data collection case study

1. DATA & ITS COLLECTION
What is Data?
- Types of Data
--Tools of Data Collection: Observation Techniques,
Questionnaires, Interview Schedule, Participatory
Rural Appraisal
Data Analysis, Data Interpretation &
Graphical Representative

2. WHAT IS IT?
 It is the process of gathering and measuring information on
variables of interest
 It is done in an established systematic fashion that enables one
to answer stated research questions, test hypotheses, and
evaluate outcomes.
 The data collection component of research is common to all
fields of study including physical and social sciences,
humanities, business, etc.
 Methods of Data Collection vary by discipline, howevere the
emphasis on ensuring accurate and honest collection remains
the same.

4. CONSEQUENCES FROM IMPROPERLY COLLECTED DATA
INCLUDE
 inability to answer research questions accurately
 inability to repeat and validate the study
 distorted findings resulting in wasted resources
 misleading other researchers to pursue fruitless avenues of
investigation
 compromising decisions for public policy
 causing harm to human participants and animal subjects

5. ISSUES RELATED TO MAINTAINING INTEGRITY OF DATA
COLLECTION:
 The primary rationale for preserving data integrity is to
support the detection of errors in the data collection
process, whether they are made intentionally (deliberate
falsifications) or not (systematic or random errors).
 There are two approaches that can preserve data
integrity and ensure the scientific validity of study
results.
 Quality assurance - activities that take place before data
collection begins
 Quality control - activities that take
place during and after data collection

6. QUALITY ASSURANCE
 Since quality assurance precedes data collection, its main
focus is ‘Prevention' (i.e., forestalling problems with data
collection).
 Prevention is the most cost-effective activity to ensure the
integrity of data collection.
 This proactive measure is best demonstrated by
the Standardization of Protocol developed in a
comprehensive and detailed procedures manual for data
collection.
 Poorly Written Manuals increase the risk of failing to
identify problems and errors early in the research
endeavor.

7. THESE FAILURES MAY BE DEMONSTRATED IN A NUMBER OF
WAYS:
 Uncertainty about the timing, methods, and identify of person(s)
responsible for reviewing data
 Partial listing of items to be collected
 Vague description of data collection instruments to be used in
lieu of rigorous step-by-step instructions on administering tests
 Failure to identify specific content and strategies for training or
retraining staff members responsible for data collection
 Obscure instructions for using, making adjustments to, and
calibrating data collection equipment (if appropriate)
 No identified mechanism to document changes in procedures
that may evolve over the course of the investigation .

8.  An important component of quality assurance is developing a
rigorous and detailed recruitment and training plan.
 Implicit in training is the need to effectively communicate the
value of accurate data collection to trainees.
 The training aspect is particularly important to address the
potential problem of staff who may unintentionally deviate from
the original protocol.
 This phenomenon, known as ‘drift’, should be corrected with
additional training, a provision that should be specified in the
procedures manual.

9. QUALITY CONTROL
 Quality control activities occur during and after data collection.
 The details should be carefully documented in the procedures
manual.
 A clearly defined communication structure is a necessary pre-
condition for establishing monitoring systems.
 There should not be any uncertainty about the flow of
information between principal investigators and staff members
following the detection of errors in data collection.
 A poorly developed communication structure encourages lax
monitoring and limits opportunities for detecting errors.

10.  Detection or monitoring can take the form of direct staff
observation during site visits, conference calls, or regular
and frequent reviews of data reports to identify
inconsistencies, extreme values or invalid codes.
 While site visits may not be appropriate for all disciplines,
failure to regularly audit records, whether quantitative or
quantitative, will make it difficult for investigators to verify
that data collection is proceeding according to procedures
established in the manual.
 In addition, if the structure of communication is not clearly
delineated in the procedures manual, transmission of any
change in procedures to staff members can be
compromised

11. Quality Control checks the following:
Errors in individual data items
Systematic errors
Violation of protocol
Problems with individual staff or site
performance
Fraud or scientific misconduct

12.  Each field of study has its preferred set of data
collection instruments.
 The hallmark of laboratory sciences is the
meticulous documentation of the lab notebook
while social sciences such as sociology and
cultural anthropology may prefer the use of
detailed field notes.
 Regardless of the discipline, comprehensive
documentation of the collection process
before, during and after the activity is
essential to preserving data integrity.

13. SO WHAT IS DATA:
The Scores obtained on the measure
of Dependent Variables.
E.g:
Average Height of students in a
class
Percentage of women that voted in
last general election

14. NOW WHAT IS VARIABLE:
 It is something which varies, i.e it takes two or more
values, it is common to a number of individuals, groups,
events, objects etc…
 Variables are characteristics/ quality/ features/ aspects
which can be measured
 They are those characteristics or conditions that can be
manipulated, controlled, observed by the researcher.
Independent Variable
Dependent Variable

15.  E.g:
Age ( young, middle aged, old),
Income class (lower, middle upper)
Education (literate, less educated, highly
educated)
Development of a country (Under-
Developed, Developing, Developed)
Achievement of an individual etc….
Intelligence, anxiety, aptitude etc etc…

16.  An INDEPENDENT VARIABLE is the presumed cause of
the DEPENDENT VARIABLE
 A DEPENDENT VARIABLE is the presumed effect of the
INDEPENDENT VARIABLE
 Suppose “A” causes “B”
 A  INDEPENDENT VARIABLE /X variable in Statistics
 B  DEPENDENT VARIABLE/Y variable in Statistics

17.  A teacher wants to know which method of teaching is more
effective in the student’s understanding.
 Options:
 Lecture Method
 Question Answer Method
 Visual Method
 Combination of two or more of these methods
 INDEPENDENT VARIABLE  method of teaching
 DEPENDENT VARIABLE  student’s understanding.

18. TYPES OF DATA

19. Data Types
Secondary Data Primary Data

20. Data/information may be classified into PRIMARY &
SECONDARY depending on the nature of data and the mode
of collection.
Primary Data are empirical observations gathered by the
researcher or research associates for the First Time for any
research & then used in the statistical analysis.
It may be Qualitative/ that which will signify some trait,
characteristics, brand, grade, status or Quantitative/ that
which will show some quantity, measurement, number, mass,
- more or less
Primary data are generally accepted as Original Data. These
data are also called Raw Data by some researchers.
These data are collected at first hand either by the
researcher for satisfying his/her purpose or by someone else
especially for the purpose of research study.

21.  The Secondary Data is also known as Published Data.
Data which are not originally collected but rather obtained
from published sources and statistically processed are
known as secondary data.
 Secondary Data have been already collected by others in
the past and used in the past.
 Following are the main sources of secondary data:
 Official Publications
 Semi-Official Publications
 Publications of Research Institutions
 Publications related to trade
 Books/Journals/Newspapers
 Publications by International Bodies
 Unpublished Sources

22. WHAT IS DATA COLLECTION?
… it is a purposive gathering of information
relevant to the subject matter of the study
under consideration.
… the methods depend on the nature, purpose
& scope of inquiry, availability of resources
and time…

23. BASIC CHARACTERISTICS OF DATA:
Following are some general characteristics of data:
 Data is an Aggregate of Facts:
 Data is Affected to a Large Extent by Multiplicity of Factors:
 Data is Collected in a Systematic Manner for a
Predetermined Objective
 The Data Must be Related to One Another:
 Data Must be Numerically Expressed:

24.  Collection of data procedure obeys a certain
method in order to sustain consistency in the
research process.
 Studies in anthropology, geography, or sociology
often require fieldwork, that is to say, using
techniques such as observation and
questionnaires.
 Postgraduate students may find it awkward.
 Overcome that awkwardness.

25. TOOLS OF DATA COLLECTION

26. Observation Method: Observing Behaviours of Participants:
 Specifies the conditions and methods at making observation.
 The information is sought by way of investigator’s own direct
observation without asking from the respondent.
 The main advantage of this method is that subjective bias is
eliminated, if observations are done accurately.
 It is the most commonly used method especially in studies
relating to behavioral science.

27. QUESTIONNAIRE METHOD:
 A list of questions pertaining to the survey (known as
questionnaire) is prepared and sent to the various informants by
post.
 The questionnaire contains questions and provides space for
answer.
 A request is made to the informants through a covering letter to
fill up the questionnaire and sent it back within a specified time.
 The respondents have to answer the questions on their own.
 The questionnaire can be delivered directly hand by hand,
through surface post or as an electronic questionnaire.
 In preparing a research questionnaire general question, question
wording to collect personal information, use of unfamiliar terms
and jargon, etc. should be avoided.

28. INTERVIEW METHOD:
 This involves listening to informants.
 The interview method of collecting data involves presentation of
oral-verbal stimuli and reply in terms of oral – verbal responses.
 So, under this method of collecting data, there is a face to face
contact with the persons from whom the information is to be
collected.
 The interviewer asks them question pertaining to the survey and
collects the desired information.
 This method can be used through personal interview, telephone
interview, Chat, Audio Conferencing, Video Conferencing, etc.
 The interview can be structured, semi structured or open interview.

29. SCHEDULES METHOD:
 The Enumerator or interviewers who are specially appointed for the
purpose along with schedules, go to the respondents, put to them the
questions from the Proforma in the order the questionnaire are listed
and record the replies in the space meant for the same in the
Proforma.
 In certain situation, schedules may be handed over to respondents
and enumerators may help them in recording their answer to various
questions in the said schedules.
 Enumerator explains the aims and objectives of the investigation and
also removes the difficulties which respondents may feel in relation to
understanding the implication of a particular question or a definition
or concept of difficult term.
 Advantage over the questionnaire method in the sense that the
respondents have no scope to misunderstand any question and
thereby putting irrelevant answer.

30. INFORMATION FROM CORRESPONDENTS:
 The Investigator/Researcher appoints local agent or
correspondents in different places to collect information.
 The Correspondents collect and transmit information to the
central office where the data are processed.
 Special advantage of this method is that it is cheap and
appropriate for extensive investigation.
 However, it may not always ensure accurate results because
of the personal prejudice and bias of the correspondents.
Newspaper agencies generally adopt this method.

31. PARTICIPATORY RURAL APPRAISAL

32. A DATA COLLECTION TOOL
 Participatory Rural Appraisal: Participatory Learning for Action
 PRA or PLA consists of a set of participatory and largely
visual techniques for:
 … Mapping/ Assessing group and community resources,
 … Identifying and Prioritizing problems and
 … Appraising strategies for solving them.
 It is a research/planning methodology in which a local
community (with or without the assistance of
outsiders) studies an issue that concerns the
population, prioritizes problems, evaluates options for
solving the problem(s) and comes up with a
Community Action Plan to address the concerns that
have been raised.

33.  PRA is a data collection tool that is particularly
concerned about the multiple perspectives that may
exist in any community.
 These various perspectives are analyzed and
represented in the mapping procedures of the
community resources that the community itself takes
the lead in evaluating its situation and finding
solutions.
 Outsiders may participate as facilitators or in
providing technical information but they should not
'take charge of the process.

35.  PRA evolved from rapid rural appraisal-a set of
informal techniques used by development practitioners
in rural areas to collect and analyze data.
 Rapid Rural Appraisal (RRA) developed in the 1970s
and 1980s in response to the perceived problems of
outsiders missing or mis-communicating with local
people in the context of development work.
 In PRA, data collection and analysis are undertaken by
local people, with outsiders facilitating rather than
controlling.
History

36.  In PRA, a number of different tools are used to
gather and analyze information.
 E.g. Draw Maps, Draw Venn Diagrams etc….
 These tools encourage participation, make it
easier for people to express their views and help
to organize information in a way that makes it
more useful and more accessible to the group that
is trying to analyze or work on a given situation.

39. SOME FEATURES OF PRA WHICH MAKE IT WELL-SUITED AS A
LEARNING AND PROBLEM-SOLVING TOOL FOR THE RURAL POOR
ARE: BUT IT IS UTILIZED FOR THE URBAN POOR TOO
 It encourages group participation and discussion
 The information to be processed is collected by group members
themselves
 It is presented in highly visual form, usually out in the open and
on the ground, using pictures, symbols and locally available
materials, using twigs of trees to draw out on the soil or sand.
 Once displayed, the information is “transparent rather than
hidden” - all members can comment on it, revise it and criticize
it.
 This assists in cross-checking and verifying collected data.

42.  PRA is an exercise in communication and transfer of
knowledge.
 Regardless of whether it is carried out as part of project
identification or appraisal or as part of country economic
and sector work, the learning-by-doing and teamwork spirit
of PRA requires transparent procedures.
 For that reason, a series of open meetings (an initial open
meeting, final meeting, and follow up meeting) generally
frame the sequence of PRA activities.
 A typical PRA activity involves a team of people working for
two to three weeks on workshop discussions, analysis and
field work

43. DATA ANALYSIS

44. DATA INTERPRETATION

45.  Data interpretation is part of daily life for most people.
 Interpretation is the process of making sense of
numerical data that has been collected, analyzed, and
presented.
 E.g. People interpret data when they turn on the
television and hear the news anchor reporting on a
poll,
 when they read advertisements claiming that one
product is better than another, or
 when they choose grocery store items that claim they
are more effective than other leading brands.

46.  Research depends a great deal on the collected data
but it should be seen that this collected data is not just
a collection of the data but should also provide good
information to the researcher during the various
research operations.
 Hence to make data good and meaningful in nature
and working, data analysis plays a very vital and
conclusive role. In this step data is made meaningful
with the help of certain statistical tools which ultimately
make data self explanatory in nature.

47. IN THE BEGINNING THE DATA IS RAW IN NATURE
BUT AFTER IT IS ARRANGED IN A CERTAIN FORMAT
OR A MEANINGFUL ORDER THIS RAW DATA TAKES
THE FORM OF THE INFORMATION. THE MOST
CRITICAL AND ESSENTIAL SUPPORTING PILLARS OF
THE RESEARCH ARE THE ANALYZATION AND THE
INTERPRETATION OF THE DATA.

48. INTERPRETATION OF THE DATA HAS BECOME A VERY
IMPORTANT AND ESSENTIAL PROCESS, MAINLY
BECAUSE OF SOME OF THE FOLLOWING FACTORS –
 Enables the researcher to have an in – depth knowledge about
the abstract principle behind his own findings.
 The researcher is able to understand his findings and the
reasons behind their existence.
 More understanding and knowledge can be obtained with the
help of the further research.
 Provides a very good guidance in the studies relating to the
research work.
 Sometimes may result in the formation of the hypothesis.

49. FIGURING OUT WHAT DATA MEANS IS JUST AS
IMPORTANT AS COLLECTING IT. EVEN IF THE
DATA COLLECTION PROCESS IS SOUND, DATA
CAN BE MISINTERPRETED. WHEN
INTERPRETING DATA, THE DATA USER MUST
NOT ONLY ATTEMPT TO DISCERN THE
DIFFERENCES BETWEEN CAUSALITY AND
COINCIDENCE, BUT ALSO MUST CONSIDER ALL
POSSIBLE FACTORS THAT MAY HAVE LED TO A
RESULT.

50. HOW TO DO THAT?
A common method of
assessing numerical data is
known as Statistical Analysis ,
and the activity of analyzing
and interpreting data in order
to make predictions is known
as Inferential Statistics

51. ORGANIZING THE DATA
 Organize all Interview schedules/Questionnaires in
one place, you might want to stack them all in one
place.
 Check for completeness & accuracy
 Remove the incomplete ones or those that do not
make any sense
 Keep noting about your decisions

52. ENTER YOUR DATA
 Manually
 By Computer
 - Excel (Spread Sheet)
 - Microsoft Access (Database Management)
 - Quantitative Analysis (SPSS) – Statistical Software -
Statistical Package for the Social Scientists
 Count Frequencies, Mean, Median, Mode, Percentage,
Range, Standard Deviation, Quartile Deviation

53. INTERPRETING INFORMATION
 Numbers do not speak for themselves
 E.g. What does it mean that 55 youth reported a
change in behavior?
 25% of the participants have rated a program?
 What is the meaning of these numbers?
 Interpretation is the process of attaching meaning
to the data

54.  Interpretation demands fair and careful judgments.
 The same data may be interpreted in different ways
 Involve others
 See how others interpret the data
 Share results with key stakeholders to discuss data
 Ask individual participants
 We often recommendations or an Action Plan
 This helps ensure that the results are used

55. DISCUSS LIMITATIONS
 When you write down reports clearly discuss
LIMITATIONS
 When you orally present the reports be prepared to
discuss the limitations of the study
 Be honest about limitations
 Know the claims that you cannot make
 …. Do not claim causation without a true
experimental design
 …. Do not generalize to the population without
random sample and quality administration of the
design (< 60% response rate on a survey)

56. GRAPHICAL REPRESENTATION
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Category 1 Category 2 Category 3 Category 4
Series 1
Series 2
Series 3

57. Sales
1st Qtr
2nd Qtr
3rd Qtr
4th Qtr

58. 0
0.5
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1.5
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2.5
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Y-Values
Y-Values