Quantitative Research
This article
describes the basic tenets of quantitative
research. The concepts of dependent and independent variables are
addressed and the concept of measurement and its associated issues, such as
error, reliability and validity, are explored. Experiments and surveys - the
principal research designs
in quantitative research -
are described and key features explained. The importance of the double-blind
randomised controlled trial is emphasised, alongside the importance of
longitudinal surveys, as opposed to cross-sectional surveys. Essential features
of data storage are covered, with an emphasis on safe, anonymous storage.
Finally, the article explores the analysis of quantitative data, considering what may be analysed
and the main uses of statistics in analysis.
QUANTITATIVE RESEARCH encompasses a range of methods
concerned with the systematic investigation of social phenomena, using statistical or numerical
data. Therefore, quantitative research
involves measurement and assumes that the phenomena under study can be
measured. It sets out to analyse data for trends and relationships and to
verify the measurements made.
Some items,
such as height and weight, are easy to measure; others, such as what people
think or feel, are difficult to measure. Quantitative research encompasses this entire spectrum. Similar
criteria are applied to verify, calculate and analyse data for all types of
measurement. Quantitative research
may be considered as a way of thinking about the world. It is essentially
deductive: measurements are made, analysis is applied and conclusions are
drawn. It is pointless to dispute whether quantitative or qualitative research is superior. The researcher may even choose
to use both quantitative
and qualitative methods in his or her research
design, in a combined or mixed methods approach ( Andrew and Halcomb 2009 ).
The mixed methods approach will be addressed in a later article in this series.
A unique feature of quantitative
research is its ability to test theories formally by formulating
hypotheses and applying statistical analyses ( Figure 1 ).
Variables
A variable
is anything that may be measured in quantitative
research, for example height, weight, attitude or wellbeing. There are
two types of variable: independent and dependent ( Pierce 2013 ). An
independent variable may influence the measurement of the dependent one. For
example, if you were studying the relationship between the frequency of
positional change and the development of pressure ulcers, positional change
would be the independent variable and pressure ulcer development would be the
dependent variable.
Measurement
Different
kinds of measurement can be placed in a hierarchy, using a theory of
measurement ( Stevens 1946 ). The different levels and their properties are
shown in Table 1 . Nominal measurement is the lowest level on the hierarchy
because it is essentially a system of classification, rather than measurement.
Ordinal level measurement begins to order phenomena, but this measurement is
limited and imprecise. Interval and ratio level are precise and accurate.
However, it is rarely possible to make ratio level measurements in quantitative research, which
involves the study of social
phenomena. Generally, measurement in quantitative
research is made at the ordinal and interval levels.
Level
|
Attributes
|
Examples
|
Ratio
|
A zero
value is meaningful, permitting direct comparisons between measurements. For
example, twice as many patients were seen in one month compared with the
previous month.
|
Height,
weight, length.
|
Interval
|
Distance
between measured variables is meaningful.
|
Temperature
scales where the zero point is arbitrary, but set intervals are meaningful,
for example, centigrade or fahrenheit.
|
Ordinal
|
Attributes
can be ordered.
|
Opinion
measured by asking if you: 'strongly agree', 'agree', 'don't know',
disagree', 'strongly disagree'.
|
Nominal
|
Attributes
are only named.
|
Hair
colour, gender, nationality.
|
(Adapted
from Trochim 2006 )
|
Error in
measurement
There is
always error associated with measurements, by whatever means they are made.
These apply to physical measurements, such as height and weight, and to other
types of measurements in the social
sciences. Error may come from several sources in measurement ( Shields and
Watson 2013 ): systematic error (introduced by an inaccuracy inherent in the
system) and random (including human) error. These should be allowed for in the
design and use of any instrument. In the social sciences, an instrument may be a questionnaire or
observational checklist. Two kinds of systematic and random error may occur:
Within
instrument (or within human).
Between
instruments (or between humans).
Within
instrument errors mean that different measurements can be given on different
occasions. Between instrument errors mean that two seemingly identical
instruments can give different measurements. Similarly, within human errors
mean that an individual using the same instrument can obtain different
measurements on separate occasions, while between human errors mean that two
people using the same instrument can obtain different measurements on the same
occasion.
Error cannot
be eliminated entirely, but steps should be taken to minimise it ( Bowling 1997
). The design of good instruments is one approach. In social research, this means having clear,
easy-to-understand questionnaires and observational checklists, and ensuring
that the questions asked address only the studied phenomena. For example, if
you are interested in measuring difficulty with feeding in older people with
dementia, you should ask questions that address that problem alone and omit any
that address other aspects of behaviour, such as agitation. In designing
instruments, a balance should be attained between 'authenticity' and
'directness' ( Messick 1994 ). An authentic instrument measures as much as
possible about a phenomenon, at the risk of becoming indirect, while a direct
instrument focuses on only the items directly concerned with the phenomenon, at
the risk of losing some authenticity.
Reliability
and validity
Reliability
and validity involve estimating - and minimising - the level of error
associated with measurements made using a given instrument ( Streiner and
Norman 2008 ). Reliability is the extent to which an instrument makes the same
measurement each time it is used. Validity is the extent to which the
measurement made by an instrument measures what the researcher is interested
in.
It is useful
to consider physical measurement to explore these concepts. For example, if we
measure a patient's blood pressure several times with a monitoring device, we
should get approximately the same measurement each time we use it, provided
that the patient's blood pressure has not changed. Allowing for human error,
the measurements would be reliable. Now imagine that the blood pressure
monitoring device is faulty, so it measures blood pressure a few millimetres of
mercury below the true value. If we take successive measurements of the
patient's blood pressure, we will still get the same measurement each time we
use the device. However, these will be wrong, because they are not valid. This
illustrates an important point, which is that measurements can be reliable, but
not valid. However, for measurements to be considered valid, they should be
reliable.
Reliability
and validity can be tested and improved by making adjustments to instruments,
if the levels of reliability and validity are too low. With questionnaires this
usually involves revising the items in the questionnaire by removing or
clarifying ambiguous questions. The principal features of the different types
of reliability and validity are provided in Box 1 .
Box 1 Type
and definition of reliability and validity
Reliability
Internal
consistency: the extent to which all the items in a questionnaire measure the
same thing.
Test-retest
reliability: the extent to which an instrument gives the same result on two
occasions.
Intra-rater
reliability: the extent to which the same person obtains the same measurement
on two occasions.
Inter-rater
reliability: the extent to which two people obtain the same measurement.
Validity
Criterion
validity: the extent of correlation with another validated measure.
Concurrent
validity: the extent of correlation with another measure of the same phenomenon
at the same time.
Predictive
validity: the extent of correlation with another measure at a later time.
Convergent
(divergent) validity: the extent (or lack) of correlation with measures of
another phenomena predicted to correlate (or not to correlate) with the new
scale.
Discriminant
validity: the ability to discriminate between cases, such as severe and mild,
and between cases and non-cases.
Quantitative research designs
There are
two broad categories of research
design in quantitative research:
experimental designs and survey designs ( Figure 1 ).
Experimental
designs
An
experiment is a study where the researcher can manipulate the independent
variable and study its effect on a dependent variable. For example, if you wished
to study the effect of the dose of an analgesic on pain levels, you could vary
the dose (the independent variable) and measure the effect on the pain level
(the dependent variable). There are many types of experimental design, and for
the purposes of this article, we focus on the randomised controlled trial
(RCT), used to test the effect of treatments on people.
The RCT is
considered to be the best method for testing the link between cause and effect
in clinical interventions. Its essential features are randomisation and use of
a control group. The RCT is rated near the top of the hierarchy of evidence, at
level II, as a method of providing evidence for clinical practice ( Centre for
Reviews and Dissemination 2009 ). It is superseded in the hierarchy only by
systematic review with meta-analysis, a method of combining the results of RCTs
and evaluating the combined evidence.
RCTs should
preferably be 'blind': either those taking part do not know if they are in the
intervention or the control group, or the person who is collecting the data
does not know this. The optimum design is the double-blind RCT where neither
the participants, nor the person collecting the data, know who is in which
group ( Smith 2008 ).
The simplest
form of RCT requires at least two groups of participants: a treatment (also
referred to as experimental or intervention) group and a control group. The
treatment group receives the treatment being tested and the control group does
not. However, the control group should be approached in exactly the same way,
or as closely to this as is possible, except that they do not receive the
treatment. When testing drugs, for example, this is achieved by administering a
'placebo', which looks identical to the treatment drug, except that it contains
no active ingredient. This matching of treatment in the two groups, as far as
is possible, is to take into account the 'placebo effect', whereby anyone
involved in a RCT - whether receiving the treatment or not - may respond as if
they were being treated. The placebo effect must be the same in both groups for
the effect of the active drug to be measured correctly. With nursing
interventions - for example, testing a support surface for pressure ulcer
prevention - it may be difficult to provide a placebo in quite the same way. In
such cases it is customary to administer the usual care that a person may
receive for pressure ulcer prevention to the control group, and to compare this
usual standard of care with the new support surface being tested.
There are many
possible methods of allocating individuals who have agreed to participate in a
RCT to either the treatment group or the control group. Randomisation is used
to minimise bias in allocating individuals to the two groups. For example,
researchers could be accused, subconsciously or even deliberately, of
allocating people who are more likely to respond to treatment to the treatment
group, and the remainder to the control group. This might introduce bias into
the experiment, which could exaggerate the effects of the treatment.
Blinding, as
explained above, is a process of concealment and can be either single-blind or
double-blind. The purpose is to minimise bias in either the participant or the
researcher, or both, by concealing to them that they are receiving the
treatment or the control (in the case of the participant), and/or which
participants are receiving the treatment or the control (in the case of the
researcher). Double-blinding is preferred, but this is difficult to achieve
with nursing interventions.
Survey
designs
Surveys are
often used in nursing research.
These frequently involve distributing questionnaires, or they may be conducted
by interview or observation. In contrast to experiments, surveys cannot easily
distinguish between cause and effect, but they are useful for gathering large
amounts of data to describe samples and populations ( Hallberg 2008 ). Surveys
may be either cross-sectional or longitudinal. Cross-sectional studies are
relatively easy to conduct, since they only have to be carried out once. The
majority of surveys are cross-sectional. Longitudinal studies are more complex,
especially those conducted over several years, since they require repeated
surveys. Attrition is a significant problem in longitudinal studies ( Aldridge
and Levine 2001 ).
There are
three types of longitudinal survey design: trend studies, cohort studies and
panel studies ( Watson 2008 ). Each has its advantages and disadvantages. Trend
studies are concerned with population trends. A classic example is the study of
voting intentions in the run-up to general elections. The population is sampled
on one day and again, at intervals. The sample surveyed is always part of the
same population but does not, necessarily, comprise the same people. Therefore,
this is a relatively simple type of survey to perform, but does not provide
information about how specific individuals change over time.
Cohort and
panel studies are similar in that they use the same group sample at each stage,
but they differ slightly in how they use the groups. A cohort study uses a
defined group (people with a shared characteristic). The people surveyed at
each stage, for example, could all belong to the nursing class of 2013, but the
same individuals may not be surveyed each time; each group surveyed will be a
sub-sample of the defined cohort. In contrast, in a panel study, exactly the
same people are surveyed at each stage. Therefore, cohort and panel studies are
more informative about how individuals change over time than trend studies, but
are more difficult to conduct and are susceptible to attrition.
Handling
data from quantitative
studies
Quantitative studies produce numbers that should be interpreted
before conclusions may be drawn. The data may be entered, stored and analysed
using some form of electronic database. Data may be entered into a Word
document or an Excel spreadsheet, for example. Some initial data analysis is
possible in Excel, but it may be imported into a statistical package, such as
SPSS (Statistical Package for the Social
Sciences), to permit more sophisticated analysis ( Pallant 2007 ). Data entry
often requires transcription from hard copies of questionnaires or
observational schedules. This has to be done carefully and double-checked.
Increasingly, surveys are distributed via the internet and data can be imported
directly into an analytical package such as SPSS.
It is
essential to store data carefully, once it has been entered into any package,
since loss can jeopardise the study. It is good practice to create a master copy
of the data, which should not be altered but may be copied in the event that
subsequent files are lost or altered inadvertently. It is also essential to
create a backup copy of the master and store it safely in case it is deleted or
altered inadvertently. Security is important if collected data are sensitive or
confidential. Files should not contain any information that could identify
individual participants and data should be password-protected.
Statistical
analysis
Quantitative data may be analysed statistically ( Watson et al
2006 ). Data may be described in terms of percentages, central tendency (mode,
median, mean) and spread (range and standard deviation) ( Box 2 ). Analysis of
the data in the sample may be used to draw inferences about the population as a
whole. Usually analysis is performed using a set of analyses known as
inferential statistics. These allow you to investigate, for example, the
differences between the mean values in the treatment and control groups in a
RCT and to investigate the associations between variables, such as pain and
analgesic dose. The important criterion in inferential statistics is whether
something is statistically significant, which is usually expressed as a
probability ( P ), measuring 'How likely was this to happen anyway?' If
the probability is very low, conventionally below 0.05 (less than a one in 20
chance), then we are justified in stating that our observation is statistically
significant at this probability. Statistical significance implies that the
observed benefits are likely to have happened as a result of the treatment
being tested, or that the observed relationship between variables is real.
Box 2 Definition
of statistical terms
Central
tendency: a description of where the central point in a dataset is.
Mode: the
most frequently occurring number.
Median: the
number in the middle of a set of ordered data.
Mean: the
number obtained by adding up all the numbers in a dataset and dividing by how
many numbers there are.
Spread: a
description of how widely the data diverge from the central tendency.
Range: the
difference between the largest and smallest values in the dataset.
Standard
deviation: a measure that describes the level of deviation from the mean
expressed in a standard form.
Conclusion
This article
has described the main principles of quantitative
research, such as variables, measurement, error, reliability and
validity, and explored the two principal research designs: experiments and surveys. Instruments of
measurement should be designed to ensure that they have good reliability and
validity. Random error cannot be eliminated in quantitative research. However, instrument and human
error can be eliminated or reduced. Experiments and surveys are used to study
the relationship between variables. Experimental designs are best for relating
cause and effect. The most effective experimental design is the double-blind
RCT. Surveys are most useful for studying people and populations; the majority
of surveys are cross-sectional, but the best survey designs are longitudinal
because these can study changes in people and populations. In quantitative research, it is
important to collect data correctly and store it securely on electronic
databases, and to analyse quantitative
data, using appropriate statistical methods.
Acknowledgement: Nursing Standard wishes to thank
Leslie Gelling, reader in nursing at Anglia Ruskin University, for
co-ordinating and developing the Research
series.
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Word count: 3275
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