Summary study book Business Research - a practical guide for undergraduate and postgraduate students of Jill Collis & Roger Hussey (Chapters 10, 11, 12, 13) - ISBN: 9780230301832, Edition: 4, Year of publication: 2014 (Chapters 10 to 14)
Chapter 10: Collecting data for statistical analysis
10.1 - Introduction
The two main methodologies associated with positivism are experimental studies and surveys. This
chapter focuses on the methods used to collect primary research data when a survey methodology
is adopted (because experimental studies are not widely used in business research for practical and
ethical reasons).
Extra information from chapter 3
Positivism is a paradigm that originated in the natural sciences. It rests on the assumption that
social reality is singular and objective, and is not affected by the act of investigating it. The
research involves a deductive process with a view to providing explanatory theories to understand
social phenomena.
Since it is assumed that social phenomena can be measured, positivism is associated with
quantitative methods of analysis based on the statistical analysis of quantitative research data.
10.2 - Main issues in collecting data for statistical analysis
Some others distinguish between data and information.
Data = Are known facts or things used as a basis for inference or reckoning.
Information = Is the knowledge created by organizing data into a useful form.
But this distinction depends on how items of data are perceived and how they are used. For
example, if you are a positivist, you may have collected data relating to the variables under study
via a questionnaire survey, which you subsequently analysed using statistics. You probably consider
that this process allowed you to turn data into information that makes a small contribution to
knowledge. On the other hand, your respondents may consider that what they gave was
information in the first place.
Research data can be quantitative (in numerical form) or qualitative (in non-numerical form, such
as text or images).
Data can also be classified by source (typical sources of secondary research data include archives,
commercial databases, government and commercially produced statistics and industry data,
statutory and voluntary corporate reports, internal documents and records of organizations, and
information in printed and web-based publications). The study may be based on an analysis of
secondary data or on an analysis of primary data.
Primary data = Are generated from an original source, such as your own experiments, surveys,
interviews or focus groups.
Secondary data = Are collected from an existing source, such as publications, databases and
internal records.
Overview of data collection for a positivist study (figure 10.1 - page 197):
Choose a sampling method → Identify the variables → Choose data collection method(s) →
Conduct pilot study and modify methods as necessary → Collect the research data
Research data can be generated or collected from different sources and more than one method can
be used.
10.2.1 - Selecting a sample for a positivist study
Sampling frame = A record of the population from which a sample can be drawn.
Sample = A subset of a population.
Population = A body of people or collection of items under consideration for statistical purposes.
Random sample = An unbiased subset of a population that is representative of the population
because every member had an equal chance of being selected.
If the population is relatively small, you can select the whole population; otherwise, you will need
to select a random sample.
Main steps in selecting a random sample (figure 10.2 - page 198):
1
, Define the target population → Obtain or construct a sampling frame → Determine the minimum
sample size → Choose a sampling method → Decide how to convert sample estimates to
population parameters
10.2.2 - Sample size
The greater variation within the sample, the larger the sample required.
The larger the sample, the better it will represent the population. Therefore, if you want to
generalize from your results, you must determine the minimum sample size to reflect the size of
the population. In a questionnaire survey, you will also need to take account of your expected
response rate, which may be 10% or less.
The minimum sample size to allow results from a random sample to be generalized to the
population is much higher for a small population than it is for a large population. 'As the population
increases, the sample size increases at a diminishing rate and remains relatively constant at
slightly more than 380 cases' (check table 10.1 - page 199).
Three main considerations are:
The statistical analysis planned
The expected variability within subsets in the sample
The tradition in your research area regarding what constitutes an appropriate sample size
10.2.3 - Methods for selecting a random sample
The sample will be biased if the researcher or someone else chooses it or asks for volunteers, or if
inducements are offered, because the sample may have characteristics that others in the
population do not possess.
One way to select a random sample is to allocate a number to every member of the population and
select a sample based on the numbers given in a random number table.
Practice: Generate random numbers in Microsoft Excel (page 199-200)
Systematic sampling = The population is divided by the required sample size (n) and the sample
chosen by taking every 'nth' subject (check box 10.1 - page 200).
Stratified sampling = Overcomes the problem that a simple random sample might result in some
members of the population being significantly under- or over-represented. It does this by taking
account of each identifiable strata of the population (check box 10.2 - page 200).
Other sampling methods include:
Quota sampling = Involves giving interviewers quotas of different types of people to question.
Cluster sampling = Involves making a random selection from a sampling frame listing groups of
units rather than individual units. Every individual belonging to the selected groups is then
interviewed or examined. This can be a useful approach, particularly for face-to-face interviews,
where for time or economy reasons it is necessary to reduce the physical areas covered.
Multi-stage sampling = Is used where the groups selected in a cluster sample are so large that a
sub-sample must be selected from each group.
10.3 - Variables
Under positivism, research is deductive and one of the purposes of the literature review is to
identify a theory or set of theories for the study.
Theory = A set of interrelated variables, definitions and propositions that specifies relationships
among the variables.
Variable = A characteristic of a phenomenon that can be observed or measured.
Researchers collect data relating to each variable and use this empirical evidence to test their
hypotheses.
Empirical evidence = Data based on observation or experience.
Hypothesis = A proposition that can be tested for association or causality against empirical
evidence.
10.3.1 - Measurement levels
2
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