PHILOSOPHY
OF QUANTITATIVE RESEARCH
By,
Hanafi Pelu (181061001001)
A. Background
Research method is a
systematic and unbiased way of solving a problem (by answering questions or
supporting hypotheses) through generating verifiable data.’ This is the fundamental
definition we need, so we need to understand systematic, unbiased, hypotheses
and verifiable, all of which we will examine later, (John Bacon-Shone, 10: 2015).
In all research, it is
important that other researchers can try to replicate your findings. Experimental
scientists talk about repeatable experiments as researchers are expected to provide
enough details that others can try to replicate their findings by repeating
their experiment.
However, some research
cannot be repeated (e.g. effect of handover on Hong Kong) because the conditions
of the data collection cannot be repeated. May be able to reanalyze the data,
but not collect a new set.
Hence high quality data
collection is a particularly important issue for social science (but also for
others where data collection is very expensive or difficult to repeat, e.g.
astronomers studying creation of stars or geologists studying volcanoes). It also
explains why in the US and Europe, research funded with public money must share
the data and journals often require public access to any data analyzed in a
journal paper.
The reality is that we
all make mistakes and have false preconceptions. Society (and the state of
knowledge) can only advance if mistakes can be identified and corrected, which
is possible with research, because people can check the findings for mistakes. Perhaps
this is the key reason that democracy works better than the alternatives,
because if the representative you choose is ineffective, you can try another
one? We will now discuss what is called the ‘scientific method’ of research,
including both qualitative and quantitative methods, which are used in the social,
biological and physical sciences.
Research methods in
education (and the other social sciences) are often divided into two main
types: quantitative and qualitative methods. This paper will discuss one of
these two main strands: quantitative methods. In this chapter we will have a
look at what is meant by the term quantitative methods, and what distinguishes
quantitative from qualitative methods. When you think of quantitative methods, you
will probably have specific things in mind. You will probably be thinking of
statistics, numbers– many of you may be feeling somewhat apprehensive because
you think quantitative methods are difficult. Apart from the last, all these
thoughts capture some of the essence of quantitative methods.
B. Discussion
Quantitative history is
the term for an array of skills and techniques used to apply the methods of statistical
data analysis to the study of history. Sometimes also called clinometric by economic
historians, the term was popularized in the 1950s and 1960s as social, political
and economic historians called for the development of a ‘social science
history’, adopted methods from the social sciences, and applied them to
historical problems.
These historians also
called for social scientists to historicize their research and consciously examine
the temporal nature of the social phenomena they explored. For both types of
questions, historians found that they needed to develop new technical skills
and data sources. That effort led to an array of activities to promote quantitative
history. Classical historical research methodology relies upon textual records,
archival research and the narrative as a form of historical writing.
The historian describes
and explains particular phenomena and events, be they large epic analyses of the
rise and fall of empires and nations, or the intimate biographical detail of an
individual life. Quantitative history is animated by similar goals but takes as
its subject the aggregate historical patterns of multiple events or phenomena.
Such a standpoint creates a different set of issues for analysis. A classic
historical analysis, for example, may treat a presidential election as a single
event.
Quantitative historians
consider a particular presidential election as one element in the universe of
all presidential elections and are interested in patterns which characterize
the universe or several units within it. The life-course patterns of one household
or family may be conceived as one element in the aggregate patterns of family history
for a nation, region, social class or ethnic group. Repeated phenomena from the
past that leave written records, which read one at a time would be insignificant,
are particularly useful if they can be aggregated, organized, converted to a
electronic database and analyzed for statistical patterns. Thus records such as
census schedules, vote tallies, vital (e.g., birth, death and marriage) records;
or the ledgers of business sales, ship crossings, or slave sales; or crime
reports permit the historian to retrieve the pattern of social, political, and
economic activity in the past and reveal the aggregate context and structures
of history.
The standpoint of
quantitative history also required a new set of skills and techniques for
historians. Most importantly, they had to incorporate the concept of the data
set and data matrix into their practice. Floud (1972: 17) defined the data set
as ‘a coherent selection of data from the whole range of historical data available
to the historian, and it is selected because it relates closely to the
questions that the historian wishes to consider.’ The myriad instances of a
phenomenon—for example, all United States presidential elections—form the cases
of the data set. The pieces of information collected about the cases—for example,
the candidates running, the year of the election or the vote totals—become the variable
characteristics of the data set, that is, the varying characteristics of any
particular case. The historian arranges the data in tabular form, that is, in a
matrix of rows and columns, ‘consisting of a number of rows, which will
normally represent cases, and a number of columns, which will normally represent
variables’ (Floud, 1972: 18).
The creation of
quantitative data sets thus required the historian to carefully compile consistent
information about the phenomenon to be investigated, and prepare the data in
tabular form. Historians then were prepared to apply the techniques of
statistical data analysis to the data set to answer the research question
posed.
In short, to make
effective use of quantitative evidence and statistical techniques for historical
analysis, practitioners had to integrate the rapidly developing skills of the social
sciences, including sampling, statistical data analysis and data archiving into
their historical work. That task led to the development of new training
programs in quantitative methods for historians, to the creation of new
academic journals and textbooks, and to the creation of data archives to support
the research.
The following
definition, taken from Aliaga and Gunderson (2002), describes what we mean by quantitative research methods very well: Quantitative research is ‘Explaining phenomena
by collecting numerical data that
are analyzed using mathematically based methods (in particular statistics).’ Let’s go through this definition step
by step. The first element is explaining phenomena. This is a key element of all research, be it
quantitative or qualitative. When we
set out do some research, we are always looking to explain something. In education this could be questions like ‘why
do teachers leave teaching?’, ‘what
factors influence pupil achievement?’ and
so on.
The specificity of
quantitative research lies in the next part of the definition. In quantitative research we collect numerical
data. This is closely connected
to the final part of the definition: analysis using mathematically based
methods. In order to be able to use mathematically based methods our data have to be in numerical form.
This is not the case for qualitative research.
Qualitative data are not necessarily or usually numerical, and therefore cannot be analyzed using statistics.
Therefore, because
quantitative research is essentially about collecting numerical data to explain a particular phenomenon, particular
questions seem immediately suited to
being answered using quantitative methods:
1. How
many males get a first-class degree at university compared to females?
2. What
percentage of teachers and school leaders belong to ethnic minority groups?
3. Has
pupil achievement in English improved in our school district over time?
These are all questions
we can look at quantitatively, as the data we need to collect are already
available to us in numerical form. However, does this not severely limit the
usefulness of quantitative research? There are many phenomena we might want to
look at, but which don’t seem to produce any quantitative data. In fact, relatively
few phenomena in education actually occur in the form of ‘naturally’
quantitative data.
Luckily, we are far
less limited than might appear from the above. Many data that do not naturally
appear in quantitative form can be collected in a quantitative way. We do this
by designing research instruments aimed specifically at converting phenomena
that don’t naturally exist in quantitative form into quantitative data, which
we can analyze statistically. Examples of this are attitudes and beliefs. We
might want to collect data on pupils’ attitudes to their school and their
teachers.
These attitudes
obviously do not naturally exist in quantitative form (we don’t form our
attitudes in the shape of numerical scales!). Yet we can develop a questionnaire
that asks pupils to rate a number of statements (for example, ‘I think school
is boring’) as either agree strongly, agree, disagree or disagree strongly, and
give the answers a number (e.g. 1 for disagree strongly, 4 for agree strongly).
Now we have quantitative data on pupil attitudes to school. In the same way, we
can collect data on a wide number of phenomena, and make them quantitative
through data collection instruments like questionnaires or tests. In the next
three chapters we will look at how we can develop instruments to do just that.
The number of phenomena
we can study in this way is almost unlimited, making quantitative research
quite flexible. However, not all phenomena are best studied using quantitative
methods. As we will see, while quantitative methods have some notable
advantages, they also have disadvantages, which mean that some phenomena are
better studied using different (qualitative) methods.
The last part of the
definition refers to the use of mathematically based methods, in
particular statistics, to analyze the data. This is what people usually
think about when they think of quantitative research, and is often seen as the
most important part of quantitative studies. This is a bit of a misconception.
While it is important to use the right data analysis tools, it is even more
important to use the right research design and data collection instruments.
However, the use of statistics to analyze the data is the element that puts a
lot of people off doing quantitative research, because the mathematics
underlying the methods seem complicated and frightening. Nevertheless, as we
will see later on in this book, most researchers do not really have to be
particularly expert in the mathematics underlying the methods, because computer
software allows us to do the analyses quickly and (relatively) easily, (Muijs,
R. D. and Reynolds, D., 15: 2002).
C. Conclusion
Quantitative history in conjunction with the larger
information technology revolution
makes the prognosis for the future
of the field better today than it
has been for many years. Almost a
half-century on, one can look back at
steady development, though not always in
a satisfyingly linear pattern.
Quantitative history as
a field was in its most rapid initial development, most traditional historians
labored much as their nineteenth-century predecessors had with pen, pencil, typewriter
and note-card as technological support. Bibliographic work entailed using library
card catalogs or reading large indexed tomes of articles, books, compilations, and
the like.
References
Aliaga and Gunderson, 2002. Interactive
Statistics. Thousand Oaks: Sage.
Daniel Muijs, 2004. Introduction to quantitative
research. Doing Quantitative Research in Education with SPSS.
SAGE Publications Ltd 1 Oliver’s Yard 55 City Road London EC1Y 1SP SAGE
Publications Inc. 2455 Teller Road Thousand Oaks, California 91320.
Floud, Roderick, 1972. An Introduction to Quantitative Methods for Historians. Princeton:
Princeton University Press.
John Bacon-Shone, 2015. Introduction
to Quantitative Research Methods. Graduate School. The
University of Hong Kong.
Margo Anderson,2002. Quantitative History. Cambridge Group, see their website, http://www-hpss.geog.cam.ac.uk.
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