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Types of research
Quantitative research
Quantitative
research is generally associated with the positivist/postpositivist
paradigm. It usually involves collecting and converting data into
numerical form so that statistical calculations can be made and
conclusions drawn.
The process
Researchers will have one or more hypotheses.
These are the questions that they want to address which include
predictions about possible relationships between the things they want to
investigate (variables). In order to find answers to these
questions, the researchers will also have various instruments and
materials (e.g. paper or computer tests, observation check lists etc.)
and a clearly defined plan of action.
Data is collected by various means following a strict procedure and prepared for statistical analysis.
Nowadays, this is carried out with the aid of sophisticated statistical
computer packages. The analysis enables the researchers to determine to
what extent there is a relationship between two or more variables. This
could be a simple association (e.g. people who exercise on a daily
basis have lower blood pressure) or a causal relationship (e.g. daily
exercise actually leads to lower blood pressure). Statistical analysis
permits researchers to discover complex causal relationships and to
determine to what extent one variable influences another.
The results of statistical analyses are presented in journals in a standard way, the end result being a P value.
For people who are not familiar with scientific research jargon, the
discussion sections at the end of articles in peer reviewed journals
usually describe the results of the study and explain the implications
of the findings in straightforward terms
Principles
Objectivity
is very important in quantitative research. Consequently, researchers
take great care to avoid their own presence, behaviour or attitude
affecting the results (e.g. by changing the situation being studied or
causing participants to behave differently). They also critically
examine their methods and conclusions for any possible bias.
Researchers
go to great lengths to ensure that they are really measuring what they
claim to be measuring. For example, if the study is about whether
background music has a positive impact on restlessness in residents in a
nursing home, the researchers must be clear about what kind of music to
include, the volume of the music, what they mean by restlessness, how
to measure restlessness and what is considered a positive impact. This
must all be considered, prepared and controlled in advance.
External
factors, which might affect the results, must also be controlled for.
In the above example, it would be important to make sure that the
introduction of the music was not accompanied by other changes (e.g. the
person who brings the CD player chatting with the residents after the
music session) as it might be the other factor which produces the
results (i.e. the social contact and not the music). Some possible
contributing factors cannot always be ruled out but should be
acknowledged by the researchers.
The main emphasis of
quantitative research is on deductive reasoning which tends to move from
the general to the specific. This is sometimes referred to as a top
down approach. The validity of conclusions is shown to be dependent on
one or more premises (prior statements, findings or conditions) being
valid. Aristotle’s famous example of deductive reasoning was: All men
are mortal àSocrates is a man à Socrates is mortal. If the premises of
an argument are inaccurate, then the argument is inaccurate. This type
of reasoning is often also associated with the fictitious character
Sherlock Holmes. However, most studies also include an element of
inductive reasoning at some stage of the research (see section on
qualitative research for more details).
Researchers rarely have
access to all the members of a particular group (e.g. all people with
dementia, carers or healthcare professionals). However, they are usually
interested in being able to make inferences from their study about
these larger groups. For this reason, it is important that the people
involved in the study are a representative sample of the wider
population/group. However, the extent to which generalizations are
possible depends to a certain extent on the number of people involved in
the study, how they were selected and whether they are representative
of the wider group. For example, generalizations about psychiatrists
should be based on a study involving psychiatrists and not one based on
psychology students. In most cases, random samples are preferred (so
that each potential participant has an equal chance of participating)
but sometimes researchers might want to ensure that they include a
certain number of people with specific characteristics and this would
not be possible using random sampling methods. Generalizability of the
results is not limited to groups of people but also to situations. It is
presumed that the results of a laboratory experiment reflect the real
life situation which the study seeks to clarify.
When looking at results, the P value
is important. P stands for probability. It measures the likelihood that
a particular finding or observed difference is due to chance. The P
value is between 0 and 1. The closer the result is to 0, the less likely
it is that the observed difference is due to chance. The closer the
result is to 1, the greater the likelihood that the finding is due to
chance (random variation) and that there is no difference between the
groups/variables.
