Dealing With Fieldwork And Data Collection Issues PDF

Dealing with Fieldwork and Data Collection Issues FINAL EXAM ROOM CHANGE Student Success Center 308 - 9:30AM Nonsampling Error Nonsampling error includes: All types of nonresponse error Data gathering errors Data handling errors Data analysis errors Interpretation errors Copyright © 2020, 2017, 2014...

Dealing with Fieldwork and Data Collection Issues FINAL EXAM ROOM CHANGE Student Success Center 308 - 9:30AM Nonsampling Error Nonsampling error includes: All types of nonresponse error Data gathering errors Data handling errors Data analysis errors Interpretation errors Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Data Collection Data collection is the phase of the marketing research process during which respondents provide their answers or information to inquiries posed to them by the researcher. Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Possible Errors in Field Data Collection Fieldworker error: errors committed by the persons who administer the questionnaires Respondent error: errors committed on the part of the respondent Errors may be either intentional or unintentional. Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Intentional Fieldworker Errors Intentional fieldworker error: errors committed when a data collection person willfully violates the data collection requirements set forth by the researcher Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Intentional Fieldworker Errors Interviewer cheating occurs when the interviewer intentionally misrepresents respondents Leading respondents occurs when the interviewer influences respondent’s answers through wording, voice inflection, or body language Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Unintentional Fieldworker Error Unintentional fieldworker error: errors committed when an interviewer believes he or she is performing correctly Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Unintentional Fieldworker Error Interviewer personal characteristics occurs because of the interviewer’s personal characteristics such as accent, sex, and demeanor. Interviewer misunderstanding occurs when the interviewer believes he or she knows how to administer a survey but instead does it incorrectly. Fatigue-related mistakes occur when interviewer becomes tired. Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Intentional Respondent Error Intentional respondent error: errors committed when there are respondents that willfully misrepresent themselves in surveys Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Intentional Respondent Error Nonresponse occurs when the prospective respondent fails to take part in a survey or to answer specific questions on the survey. Falsehoods occur when respondents fail to tell the truth in surveys. Speeding is when the respondent rushes to complete the survey. Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Unintentional Respondent Error Unintentional respondent error: errors committed when a respondent gives a response that is not valid but that he or she believes is the truth Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Unintentional Respondent Error Respondent misunderstanding occurs when a respondent gives an answer without comprehending the question and/or the accompanying instructions. Guessing occurs when a respondent gives an answer when he or she is uncertain of its accuracy. Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Unintentional Respondent Error Attention loss occurs when a respondent’s interest in the survey wanes Distractions (such as interruptions) may occur while questionnaire administration takes place Fatigue occurs when a respondent becomes tired of participating in a survey Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Online Survey Completion Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Field Data Collection Quality Controls Control of intentional fieldworker error – Supervision uses administrators to oversee the work of field data collection workers. – Validation verifies that the interviewer did the work. Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Field Data Collection Quality Controls Control of unintentional fieldworker error – Orientation sessions are meetings in which the supervisor introduces the survey and questionnaire administration. – Role-playing sessions, which are dry runs or dress rehearsals of the questionnaire with the supervisor or some other interviewer playing the respondent’s role. Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Field Data Collection Quality Controls Control of intentional respondent error – Anonymity occurs when the respondent is assured that his or her name will not be associated with his or her answers. – Confidentiality occurs when the respondent is given assurances that his or her answers will remain private. Both assurances are believed to be helpful in forestalling falsehoods. Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Field Data Collection Quality Controls Control of intentional respondent error – One tactic for reducing falsehoods and nonresponse error is the use of incentives, which are cash payments, gifts, or something of value promised to respondents in return for their participation. Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Field Data Collection Quality Controls Control of intentional respondent error – Another approach for reducing falsehoods is the use of validation checks, in which information provided by a respondent is confirmed during the interview. – A third-person technique can be used in a question, in which instead of directly quizzing the respondent, the question is couched in terms of a third person who is similar to the respondent. Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Control of Unintentional Respondent Error Well-drafted questionnaire instructions and examples are commonly used as a way of avoiding respondent confusion. The researcher can switch the positions of a few items on a scale, called reversals of scale endpoints, instead of putting all of the negative adjectives on one side and all the positive ones on the other side. Prompters are used to keep respondents on task and alert. Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Nonresponse Error Nonresponse: failure on the part of a prospective respondent to take part in a survey or to answer specific questions on the survey Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved The Three Types of Nonresponses with Surveys Name Description Refusal The prospective respondent declines to participate in the survey. Break-off After answering some questions in the survey, the respondent stops participating. Item omission The respondent does not answer a particular question but does answer other questions. Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved What Is a Completed Interview? The marketing researcher must define what is a “completed” interview. A completed interview is often defined as one in which all the primary questions have been answered. Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Illustrative Best Practices for Online Panels Quality Assurance Best Practice Explanation Panel purpose. Ensure that the panel is used solely for market research. Some panel companies have members who are recruited via third-party product registration rather than for “marketing research purposes” Recruitment. Panels should be ethically invited or otherwise accorded an “opt-in” relationship with the panel company to participate in bona fide marketing surveys Some panel companies use spambots, spiders, or other dubious and unethical methods to gain respondents Privacy. There should be a formal, published privacy policy concerning anonymity, confidentiality, and privacy of panel members’ personal data Researchers who assure privacy must be guaranteed by the panel company that panel member data is undiscoverable Data security. Measures should be in place to ensure the security of panelists’ identifiable information. Panelists should be assured that their confidentiality is protected by firewall or other appropriate database server protections Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Illustrative Best Practices for Online Panels Quality Assurance Best Practice Explanation Data quality management. Panel members should not participate in multiple surveys in short periods of time. Also, provisions should be in place to guard against straightlining, rushed answers, illogical responses, and automated responses. Participation limits eliminate “professional respondents” and otherwise limit overrepresentation of individual panel members in surveys Panel replacement. There should be a healthy attrition and replacement of panelists per annum The IMRA states that natural attrition, sometimes called “churn rate,” ranges between 25% and 30% per year due to members who lose interest or are otherwise delisted Screening and quota management. There should be abundant, up-to-date classification information on panel members Clients often wish to sample very specific subpopulations Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Dataset, Coding Data and the Data Code Book A dataset is an arrangement of numbers (mainly) in rows and columns. The dataset is created by an operation called data coding, defined as the identification of code values that are associated with the possible responses for each question on the questionnaire. Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Dataset, Coding Data and the Data Code Book In large-scale projects, and especially in cases in which the data entry is performed by a subcontractor, researchers use a data code book which identifies: – The questions on the questionnaire – The variable name or label that is associated with each question or question part – The code numbers associated with each possible response to each question Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Data Coding 1st Step: Create an appendix of all of your questions and possible responses and assign them a number. Ex: What is your gender? (Female = 1, Male = 2) What is your income? (Under $15,000 = 1, $150,000+ = 2, etc) Likert scale (Strongly disagree = 1, Strongly agree = 5) Data Coding 2nd Step: Use the find and replace function in excel for each response Tips: You need to do the following examples first: Female No Neither disagree or agree (1st) Strongly agree (2nd) Strongly disagree (3rd) Disagree (4th) Agree (5th) Dissatisfied (2nd to last) Satisfied (Last) Important (Last)