1. The document outlines the steps for implementing a data collection plan, including identifying research personnel, training data collectors, and carrying out the collection procedures. 2. Key considerations for research personnel include their experience, congruity with sample characteristics, appearance, personality, and availability. Training covers study procedures, administration of questions, and trial runs. 3. The eight steps for implementing the plan are: identifying questions, collecting available data, determining needed data amount, measuring data, appointing collectors, collecting from sources, deciding on sampling, and determining display formats. Careful planning of personnel, training, and procedures is essential.

1. 1
Implementing
Data collection Plan
Prof.Dr.Chinna Chadayan.N
RN.RM., B.Sc (N)., M.Sc (N)., Ph.D (N).,
Professor
Enam Nursing College
M.Sc (N) I year
Unit 16d
Implementing data
collection Plan
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2. Meaning of
Implementing Data collection plan
A data collection plan is a detailed document.
It describes the exact steps as well as the sequence that
needs to be followed in gathering the data for the given
project.
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3. Selecting Research Personnel
An important decision concerns who will actually collect the research data. In small studies, the
researchers in charge often collect the data themselves.
In larger studies, however, this may not be feasible. When data are collected by others, it is
important to select appropriate people. In general, they should be neutral agents through whom
data passes—that is, their characteristics or behavior should not affect the substance of the data.
Some considerations that should be kept in mind when selecting research personnel are as
follows:
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4. Experience. Research staff ideally have had prior experience collecting data. For example, for a self-report
study, it is advantageous to use people with interviewing experience. If it is necessary to use those without
experience, look for
people who can readily acquire the necessary skills (e.g., an interviewer should have good verbal and social
skills).
Congruity with sample characteristics. To the extent possible, data collectors should match study
participants with respect to such characteristics as racial or cultural background and gender. In some studies,
this is an absolute requirement (e.g., hiring a person who speaks the language of an immigrant sample). The
greater the sensitivity
of the questions, the greater the desirability of matching characteristics. For example, in a study of the sexual
behavior of pregnant African- American teenagers, the interviewers would ideally be African-American
women.
Unremarkable appearance. Extremes of appearance should be avoided because participants may react to
extremes and alter their behavior or responses accordingly. For example, data collectors should in general
not be very old or very young. They should not dress extremely casually (e.g., in shorts and tee shirts), nor
very formally (e.g., with elaborate jewelry). While on the job, data collectors should never wear anything
that conveys their political, social, or religious views (e.g., political buttons, jewelry with peace symbols).
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5. Personality. Data collectors should be pleasant (but not effusive), sociable (but not overly
talkative or overbearing), and nonjudgmental (but not apathetic or unfeeling about
participants’ lives). The goal is to have nonthreatening staff who can encourage candor
and put participants at ease without interjecting their own values and biases.
Availability. Data collectors should ideally be available for the entire data collection
period to avoid having to recruit and train new staff. If the study is longitudinal, it is
advantageous to hire people who could potentially be available for subsequent rounds of
data collection.
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6. In some situations, researchers cannot select research personnel.
For example, the data collectors may be staff nurses employed at a hospital or graduate
students in a school of nursing. Training of the data collection staff is particularly important
in such situations.
Example of care in selecting research personnel:
Nyamathi, Leake, Keenan, and Gelberg (2000) studied health behaviors and the use of
health services among homeless women in Los Angeles. Participants were recruited by
African-American or Latina nurses who were trained in working with this population. Data
were collected in face-to-face interviews done in English and Spanish by nurses and
outreach workers of the same ethnicity as participants
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7. Training Data Collectors
• Depending on the prior experience of data collectors, training will need to cover both general
procedures (e.g., how to conduct a research interview) and ones specific to the study (e.g., how
to administer a particular set of questions or make certain observations). Training can often be
accomplished in a single day, but in complex projects, it may require more time.
• The lead researcher is usually the best person to conduct the training and to develop training
materials.
• The data collection protocols, discussed in the previous section, usually are a good foundation
for a training manual. The manual normally includes background materials (e.g., the study
aims), general instructions, specific instructions, and copies of all the data collection and
administrative forms.
• Table 14-1 presents an example of a table of contents for a training manual in a quantitative
interview study.
• The agenda for the training should cover the content of the training manual, elaborating on any
portion that is especially difficult or complex.
• The training usually includes demonstrations of fictitious data collection sessions, performed
either live or on videotape. Finally, the training usually involves having the trainees do trial runs
of data collection in front of the trainers to demonstrate their understanding of the instructions. 7

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9. Implement data collection procedures.
1. It will be vital to find reliable and trustworthy people to collect and manage the data.
2. Consider who will collect the data and how you will recruit these people.
3. What steps will they need to take to collect the data? How will you train them?
4. Finally, who will be responsible for monitoring the data collection process to ensure you are
getting what is needed for the research?
5. It’s important to answer each of these questions during the planning.
6. Researcher don’t want to be surprised halfway through the process to discover the three-month
follow-up surveys were not mailed out because the researcher didn’t identify who would do so!
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10. Steps in Implementation of Data Collection Plan
1) Identify the questions that want to be answered
The first step in implementing a Data Collection Plan is to identify the questions we want to
answer.
2) Collect the needed data that is available
The second step in implementing a Data Collection Plan is to find out what kind of data is
available to collect.
3) Collect how much data is needed
The third step in implementing a Data Collection Plan is to decide how much data we need.
We want to get enough data so that we can see patterns and trends. For each data
element on the list, write down how much data is actually needed.
4) Measure the data
The fourth step in implementing a Data Collection Plan is to see how to measure the data.
As we know, data can be measured in different ways: check sheets, survey answers, etc.
The way we measure will be dependent upon the type of data we seek.
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11. 5) Appoint the data collector.
The fifth step in implementing a Data Collection Plan is to decide who is going to collect the data.
Nowadays, the data can also be collected through automated software. We may be required to liaise with
the person in charge of the software to ensure the data is available and in the correct format.
6) Collect the Data from the exact location
The sixth step is to check where to collect the data from. It means deciding on the location and/or source
of data. The location does not mean any physical location. It is the location within the process. The data
collection plan must explicitly specify where in the process data must be collected from.
7) Decide whether to measure a sample or the whole population
The seventh step is to decide whether to sample the data or not. Sometimes it is impractical to measure an
entire population of data. In such a case, we then take a sample of data. The question that the project team
needs to look into could be: What should be our sampling method and sample size be to make statistically-
sound judgments?
8) Determine in what format the data will be displayed
The eighth step is to decide the format of displaying the data. We can display data in many ways such as
Pareto Diagrams, Scatter Diagram, etc.
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12. Thank u
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