The Nature of QUANTITATIVE Research – Global Homework Experts

Week 5: The Nature of QUANTITATIVE
Research
Module: BUSINESS PROJECT
SESSIONAL SCHEDULE

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WEEK TOPIC
WEEK 1 Introduction to the Module/Assessment Strategy/The Nature of Business Management Research
WEEK 2 Planning a Research Project
WEEK 3 Critically Reviewing the Literature
WEEK 4 The Nature of Qualitative Research
WEEK 5 The Nature of Quantitative Research
WEEK 6 (a) Mixed Methods Research (b) e-Research
WEEK 7 (a) Analysing Qualitative Data (b) Qualitative Analysis – Nvivo
WEEK 8 (a) Analysing Quantitative Data (b) Quantitative Analysis – SPSS
WEEK 9 (a) Writing up Business Management Research (b) Writing up Business Research Outputs
WEEK 10 Assessment Week – Submission of INDIVIDUAL RESEARCH PROPOSALS

3
1. To define quantitative research and what is unique about it
2. To explain the quantitative research process
3. Understand the terminology and language of quantitative
research
4. Be aware of the main criticisms of quantitative research and how
to address them
LEARNING OUTCOMES
What are concepts?
Concepts are:
Building blocks of theory
Labels that we give to elements of the social world
Categories for the organization of ideas and observations (Bulmer, 1984)
Concepts are useful for:
Providing an explanation of a certain aspect of the social world
Standing for things we want to explain
Giving a basis for measuring variation
Why measure?
To delineate fine differences between people, organizations, or any other unit
of analysis
To provide a consistent device for gauging distinctions
To produce precise estimates of the degree of the relationship between
concepts

Indicators of concepts
produced by the operational definition of a concept
less directly quantifiable than measures
common sense understandings of the form a concept might take
multiple-indicator measures
concept may have different dimensions
Why use more than one indicator?
Single indicators may incorrectly classify many individuals
Single indicators may capture only a portion of the underlying concept or be
too general
Multiple indicators can make finer distinctions between individuals
Multiple indicators can capture different dimensions of a concept
What does reliability mean?
Stability
is the measure stable over time?
e.g. test–retest method
Internal reliability
are the indicators consistent?
e.g. split-half method
Inter-observer consistency
is the measure consistent between observers?
What does validity mean?
Does the indicator measure the concept?
It does if it has:
Face validity (right for the concept?)
Concurrent validity (supported by a relevant criterion today?)
Predictive validity (likely to be supported by a relevant criterion tomorrow?)
Construct validity (are useful hypotheses produced?)
Convergent validity (supported by results from other methods?
Causality
Explanation
why things are the way they are
Direction of causal influence
relationship between dependent & independent variables
Confidence
in the researcher’s causal inferences
Generalisation
Can findings be generalised beyond the confines of the particular
context?
Can findings be generalised from sample to population?
How representative are samples?
Replication
Minimizing contamination from researcher biases or
values
Explicit description of procedures
Control of conditions of study
Ability to replicate in differing contexts
The process of Quantitative Research
Criticisms of quantitative research
Failure to distinguish between objects in the natural world and social phenomena
Artificial and spurious sense of precision and accuracy
Lack of ecological validity
reliance on instruments and measurements
Static view of social life
Is it always like this?
Quantitative research design is an
ideal-typical approach
Useful as a guide of good practice
But discrepancy between ideal type and actual
practice of business research
Pragmatic concerns mean that researchers may not
adhere rigidly to these principles

One reason for the discrepancy between the ideal
and typical approaches
Quantitative research is usually deductive (operational
definition of concepts)
But measurements can sometimes lead to inductive
theorising
And this means the factors give rise to the concepts,
rather than making them operational.
Bryman (1988:28) calls this ‘reverse operationism’.
…and another reason
Published accounts of quantitative research rarely report
evidence of reliability and validity (Podsakoff & Dalton, 1987)
Researchers are primarily interested in the substantive
content and findings of their research
Running tests of reliability and validity may seem an
unappealing alternative!
But researchers remain committed to the principles of good
practice

Operational Definitions
Defined
Defines how a concept or idea will be measured
Does not define the idea or concept
Types of Scales
There are four types of scales used to measure a dependent variable:
1. Nominal
2. Ordinal
3. Interval
4. Ratio

Types of Scales
Nominal Scale
Classification of data into one of two or more categories
Also known as categorical
Example: What is your gender?
Ordinal Scale
Classification of data into an order or rank of magnitude
Example:
Class Rank
Order of finish in a race
Interval Scale
Classification of data on a scale that assumes equal distance between numbers
Example: Are you a morning person (select a number)? Not at All 1—–2—–3—-4—-5—-6—-7 Very Much
Ratio Scale
Classification of data on a scale that assumes equal distances and has a true zero value
Example:
Please indicate how much time you spent studying this weekend. hr(s)
Variables: Independent and Dependent
Variables are given a special name that only applies to experimental investigations.
One is called the dependent variable and the other the independent variable.
The
independent variable (IV) is the variable the experimenter manipulates or
changes and is assumed to have a direct effect on the dependent variable.
An IV stands alone and does not change due to the impact of any other variable. The
IV is the one that is being manipulated or the one that varies
to measure its impact on
other variables
. It is sometimes called the ‘predictor’ or ‘treatment’ variable.
A dependent variable (DV) depends on other variables; and is the variable that is being tested
in the experiment.
The dependent variable is the outcome (or response) variable
Examples of Independent and Dependent Variables
o How does the amount of sleep impact test scores?
1. IV: Time spent sleeping before the exam
2. DV: Test Score
o What is the effect of fast food on blood pressure?
1. IV: Consumption of fast food
2. DV: Blood Pressure
o What is the effect of caffeine on sleep?
1. IV: the amount of caffeine consumed
2. DV: Sleep
© 2021 ULaw and ULBS 22
© 2021 ULaw and ULBS 23
Measuring the Dependent Variable
Types of Measures
Behavioral Measures
Attitudinal Measures
Cognitive Measures
Physiological Measures
Measuring the Dependent Variable
Types of Measures
Behavioral Measures
A measure to investigate a person’s behavior
Attitudinal Measures
A measure assessing a person’s attitudes on a topic
Cognitive Measures
A measure of one’s ability or knowledge of a topic
Physiological Measures
Measures that are biological in nature
Heart rate
Pulse
Blood pressure, etc.
Measuring the Dependent Variable
Assessing the measures:
Percent Correct
Average of correct responses to overall responses
Presented as a percentage
Frequency of Responding
A sum of the number of times a person/group responds to a question
Degree of Response
A measure of intensity of a response
Measuring the Dependent Variable
Ways to assess the measures
Several ways to assess each of the measures
Each measure can be assessed in multiple ways
Additional Considerations
Multiple Dependent Variables
Most studies have more than one dependent variable
Allows the researcher to collect more information about participants in the study
Floor Effect
When scores fall primarily at the lower range of a response option
Ceiling Effect
When scores fall primarily at the upper range of a response option
Pitfalls to Be Aware of when Testing Variables
Aside from dependent and independent variables, you must be aware of
other variables that may influence the result of your experiment.
Extraneous Variables
• may influence the relationships between the Independent and the Dependent
variables. Researchers try to identify these variables in order to control them.
Confounding Variables
• are those variables that cannot be controlled in research. In non-experimental
research, there may be other variables that you have not identified. These variables
may be influencing changes in the outcome.
© 2021 ULaw and ULBS 28
Exercise
Read the Case Study “Case 5c – Job Satisfaction in the Banking Industry”, then
answer the case questions.
1. Research consortium & ethics
2. Sample size
3. Social context
4. Language issue
5. Over-representation in a sample
6. Response rate/reliability/generalisation
7. Presentation of results

Thank you

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