This document discusses the process of data collection and management. It involves selecting, training, supervising, validating and evaluating personnel involved in data collection. Key steps include selecting interviewers, training them on proper procedures, supervising their work, validating the quality of data collected, editing for errors, coding responses, entering data into software, and cleaning the data by checking for errors or inconsistencies before analysis. The goal is to ensure high quality, accurate data through establishing proper collection and management protocols.

1. Lecturer: Mrs Precious Chanaiwa

2.  It involves:
 selection
 training
 supervision
 Validation
 Evaluation
Of the persons involved in the collection of data

3. SELECTION
 Involves careful selection of people who will
conduct the interviews or the respondents from
where data is going to be collected
 Type of data to be collected will influence the
type of persons to be recruited e.g. qualitative
data that involves probing questions would
require experienced interviewers

4. TRAINING
 Involves training personnel that will go out to
collect data
 Training helps to achieve uniformity in data
collection
 Training covers aspects such as the approach,
administering of instruments, probing,
recording

5. SUPERVISION
 Interviewers need to be supervised to ensure
that they are proceeding on schedule
 There is need to keep control of day-to-day
activities
 Unethical practices might occur e.g. when field
workers complete questionnaires themselves.

6. VALIDATION
 Quality of data collect is critical
therefore there is need for
supervisor to check accurately for
mistakes that might occur during
data gathering and recording.

7. EVALUATION
 All data gathered have to be evaluated to
check for consistency.
 How many people completed the
questionnaire, quality of the interviews
 Two errors might occur:
 Intentional errors- falsehood and non-
response
 Unintentional errors- misunderstanding,
fatigue, attention loss

8.  The process of checking the quality of data
gathered during fieldwork, thus converting it
into useful format for further analysis
 Involves:
 Validating
 Editing
 Coding
 Data entry
 Data cleaning

9. VALIDATING
 An analysis to check whether proper
procedures were followed in the collection of
data using different instruments
 Factors to look for include:
 Fraud
 Screening
 Procedure
 Completeness
 Courtesy

10. EDITING
 Checking of mistakes emanating either from
interviewer or respondent
 Editing can start when the supervisor visits the
fieldwork when data is being collected and
after completion of questionnaires for
omissions and inaccuracies
 Further editing might be done at the central
office of the research firm

11. CODING
 Process of assigning a code, symbol, or a
number to each possible answer.
 It is easier for closed ended questions where
pre-coding can be conducted
 Closed ended questions would require coming
up with themes as same questions can be
responded to differently but with the same
meaning

12. CODING-cont’
Missing data is usually caused by:
 The question did not apply to the respondent
 The respondent refused to answer the question
 The respondent did not know the answer to
the question
 The respondent or interviewer forgot to record
the answer

13. DATA ENTRY
 Involves the direct input of coded data into
software packages that will allow the
researcher to analyse and transform the raw
data
 Software packages include SPSS for
quantitative data and NVIVO 7 for qualitative
data

14. DATA CLEANING
It is the process of checking data
before starting data analysis
especially if data was entered
manually in a computer

15. DATA CLEANING- cont’
Done to detect obvious errors such as:
 Does the number of questionnaires match the
number of respondents
 Calculation of minimum and maximum variables to
check whether they are outside the expected range
 Is there consistency in the number of respondents
who provided answers
 Comparison of recorded answers with the values in
the data matrix for a few selected questionnaires

16. Options for large percentages of missing
answers:
 Casewise deletion- discard any cases with
missing responses
 Pairwise deletion- use all the cases with
complete responses for specific calculation
 Rules of thumb- establish your own rules as
to when to include or discard individual
questionnaires