Research Methodology
Research Methodology is the complete plan of attack on the central research problem. It provides the general structure for the procedures that the researcher follows, the info that the researcher collects, and therefore the data analyses that the researcher conducts, thus involves planning. It's an idea with the central goal of solving the research problem in mind. Research methodology concerning how the study was held. It includes; research design, study population, sample and sample size, methods of knowledge collection, methods of knowledge analysis, and anticipation of the study. Research methodology concern with a philosophy of research development. It includes the assumptions and values that serve a rationale for research and therefore the standards or criteria the researcher uses for collecting and interpreting data and reaching conclusions (Martin and Amin, 2005:63). In other words research methodology determines the factors like the way to write a hypothesis and what level of evidence is important to form decisions on whether to simply accept or reject the hypothesis.
Data Collection
Data collection is defined
because of the procedure of collecting, measuring, and analyzing accurate insights
for research using standard validated techniques. A researcher can evaluate
their hypothesis on the idea of collected data. In most cases, data collection
is the primary and most vital step for research, regardless of the sector
of research. The approach of knowledge collection is different for various
fields of study, counting on the specified information.
Data collection is the
method of gathering and measurement data on variables of interest, in a very
old systematic fashion that allows one to answer expressed research questions,
check hypotheses and evaluate outcomes. The info collection component of
research is common to all or any fields of study including physical and social
sciences, humanities, business, etc. While methods vary by discipline, the
stress on ensuring accurate and honest collection remains equivalent.
Data collection may be the systematic process of gathering observations or measurements. Whether you are
activity research for business, governmental or educational purposes,
information collection permits you to comprehend first-hand data and original
insights into your research problem. While methods and aims may differ between
fields, the general process of knowledge collection remains largely equivalent.
Before you start collecting data, you would like to consider:
• The aim of the research
• The sort of data that you
simply will collect
• The methods and procedures
you'll use to gather, store, and process the info
The importance of making
certain correct and acceptable information collection
Regardless of the arena of
study or preference for outlining information (quantitative, qualitative),
correct data collection is very important in maintaining the integrity of
research. Each the selection of acceptable data collection instruments
(existing, modified, or new developed) and clearly represented instructions for
his or her correct use reduces the probability of errors occurring.
Consequences from improperly
collected information include
• Inability to answer the research
questions accurately
• Inability to repeat and
validate the study
• Distorted findings resulting
in wasted resources
• Deceptive different
researchers to pursue unproductive avenues of investigation
• Compromising decisions for
public policy
• Inflicting hurt to human
participants and animal subjects
While the degree of impact
from faulty data collection could vary by discipline and so the character of the investigation, there is the potential to cause disproportionate damage once
these research results are wont to support public policy recommendations. Issues
related to maintaining the integrity of information collection:
The primary principle for preserving data integrity is to support the detection of errors inside the data collection methodology, whether or not or not or not they are created designedly (deliberate falsifications) or not (systematic or random errors).Most, Craddick, Crawford, Redican, Rhodes, Rukenbrod, and Laws (2003) describe ‘quality assurance’ and ‘quality control’ as two approaches that will preserve data integrity and make sure the scientific validity of study results. Each approach is implemented at different points within the research timeline (Whitney, Lind, Wahl, 1998):
1. Quality assurance -
activities that happen before data collection begins
2. internal control -
activities that happen during and after data collection
Since quality declaration
precedes data collection, its main meeting point is 'prevention' (i.e., averting
problems with data collection). Prevention is the most cost-effective
activity to make sure the integrity of data collection. This practical measure
is best verified by the standardization of procedures developed during a broad
and detailed procedure manual for data collection. Poorly written manuals
increase the danger of failing to spot problems and errors early within the
research endeavor. These failures could also be demonstrated in a number of
ways:
• Uncertainty about the
timing, methods, and identity of the person(s) liable for reviewing data
• Partial listing of things to
be collected
• Vague description of
knowledge collection instruments to be utilized in lieu of rigorous
step-by-step instructions on administering tests
• Failure to spot specific
content and methods for training or retraining staff members liable for data
collection
• Vague instructions for
using, making adjustments to, and calibrating data collection tools (if
appropriate)
• No identified mechanism to
document changes in procedures that will evolve over the course of the investigation.
An essential component of
quality assurance is developing a rigorous and detailed recruitment and
training plan. Inherent training is that they got to effectively communicate
the worth of accurate data collection to trainees (Knatterud, Rockhold, George,
Barton, Davis, Fairweather, Honohan, Mowery, O'Neill, 1998). The training
aspect is especially important to deal with the potential problem of staff that
may unintentionally deviate from the first protocol. This phenomenon referred
to as ‘drift’, should be corrected with additional training, a provision that
ought to be laid out in the procedures manual.
Given the variety of qualitative
research methods (non-participant/ participant observation, interview,
archival, field study, ethnography, content analysis, oral history, biography,
retiring research) it's troublesome to make generalized statements regarding
however one should establish a search protocol so as to facilitate quality
assurance. Certainly, researchers conducting non-participant/participant
observation may have only the broadest research inquiries to guide the initial
research efforts. Since the researcher is that the most measurement device
throughout a study, repeatedly there are very little or no different
information collection instruments. Indeed, instruments may have to be
developed on the spot to accommodate unanticipated findings.
While internal control
activities (detection/monitoring and action) occur during and after data
collection, the small print should be carefully documented within the
procedures manual. A clearly outlined communication structure could also be a
necessary pre-condition for establishing observation systems. There should not
be any uncertainty concerning the flow of data of knowledge of information}
between principal investigators and employees members following the detection
of errors in data collection. A poorly-developed communication structure
encourages lax observation and limits opportunities for detecting errors.
Detection or monitoring can
take the shape of direct staff observation during site visits, conference
calls or regular and frequent reviews of knowledge reports spotting
inconsistencies, extreme values or invalid codes. While site visits may not be
acceptable for all disciplines, failure to often audit records, whether or not
quantitative or quantitative, can create it difficult for investigators to
verify that knowledge collection is proceeding consistent with procedures
established within the manual. Additionally, if the structure of communication
isn't clearly delineated within the procedures manual, the transmission of any change
in procedures to staff members are often compromised
Quality control additionally
identifies the required responses, or ‘actions’ necessary to correct faultily
data collection practices and also minimize future occurrences. These actions
are less likely to occur if data collection procedures are vaguely written and
therefore the necessary steps to attenuate recurrence aren't implemented
through feedback and education (Knatterud, et al, 1998)
Examples of data collection
problems that need prompt action include:
• Errors in individual data
items
• Systematic errors
• Violation of protocol
• Problems with individual
staff or site performance
• Fraud or scientific
misconduct
In the social/behavioral
sciences where primary data collection involves human subjects, researchers are
taught to include one or more secondary measures which will be wont to verify
the standard of data being collected from the human subject. For instance, the researcher conducting a survey could be curious about gaining a far better
insight into the occurrence of risky behaviors among young adult also because
the social conditions that increase the likelihood and frequency of those risky
behaviors.
To verify data quality,
respondents could be queried about equivalent information but asked at
different points of the survey and during a number of various ways. Measures of
‘Social Desirability’ may additionally be wont to get a measure of the honesty
of responses. There are two points that require to be raised here, 1)
cross-checks within the info collection process and 2) data quality being the
maximum amount an observation-level issue because it may be a complete data set
issue. Thus, data quality should be addressed for every individual measurement,
for every individual observation, and for the whole data set.
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