Lecture 02 Data analysis Performance Measurement of the Field

Lecture 02
Data analysis
Performance Measurement of the Field Force
Performance measurement and management are two important aspects of field work.
Supervisor should ensure that the procedures are being followed. If not, give additional training
to the field workers. Some guidelines are:
Collected questioners be examined and edited
Ensure sampling plan being followed strictly not on convenience & accessibility
Cheating and fake answers be controlled by calling the respondents on phone.
However, sometimes this thing is difficult to control.
Authentication & Evaluation
A good method to authenticate that the data were collected genuinely, call 10 to 15% of
respondents to validate. Ask their basic demographic data & cross check. You also ask about
length, quality, and their reaction to interviewer from the respondents
In order to evaluate the performance, check in each of the field worker:
se rates
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d and
Quality of interviewing including precision, ability to probe, ability to ask sensitive
interpersonal skills and termination of interview Quality of data: legibility, non
response, instruction followed or not, answers completely recorded or not.
Data Preparation
We should understand that data is recorded measures of phenomenon but information is body of
facts in a format suitable for decision making. The purpose of research is to provide
Data analysis is a process whereby data re converted into information. There are many
methods and techniques of data analysis.
Understanding principles of data analysis is important because it
m. Leads the researcher to develop insight into information and data
n. Helps avoid erroneous judgment and conclusions.
o. Helps interpret the analysis of others.
Knowledge and power of data analysis can constructively influence research design but it should
be remembered that it cannot rescue or compensate study not well conceived. If research
hypotheses were non viable or uninteresting, or questions asked were irrelevant, or sampling
was inadequate or if fieldwork was sloppy, data analysis will not provide any remedy for these
deficiencies. On the other hand, selecting inappropriate data analysis techniques has the
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potential to ruin a well designed study. It will bring unclear, incomplete and erroneous
conclusions; which in their turn will lead to inferior decisions.
We can avoid these pitfalls only by having an adequate understanding about techniques of
data analysis.
Data Editing
Although data analysis techniques are unique for each study but all studies require data
editing. Data editing mean identifying omissions, ambiguities, and errors in the responses and
taking necessary corrective actions.
Who is responsible for such errors or omissions, could be all or any of the interviewer,
supervisor, or data analyst.
Problems that have been identified through various studies and need to be fixed during editing
of data include but not limited to the following:
Incorrect instructions by the interviewer;
Omissions, ambiguities (two boxes checked in
MCQ);Inconsistencies (not married but
Lack of cooperation (all
agree), Ineligible respondents
(age under 18)
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There are several alternatives for data editing. Some possibilities are:
Approach the respondent again for clarification;
Devise a new category;
Create a category of “no answer” or “missing”; or
Don’t include a question or the whole questionnaire in your analysis.
Non Sampling Errors
Sampling and Non Sampling Errors
All errors in survey except sampling plan and sampling size are non sampling errors. These
 Non response errors;
 Data gathering errors;
 Data handling errors;
 Data analysis errors;
 Interpretation errors;
 Ambiguity in problem definition;
 Inappropriate wording of questions etc
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Greatest potential for these errors is at data collection stage or field work.
Such error may occur due to fieldworker or respondents.
Fieldworker Errors
These errors are committed by the person who administers the questionnaire or takes interview.
The errors may be intentional (committed deliberately) or unintentional (occur without willful
intent). Examples of each are listed below.
Intentional Fieldworker Errors:
Cheating i.e. intentionally falsifying responses due to compensation e.g. if Rs. 500 per
completed interview are being paid to the interviewer, he/she may inflate the number
of interviews complete to get more money.
Convenient interviewee may be contacted instead of a genuine sample.
Less than agreed questionnaires may be completed without cheating.
Lead the respondent to a particular answer through wording, voice inflection, or body
Re-wording the question. (Isn’t it?)
Subtle influence on the respondent by shaking the head to yes or no, or saying uhu, okay etc.
Unintentional Fieldworker Errors:
Three sources
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Personal characteristics, accent, gender
Interviewer’s misunderstanding of instruction about scales, recording responses etc.
Gap between the educational level of research designer and the fieldworker. It creates
communication gap.
Miscellaneous sources like fatigue, monotony, at the end less alert, fail to check the reply,
does not follow skip-pattern, or hurry.
Respondent Errors
These errors are committed by the person who fills the questionnaire or responds to interview.
Like fieldworker errors, respondent errors are intentional or unintentional. See examples below.
Respondents’ intentional Errors:
Unfortunately respondents willfully misrepresent themselves in two ways
Tell a lie due to privacy or embarrassments e.g. income, marital status for lonely women, age
Don’t give response in whole or part due to busy schedule or privacy.
Respondents’ unintentional Errors:
Respondents unintentionally believe that an invalid response is a truth. Some instances are:
Answering without an understanding whether income is with or without taxes
Checking two answers instead of one
Guessing, uncertain of accuracy, little knowledge, low recall but feel compelled to answer.
Electricity consumption in K/hr
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Attention Loss, not interested in survey
Distraction, interruption, off track
Acquaintance on mall, Toddler in telephone survey, telephone in mail
survey Respondent Fatigue, Cause no opinion.
How to Control Field Errors
Precautions should be taken to minimize such errors, but these cannot be eliminated. Potential
for these errors always exists in field activities. What are the controls for these errors? Some
methods have been suggested for controlling field errors. These are described below.
Intentional fieldwork errors can be controlled by adopting these methods:
Supervision: Oversee the work. Central telephone monitoring can be used for this. Spot
cheating. Reprimand the interviewer if needed. It is always good to inform the field workers
about monitoring so that they remain alert. In the beginning accompany the personnel to
ensure that field workers have been adequately trained.
Validation: Validate the work by re-contacting about 10% respondents. Sometimes readminister the instrument for comparison.
Unintentional fieldworker errors can be controlled by three mechanisms.
9. Selection and training can take best care of such errors.
10. Orientation session: Meeting of field workers with the supervisor. He/she gives
instructions and tells about requirements of the questionnaire administration.
11. Role Playing (Rehearsals): Supervisor or somebody plays the role of respondent and
checks the interviewing skill. Alternating of the roles may take place between two
field workers.
Control of Intentional Respondent Errors
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Minimize falsehood and non response errors by following these tactics:
Anonymity: assure confidentiality of respondent’s name
Incentives: Give or promise gifts like t-shirts, ballpoints, cash, diaries etc.
Validation Checks: confirm the response by cross checking with other ways, for
example see the effect of medicine for baldness.
Third Person Techniques: Design of question in such a way that it does not seem to be a
direct personal question rather indirect in the name of a friend, colleagues or neighbors.
Control of Unintentional Respondent Errors
Unintentional errors on the part of respondents can be controlled by adopting these techniques.
Well Drafted Questionnaire: Clear instructions and examples remove misunderstandings and
1. Reversal of Scale on Endpoints: Negative adjectives may be placed not on one side. If
you place these adjectives sometimes on right and sometimes on left side, it will compel
the respondent to think and give thoughtful answer.
2. Prompters: Keep respondents on task. Give written or oral statements like “this was the
most difficult section to answer”, “we are almost finished” This reduces the fatigue also.
In the end we can say that there are different errors and different control but good design of
questionnaire and technology can reduce the errors of data collection in general.
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