questions in a survey
o Intentional respondent errors: those committed when the respondent knowingly provides false answers or fails to
give an answer
o Unintentional respondent errors: occurs when respondent is confused, distracted, or otherwise inattentive
DATA COLLECTION ERROR WITH ONLINE SURVEYS
Unless controls are in place, there can be misrepresentations in online surveys
Three data collection errors unique to online surveys
o Multiple submissions: unless controlled, it is possible for a respondent to submit his or her completed
questionnaire multiple times in a matter of minutes
o Bogus respondents and responses by giving nonsense, polarized, or otherwise false responses
o Population misrepresentation: some segments of the population are not good prospects for an online survey
TYPES OF NONRESPONSE
Nonresponse: failure on the part of a prospective respondent to take part in the survey or to answer specific questions on
the questionnaire
Three different types of potential nonresponse error:
o Refusal: occurs when a potential respondent rejects the offer to take part in the survey
o Break-off: occurs when respondent reaches a certain point and then decides not to answer any more questions of
the survey
o Item omission: signify that some respondents refused to answer a particular question
o Completed interview: if sufficient number of questions are answered to allow the questionnaire to move into the
data analysis stage, the interview can be considered completed
CODING DATA AND THE DATA CODE BOOK
Data entry: creation of a computer file that holds the raw data taken from all of the completed questionnaires
Data coding: identification of computer code values that pertain to the possible responses for each question on the
questionnaire
o These codes are numerical because numbers are quick and easy to input
Data code book: identifies all of the variable names and code numbers associated with each possible response to each
question that makes up the data set
TYPES OF DATA ANALYSES USED IN MARKETING RESEARCH
Data set: matrix of numbers and other codes that includes all of the relevant answers of all respondents in a survey
Data analysis: process of describing a data set by computing a small number of measures that characterize the data set by
computing a small number of measures that characterize the data set in a way that are meaningful to the client
Data analysis accomplishes one or more of the following functions:
o It summarizes data
Summarizing sample data with categorical data (percentages and percentage distribution) and metric
data (averages, ranges, and standard deviation)
o It generalizes sample findings to the population
Generalizing the sample findings to the population with hypothesis tests and confidence intervals
o It compares for meaningful differences
Comparing averages or percentages in sample data to see if there are meaningful differences with
percentage difference tests (variable 1 is categorical, variable 2 is categorical) and average difference
tests (variable 1 is categorical, variable 2 is metric)
o It relates underlying patterns
You're Reading a Preview

Unlock to view full version