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Summary Quantitative Business Research Methods
Quantitative Business Research Methods Bryman & Bell | SBM
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Summary quantitative business researchChapter 7 | The nature of qualitative research
Quantitative research is a distinctive research strategy. It’s about entailing the collection of
numerical data and exhibiting view of the relationship between theory and research as
deductive, a predilection for a natural science approach (and of positivism in particular), and
as having an objectivist conception of social reality.
- It is common for outlines of the main steps of quantitative research to suggest that a
hypothesis is deduced from the theory and is tested (however, does not always
contain a hypothesis).
- Hypothesises are most likely in experimental researches.
, - Coding information = transforming data into numbers to facilitate the quantitative
analysis of the data, particularly if the analysis is going to be carried out by
computers. Codes act as tags that are placed on data about people to allow the
information to be processed by the computer (used in step 8).
- A significant part of the research process entails convincing others of the significance
and validity of one’s findings.
- When all steps are followed, you go back to step 1. The presence of both an
elements of deductivism (Step 2) and inductivism (the feedback loop) is indicative of
the positivist foundation of qualitative research. Moreover, the emphasis on the
translation of concepts into measures (step 4) is also a feature of positivism.
Concepts and their measurement
Concepts = the building blocks of theory and represent the points around which business
research is conducted. Bulmer: Concepts are categories for the organization of ideas and
observations. E.G. satisfaction (Concept = intelligence and IQ = measurement of the concept
intelligence).
We use theories to explain variations between concepts.
If a concept is to be employed in quantitative research, it will have to be measured. Once
they are measured, concepts can be in the form of independent or dependent variables. In
other words, concepts may provide an explanation of a certain aspect of the social world, or
they may stand for things we want to explain.
MEASURES V.S. INDICATORS
Why measure?
Measures are quantities. Preoccupations of measurement in qualitative research:
- Measurement allows us to delineate fine differences between people in terms of the
characteristic in question.
- Measurement gives us a consistent device or yardstick for making such distinctions. A
measurement device provides a consistent instrument for gauging differences. This
consistency relates to two things: our ability to be consistent over time and our ability
to be consistent with other researchers. A measure should be something that is
influenced neither by the timing of its administration nor by the person who
administers it (reliability).
- Measurement provides the basis for more precise estimates of the degree of
relationship between concepts.
Indicators
We use indicators to tap concepts that are less directly quantifiable.
In order to provide a measure of a concept (an operational definition), it is necessary to have
an indicator or indicators that will stand for the concept. There are a number of ways in
which indicators can be devised:
, - through a question that is part of a structured interview schedule or self-completion
questionnaire. The questions should be concerned with the respondent’s report of an
attitude, employment status or of their behaviour.
- through the recording of individual’s behaviour using a structured observation
schedule
- through official statistics
- through an examination of mass media content through content analysis
Very often the researcher has to consider whether one indicator of a concept will be
sufficient. Multiple indicator measure of a concept.
The main reason for using a multiple-indicator measure of a concept is a recognition that
there are potential problems with a reliance on just a single indicator:
- a single indicator will incorrectly classify many individuals
- one indicator may capture only a portion of the underlying concept or to be too
general
- you can make much finer distinctions
Dimensions of concepts
One elaboration of the general approach to measurement is to consider the possibility that
the concept in which you are interested compromise different dimensions.
Lazarsfeld (1958): when the researcher is seeking to develop a measure of a concept, the
different aspects or components of that concept should be considered. This specification of
the dimensions of a concept would be undertaken with reference to theory and research
associated with that concept.
Reliability and validity of measures
It is crucial whether or not measures are reliable and whether or not they are valid
representations of the concepts they are supposed to be tapping.
Reliability
Reliability refers to the consistency of a measure of a concept. The following are three
prominent factors involved when considering whether a measure is reliable.
- Stability: This consideration entails asking whether or not a measure is stable over
time, so that we can be confident that the results relating to that measure for a
sample of respondents do not fluctuate. The most obvious way of testing the stability
of a measure is the test-retest method. This involves administering a test or measure
on one occasion and then read ministering it to the sample on another occasion. We
should expect to find a high correlation between the two occasions: correlation is a
measure of the strength of the relationship between two variables.
- Internal reliability: The key issue is whether or not the indicators that make up the
scale or index are consistent, so whether or not respondents’ scores on any one
indicator tend to be related to their scores on the other indicators. Nowadays, most
researchers use a test of internal reliability as Cronbach’s alpha. It essentially
calculates the average of all split-half reliability coefficients. A computed alpha
coefficient will vary between 1 (denoting perfect internal reliability) and 0 (denoting no
, internal reliability). The figure 0.80 is typically employed as a rule of thumb to denote
an acceptable level of internal reliability.
- Inter-observer consistency: When a great deal of subjective judgement is involved
in such activities as the recording of observations or the translation of data into
categories and where more than one ‘observer’ is involved in such activities, there is
the possibility that there is a lack of consistency in their decisions.
Validity
Validity refers to the issue of whether or not an indicator (or set of indicators) that is devised
to gauge a concept really measures that concept. Here the term is being used as a
shorthand for what was referred to as measurement validity. A distinction is made between a
number of types of validity. These types really reflect different ways of gauging the validity of
a measure of a concept.
- Face validity: The measure apparently reflects the content of the concept in
question. People might be asked to act as judges to determine whether or not on the
face of it the measure seems to reflect the concept concerned. It is an essentially
intuitive process.
- Concurrent validity: The researcher employs a criterion on which cases are known
to differ and that is relevant to the concept in question.
- Predictive validity: The researcher uses a future criterion measure, rather than a
contemporary one, as in the case of concurrent validity.
- Construct validity: The researcher is encouraged to deduce hypotheses from a
theory that is relevant to the concept.
- Convergent validity: Comparing it to measures of the same concept developed
through other methods.
Reflections on reliability and validity
It should be borne in mind that, although reliability and validity are analytically
distinguishable, they are related because validity presumes reliability. This means that if your
measure is not reliable, it cannot be valid.
The main preoccupations of quantitative researchers
Measurement
From the position of quantitative research, measurement carries a number of advantages.
Issues of reliability and validity are a concern for quantitative researchers, though this is not
always manifested in research practice.
Causality
There is a very strong concern in most quantitative research with explanation. Quantitative
researchers are rarely concerned merely to describe how things are, but are keen to say why
things are the way they are. They are likely to want to explain it, which means examining its
causes. The idea of ‘independent’ and ‘dependent’ variables reflect the tendency to think in
terms of causes and effects. (independent variable = manipulated) A criterion of good
quantitative research is frequently the extent to which there is confidence in the researcher’s
causal inferences.
Generalization
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